WorldWideScience

Sample records for deep sequencing approach

  1. DeepSimulator: a deep simulator for Nanopore sequencing

    KAUST Repository

    Li, Yu

    2017-12-23

    Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals. Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection. Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.

  2. Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.

    Science.gov (United States)

    Shafiee, Mohammad Javad; Chung, Audrey G; Khalvati, Farzad; Haider, Masoom A; Wong, Alexander

    2017-10-01

    While lung cancer is the second most diagnosed form of cancer in men and women, a sufficiently early diagnosis can be pivotal in patient survival rates. Imaging-based, or radiomics-driven, detection methods have been developed to aid diagnosticians, but largely rely on hand-crafted features that may not fully encapsulate the differences between cancerous and healthy tissue. Recently, the concept of discovery radiomics was introduced, where custom abstract features are discovered from readily available imaging data. We propose an evolutionary deep radiomic sequencer discovery approach based on evolutionary deep intelligence. Motivated by patient privacy concerns and the idea of operational artificial intelligence, the evolutionary deep radiomic sequencer discovery approach organically evolves increasingly more efficient deep radiomic sequencers that produce significantly more compact yet similarly descriptive radiomic sequences over multiple generations. As a result, this framework improves operational efficiency and enables diagnosis to be run locally at the radiologist's computer while maintaining detection accuracy. We evaluated the evolved deep radiomic sequencer (EDRS) discovered via the proposed evolutionary deep radiomic sequencer discovery framework against state-of-the-art radiomics-driven and discovery radiomics methods using clinical lung CT data with pathologically proven diagnostic data from the LIDC-IDRI dataset. The EDRS shows improved sensitivity (93.42%), specificity (82.39%), and diagnostic accuracy (88.78%) relative to previous radiomics approaches.

  3. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

    Science.gov (United States)

    Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

    2018-02-01

    Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

  4. Quantitative phenotyping via deep barcode sequencing.

    Science.gov (United States)

    Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

    2009-10-01

    Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

  5. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

    KAUST Repository

    Kulmanov, Maxat

    2017-09-27

    Motivation A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. Results We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein–protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations.

  6. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

    Science.gov (United States)

    Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

    2018-02-15

    A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

  7. deepTools2: a next generation web server for deep-sequencing data analysis.

    Science.gov (United States)

    Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

    2016-07-08

    We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  8. Exploring fungal diversity in deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing

    Science.gov (United States)

    Zhang, Xiao-Yong; Wang, Guang-Hua; Xu, Xin-Ya; Nong, Xu-Hua; Wang, Jie; Amin, Muhammad; Qi, Shu-Hua

    2016-10-01

    The present study investigated the fungal diversity in four different deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing of the nuclear ribosomal internal transcribed spacer-1 (ITS1). A total of 40,297 fungal ITS1 sequences clustered into 420 operational taxonomic units (OTUs) with 97% sequence similarity and 170 taxa were recovered from these sediments. Most ITS1 sequences (78%) belonged to the phylum Ascomycota, followed by Basidiomycota (17.3%), Zygomycota (1.5%) and Chytridiomycota (0.8%), and a small proportion (2.4%) belonged to unassigned fungal phyla. Compared with previous studies on fungal diversity of sediments from deep-sea environments by culture-dependent approach and clone library analysis, the present result suggested that Illumina sequencing had been dramatically accelerating the discovery of fungal community of deep-sea sediments. Furthermore, our results revealed that Sordariomycetes was the most diverse and abundant fungal class in this study, challenging the traditional view that the diversity of Sordariomycetes phylotypes was low in the deep-sea environments. In addition, more than 12 taxa accounted for 21.5% sequences were found to be rarely reported as deep-sea fungi, suggesting the deep-sea sediments from Okinawa Trough harbored a plethora of different fungal communities compared with other deep-sea environments. To our knowledge, this study is the first exploration of the fungal diversity in deep-sea sediments from Okinawa Trough using high-throughput Illumina sequencing.

  9. DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data.

    Science.gov (United States)

    Yang, Jian-Hua; Qu, Liang-Hu

    2012-01-01

    Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.

  10. DeepSimulator: a deep simulator for Nanopore sequencing

    KAUST Repository

    Li, Yu; Han, Renmin; Bi, Chongwei; Li, Mo; Wang, Sheng; Gao, Xin

    2017-01-01

    or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments

  11. Unified Deep Learning Architecture for Modeling Biology Sequence.

    Science.gov (United States)

    Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

    2017-10-09

    Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

  12. Geoseq: a tool for dissecting deep-sequencing datasets

    Directory of Open Access Journals (Sweden)

    Homann Robert

    2010-10-01

    Full Text Available Abstract Background Datasets generated on deep-sequencing platforms have been deposited in various public repositories such as the Gene Expression Omnibus (GEO, Sequence Read Archive (SRA hosted by the NCBI, or the DNA Data Bank of Japan (ddbj. Despite being rich data sources, they have not been used much due to the difficulty in locating and analyzing datasets of interest. Results Geoseq http://geoseq.mssm.edu provides a new method of analyzing short reads from deep sequencing experiments. Instead of mapping the reads to reference genomes or sequences, Geoseq maps a reference sequence against the sequencing data. It is web-based, and holds pre-computed data from public libraries. The analysis reduces the input sequence to tiles and measures the coverage of each tile in a sequence library through the use of suffix arrays. The user can upload custom target sequences or use gene/miRNA names for the search and get back results as plots and spreadsheet files. Geoseq organizes the public sequencing data using a controlled vocabulary, allowing identification of relevant libraries by organism, tissue and type of experiment. Conclusions Analysis of small sets of sequences against deep-sequencing datasets, as well as identification of public datasets of interest, is simplified by Geoseq. We applied Geoseq to, a identify differential isoform expression in mRNA-seq datasets, b identify miRNAs (microRNAs in libraries, and identify mature and star sequences in miRNAS and c to identify potentially mis-annotated miRNAs. The ease of using Geoseq for these analyses suggests its utility and uniqueness as an analysis tool.

  13. Detection of Emerging Vaccine-Related Polioviruses by Deep Sequencing.

    Science.gov (United States)

    Sahoo, Malaya K; Holubar, Marisa; Huang, ChunHong; Mohamed-Hadley, Alisha; Liu, Yuanyuan; Waggoner, Jesse J; Troy, Stephanie B; Garcia-Garcia, Lourdes; Ferreyra-Reyes, Leticia; Maldonado, Yvonne; Pinsky, Benjamin A

    2017-07-01

    Oral poliovirus vaccine can mutate to regain neurovirulence. To date, evaluation of these mutations has been performed primarily on culture-enriched isolates by using conventional Sanger sequencing. We therefore developed a culture-independent, deep-sequencing method targeting the 5' untranslated region (UTR) and P1 genomic region to characterize vaccine-related poliovirus variants. Error analysis of the deep-sequencing method demonstrated reliable detection of poliovirus mutations at levels of vaccinated, asymptomatic children and their close contacts collected during a prospective cohort study in Veracruz, Mexico, revealed no vaccine-derived polioviruses. This was expected given that the longest duration between sequenced sample collection and the end of the most recent national immunization week was 66 days. However, we identified many low-level variants (Sabin serotypes, as well as vaccine-related viruses with multiple canonical mutations associated with phenotypic reversion present at high levels (>90%). These results suggest that monitoring emerging vaccine-related poliovirus variants by deep sequencing may aid in the poliovirus endgame and efforts to ensure global polio eradication. Copyright © 2017 Sahoo et al.

  14. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

    KAUST Repository

    Teng, Haotian; Cao, Minh Duc; Hall, Michael B; Duarte, Tania; Wang, Sheng; Coin, Lachlan J M

    2018-01-01

    Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

  15. Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning

    KAUST Repository

    Teng, Haotian

    2018-04-10

    Sequencing by translocating DNA fragments through an array of nanopores is a rapidly maturing technology that offers faster and cheaper sequencing than other approaches. However, accurately deciphering the DNA sequence from the noisy and complex electrical signal is challenging. Here, we report Chiron, the first deep learning model to achieve end-to-end basecalling and directly translate the raw signal to DNA sequence without the error-prone segmentation step. Trained with only a small set of 4,000 reads, we show that our model provides state-of-the-art basecalling accuracy, even on previously unseen species. Chiron achieves basecalling speeds of more than 2,000 bases per second using desktop computer graphics processing units.

  16. Sequencing Infrastructure Investments under Deep Uncertainty Using Real Options Analysis

    Directory of Open Access Journals (Sweden)

    Nishtha Manocha

    2018-02-01

    Full Text Available The adaptation tipping point and adaptation pathway approach developed to make decisions under deep uncertainty do not shed light on which among the multiple available pathways should be chosen as the preferred pathway. This creates the need to extend these approaches by means of suitable tools that can help sequence actions and subsequently enable the outlining of relevant policies. This paper presents two sequencing approaches, namely, the “Build to Target” and “Build Up” approach, to aid in sub-selecting a set of preferred pathways. Both approaches differ in the levels of flexibility they offer. They are exemplified by means of two case studies wherein the Net Present Valuation and the Real Options Analysis are employed as selection criterions. The results demonstrate the benefit of these two approaches when used in conjunction with the adaptation pathways and show how the pathways selected by means of a Build to Target approach generally have a value greater than, or at least the same as, the pathways selected by the Build Up approach. Further, this paper also demonstrates the capacity of Real Options to quantify and capture the economic value of flexibility, which cannot be done by traditional valuation approaches such as Net Present Valuation.

  17. DNA Replication Profiling Using Deep Sequencing.

    Science.gov (United States)

    Saayman, Xanita; Ramos-Pérez, Cristina; Brown, Grant W

    2018-01-01

    Profiling of DNA replication during progression through S phase allows a quantitative snap-shot of replication origin usage and DNA replication fork progression. We present a method for using deep sequencing data to profile DNA replication in S. cerevisiae.

  18. Exploring the Mechanisms of Gastrointestinal Cancer Development Using Deep Sequencing Analysis

    International Nuclear Information System (INIS)

    Matsumoto, Tomonori; Shimizu, Takahiro; Takai, Atsushi; Marusawa, Hiroyuki

    2015-01-01

    Next-generation sequencing (NGS) technologies have revolutionized cancer genomics due to their high throughput sequencing capacity. Reports of the gene mutation profiles of various cancers by many researchers, including international cancer genome research consortia, have increased over recent years. In addition to detecting somatic mutations in tumor cells, NGS technologies enable us to approach the subject of carcinogenic mechanisms from new perspectives. Deep sequencing, a method of optimizing the high throughput capacity of NGS technologies, allows for the detection of genetic aberrations in small subsets of premalignant and/or tumor cells in noncancerous chronically inflamed tissues. Genome-wide NGS data also make it possible to clarify the mutational signatures of each cancer tissue by identifying the precise pattern of nucleotide alterations in the cancer genome, providing new information regarding the mechanisms of tumorigenesis. In this review, we highlight these new methods taking advantage of NGS technologies, and discuss our current understanding of carcinogenic mechanisms elucidated from such approaches

  19. Exploring the Mechanisms of Gastrointestinal Cancer Development Using Deep Sequencing Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Matsumoto, Tomonori; Shimizu, Takahiro; Takai, Atsushi; Marusawa, Hiroyuki, E-mail: maru@kuhp.kyoto-u.ac.jp [Department of Gastroenterology and Hepatology, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507 (Japan)

    2015-06-15

    Next-generation sequencing (NGS) technologies have revolutionized cancer genomics due to their high throughput sequencing capacity. Reports of the gene mutation profiles of various cancers by many researchers, including international cancer genome research consortia, have increased over recent years. In addition to detecting somatic mutations in tumor cells, NGS technologies enable us to approach the subject of carcinogenic mechanisms from new perspectives. Deep sequencing, a method of optimizing the high throughput capacity of NGS technologies, allows for the detection of genetic aberrations in small subsets of premalignant and/or tumor cells in noncancerous chronically inflamed tissues. Genome-wide NGS data also make it possible to clarify the mutational signatures of each cancer tissue by identifying the precise pattern of nucleotide alterations in the cancer genome, providing new information regarding the mechanisms of tumorigenesis. In this review, we highlight these new methods taking advantage of NGS technologies, and discuss our current understanding of carcinogenic mechanisms elucidated from such approaches.

  20. Discovery of Bovine Digital Dermatitis-Associated Treponema spp. in the Dairy Herd Environment by a Targeted Deep-Sequencing Approach

    DEFF Research Database (Denmark)

    Schou, Kirstine Klitgaard; Weiss Nielsen, Martin; Ingerslev, Hans-Christian

    2014-01-01

    The bacteria associated with the infectious claw disease bovine digital dermatitis (DD) are spirochetes of the genus Treponema; however, their environmental reservoir remains unknown. To our knowledge, the current study is the first report of the discovery and phylogenetic characterization of r...... of this disease among cows within a herd as well as between herds. To address the issue of DD infection reservoirs, we searched for evidence of DD-associated treponemes in fresh feces, in slurry, and in hoof lesions by deep sequencing of the V3 and V4 hypervariable regions of the 16S rRNA gene coupled...... with identification at the operational-taxonomic-unit level. Using treponeme-specific primers in this high-throughput approach, we identified small amounts of DNA (on average 0.6% of the total amount of sequence reads) from DD-associated treponemes in 43 of 64 samples from slurry and cow feces collected from six...

  1. CPSS: a computational platform for the analysis of small RNA deep sequencing data.

    Science.gov (United States)

    Zhang, Yuanwei; Xu, Bo; Yang, Yifan; Ban, Rongjun; Zhang, Huan; Jiang, Xiaohua; Cooke, Howard J; Xue, Yu; Shi, Qinghua

    2012-07-15

    Next generation sequencing (NGS) techniques have been widely used to document the small ribonucleic acids (RNAs) implicated in a variety of biological, physiological and pathological processes. An integrated computational tool is needed for handling and analysing the enormous datasets from small RNA deep sequencing approach. Herein, we present a novel web server, CPSS (a computational platform for the analysis of small RNA deep sequencing data), designed to completely annotate and functionally analyse microRNAs (miRNAs) from NGS data on one platform with a single data submission. Small RNA NGS data can be submitted to this server with analysis results being returned in two parts: (i) annotation analysis, which provides the most comprehensive analysis for small RNA transcriptome, including length distribution and genome mapping of sequencing reads, small RNA quantification, prediction of novel miRNAs, identification of differentially expressed miRNAs, piwi-interacting RNAs and other non-coding small RNAs between paired samples and detection of miRNA editing and modifications and (ii) functional analysis, including prediction of miRNA targeted genes by multiple tools, enrichment of gene ontology terms, signalling pathway involvement and protein-protein interaction analysis for the predicted genes. CPSS, a ready-to-use web server that integrates most functions of currently available bioinformatics tools, provides all the information wanted by the majority of users from small RNA deep sequencing datasets. CPSS is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/db/cpss/index.html or http://mcg.ustc.edu.cn/sdap1/cpss/index.html.

  2. Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

    Directory of Open Access Journals (Sweden)

    Ehsaneddin Asgari

    Full Text Available We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec to refer to biological sequences in general with protein-vectors (ProtVec for proteins (amino-acid sequences and gene-vectors (GeneVec for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups. Using support vector machine classifiers, FG-Nup sequences are distinguished from structured protein sequences found in Protein Data Bank (PDB with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be

  3. Predicting effects of noncoding variants with deep learning-based sequence model.

    Science.gov (United States)

    Zhou, Jian; Troyanskaya, Olga G

    2015-10-01

    Identifying functional effects of noncoding variants is a major challenge in human genetics. To predict the noncoding-variant effects de novo from sequence, we developed a deep learning-based algorithmic framework, DeepSEA (http://deepsea.princeton.edu/), that directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity. We further used this capability to improve prioritization of functional variants including expression quantitative trait loci (eQTLs) and disease-associated variants.

  4. Transcriptome sequences resolve deep relationships of the grape family.

    Science.gov (United States)

    Wen, Jun; Xiong, Zhiqiang; Nie, Ze-Long; Mao, Likai; Zhu, Yabing; Kan, Xian-Zhao; Ickert-Bond, Stefanie M; Gerrath, Jean; Zimmer, Elizabeth A; Fang, Xiao-Dong

    2013-01-01

    Previous phylogenetic studies of the grape family (Vitaceae) yielded poorly resolved deep relationships, thus impeding our understanding of the evolution of the family. Next-generation sequencing now offers access to protein coding sequences very easily, quickly and cost-effectively. To improve upon earlier work, we extracted 417 orthologous single-copy nuclear genes from the transcriptomes of 15 species of the Vitaceae, covering its phylogenetic diversity. The resulting transcriptome phylogeny provides robust support for the deep relationships, showing the phylogenetic utility of transcriptome data for plants over a time scale at least since the mid-Cretaceous. The pros and cons of transcriptome data for phylogenetic inference in plants are also evaluated.

  5. miRBase: integrating microRNA annotation and deep-sequencing data.

    Science.gov (United States)

    Kozomara, Ana; Griffiths-Jones, Sam

    2011-01-01

    miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences. Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. We have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations. miRBase is available online at: http://www.mirbase.org/.

  6. Transcriptome sequences resolve deep relationships of the grape family.

    Directory of Open Access Journals (Sweden)

    Jun Wen

    Full Text Available Previous phylogenetic studies of the grape family (Vitaceae yielded poorly resolved deep relationships, thus impeding our understanding of the evolution of the family. Next-generation sequencing now offers access to protein coding sequences very easily, quickly and cost-effectively. To improve upon earlier work, we extracted 417 orthologous single-copy nuclear genes from the transcriptomes of 15 species of the Vitaceae, covering its phylogenetic diversity. The resulting transcriptome phylogeny provides robust support for the deep relationships, showing the phylogenetic utility of transcriptome data for plants over a time scale at least since the mid-Cretaceous. The pros and cons of transcriptome data for phylogenetic inference in plants are also evaluated.

  7. DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

    OpenAIRE

    Yin, Zi; Chang, Keng-hao; Zhang, Ruofei

    2017-01-01

    Information extraction and user intention identification are central topics in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes...

  8. Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

    Science.gov (United States)

    Ouyang, Zhangyi; Liu, Feng; Zhao, Chenghui; Ren, Chao; An, Gaole; Mei, Chuan; Bo, Xiaochen; Shu, Wenjie

    2018-04-16

    RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.

  9. A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

    Science.gov (United States)

    Yildirim, Özal

    2018-05-01

    Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. DSAP: deep-sequencing small RNA analysis pipeline.

    Science.gov (United States)

    Huang, Po-Jung; Liu, Yi-Chung; Lee, Chi-Ching; Lin, Wei-Chen; Gan, Richie Ruei-Chi; Lyu, Ping-Chiang; Tang, Petrus

    2010-07-01

    DSAP is an automated multiple-task web service designed to provide a total solution to analyzing deep-sequencing small RNA datasets generated by next-generation sequencing technology. DSAP uses a tab-delimited file as an input format, which holds the unique sequence reads (tags) and their corresponding number of copies generated by the Solexa sequencing platform. The input data will go through four analysis steps in DSAP: (i) cleanup: removal of adaptors and poly-A/T/C/G/N nucleotides; (ii) clustering: grouping of cleaned sequence tags into unique sequence clusters; (iii) non-coding RNA (ncRNA) matching: sequence homology mapping against a transcribed sequence library from the ncRNA database Rfam (http://rfam.sanger.ac.uk/); and (iv) known miRNA matching: detection of known miRNAs in miRBase (http://www.mirbase.org/) based on sequence homology. The expression levels corresponding to matched ncRNAs and miRNAs are summarized in multi-color clickable bar charts linked to external databases. DSAP is also capable of displaying miRNA expression levels from different jobs using a log(2)-scaled color matrix. Furthermore, a cross-species comparative function is also provided to show the distribution of identified miRNAs in different species as deposited in miRBase. DSAP is available at http://dsap.cgu.edu.tw.

  11. High Class-Imbalance in pre-miRNA Prediction: A Novel Approach Based on deepSOM.

    Science.gov (United States)

    Stegmayer, Georgina; Yones, Cristian; Kamenetzky, Laura; Milone, Diego H

    2017-01-01

    The computational prediction of novel microRNA within a full genome involves identifying sequences having the highest chance of being a miRNA precursor (pre-miRNA). These sequences are usually named candidates to miRNA. The well-known pre-miRNAs are usually only a few in comparison to the hundreds of thousands of potential candidates to miRNA that have to be analyzed, which makes this task a high class-imbalance classification problem. The classical way of approaching it has been training a binary classifier in a supervised manner, using well-known pre-miRNAs as positive class and artificially defining the negative class. However, although the selection of positive labeled examples is straightforward, it is very difficult to build a set of negative examples in order to obtain a good set of training samples for a supervised method. In this work, we propose a novel and effective way of approaching this problem using machine learning, without the definition of negative examples. The proposal is based on clustering unlabeled sequences of a genome together with well-known miRNA precursors for the organism under study, which allows for the quick identification of the best candidates to miRNA as those sequences clustered with known precursors. Furthermore, we propose a deep model to overcome the problem of having very few positive class labels. They are always maintained in the deep levels as positive class while less likely pre-miRNA sequences are filtered level after level. Our approach has been compared with other methods for pre-miRNAs prediction in several species, showing effective predictivity of novel miRNAs. Additionally, we will show that our approach has a lower training time and allows for a better graphical navegability and interpretation of the results. A web-demo interface to try deepSOM is available at http://fich.unl.edu.ar/sinc/web-demo/deepsom/.

  12. LookSeq: A browser-based viewer for deep sequencing data

    OpenAIRE

    Manske, Heinrich Magnus; Kwiatkowski, Dominic P.

    2009-01-01

    Sequencing a genome to great depth can be highly informative about heterogeneity within an individual or a population. Here we address the problem of how to visualize the multiple layers of information contained in deep sequencing data. We propose an interactive AJAX-based web viewer for browsing large data sets of aligned sequence reads. By enabling seamless browsing and fast zooming, the LookSeq program assists the user to assimilate information at different levels of resolution, from an ov...

  13. Deep amplicon sequencing reveals mixed phytoplasma infection within single grapevine plants

    DEFF Research Database (Denmark)

    Nicolaisen, Mogens; Contaldo, Nicoletta; Makarova, Olga

    2011-01-01

    The diversity of phytoplasmas within single plants has not yet been fully investigated. In this project, deep amplicon sequencing was used to generate 50,926 phytoplasma sequences from 11 phytoplasma-infected grapevine samples from a PCR amplicon in the 5' end of the 16S region. After clustering ...

  14. A simple method for the parallel deep sequencing of full influenza A genomes

    DEFF Research Database (Denmark)

    Kampmann, Marie-Louise; Fordyce, Sarah Louise; Avila Arcos, Maria del Carmen

    2011-01-01

    Given the major threat of influenza A to human and animal health, and its ability to evolve rapidly through mutation and reassortment, tools that enable its timely characterization are necessary to help monitor its evolution and spread. For this purpose, deep sequencing can be a very valuable tool....... This study reports a comprehensive method that enables deep sequencing of the complete genomes of influenza A subtypes using the Illumina Genome Analyzer IIx (GAIIx). By using this method, the complete genomes of nine viruses were sequenced in parallel, representing the 2009 pandemic H1N1 virus, H5N1 virus...

  15. Enhanced arbovirus surveillance with deep sequencing: Identification of novel rhabdoviruses and bunyaviruses in Australian mosquitoes.

    Science.gov (United States)

    Coffey, Lark L; Page, Brady L; Greninger, Alexander L; Herring, Belinda L; Russell, Richard C; Doggett, Stephen L; Haniotis, John; Wang, Chunlin; Deng, Xutao; Delwart, Eric L

    2014-01-05

    Viral metagenomics characterizes known and identifies unknown viruses based on sequence similarities to any previously sequenced viral genomes. A metagenomics approach was used to identify virus sequences in Australian mosquitoes causing cytopathic effects in inoculated mammalian cell cultures. Sequence comparisons revealed strains of Liao Ning virus (Reovirus, Seadornavirus), previously detected only in China, livestock-infecting Stretch Lagoon virus (Reovirus, Orbivirus), two novel dimarhabdoviruses, named Beaumont and North Creek viruses, and two novel orthobunyaviruses, named Murrumbidgee and Salt Ash viruses. The novel virus proteomes diverged by ≥ 50% relative to their closest previously genetically characterized viral relatives. Deep sequencing also generated genomes of Warrego and Wallal viruses, orbiviruses linked to kangaroo blindness, whose genomes had not been fully characterized. This study highlights viral metagenomics in concert with traditional arbovirus surveillance to characterize known and new arboviruses in field-collected mosquitoes. Follow-up epidemiological studies are required to determine whether the novel viruses infect humans. © 2013 Elsevier Inc. All rights reserved.

  16. Analysis of microRNA profile of Anopheles sinensis by deep sequencing and bioinformatic approaches.

    Science.gov (United States)

    Feng, Xinyu; Zhou, Xiaojian; Zhou, Shuisen; Wang, Jingwen; Hu, Wei

    2018-03-12

    microRNAs (miRNAs) are small non-coding RNAs widely identified in many mosquitoes. They are reported to play important roles in development, differentiation and innate immunity. However, miRNAs in Anopheles sinensis, one of the Chinese malaria mosquitoes, remain largely unknown. We investigated the global miRNA expression profile of An. sinensis using Illumina Hiseq 2000 sequencing. Meanwhile, we applied a bioinformatic approach to identify potential miRNAs in An. sinensis. The identified miRNA profiles were compared and analyzed by two approaches. The selected miRNAs from the sequencing result and the bioinformatic approach were confirmed with qRT-PCR. Moreover, target prediction, GO annotation and pathway analysis were carried out to understand the role of miRNAs in An. sinensis. We identified 49 conserved miRNAs and 12 novel miRNAs by next-generation high-throughput sequencing technology. In contrast, 43 miRNAs were predicted by the bioinformatic approach, of which two were assigned as novel. Comparative analysis of miRNA profiles by two approaches showed that 21 miRNAs were shared between them. Twelve novel miRNAs did not match any known miRNAs of any organism, indicating that they are possibly species-specific. Forty miRNAs were found in many mosquito species, indicating that these miRNAs are evolutionally conserved and may have critical roles in the process of life. Both the selected known and novel miRNAs (asi-miR-281, asi-miR-184, asi-miR-14, asi-miR-nov5, asi-miR-nov4, asi-miR-9383, and asi-miR-2a) could be detected by quantitative real-time PCR (qRT-PCR) in the sequenced sample, and the expression patterns of these miRNAs measured by qRT-PCR were in concordance with the original miRNA sequencing data. The predicted targets for the known and the novel miRNAs covered many important biological roles and pathways indicating the diversity of miRNA functions. We also found 21 conserved miRNAs and eight counterparts of target immune pathway genes in An. sinensis

  17. Deep-sequencing to resolve complex diversity of apicomplexan parasites in platypuses and echidnas: Proof of principle for wildlife disease investigation.

    Science.gov (United States)

    Šlapeta, Jan; Saverimuttu, Stefan; Vogelnest, Larry; Sangster, Cheryl; Hulst, Frances; Rose, Karrie; Thompson, Paul; Whittington, Richard

    2017-11-01

    The short-beaked echidna (Tachyglossus aculeatus) and the platypus (Ornithorhynchus anatinus) are iconic egg-laying monotremes (Mammalia: Monotremata) from Australasia. The aim of this study was to demonstrate the utility of diversity profiles in disease investigations of monotremes. Using small subunit (18S) rDNA amplicon deep-sequencing we demonstrated the presence of apicomplexan parasites and confirmed by direct and cloned amplicon gene sequencing Theileria ornithorhynchi, Theileria tachyglossi, Eimeria echidnae and Cryptosporidium fayeri. Using a combination of samples from healthy and diseased animals, we show a close evolutionary relationship between species of coccidia (Eimeria) and piroplasms (Theileria) from the echidna and platypus. The presence of E. echidnae was demonstrated in faeces and tissues affected by disseminated coccidiosis. Moreover, the presence of E. echidnae DNA in the blood of echidnas was associated with atoxoplasma-like stages in white blood cells, suggesting Hepatozoon tachyglossi blood stages are disseminated E. echidnae stages. These next-generation DNA sequencing technologies are suited to material and organisms that have not been previously characterised and for which the material is scarce. The deep sequencing approach supports traditional diagnostic methods, including microscopy, clinical pathology and histopathology, to better define the status quo. This approach is particularly suitable for wildlife disease investigation. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Deep whole-genome sequencing of 90 Han Chinese genomes.

    Science.gov (United States)

    Lan, Tianming; Lin, Haoxiang; Zhu, Wenjuan; Laurent, Tellier Christian Asker Melchior; Yang, Mengcheng; Liu, Xin; Wang, Jun; Wang, Jian; Yang, Huanming; Xu, Xun; Guo, Xiaosen

    2017-09-01

    Next-generation sequencing provides a high-resolution insight into human genetic information. However, the focus of previous studies has primarily been on low-coverage data due to the high cost of sequencing. Although the 1000 Genomes Project and the Haplotype Reference Consortium have both provided powerful reference panels for imputation, low-frequency and novel variants remain difficult to discover and call with accuracy on the basis of low-coverage data. Deep sequencing provides an optimal solution for the problem of these low-frequency and novel variants. Although whole-exome sequencing is also a viable choice for exome regions, it cannot account for noncoding regions, sometimes resulting in the absence of important, causal variants. For Han Chinese populations, the majority of variants have been discovered based upon low-coverage data from the 1000 Genomes Project. However, high-coverage, whole-genome sequencing data are limited for any population, and a large amount of low-frequency, population-specific variants remain uncharacterized. We have performed whole-genome sequencing at a high depth (∼×80) of 90 unrelated individuals of Chinese ancestry, collected from the 1000 Genomes Project samples, including 45 Northern Han Chinese and 45 Southern Han Chinese samples. Eighty-three of these 90 have been sequenced by the 1000 Genomes Project. We have identified 12 568 804 single nucleotide polymorphisms, 2 074 210 short InDels, and 26 142 structural variations from these 90 samples. Compared to the Han Chinese data from the 1000 Genomes Project, we have found 7 000 629 novel variants with low frequency (defined as minor allele frequency genome. Compared to the 1000 Genomes Project, these Han Chinese deep sequencing data enhance the characterization of a large number of low-frequency, novel variants. This will be a valuable resource for promoting Chinese genetics research and medical development. Additionally, it will provide a valuable supplement to the 1000

  19. Error Analysis of Deep Sequencing of Phage Libraries: Peptides Censored in Sequencing

    Directory of Open Access Journals (Sweden)

    Wadim L. Matochko

    2013-01-01

    Full Text Available Next-generation sequencing techniques empower selection of ligands from phage-display libraries because they can detect low abundant clones and quantify changes in the copy numbers of clones without excessive selection rounds. Identification of errors in deep sequencing data is the most critical step in this process because these techniques have error rates >1%. Mechanisms that yield errors in Illumina and other techniques have been proposed, but no reports to date describe error analysis in phage libraries. Our paper focuses on error analysis of 7-mer peptide libraries sequenced by Illumina method. Low theoretical complexity of this phage library, as compared to complexity of long genetic reads and genomes, allowed us to describe this library using convenient linear vector and operator framework. We describe a phage library as N×1 frequency vector n=ni, where ni is the copy number of the ith sequence and N is the theoretical diversity, that is, the total number of all possible sequences. Any manipulation to the library is an operator acting on n. Selection, amplification, or sequencing could be described as a product of a N×N matrix and a stochastic sampling operator (Sa. The latter is a random diagonal matrix that describes sampling of a library. In this paper, we focus on the properties of Sa and use them to define the sequencing operator (Seq. Sequencing without any bias and errors is Seq=Sa IN, where IN is a N×N unity matrix. Any bias in sequencing changes IN to a nonunity matrix. We identified a diagonal censorship matrix (CEN, which describes elimination or statistically significant downsampling, of specific reads during the sequencing process.

  20. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  1. Deep sequencing methods for protein engineering and design.

    Science.gov (United States)

    Wrenbeck, Emily E; Faber, Matthew S; Whitehead, Timothy A

    2017-08-01

    The advent of next-generation sequencing (NGS) has revolutionized protein science, and the development of complementary methods enabling NGS-driven protein engineering have followed. In general, these experiments address the functional consequences of thousands of protein variants in a massively parallel manner using genotype-phenotype linked high-throughput functional screens followed by DNA counting via deep sequencing. We highlight the use of information rich datasets to engineer protein molecular recognition. Examples include the creation of multiple dual-affinity Fabs targeting structurally dissimilar epitopes and engineering of a broad germline-targeted anti-HIV-1 immunogen. Additionally, we highlight the generation of enzyme fitness landscapes for conducting fundamental studies of protein behavior and evolution. We conclude with discussion of technological advances. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. miRBase: annotating high confidence microRNAs using deep sequencing data.

    Science.gov (United States)

    Kozomara, Ana; Griffiths-Jones, Sam

    2014-01-01

    We describe an update of the miRBase database (http://www.mirbase.org/), the primary microRNA sequence repository. The latest miRBase release (v20, June 2013) contains 24 521 microRNA loci from 206 species, processed to produce 30 424 mature microRNA products. The rate of deposition of novel microRNAs and the number of researchers involved in their discovery continue to increase, driven largely by small RNA deep sequencing experiments. In the face of these increases, and a range of microRNA annotation methods and criteria, maintaining the quality of the microRNA sequence data set is a significant challenge. Here, we describe recent developments of the miRBase database to address this issue. In particular, we describe the collation and use of deep sequencing data sets to assign levels of confidence to miRBase entries. We now provide a high confidence subset of miRBase entries, based on the pattern of mapped reads. The high confidence microRNA data set is available alongside the complete microRNA collection at http://www.mirbase.org/. We also describe embedding microRNA-specific Wikipedia pages on the miRBase website to encourage the microRNA community to contribute and share textual and functional information.

  3. LookSeq: a browser-based viewer for deep sequencing data.

    Science.gov (United States)

    Manske, Heinrich Magnus; Kwiatkowski, Dominic P

    2009-11-01

    Sequencing a genome to great depth can be highly informative about heterogeneity within an individual or a population. Here we address the problem of how to visualize the multiple layers of information contained in deep sequencing data. We propose an interactive AJAX-based web viewer for browsing large data sets of aligned sequence reads. By enabling seamless browsing and fast zooming, the LookSeq program assists the user to assimilate information at different levels of resolution, from an overview of a genomic region to fine details such as heterogeneity within the sample. A specific problem, particularly if the sample is heterogeneous, is how to depict information about structural variation. LookSeq provides a simple graphical representation of paired sequence reads that is more revealing about potential insertions and deletions than are conventional methods.

  4. AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

    Science.gov (United States)

    Wang, Sheng; Sun, Siqi; Xu, Jinbo

    2016-09-01

    Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

  5. Genomic variation in macrophage-cultured European porcine reproductive and respiratory syndrome virus Olot/91 revealed using ultra-deep next generation sequencing.

    Science.gov (United States)

    Lu, Zen H; Brown, Alexander; Wilson, Alison D; Calvert, Jay G; Balasch, Monica; Fuentes-Utrilla, Pablo; Loecherbach, Julia; Turner, Frances; Talbot, Richard; Archibald, Alan L; Ait-Ali, Tahar

    2014-03-04

    Porcine Reproductive and Respiratory Syndrome (PRRS) is a disease of major economic impact worldwide. The etiologic agent of this disease is the PRRS virus (PRRSV). Increasing evidence suggest that microevolution within a coexisting quasispecies population can give rise to high sequence heterogeneity in PRRSV. We developed a pipeline based on the ultra-deep next generation sequencing approach to first construct the complete genome of a European PRRSV, strain Olot/9, cultured on macrophages and then capture the rare variants representative of the mixed quasispecies population. Olot/91 differs from the reference Lelystad strain by about 5% and a total of 88 variants, with frequencies as low as 1%, were detected in the mixed population. These variants included 16 non-synonymous variants concentrated in the genes encoding structural and nonstructural proteins; including Glycoprotein 2a and 5. Using an ultra-deep sequencing methodology, the complete genome of Olot/91 was constructed without any prior knowledge of the sequence. Rare variants that constitute minor fractions of the heterogeneous PRRSV population could successfully be detected to allow further exploration of microevolutionary events.

  6. Deep RNA Sequencing of the Skeletal Muscle Transcriptome in Swimming Fish

    NARCIS (Netherlands)

    Palstra, A.P.; Beltran, S.; Burgerhout, E.; Brittijn, S.A.; Magnoni, L.J.; Henkel, C.V.; Jansen, A.; Thillart, G.E.E.J.M.; Spaink, H.P.; Planas, J.V.

    2013-01-01

    Deep RNA sequencing (RNA-seq) was performed to provide an in-depth view of the transcriptome of red and white skeletal muscle of exercised and non-exercised rainbow trout (Oncorhynchus mykiss) with the specific objective to identify expressed genes and quantify the transcriptomic effects of

  7. Ultra-deep sequencing of intra-host rabies virus populations during cross-species transmission.

    Directory of Open Access Journals (Sweden)

    Monica K Borucki

    2013-11-01

    Full Text Available One of the hurdles to understanding the role of viral quasispecies in RNA virus cross-species transmission (CST events is the need to analyze a densely sampled outbreak using deep sequencing in order to measure the amount of mutation occurring on a small time scale. In 2009, the California Department of Public Health reported a dramatic increase (350 in the number of gray foxes infected with a rabies virus variant for which striped skunks serve as a reservoir host in Humboldt County. To better understand the evolution of rabies, deep-sequencing was applied to 40 unpassaged rabies virus samples from the Humboldt outbreak. For each sample, approximately 11 kb of the 12 kb genome was amplified and sequenced using the Illumina platform. Average coverage was 17,448 and this allowed characterization of the rabies virus population present in each sample at unprecedented depths. Phylogenetic analysis of the consensus sequence data demonstrated that samples clustered according to date (1995 vs. 2009 and geographic location (northern vs. southern. A single amino acid change in the G protein distinguished a subset of northern foxes from a haplotype present in both foxes and skunks, suggesting this mutation may have played a role in the observed increased transmission among foxes in this region. Deep-sequencing data indicated that many genetic changes associated with the CST event occurred prior to 2009 since several nonsynonymous mutations that were present in the consensus sequences of skunk and fox rabies samples obtained from 20032010 were present at the sub-consensus level (as rare variants in the viral population in skunk and fox samples from 1995. These results suggest that analysis of rare variants within a viral population may yield clues to ancestral genomes and identify rare variants that have the potential to be selected for if environment conditions change.

  8. Determining mutant spectra of three RNA viral samples using ultra-deep sequencing

    Energy Technology Data Exchange (ETDEWEB)

    Chen, H

    2012-06-06

    RNA viruses have extremely high mutation rates that enable the virus to adapt to new host environments and even jump from one species to another. As part of a viral transmission study, three viral samples collected from naturally infected animals were sequenced using Illumina paired-end technology at ultra-deep coverage. In order to determine the mutant spectra within the viral quasispecies, it is critical to understand the sequencing error rates and control for false positive calls of viral variants (point mutantations). I will estimate the sequencing error rate from two control sequences and characterize the mutant spectra in the natural samples with this error rate.

  9. Deep sequencing reveals double mutations in cis of MPL exon 10 in myeloproliferative neoplasms.

    Science.gov (United States)

    Pietra, Daniela; Brisci, Angela; Rumi, Elisa; Boggi, Sabrina; Elena, Chiara; Pietrelli, Alessandro; Bordoni, Roberta; Ferrari, Maurizio; Passamonti, Francesco; De Bellis, Gianluca; Cremonesi, Laura; Cazzola, Mario

    2011-04-01

    Somatic mutations of MPL exon 10, mainly involving a W515 substitution, have been described in JAK2 (V617F)-negative patients with essential thrombocythemia and primary myelofibrosis. We used direct sequencing and high-resolution melt analysis to identify mutations of MPL exon 10 in 570 patients with myeloproliferative neoplasms, and allele specific PCR and deep sequencing to further characterize a subset of mutated patients. Somatic mutations were detected in 33 of 221 patients (15%) with JAK2 (V617F)-negative essential thrombocythemia or primary myelofibrosis. Only one patient with essential thrombocythemia carried both JAK2 (V617F) and MPL (W515L). High-resolution melt analysis identified abnormal patterns in all the MPL mutated cases, while direct sequencing did not detect the mutant MPL in one fifth of them. In 3 cases carrying double MPL mutations, deep sequencing analysis showed identical load and location in cis of the paired lesions, indicating their simultaneous occurrence on the same chromosome.

  10. Deep-Sea Microbes: Linking Biogeochemical Rates to -Omics Approaches

    Science.gov (United States)

    Herndl, G. J.; Sintes, E.; Bayer, B.; Bergauer, K.; Amano, C.; Hansman, R.; Garcia, J.; Reinthaler, T.

    2016-02-01

    Over the past decade substantial progress has been made in determining deep ocean microbial activity and resolving some of the enigmas in understanding the deep ocean carbon flux. Also, metagenomics approaches have shed light onto the dark ocean's microbes but linking -omics approaches to biogeochemical rate measurements are generally rare in microbial oceanography and even more so for the deep ocean. In this presentation, we will show by combining metagenomics, -proteomics and biogeochemical rate measurements on the bulk and single-cell level that deep-sea microbes exhibit characteristics of generalists with a large genome repertoire, versatile in utilizing substrate as revealed by metaproteomics. This is in striking contrast with the apparently rather uniform dissolved organic matter pool in the deep ocean. Combining the different -omics approaches with metabolic rate measurements, we will highlight some major inconsistencies and enigmas in our understanding of the carbon cycling and microbial food web structure in the dark ocean.

  11. Deep sequencing-based transcriptome analysis of Plutella xylostella larvae parasitized by Diadegma semiclausum

    Science.gov (United States)

    2011-01-01

    Background Parasitoid insects manipulate their hosts' physiology by injecting various factors into their host upon parasitization. Transcriptomic approaches provide a powerful approach to study insect host-parasitoid interactions at the molecular level. In order to investigate the effects of parasitization by an ichneumonid wasp (Diadegma semiclausum) on the host (Plutella xylostella), the larval transcriptome profile was analyzed using a short-read deep sequencing method (Illumina). Symbiotic polydnaviruses (PDVs) associated with ichneumonid parasitoids, known as ichnoviruses, play significant roles in host immune suppression and developmental regulation. In the current study, D. semiclausum ichnovirus (DsIV) genes expressed in P. xylostella were identified and their sequences compared with other reported PDVs. Five of these genes encode proteins of unknown identity, that have not previously been reported. Results De novo assembly of cDNA sequence data generated 172,660 contigs between 100 and 10000 bp in length; with 35% of > 200 bp in length. Parasitization had significant impacts on expression levels of 928 identified insect host transcripts. Gene ontology data illustrated that the majority of the differentially expressed genes are involved in binding, catalytic activity, and metabolic and cellular processes. In addition, the results show that transcription levels of antimicrobial peptides, such as gloverin, cecropin E and lysozyme, were up-regulated after parasitism. Expression of ichnovirus genes were detected in parasitized larvae with 19 unique sequences identified from five PDV gene families including vankyrin, viral innexin, repeat elements, a cysteine-rich motif, and polar residue rich protein. Vankyrin 1 and repeat element 1 genes showed the highest transcription levels among the DsIV genes. Conclusion This study provides detailed information on differential expression of P. xylostella larval genes following parasitization, DsIV genes expressed in the

  12. Deep and shallow approaches to learning mathematics are not mutually exclusive.

    OpenAIRE

    Mathias, J.; Newton, D.P.

    2016-01-01

    From time to time, students are characterised as having a deep or shallow approach to learning. A deep approach to learning tends to attract more approval than a shallow approach, at least in the West. Students on a university-based Foundation course to prepare them for undergraduate studies were divided into those likely to have a deep approach (26) and those likely to have a shallow approach (18). Their performance in a test of problem solving in an aspect of applied mathematics was compare...

  13. Genomic region operation kit for flexible processing of deep sequencing data.

    Science.gov (United States)

    Ovaska, Kristian; Lyly, Lauri; Sahu, Biswajyoti; Jänne, Olli A; Hautaniemi, Sampsa

    2013-01-01

    Computational analysis of data produced in deep sequencing (DS) experiments is challenging due to large data volumes and requirements for flexible analysis approaches. Here, we present a mathematical formalism based on set algebra for frequently performed operations in DS data analysis to facilitate translation of biomedical research questions to language amenable for computational analysis. With the help of this formalism, we implemented the Genomic Region Operation Kit (GROK), which supports various DS-related operations such as preprocessing, filtering, file conversion, and sample comparison. GROK provides high-level interfaces for R, Python, Lua, and command line, as well as an extension C++ API. It supports major genomic file formats and allows storing custom genomic regions in efficient data structures such as red-black trees and SQL databases. To demonstrate the utility of GROK, we have characterized the roles of two major transcription factors (TFs) in prostate cancer using data from 10 DS experiments. GROK is freely available with a user guide from >http://csbi.ltdk.helsinki.fi/grok/.

  14. Workup of Human Blood Samples for Deep Sequencing of HIV-1 Genomes

    NARCIS (Netherlands)

    Cornelissen, Marion; Gall, Astrid; van der Kuyl, Antoinette; Wymant, Chris; Blanquart, François; Fraser, Christophe; Berkhout, Ben

    2018-01-01

    We describe a detailed protocol for the manual workup of blood (plasma/serum) samples from individuals infected with the human immunodeficiency virus type 1 (HIV-1) for deep sequence analysis of the viral genome. The study optimizing the assay was performed in the context of the BEEHIVE (Bridging

  15. A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology.

    Science.gov (United States)

    Kravatsky, Yuri; Chechetkin, Vladimir; Fedoseeva, Daria; Gorbacheva, Maria; Kravatskaya, Galina; Kretova, Olga; Tchurikov, Nickolai

    2017-11-23

    The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs), requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s). Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s). The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi) targets in human immunodeficiency virus 1 (HIV-1) subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented.

  16. Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

    NARCIS (Netherlands)

    K.L. Groenland (Koen); S.M. Bohte (Sander)

    2016-01-01

    textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order

  17. Deep sequencing of foot-and-mouth disease virus reveals RNA sequences involved in genome packaging.

    Science.gov (United States)

    Logan, Grace; Newman, Joseph; Wright, Caroline F; Lasecka-Dykes, Lidia; Haydon, Daniel T; Cottam, Eleanor M; Tuthill, Tobias J

    2017-10-18

    Non-enveloped viruses protect their genomes by packaging them into an outer shell or capsid of virus-encoded proteins. Packaging and capsid assembly in RNA viruses can involve interactions between capsid proteins and secondary structures in the viral genome as exemplified by the RNA bacteriophage MS2 and as proposed for other RNA viruses of plants, animals and human. In the picornavirus family of non-enveloped RNA viruses, the requirements for genome packaging remain poorly understood. Here we show a novel and simple approach to identify predicted RNA secondary structures involved in genome packaging in the picornavirus foot-and-mouth disease virus (FMDV). By interrogating deep sequencing data generated from both packaged and unpackaged populations of RNA we have determined multiple regions of the genome with constrained variation in the packaged population. Predicted secondary structures of these regions revealed stem loops with conservation of structure and a common motif at the loop. Disruption of these features resulted in attenuation of virus growth in cell culture due to a reduction in assembly of mature virions. This study provides evidence for the involvement of predicted RNA structures in picornavirus packaging and offers a readily transferable methodology for identifying packaging requirements in many other viruses. Importance In order to transmit their genetic material to a new host, non-enveloped viruses must protect their genomes by packaging them into an outer shell or capsid of virus-encoded proteins. For many non-enveloped RNA viruses the requirements for this critical part of the viral life cycle remain poorly understood. We have identified RNA sequences involved in genome packaging of the picornavirus foot-and-mouth disease virus. This virus causes an economically devastating disease of livestock affecting both the developed and developing world. The experimental methods developed to carry out this work are novel, simple and transferable to the

  18. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

    Science.gov (United States)

    Pan, Xiaoyong; Shen, Hong-Bin

    2017-02-28

    RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6

  19. A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.

    Directory of Open Access Journals (Sweden)

    James X Sun

    2018-02-01

    Full Text Available A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity, a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration's allele frequency (AF, taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs. Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10-75%. Overall, predictions were made in 85% of cases, with 95-99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants

  20. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

    NARCIS (Netherlands)

    I. Tachmazidou (Ioanna); Süveges, D. (Dániel); J. Min (Josine); G.R.S. Ritchie (Graham R.S.); Steinberg, J. (Julia); K. Walter (Klaudia); V. Iotchkova (Valentina); J.A. Schwartzentruber (Jeremy); J. Huang (Jian); Y. Memari (Yasin); McCarthy, S. (Shane); Crawford, A.A. (Andrew A.); C. Bombieri (Cristina); M. Cocca (Massimiliano); A.-E. Farmaki (Aliki-Eleni); T.R. Gaunt (Tom); P. Jousilahti (Pekka); M.N. Kooijman (Marjolein ); Lehne, B. (Benjamin); G. Malerba (Giovanni); S. Männistö (Satu); A. Matchan (Angela); M.C. Medina-Gomez (Carolina); S. Metrustry (Sarah); A. Nag (Abhishek); I. Ntalla (Ioanna); L. Paternoster (Lavinia); N.W. Rayner (Nigel William); C. Sala (Cinzia); W.R. Scott (William R.); H.A. Shihab (Hashem A.); L. Southam (Lorraine); B. St Pourcain (Beate); M. Traglia (Michela); K. Trajanoska (Katerina); Zaza, G. (Gialuigi); W. Zhang (Weihua); M.S. Artigas; Bansal, N. (Narinder); M. Benn (Marianne); Chen, Z. (Zhongsheng); P. Danecek (Petr); Lin, W.-Y. (Wei-Yu); A. Locke (Adam); J. Luan (Jian'An); A.K. Manning (Alisa); Mulas, A. (Antonella); C. Sidore (Carlo); A. Tybjaerg-Hansen; A. Varbo (Anette); M. Zoledziewska (Magdalena); C. Finan (Chris); Hatzikotoulas, K. (Konstantinos); A.E. Hendricks (Audrey E.); J.P. Kemp (John); A. Moayyeri (Alireza); Panoutsopoulou, K. (Kalliope); Szpak, M. (Michal); S.G. Wilson (Scott); M. Boehnke (Michael); F. Cucca (Francesco); Di Angelantonio, E. (Emanuele); C. Langenberg (Claudia); C.M. Lindgren (Cecilia M.); McCarthy, M.I. (Mark I.); A.P. Morris (Andrew); B.G. Nordestgaard (Børge); R.A. Scott (Robert); M.D. Tobin (Martin); N.J. Wareham (Nick); P.R. Burton (Paul); J.C. Chambers (John); Smith, G.D. (George Davey); G.V. Dedoussis (George); J.F. Felix (Janine); O.H. Franco (Oscar); Gambaro, G. (Giovanni); P. Gasparini (Paolo); C.J. Hammond (Christopher J.); A. Hofman (Albert); V.W.V. Jaddoe (Vincent); M.E. Kleber (Marcus); J.S. Kooner (Jaspal S.); M. Perola (Markus); C.L. Relton (Caroline); S.M. Ring (Susan); F. Rivadeneira Ramirez (Fernando); V. Salomaa (Veikko); T.D. Spector (Timothy); O. Stegle (Oliver); D. Toniolo (Daniela); A.G. Uitterlinden (André); I.E. Barroso (Inês); C.M.T. Greenwood (Celia); Perry, J.R.B. (John R.B.); Walker, B.R. (Brian R.); A.S. Butterworth (Adam); Y. Xue (Yali); R. Durbin (Richard); K.S. Small (Kerrin); N. Soranzo (Nicole); N.J. Timpson (Nicholas); E. Zeggini (Eleftheria)

    2016-01-01

    textabstractDeep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the

  1. Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits

    DEFF Research Database (Denmark)

    Tachmazidou, Ioanna; Süveges, Dániel; Min, Josine L

    2017-01-01

    Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader alleli...

  2. Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma

    Science.gov (United States)

    Lahuerta, Juan J.; Pepin, François; González, Marcos; Barrio, Santiago; Ayala, Rosa; Puig, Noemí; Montalban, María A.; Paiva, Bruno; Weng, Li; Jiménez, Cristina; Sopena, María; Moorhead, Martin; Cedena, Teresa; Rapado, Immaculada; Mateos, María Victoria; Rosiñol, Laura; Oriol, Albert; Blanchard, María J.; Martínez, Rafael; Bladé, Joan; San Miguel, Jesús; Faham, Malek; García-Sanz, Ramón

    2014-01-01

    We assessed the prognostic value of minimal residual disease (MRD) detection in multiple myeloma (MM) patients using a sequencing-based platform in bone marrow samples from 133 MM patients in at least very good partial response (VGPR) after front-line therapy. Deep sequencing was carried out in patients in whom a high-frequency myeloma clone was identified and MRD was assessed using the IGH-VDJH, IGH-DJH, and IGK assays. The results were contrasted with those of multiparametric flow cytometry (MFC) and allele-specific oligonucleotide polymerase chain reaction (ASO-PCR). The applicability of deep sequencing was 91%. Concordance between sequencing and MFC and ASO-PCR was 83% and 85%, respectively. Patients who were MRD– by sequencing had a significantly longer time to tumor progression (TTP) (median 80 vs 31 months; P < .0001) and overall survival (median not reached vs 81 months; P = .02), compared with patients who were MRD+. When stratifying patients by different levels of MRD, the respective TTP medians were: MRD ≥10−3 27 months, MRD 10−3 to 10−5 48 months, and MRD <10−5 80 months (P = .003 to .0001). Ninety-two percent of VGPR patients were MRD+. In complete response patients, the TTP remained significantly longer for MRD– compared with MRD+ patients (131 vs 35 months; P = .0009). PMID:24646471

  3. A Bioinformatic Pipeline for Monitoring of the Mutational Stability of Viral Drug Targets with Deep-Sequencing Technology

    Directory of Open Access Journals (Sweden)

    Yuri Kravatsky

    2017-11-01

    Full Text Available The efficient development of antiviral drugs, including efficient antiviral small interfering RNAs (siRNAs, requires continuous monitoring of the strict correspondence between a drug and the related highly variable viral DNA/RNA target(s. Deep sequencing is able to provide an assessment of both the general target conservation and the frequency of particular mutations in the different target sites. The aim of this study was to develop a reliable bioinformatic pipeline for the analysis of millions of short, deep sequencing reads corresponding to selected highly variable viral sequences that are drug target(s. The suggested bioinformatic pipeline combines the available programs and the ad hoc scripts based on an original algorithm of the search for the conserved targets in the deep sequencing data. We also present the statistical criteria for the threshold of reliable mutation detection and for the assessment of variations between corresponding data sets. These criteria are robust against the possible sequencing errors in the reads. As an example, the bioinformatic pipeline is applied to the study of the conservation of RNA interference (RNAi targets in human immunodeficiency virus 1 (HIV-1 subtype A. The developed pipeline is freely available to download at the website http://virmut.eimb.ru/. Brief comments and comparisons between VirMut and other pipelines are also presented.

  4. InFusion: Advancing Discovery of Fusion Genes and Chimeric Transcripts from Deep RNA-Sequencing Data.

    Directory of Open Access Journals (Sweden)

    Konstantin Okonechnikov

    Full Text Available Analysis of fusion transcripts has become increasingly important due to their link with cancer development. Since high-throughput sequencing approaches survey fusion events exhaustively, several computational methods for the detection of gene fusions from RNA-seq data have been developed. This kind of analysis, however, is complicated by native trans-splicing events, the splicing-induced complexity of the transcriptome and biases and artefacts introduced in experiments and data analysis. There are a number of tools available for the detection of fusions from RNA-seq data; however, certain differences in specificity and sensitivity between commonly used approaches have been found. The ability to detect gene fusions of different types, including isoform fusions and fusions involving non-coding regions, has not been thoroughly studied yet. Here, we propose a novel computational toolkit called InFusion for fusion gene detection from RNA-seq data. InFusion introduces several unique features, such as discovery of fusions involving intergenic regions, and detection of anti-sense transcription in chimeric RNAs based on strand-specificity. Our approach demonstrates superior detection accuracy on simulated data and several public RNA-seq datasets. This improved performance was also evident when evaluating data from RNA deep-sequencing of two well-established prostate cancer cell lines. InFusion identified 26 novel fusion events that were validated in vitro, including alternatively spliced gene fusion isoforms and chimeric transcripts that include intergenic regions. The toolkit is freely available to download from http:/bitbucket.org/kokonech/infusion.

  5. Deep sequencing analysis of the developing mouse brain reveals a novel microRNA

    Directory of Open Access Journals (Sweden)

    Piltz Sandra

    2011-04-01

    Full Text Available Abstract Background MicroRNAs (miRNAs are small non-coding RNAs that can exert multilevel inhibition/repression at a post-transcriptional or protein synthesis level during disease or development. Characterisation of miRNAs in adult mammalian brains by deep sequencing has been reported previously. However, to date, no small RNA profiling of the developing brain has been undertaken using this method. We have performed deep sequencing and small RNA analysis of a developing (E15.5 mouse brain. Results We identified the expression of 294 known miRNAs in the E15.5 developing mouse brain, which were mostly represented by let-7 family and other brain-specific miRNAs such as miR-9 and miR-124. We also discovered 4 putative 22-23 nt miRNAs: mm_br_e15_1181, mm_br_e15_279920, mm_br_e15_96719 and mm_br_e15_294354 each with a 70-76 nt predicted pre-miRNA. We validated the 4 putative miRNAs and further characterised one of them, mm_br_e15_1181, throughout embryogenesis. Mm_br_e15_1181 biogenesis was Dicer1-dependent and was expressed in E3.5 blastocysts and E7 whole embryos. Embryo-wide expression patterns were observed at E9.5 and E11.5 followed by a near complete loss of expression by E13.5, with expression restricted to a specialised layer of cells within the developing and early postnatal brain. Mm_br_e15_1181 was upregulated during neurodifferentiation of P19 teratocarcinoma cells. This novel miRNA has been identified as miR-3099. Conclusions We have generated and analysed the first deep sequencing dataset of small RNA sequences of the developing mouse brain. The analysis revealed a novel miRNA, miR-3099, with potential regulatory effects on early embryogenesis, and involvement in neuronal cell differentiation/function in the brain during late embryonic and early neonatal development.

  6. Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing

    OpenAIRE

    Manske, Magnus; Miotto, Olivo; Campino, Susana; Auburn, Sarah; Almagro-Garcia, Jacob; Maslen, Gareth; O?Brien, Jack; Djimde, Abdoulaye; Doumbo, Ogobara; Zongo, Issaka; Ouedraogo, Jean-Bosco; Michon, Pascal; Mueller, Ivo; Siba, Peter; Nzila, Alexis

    2012-01-01

    : Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance. Here we describe methods for the large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short-term culture. Analysis of 86,158 exonic single nucleotide polymorphisms that passed genotyping quality c...

  7. An adaptive deep learning approach for PPG-based identification.

    Science.gov (United States)

    Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

    2016-08-01

    Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

  8. Identification of microRNAs Involved in the Host Response to Enterovirus 71 Infection by a Deep Sequencing Approach

    Directory of Open Access Journals (Sweden)

    Lunbiao Cui

    2010-01-01

    Full Text Available Role of microRNA (miRNA has been highlighted in pathogen-host interactions recently. To identify cellular miRNAs involved in the host response to enterovirus 71 (EV71 infection, we performed a comprehensive miRNA profiling in EV71-infected Hep2 cells through deep sequencing. 64 miRNAs were found whose expression levels changed for more than 2-fold in response to EV71 infection. Gene ontology analysis revealed that many of these mRNAs play roles in neurological process, immune response, and cell death pathways, which are known to be associated with the extreme virulence of EV71. To our knowledge, this is the first study on host miRNAs expression alteration response to EV71 infection. Our findings supported the hypothesis that certain miRNAs might be essential in the host-pathogen interactions.

  9. Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata

    Directory of Open Access Journals (Sweden)

    Yu Huaping

    2010-07-01

    Full Text Available Abstract Background MicroRNAs (miRNAs play a critical role in post-transcriptional gene regulation and have been shown to control many genes involved in various biological and metabolic processes. There have been extensive studies to discover miRNAs and analyze their functions in model plant species, such as Arabidopsis and rice. Deep sequencing technologies have facilitated identification of species-specific or lowly expressed as well as conserved or highly expressed miRNAs in plants. Results In this research, we used Solexa sequencing to discover new microRNAs in trifoliate orange (Citrus trifoliata which is an important rootstock of citrus. A total of 13,106,753 reads representing 4,876,395 distinct sequences were obtained from a short RNA library generated from small RNA extracted from C. trifoliata flower and fruit tissues. Based on sequence similarity and hairpin structure prediction, we found that 156,639 reads representing 63 sequences from 42 highly conserved miRNA families, have perfect matches to known miRNAs. We also identified 10 novel miRNA candidates whose precursors were all potentially generated from citrus ESTs. In addition, five miRNA* sequences were also sequenced. These sequences had not been earlier described in other plant species and accumulation of the 10 novel miRNAs were confirmed by qRT-PCR analysis. Potential target genes were predicted for most conserved and novel miRNAs. Moreover, four target genes including one encoding IRX12 copper ion binding/oxidoreductase and three genes encoding NB-LRR disease resistance protein have been experimentally verified by detection of the miRNA-mediated mRNA cleavage in C. trifoliata. Conclusion Deep sequencing of short RNAs from C. trifoliata flowers and fruits identified 10 new potential miRNAs and 42 highly conserved miRNA families, indicating that specific miRNAs exist in C. trifoliata. These results show that regulatory miRNAs exist in agronomically important trifoliate orange

  10. Sequence2Vec: A novel embedding approach for modeling transcription factor binding affinity landscape

    KAUST Repository

    Dai, Hanjun

    2017-07-26

    Motivation: An accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem. Results: Here we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model (HMM) which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these HMMs into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA data sets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.

  11. Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data

    DEFF Research Database (Denmark)

    Krøigård, Anne Bruun; Thomassen, Mads; Lænkholm, Anne Vibeke

    2016-01-01

    a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2...

  12. Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

    OpenAIRE

    Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao

    2016-01-01

    A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...

  13. Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data.

    Science.gov (United States)

    Krøigård, Anne Bruun; Thomassen, Mads; Lænkholm, Anne-Vibeke; Kruse, Torben A; Larsen, Martin Jakob

    2016-01-01

    Next generation sequencing is extensively applied to catalogue somatic mutations in cancer, in research settings and increasingly in clinical settings for molecular diagnostics, guiding therapy decisions. Somatic variant callers perform paired comparisons of sequencing data from cancer tissue and matched normal tissue in order to detect somatic mutations. The advent of many new somatic variant callers creates a need for comparison and validation of the tools, as no de facto standard for detection of somatic mutations exists and only limited comparisons have been reported. We have performed a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2 and Virmid for the detection of single nucleotide mutations and small deletions and insertions. We report a large variation in the number of calls from the nine somatic variant callers on the same sequencing data and highly variable agreement. Sequencing depth had markedly diverse impact on individual callers, as for some callers, increased sequencing depth highly improved sensitivity. For SNV calling, we report EBCall, Mutect, Virmid and Strelka to be the most reliable somatic variant callers for both exome sequencing and targeted deep sequencing. For indel calling, EBCall is superior due to high sensitivity and robustness to changes in sequencing depths.

  14. High-throughput sequencing and analysis of the gill tissue transcriptome from the deep-sea hydrothermal vent mussel Bathymodiolus azoricus

    Directory of Open Access Journals (Sweden)

    Gomes Paula

    2010-10-01

    Full Text Available Abstract Background Bathymodiolus azoricus is a deep-sea hydrothermal vent mussel found in association with large faunal communities living in chemosynthetic environments at the bottom of the sea floor near the Azores Islands. Investigation of the exceptional physiological reactions that vent mussels have adopted in their habitat, including responses to environmental microbes, remains a difficult challenge for deep-sea biologists. In an attempt to reveal genes potentially involved in the deep-sea mussel innate immunity we carried out a high-throughput sequence analysis of freshly collected B. azoricus transcriptome using gills tissues as the primary source of immune transcripts given its strategic role in filtering the surrounding waterborne potentially infectious microorganisms. Additionally, a substantial EST data set was produced and from which a comprehensive collection of genes coding for putative proteins was organized in a dedicated database, "DeepSeaVent" the first deep-sea vent animal transcriptome database based on the 454 pyrosequencing technology. Results A normalized cDNA library from gills tissue was sequenced in a full 454 GS-FLX run, producing 778,996 sequencing reads. Assembly of the high quality reads resulted in 75,407 contigs of which 3,071 were singletons. A total of 39,425 transcripts were conceptually translated into amino-sequences of which 22,023 matched known proteins in the NCBI non-redundant protein database, 15,839 revealed conserved protein domains through InterPro functional classification and 9,584 were assigned with Gene Ontology terms. Queries conducted within the database enabled the identification of genes putatively involved in immune and inflammatory reactions which had not been previously evidenced in the vent mussel. Their physical counterpart was confirmed by semi-quantitative quantitative Reverse-Transcription-Polymerase Chain Reactions (RT-PCR and their RNA transcription level by quantitative PCR (q

  15. Development of genic-SSR markers by deep transcriptome sequencing in pigeonpea [Cajanus cajan (L.) Millspaugh

    Science.gov (United States)

    2011-01-01

    Background Pigeonpea [Cajanus cajan (L.) Millspaugh], one of the most important food legumes of semi-arid tropical and subtropical regions, has limited genomic resources, particularly expressed sequence based (genic) markers. We report a comprehensive set of validated genic simple sequence repeat (SSR) markers using deep transcriptome sequencing, and its application in genetic diversity analysis and mapping. Results In this study, 43,324 transcriptome shotgun assembly unigene contigs were assembled from 1.696 million 454 GS-FLX sequence reads of separate pooled cDNA libraries prepared from leaf, root, stem and immature seed of two pigeonpea varieties, Asha and UPAS 120. A total of 3,771 genic-SSR loci, excluding homopolymeric and compound repeats, were identified; of which 2,877 PCR primer pairs were designed for marker development. Dinucleotide was the most common repeat motif with a frequency of 60.41%, followed by tri- (34.52%), hexa- (2.62%), tetra- (1.67%) and pentanucleotide (0.76%) repeat motifs. Primers were synthesized and tested for 772 of these loci with repeat lengths of ≥18 bp. Of these, 550 markers were validated for consistent amplification in eight diverse pigeonpea varieties; 71 were found to be polymorphic on agarose gel electrophoresis. Genetic diversity analysis was done on 22 pigeonpea varieties and eight wild species using 20 highly polymorphic genic-SSR markers. The number of alleles at these loci ranged from 4-10 and the polymorphism information content values ranged from 0.46 to 0.72. Neighbor-joining dendrogram showed distinct separation of the different groups of pigeonpea cultivars and wild species. Deep transcriptome sequencing of the two parental lines helped in silico identification of polymorphic genic-SSR loci to facilitate the rapid development of an intra-species reference genetic map, a subset of which was validated for expected allelic segregation in the reference mapping population. Conclusion We developed 550 validated genic

  16. Deep Ion Torrent sequencing identifies soil fungal community shifts after frequent prescribed fires in a southeastern US forest ecosystem.

    Science.gov (United States)

    Brown, Shawn P; Callaham, Mac A; Oliver, Alena K; Jumpponen, Ari

    2013-12-01

    Prescribed burning is a common management tool to control fuel loads, ground vegetation, and facilitate desirable game species. We evaluated soil fungal community responses to long-term prescribed fire treatments in a loblolly pine forest on the Piedmont of Georgia and utilized deep Internal Transcribed Spacer Region 1 (ITS1) amplicon sequencing afforded by the recent Ion Torrent Personal Genome Machine (PGM). These deep sequence data (19,000 + reads per sample after subsampling) indicate that frequent fires (3-year fire interval) shift soil fungus communities, whereas infrequent fires (6-year fire interval) permit system resetting to a state similar to that without prescribed fire. Furthermore, in nonmetric multidimensional scaling analyses, primarily ectomycorrhizal taxa were correlated with axes associated with long fire intervals, whereas soil saprobes tended to be correlated with the frequent fire recurrence. We conclude that (1) multiplexed Ion Torrent PGM analyses allow deep cost effective sequencing of fungal communities but may suffer from short read lengths and inconsistent sequence quality adjacent to the sequencing adaptor; (2) frequent prescribed fires elicit a shift in soil fungal communities; and (3) such shifts do not occur when fire intervals are longer. Our results emphasize the general responsiveness of these forests to management, and the importance of fire return intervals in meeting management objectives. © 2013 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

  17. DeepMirTar: a deep-learning approach for predicting human miRNA targets.

    Science.gov (United States)

    Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

    2018-06-01

    MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

  18. Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data.

    Directory of Open Access Journals (Sweden)

    Anne Bruun Krøigård

    Full Text Available Next generation sequencing is extensively applied to catalogue somatic mutations in cancer, in research settings and increasingly in clinical settings for molecular diagnostics, guiding therapy decisions. Somatic variant callers perform paired comparisons of sequencing data from cancer tissue and matched normal tissue in order to detect somatic mutations. The advent of many new somatic variant callers creates a need for comparison and validation of the tools, as no de facto standard for detection of somatic mutations exists and only limited comparisons have been reported. We have performed a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2 and Virmid for the detection of single nucleotide mutations and small deletions and insertions. We report a large variation in the number of calls from the nine somatic variant callers on the same sequencing data and highly variable agreement. Sequencing depth had markedly diverse impact on individual callers, as for some callers, increased sequencing depth highly improved sensitivity. For SNV calling, we report EBCall, Mutect, Virmid and Strelka to be the most reliable somatic variant callers for both exome sequencing and targeted deep sequencing. For indel calling, EBCall is superior due to high sensitivity and robustness to changes in sequencing depths.

  19. Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

    Science.gov (United States)

    Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

    2018-03-01

    The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

  20. High-Quality Draft Single-Cell Genome Sequence Belonging to the Archaeal Candidate Division SA1, Isolated from Nereus Deep in the Red Sea

    KAUST Repository

    Ngugi, David; Stingl, Ulrich

    2018-01-01

    Candidate division SA1 encompasses a phylogenetically coherent archaeal group ubiquitous in deep hypersaline anoxic brines around the globe. Recently, the genome sequences of two cultivated representatives from hypersaline soda lake sediments were published. Here, we present a single-cell genome sequence from Nereus Deep in the Red Sea that represents a putatively novel family within SA1.

  1. High-Quality Draft Single-Cell Genome Sequence Belonging to the Archaeal Candidate Division SA1, Isolated from Nereus Deep in the Red Sea

    KAUST Repository

    Ngugi, David

    2018-05-09

    Candidate division SA1 encompasses a phylogenetically coherent archaeal group ubiquitous in deep hypersaline anoxic brines around the globe. Recently, the genome sequences of two cultivated representatives from hypersaline soda lake sediments were published. Here, we present a single-cell genome sequence from Nereus Deep in the Red Sea that represents a putatively novel family within SA1.

  2. An introduction to deep learning on biological sequence data: examples and solutions.

    Science.gov (United States)

    Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

    2017-11-15

    Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  3. Mathematical beauty in service of deep approach to learning

    DEFF Research Database (Denmark)

    Karamehmedovic, Mirza

    2015-01-01

    was hands-on MATLAB programming, where the algorithms were tested and applied to solve physical modelbased problems. To encourage a deep approach, and discourage a surface approach to learning, I introduced into the lectures a basic but rigorous mathematical treatment of crucial theoretical points...

  4. Poly(A)-tag deep sequencing data processing to extract poly(A) sites.

    Science.gov (United States)

    Wu, Xiaohui; Ji, Guoli; Li, Qingshun Quinn

    2015-01-01

    Polyadenylation [poly(A)] is an essential posttranscriptional processing step in the maturation of eukaryotic mRNA. The advent of next-generation sequencing (NGS) technology has offered feasible means to generate large-scale data and new opportunities for intensive study of polyadenylation, particularly deep sequencing of the transcriptome targeting the junction of 3'-UTR and the poly(A) tail of the transcript. To take advantage of this unprecedented amount of data, we present an automated workflow to identify polyadenylation sites by integrating NGS data cleaning, processing, mapping, normalizing, and clustering. In this pipeline, a series of Perl scripts are seamlessly integrated to iteratively map the single- or paired-end sequences to the reference genome. After mapping, the poly(A) tags (PATs) at the same genome coordinate are grouped into one cleavage site, and the internal priming artifacts removed. Then the ambiguous region is introduced to parse the genome annotation for cleavage site clustering. Finally, cleavage sites within a close range of 24 nucleotides and from different samples can be clustered into poly(A) clusters. This procedure could be used to identify thousands of reliable poly(A) clusters from millions of NGS sequences in different tissues or treatments.

  5. Identification and Removal of Contaminant Sequences From Ribosomal Gene Databases: Lessons From the Census of Deep Life.

    Science.gov (United States)

    Sheik, Cody S; Reese, Brandi Kiel; Twing, Katrina I; Sylvan, Jason B; Grim, Sharon L; Schrenk, Matthew O; Sogin, Mitchell L; Colwell, Frederick S

    2018-01-01

    Earth's subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium , Aquabacterium , Ralstonia , and Acinetobacter . While the top five most frequently observed genera were Pseudomonas , Propionibacterium , Acinetobacter , Ralstonia , and Sphingomonas . The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA

  6. Protein model discrimination using mutational sensitivity derived from deep sequencing.

    Science.gov (United States)

    Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan

    2012-02-08

    A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons.

    Directory of Open Access Journals (Sweden)

    Magdalena Guardiola

    Full Text Available Marine sediments are home to one of the richest species pools on Earth, but logistics and a dearth of taxonomic work-force hinders the knowledge of their biodiversity. We characterized α- and β-diversity of deep-sea assemblages from submarine canyons in the western Mediterranean using an environmental DNA metabarcoding. We used a new primer set targeting a short eukaryotic 18S sequence (ca. 110 bp. We applied a protocol designed to obtain extractions enriched in extracellular DNA from replicated sediment corers. With this strategy we captured information from DNA (local or deposited from the water column that persists adsorbed to inorganic particles and buffered short-term spatial and temporal heterogeneity. We analysed replicated samples from 20 localities including 2 deep-sea canyons, 1 shallower canal, and two open slopes (depth range 100-2,250 m. We identified 1,629 MOTUs, among which the dominant groups were Metazoa (with representatives of 19 phyla, Alveolata, Stramenopiles, and Rhizaria. There was a marked small-scale heterogeneity as shown by differences in replicates within corers and within localities. The spatial variability between canyons was significant, as was the depth component in one of the canyons where it was tested. Likewise, the composition of the first layer (1 cm of sediment was significantly different from deeper layers. We found that qualitative (presence-absence and quantitative (relative number of reads data showed consistent trends of differentiation between samples and geographic areas. The subset of exclusively benthic MOTUs showed similar patterns of β-diversity and community structure as the whole dataset. Separate analyses of the main metazoan phyla (in number of MOTUs showed some differences in distribution attributable to different lifestyles. Our results highlight the differentiation that can be found even between geographically close assemblages, and sets the ground for future monitoring and conservation

  8. Hybridization-based reconstruction of small non-coding RNA transcripts from deep sequencing data.

    Science.gov (United States)

    Ragan, Chikako; Mowry, Bryan J; Bauer, Denis C

    2012-09-01

    Recent advances in RNA sequencing technology (RNA-Seq) enables comprehensive profiling of RNAs by producing millions of short sequence reads from size-fractionated RNA libraries. Although conventional tools for detecting and distinguishing non-coding RNAs (ncRNAs) from reference-genome data can be applied to sequence data, ncRNA detection can be improved by harnessing the full information content provided by this new technology. Here we present NorahDesk, the first unbiased and universally applicable method for small ncRNAs detection from RNA-Seq data. NorahDesk utilizes the coverage-distribution of small RNA sequence data as well as thermodynamic assessments of secondary structure to reliably predict and annotate ncRNA classes. Using publicly available mouse sequence data from brain, skeletal muscle, testis and ovary, we evaluated our method with an emphasis on the performance for microRNAs (miRNAs) and piwi-interacting small RNA (piRNA). We compared our method with Dario and mirDeep2 and found that NorahDesk produces longer transcripts with higher read coverage. This feature makes it the first method particularly suitable for the prediction of both known and novel piRNAs.

  9. Draft Genome Sequence of Deep-Sea Alteromonas sp. Strain V450 Isolated from the Marine Sponge Leiodermatium sp.

    Science.gov (United States)

    Wang, Guojun; Barrett, Nolan H; McCarthy, Peter J

    2017-02-02

    The proteobacterium Alteromonas sp. strain V450 was isolated from the Atlantic deep-sea sponge Leiodermatium sp. Here, we report the draft genome sequence of this strain, with a genome size of approx. 4.39 Mb and a G+C content of 44.01%. The results will aid deep-sea microbial ecology, evolution, and sponge-microbe association studies. Copyright © 2017 Wang et al.

  10. A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

    Directory of Open Access Journals (Sweden)

    Junkai Yi

    2017-01-01

    Full Text Available Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

  11. Improving High-Throughput Sequencing Approaches for Reconstructing the Evolutionary Dynamics of Upper Paleolithic Human Groups

    DEFF Research Database (Denmark)

    Seguin-Orlando, Andaine

    the development and testing of innovative molecular approaches aiming at improving the amount of informative HTS data one can recover from ancient DNA extracts. We have characterized important ligation and amplification biases in the sequencing library building and enrichment steps, which can impede further...... been mainly driven by the development of High-Throughput DNA Sequencing (HTS) technologies but also by the implementation of novel molecular tools tailored to the manipulation of ultra short and damaged DNA molecules. Our ability to retrieve traces of genetic material has tremendously improved, pushing......, that impact on the overall efficacy of the method. In a second part, we implemented some of these molecular tools to the processing of five Upper Paleolithic human samples from the Kostenki and Sunghir sites in Western Eurasia, in order to reconstruct the deep genomic history of European populations...

  12. A novel deep learning approach for classification of EEG motor imagery signals.

    Science.gov (United States)

    Tabar, Yousef Rezaei; Halici, Ugur

    2017-02-01

    Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

  13. Screening for single nucleotide variants, small indels and exon deletions with a next-generation sequencing based gene panel approach for Usher syndrome.

    Science.gov (United States)

    Krawitz, Peter M; Schiska, Daniela; Krüger, Ulrike; Appelt, Sandra; Heinrich, Verena; Parkhomchuk, Dmitri; Timmermann, Bernd; Millan, Jose M; Robinson, Peter N; Mundlos, Stefan; Hecht, Jochen; Gross, Manfred

    2014-09-01

    Usher syndrome is an autosomal recessive disorder characterized both by deafness and blindness. For the three clinical subtypes of Usher syndrome causal mutations in altogether 12 genes and a modifier gene have been identified. Due to the genetic heterogeneity of Usher syndrome, the molecular analysis is predestined for a comprehensive and parallelized analysis of all known genes by next-generation sequencing (NGS) approaches. We describe here the targeted enrichment and deep sequencing for exons of Usher genes and compare the costs and workload of this approach compared to Sanger sequencing. We also present a bioinformatics analysis pipeline that allows us to detect single-nucleotide variants, short insertions and deletions, as well as copy number variations of one or more exons on the same sequence data. Additionally, we present a flexible in silico gene panel for the analysis of sequence variants, in which newly identified genes can easily be included. We applied this approach to a cohort of 44 Usher patients and detected biallelic pathogenic mutations in 35 individuals and monoallelic mutations in eight individuals of our cohort. Thirty-nine of the sequence variants, including two heterozygous deletions comprising several exons of USH2A, have not been reported so far. Our NGS-based approach allowed us to assess single-nucleotide variants, small indels, and whole exon deletions in a single test. The described diagnostic approach is fast and cost-effective with a high molecular diagnostic yield.

  14. MANGO: a new approach to multiple sequence alignment.

    Science.gov (United States)

    Zhang, Zefeng; Lin, Hao; Li, Ming

    2007-01-01

    Multiple sequence alignment is a classical and challenging task for biological sequence analysis. The problem is NP-hard. The full dynamic programming takes too much time. The progressive alignment heuristics adopted by most state of the art multiple sequence alignment programs suffer from the 'once a gap, always a gap' phenomenon. Is there a radically new way to do multiple sequence alignment? This paper introduces a novel and orthogonal multiple sequence alignment method, using multiple optimized spaced seeds and new algorithms to handle these seeds efficiently. Our new algorithm processes information of all sequences as a whole, avoiding problems caused by the popular progressive approaches. Because the optimized spaced seeds are provably significantly more sensitive than the consecutive k-mers, the new approach promises to be more accurate and reliable. To validate our new approach, we have implemented MANGO: Multiple Alignment with N Gapped Oligos. Experiments were carried out on large 16S RNA benchmarks showing that MANGO compares favorably, in both accuracy and speed, against state-of-art multiple sequence alignment methods, including ClustalW 1.83, MUSCLE 3.6, MAFFT 5.861, Prob-ConsRNA 1.11, Dialign 2.2.1, DIALIGN-T 0.2.1, T-Coffee 4.85, POA 2.0 and Kalign 2.0.

  15. Radar Rainfall Bias Correction based on Deep Learning Approach

    Science.gov (United States)

    Song, Yang; Han, Dawei; Rico-Ramirez, Miguel A.

    2017-04-01

    Radar rainfall measurement errors can be considerably attributed to various sources including intricate synoptic regimes. Temperature, humidity and wind are typically acknowledged as critical meteorological factors in inducing the precipitation discrepancies aloft and on the ground. The conventional practices mainly use the radar-gauge or geostatistical techniques by direct weighted interpolation algorithms as bias correction schemes whereas rarely consider the atmospheric effects. This study aims to comprehensively quantify those meteorological elements' impacts on radar-gauge rainfall bias correction based on a deep learning approach. The deep learning approach employs deep convolutional neural networks to automatically extract three-dimensional meteorological features for target recognition based on high range resolution profiles. The complex nonlinear relationships between input and target variables can be implicitly detected by such a scheme, which is validated on the test dataset. The proposed bias correction scheme is expected to be a promising improvement in systematically minimizing the synthesized atmospheric effects on rainfall discrepancies between radar and rain gauges, which can be useful in many meteorological and hydrological applications (e.g., real-time flood forecasting) especially for regions with complex atmospheric conditions.

  16. Deep sequence characterisation of a divergent HPIV-4a from an adult with prolonged influenza-like illness

    Directory of Open Access Journals (Sweden)

    Katherine E. Arden

    2015-12-01

    Deep sequencing allowed identification and genomic characterisation of a possible pathogen from an ILI as well as being an important tool to aid future understanding of the linkages between viral genetic variation, transmission and disease prognosis.

  17. 3' terminal diversity of MRP RNA and other human noncoding RNAs revealed by deep sequencing.

    Science.gov (United States)

    Goldfarb, Katherine C; Cech, Thomas R

    2013-09-21

    Post-transcriptional 3' end processing is a key component of RNA regulation. The abundant and essential RNA subunit of RNase MRP has been proposed to function in three distinct cellular compartments and therefore may utilize this mode of regulation. Here we employ 3' RACE coupled with high-throughput sequencing to characterize the 3' terminal sequences of human MRP RNA and other noncoding RNAs that form RNP complexes. The 3' terminal sequence of MRP RNA from HEK293T cells has a distinctive distribution of genomically encoded termini (including an assortment of U residues) with a portion of these selectively tagged by oligo(A) tails. This profile contrasts with the relatively homogenous 3' terminus of an in vitro transcribed MRP RNA control and the differing 3' terminal profiles of U3 snoRNA, RNase P RNA, and telomerase RNA (hTR). 3' RACE coupled with deep sequencing provides a valuable framework for the functional characterization of 3' terminal sequences of noncoding RNAs.

  18. A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

    Science.gov (United States)

    Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

    2017-11-01

    Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

  19. Deep sequencing analysis of HBV genotype shift and correlation with antiviral efficiency during adefovir dipivoxil therapy.

    Directory of Open Access Journals (Sweden)

    Yuwei Wang

    Full Text Available Viral genotype shift in chronic hepatitis B (CHB patients during antiviral therapy has been reported, but the underlying mechanism remains elusive.38 CHB patients treated with ADV for one year were selected for studying genotype shift by both deep sequencing and Sanger sequencing method.Sanger sequencing method found that 7.9% patients showed mixed genotype before ADV therapy. In contrast, all 38 patients showed mixed genotype before ADV treatment by deep sequencing. 95.5% mixed genotype rate was also obtained from additional 200 treatment-naïve CHB patients. Of the 13 patients with genotype shift, the fraction of the minor genotype in 5 patients (38% increased gradually during the course of ADV treatment. Furthermore, responses to ADV and HBeAg seroconversion were associated with the high rate of genotype shift, suggesting drug and immune pressure may be key factors to induce genotype shift. Interestingly, patients with genotype C had a significantly higher rate of genotype shift than genotype B. In genotype shift group, ADV treatment induced a marked enhancement of genotype B ratio accompanied by a reduction of genotype C ratio, suggesting genotype C may be more sensitive to ADV than genotype B. Moreover, patients with dominant genotype C may have a better therapeutic effect. Finally, genotype shifts was correlated with clinical improvement in terms of ALT.Our findings provided a rational explanation for genotype shift among ADV-treated CHB patients. The genotype and genotype shift might be associated with antiviral efficiency.

  20. Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

    Science.gov (United States)

    Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

    2015-01-01

    Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for

  1. The subclonal structure and genomic evolution of oral squamous cell carcinoma revealed by ultra-deep sequencing

    DEFF Research Database (Denmark)

    Tabatabaeifar, Siavosh; Thomassen, Mads; Larsen, Martin J

    2017-01-01

    Recent studies suggest that head and neck squamous cell carcinomas are very heterogeneous between patients; however the subclonal structure remains unexplored mainly due to studies using only a single biopsy per patient. To deconvolutethe clonal structure and describe the genomic cancer evolution......, we applied whole-exome sequencing combined with ultra-deep targeted sequencing on oral squamous cell carcinomas (OSCC). From each patient, a set of biopsies was sampled from distinct geographical sites in primary tumor and lymph node metastasis.We demonstrate that the included OSCCs show a high...

  2. Improved detection of CXCR4-using HIV by V3 genotyping: application of population-based and "deep" sequencing to plasma RNA and proviral DNA.

    Science.gov (United States)

    Swenson, Luke C; Moores, Andrew; Low, Andrew J; Thielen, Alexander; Dong, Winnie; Woods, Conan; Jensen, Mark A; Wynhoven, Brian; Chan, Dennison; Glascock, Christopher; Harrigan, P Richard

    2010-08-01

    Tropism testing should rule out CXCR4-using HIV before treatment with CCR5 antagonists. Currently, the recombinant phenotypic Trofile assay (Monogram) is most widely utilized; however, genotypic tests may represent alternative methods. Independent triplicate amplifications of the HIV gp120 V3 region were made from either plasma HIV RNA or proviral DNA. These underwent standard, population-based sequencing with an ABI3730 (RNA n = 63; DNA n = 40), or "deep" sequencing with a Roche/454 Genome Sequencer-FLX (RNA n = 12; DNA n = 12). Position-specific scoring matrices (PSSMX4/R5) (-6.96 cutoff) and geno2pheno[coreceptor] (5% false-positive rate) inferred tropism from V3 sequence. These methods were then independently validated with a separate, blinded dataset (n = 278) of screening samples from the maraviroc MOTIVATE trials. Standard sequencing of HIV RNA with PSSM yielded 69% sensitivity and 91% specificity, relative to Trofile. The validation dataset gave 75% sensitivity and 83% specificity. Proviral DNA plus PSSM gave 77% sensitivity and 71% specificity. "Deep" sequencing of HIV RNA detected >2% inferred-CXCR4-using virus in 8/8 samples called non-R5 by Trofile, and <2% in 4/4 samples called R5. Triplicate analyses of V3 standard sequence data detect greater proportions of CXCR4-using samples than previously achieved. Sequencing proviral DNA and "deep" V3 sequencing may also be useful tools for assessing tropism.

  3. Viral metagenomics: Analysis of begomoviruses by illumina high-throughput sequencing

    KAUST Repository

    Idris, Ali

    2014-03-12

    Traditional DNA sequencing methods are inefficient, lack the ability to discern the least abundant viral sequences, and ineffective for determining the extent of variability in viral populations. Here, populations of single-stranded DNA plant begomoviral genomes and their associated beta- and alpha-satellite molecules (virus-satellite complexes) (genus, Begomovirus; family, Geminiviridae) were enriched from total nucleic acids isolated from symptomatic, field-infected plants, using rolling circle amplification (RCA). Enriched virus-satellite complexes were subjected to Illumina-Next Generation Sequencing (NGS). CASAVA and SeqMan NGen programs were implemented, respectively, for quality control and for de novo and reference-guided contig assembly of viral-satellite sequences. The authenticity of the begomoviral sequences, and the reproducibility of the Illumina-NGS approach for begomoviral deep sequencing projects, were validated by comparing NGS results with those obtained using traditional molecular cloning and Sanger sequencing of viral components and satellite DNAs, also enriched by RCA or amplified by polymerase chain reaction. As the use of NGS approaches, together with advances in software development, make possible deep sequence coverage at a lower cost; the approach described herein will streamline the exploration of begomovirus diversity and population structure from naturally infected plants, irrespective of viral abundance. This is the first report of the implementation of Illumina-NGS to explore the diversity and identify begomoviral-satellite SNPs directly from plants naturally-infected with begomoviruses under field conditions. 2014 by the authors; licensee MDPI, Basel, Switzerland.

  4. Viral Metagenomics: Analysis of Begomoviruses by Illumina High-Throughput Sequencing

    Directory of Open Access Journals (Sweden)

    Ali Idris

    2014-03-01

    Full Text Available Traditional DNA sequencing methods are inefficient, lack the ability to discern the least abundant viral sequences, and ineffective for determining the extent of variability in viral populations. Here, populations of single-stranded DNA plant begomoviral genomes and their associated beta- and alpha-satellite molecules (virus-satellite complexes (genus, Begomovirus; family, Geminiviridae were enriched from total nucleic acids isolated from symptomatic, field-infected plants, using rolling circle amplification (RCA. Enriched virus-satellite complexes were subjected to Illumina-Next Generation Sequencing (NGS. CASAVA and SeqMan NGen programs were implemented, respectively, for quality control and for de novo and reference-guided contig assembly of viral-satellite sequences. The authenticity of the begomoviral sequences, and the reproducibility of the Illumina-NGS approach for begomoviral deep sequencing projects, were validated by comparing NGS results with those obtained using traditional molecular cloning and Sanger sequencing of viral components and satellite DNAs, also enriched by RCA or amplified by polymerase chain reaction. As the use of NGS approaches, together with advances in software development, make possible deep sequence coverage at a lower cost; the approach described herein will streamline the exploration of begomovirus diversity and population structure from naturally infected plants, irrespective of viral abundance. This is the first report of the implementation of Illumina-NGS to explore the diversity and identify begomoviral-satellite SNPs directly from plants naturally-infected with begomoviruses under field conditions.

  5. Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments

    Science.gov (United States)

    Postareff, Liisa; Parpala, Anna; Lindblom-Ylänne, Sari

    2015-01-01

    The study explored factors explaining changes in a deep approach to learning. The data consisted of interviews with 12 students from four Bachelor-level courses representing different disciplines. We analysed and compared descriptions of students whose deep approach either increased, decreased or remained relatively unchanged during their courses.…

  6. Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing

    Directory of Open Access Journals (Sweden)

    Chen Shou-Yi

    2011-01-01

    Full Text Available Abstract Background MicroRNAs (miRNAs regulate gene expression by mediating gene silencing at transcriptional and post-transcriptional levels in higher plants. miRNAs and related target genes have been widely studied in model plants such as Arabidopsis and rice; however, the number of identified miRNAs in soybean (Glycine max is limited, and global identification of the related miRNA targets has not been reported in previous research. Results In our study, a small RNA library and a degradome library were constructed from developing soybean seeds for deep sequencing. We identified 26 new miRNAs in soybean by bioinformatic analysis and further confirmed their expression by stem-loop RT-PCR. The miRNA star sequences of 38 known miRNAs and 8 new miRNAs were also discovered, providing additional evidence for the existence of miRNAs. Through degradome sequencing, 145 and 25 genes were identified as targets of annotated miRNAs and new miRNAs, respectively. GO analysis indicated that many of the identified miRNA targets may function in soybean seed development. Additionally, a soybean homolog of Arabidopsis SUPPRESSOR OF GENE SLIENCING 3 (AtSGS3 was detected as a target of the newly identified miRNA Soy_25, suggesting the presence of feedback control of miRNA biogenesis. Conclusions We have identified large numbers of miRNAs and their related target genes through deep sequencing of a small RNA library and a degradome library. Our study provides more information about the regulatory network of miRNAs in soybean and advances our understanding of miRNA functions during seed development.

  7. Deep RNA sequencing of the skeletal muscle transcriptome in swimming fish.

    Directory of Open Access Journals (Sweden)

    Arjan P Palstra

    Full Text Available Deep RNA sequencing (RNA-seq was performed to provide an in-depth view of the transcriptome of red and white skeletal muscle of exercised and non-exercised rainbow trout (Oncorhynchus mykiss with the specific objective to identify expressed genes and quantify the transcriptomic effects of swimming-induced exercise. Pubertal autumn-spawning seawater-raised female rainbow trout were rested (n = 10 or swum (n = 10 for 1176 km at 0.75 body-lengths per second in a 6,000-L swim-flume under reproductive conditions for 40 days. Red and white muscle RNA of exercised and non-exercised fish (4 lanes was sequenced and resulted in 15-17 million reads per lane that, after de novo assembly, yielded 149,159 red and 118,572 white muscle contigs. Most contigs were annotated using an iterative homology search strategy against salmonid ESTs, the zebrafish Danio rerio genome and general Metazoan genes. When selecting for large contigs (>500 nucleotides, a number of novel rainbow trout gene sequences were identified in this study: 1,085 and 1,228 novel gene sequences for red and white muscle, respectively, which included a number of important molecules for skeletal muscle function. Transcriptomic analysis revealed that sustained swimming increased transcriptional activity in skeletal muscle and specifically an up-regulation of genes involved in muscle growth and developmental processes in white muscle. The unique collection of transcripts will contribute to our understanding of red and white muscle physiology, specifically during the long-term reproductive migration of salmonids.

  8. Microbial Dark Matter: Unusual intervening sequences in 16S rRNA genes of candidate phyla from the deep subsurface

    Energy Technology Data Exchange (ETDEWEB)

    Jarett, Jessica; Stepanauskas, Ramunas; Kieft, Thomas; Onstott, Tullis; Woyke, Tanja

    2014-03-17

    The Microbial Dark Matter project has sequenced genomes from over 200 single cells from candidate phyla, greatly expanding our knowledge of the ecology, inferred metabolism, and evolution of these widely distributed, yet poorly understood lineages. The second phase of this project aims to sequence an additional 800 single cells from known as well as potentially novel candidate phyla derived from a variety of environments. In order to identify whole genome amplified single cells, screening based on phylogenetic placement of 16S rRNA gene sequences is being conducted. Briefly, derived 16S rRNA gene sequences are aligned to a custom version of the Greengenes reference database and added to a reference tree in ARB using parsimony. In multiple samples from deep subsurface habitats but not from other habitats, a large number of sequences proved difficult to align and therefore to place in the tree. Based on comparisons to reference sequences and structural alignments using SSU-ALIGN, many of these ?difficult? sequences appear to originate from candidate phyla, and contain intervening sequences (IVSs) within the 16S rRNA genes. These IVSs are short (39 - 79 nt) and do not appear to be self-splicing or to contain open reading frames. IVSs were found in the loop regions of stem-loop structures in several different taxonomic groups. Phylogenetic placement of sequences is strongly affected by IVSs; two out of three groups investigated were classified as different phyla after their removal. Based on data from samples screened in this project, IVSs appear to be more common in microbes occurring in deep subsurface habitats, although the reasons for this remain elusive.

  9. Deep sequencing reveals persistence of cell-associated mumps vaccine virus in chronic encephalitis.

    Science.gov (United States)

    Morfopoulou, Sofia; Mee, Edward T; Connaughton, Sarah M; Brown, Julianne R; Gilmour, Kimberly; Chong, W K 'Kling'; Duprex, W Paul; Ferguson, Deborah; Hubank, Mike; Hutchinson, Ciaran; Kaliakatsos, Marios; McQuaid, Stephen; Paine, Simon; Plagnol, Vincent; Ruis, Christopher; Virasami, Alex; Zhan, Hong; Jacques, Thomas S; Schepelmann, Silke; Qasim, Waseem; Breuer, Judith

    2017-01-01

    Routine childhood vaccination against measles, mumps and rubella has virtually abolished virus-related morbidity and mortality. Notwithstanding this, we describe here devastating neurological complications associated with the detection of live-attenuated mumps virus Jeryl Lynn (MuV JL5 ) in the brain of a child who had undergone successful allogeneic transplantation for severe combined immunodeficiency (SCID). This is the first confirmed report of MuV JL5 associated with chronic encephalitis and highlights the need to exclude immunodeficient individuals from immunisation with live-attenuated vaccines. The diagnosis was only possible by deep sequencing of the brain biopsy. Sequence comparison of the vaccine batch to the MuV JL5 isolated from brain identified biased hypermutation, particularly in the matrix gene, similar to those found in measles from cases of SSPE. The findings provide unique insights into the pathogenesis of paramyxovirus brain infections.

  10. Sequence-specific bias correction for RNA-seq data using recurrent neural networks.

    Science.gov (United States)

    Zhang, Yao-Zhong; Yamaguchi, Rui; Imoto, Seiya; Miyano, Satoru

    2017-01-25

    The recent success of deep learning techniques in machine learning and artificial intelligence has stimulated a great deal of interest among bioinformaticians, who now wish to bring the power of deep learning to bare on a host of bioinformatical problems. Deep learning is ideally suited for biological problems that require automatic or hierarchical feature representation for biological data when prior knowledge is limited. In this work, we address the sequence-specific bias correction problem for RNA-seq data redusing Recurrent Neural Networks (RNNs) to model nucleotide sequences without pre-determining sequence structures. The sequence-specific bias of a read is then calculated based on the sequence probabilities estimated by RNNs, and used in the estimation of gene abundance. We explore the application of two popular RNN recurrent units for this task and demonstrate that RNN-based approaches provide a flexible way to model nucleotide sequences without knowledge of predetermined sequence structures. Our experiments show that training a RNN-based nucleotide sequence model is efficient and RNN-based bias correction methods compare well with the-state-of-the-art sequence-specific bias correction method on the commonly used MAQC-III data set. RNNs provides an alternative and flexible way to calculate sequence-specific bias without explicitly pre-determining sequence structures.

  11. A new approach to irreversibility in deep inelastic collisions

    International Nuclear Information System (INIS)

    Nemes, M.C.

    1982-01-01

    We use concepts of statistical mechanics to discuss the irreversible character of the experimental data in deep inelastic collisions. A definition of irreversibility proposed by Ruch permits a unified overview on current theories which describe these reactions. An information theoretical analysis of the data leads to a Fokker-Planck equation for the collective variables (excitation energy, charge and mass). The concept of mixing distance can serve as a quantitative measure to characterize the 'approach to equilibrium'. We apply it to the brownian motion as an illustration and also to the phenomenological analysis of deep inelastic scattering data with interesting results. (orig.)

  12. An improved advertising CTR prediction approach based on the fuzzy deep neural network.

    Science.gov (United States)

    Jiang, Zilong; Gao, Shu; Li, Mingjiang

    2018-01-01

    Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

  13. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  14. Acoustic emission localization on ship hull structures using a deep learning approach

    DEFF Research Database (Denmark)

    Georgoulas, George; Kappatos, Vassilios; Nikolakopoulos, George

    2016-01-01

    In this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimension......In this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high...

  15. Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

    Science.gov (United States)

    Lai, Ying-Hui; Tsao, Yu; Lu, Xugang; Chen, Fei; Su, Yu-Ting; Chen, Kuang-Chao; Chen, Yu-Hsuan; Chen, Li-Ching; Po-Hung Li, Lieber; Lee, Chin-Hui

    2018-01-20

    We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients. The deep learning-based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach. Moreover, we evaluate the speech recognition performance of the NC + DDAE NR and classical single-microphone NR approaches for Mandarin-speaking CI recipients under different noisy conditions. The testing set contains Mandarin sentences corrupted by two types of maskers, two-talker babble noise, and a construction jackhammer noise, at 0 and 5 dB SNR levels. Two conventional NR techniques and the proposed deep learning-based approach are used to process the noisy utterances. We qualitatively compare the NR approaches by the amplitude envelope and spectrogram plots of the processed utterances. Quantitative objective measures include (1) normalized covariance measure to test the intelligibility of the utterances processed by each of the NR approaches; and (2) speech recognition tests conducted by nine Mandarin-speaking CI recipients. These nine CI recipients use their own clinical speech processors during testing. The experimental results of objective evaluation and listening test indicate that under challenging listening conditions, the proposed NC + DDAE NR approach yields higher intelligibility scores than the two compared classical NR techniques, under both matched and mismatched training-testing conditions. When compared to the two well-known conventional NR techniques under challenging listening condition, the proposed NC + DDAE NR approach has superior noise suppression capabilities and gives less distortion

  16. Position-specific automated processing of V3 env ultra-deep pyrosequencing data for predicting HIV-1 tropism.

    Science.gov (United States)

    Jeanne, Nicolas; Saliou, Adrien; Carcenac, Romain; Lefebvre, Caroline; Dubois, Martine; Cazabat, Michelle; Nicot, Florence; Loiseau, Claire; Raymond, Stéphanie; Izopet, Jacques; Delobel, Pierre

    2015-11-20

    HIV-1 coreceptor usage must be accurately determined before starting CCR5 antagonist-based treatment as the presence of undetected minor CXCR4-using variants can cause subsequent virological failure. Ultra-deep pyrosequencing of HIV-1 V3 env allows to detect low levels of CXCR4-using variants that current genotypic approaches miss. However, the computation of the mass of sequence data and the need to identify true minor variants while excluding artifactual sequences generated during amplification and ultra-deep pyrosequencing is rate-limiting. Arbitrary fixed cut-offs below which minor variants are discarded are currently used but the errors generated during ultra-deep pyrosequencing are sequence-dependant rather than random. We have developed an automated processing of HIV-1 V3 env ultra-deep pyrosequencing data that uses biological filters to discard artifactual or non-functional V3 sequences followed by statistical filters to determine position-specific sensitivity thresholds, rather than arbitrary fixed cut-offs. It allows to retain authentic sequences with point mutations at V3 positions of interest and discard artifactual ones with accurate sensitivity thresholds.

  17. Deep Sequencing Reveals the Complete Genome and Evidence for Transcriptional Activity of the First Virus-Like Sequences Identified in Aristotelia chilensis (Maqui Berry

    Directory of Open Access Journals (Sweden)

    Javier Villacreses

    2015-04-01

    Full Text Available Here, we report the genome sequence and evidence for transcriptional activity of a virus-like element in the native Chilean berry tree Aristotelia chilensis. We propose to name the endogenous sequence as Aristotelia chilensis Virus 1 (AcV1. High-throughput sequencing of the genome of this tree uncovered an endogenous viral element, with a size of 7122 bp, corresponding to the complete genome of AcV1. Its sequence contains three open reading frames (ORFs: ORFs 1 and 2 shares 66%–73% amino acid similarity with members of the Caulimoviridae virus family, especially the Petunia vein clearing virus (PVCV, Petuvirus genus. ORF1 encodes a movement protein (MP; ORF2 a Reverse Transcriptase (RT and a Ribonuclease H (RNase H domain; and ORF3 showed no amino acid sequence similarity with any other known virus proteins. Analogous to other known endogenous pararetrovirus sequences (EPRVs, AcV1 is integrated in the genome of Maqui Berry and showed low viral transcriptional activity, which was detected by deep sequencing technology (DNA and RNA-seq. Phylogenetic analysis of AcV1 and other pararetroviruses revealed a closer resemblance with Petuvirus. Overall, our data suggests that AcV1 could be a new member of Caulimoviridae family, genus Petuvirus, and the first evidence of this kind of virus in a fruit plant.

  18. 3′ terminal diversity of MRP RNA and other human noncoding RNAs revealed by deep sequencing

    Science.gov (United States)

    2013-01-01

    Background Post-transcriptional 3′ end processing is a key component of RNA regulation. The abundant and essential RNA subunit of RNase MRP has been proposed to function in three distinct cellular compartments and therefore may utilize this mode of regulation. Here we employ 3′ RACE coupled with high-throughput sequencing to characterize the 3′ terminal sequences of human MRP RNA and other noncoding RNAs that form RNP complexes. Results The 3′ terminal sequence of MRP RNA from HEK293T cells has a distinctive distribution of genomically encoded termini (including an assortment of U residues) with a portion of these selectively tagged by oligo(A) tails. This profile contrasts with the relatively homogenous 3′ terminus of an in vitro transcribed MRP RNA control and the differing 3′ terminal profiles of U3 snoRNA, RNase P RNA, and telomerase RNA (hTR). Conclusions 3′ RACE coupled with deep sequencing provides a valuable framework for the functional characterization of 3′ terminal sequences of noncoding RNAs. PMID:24053768

  19. Shakeout: A New Approach to Regularized Deep Neural Network Training.

    Science.gov (United States)

    Kang, Guoliang; Li, Jun; Tao, Dacheng

    2018-05-01

    Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

  20. Deep sequencing of cardiac microRNA-mRNA interactomes in clinical and experimental cardiomyopathy.

    Science.gov (United States)

    Matkovich, Scot J; Dorn, Gerald W

    2015-01-01

    MicroRNAs are a family of short (~21 nucleotide) noncoding RNAs that serve key roles in cellular growth and differentiation and the response of the heart to stress stimuli. As the sequence-specific recognition element of RNA-induced silencing complexes (RISCs), microRNAs bind mRNAs and prevent their translation via mechanisms that may include transcript degradation and/or prevention of ribosome binding. Short microRNA sequences and the ability of microRNAs to bind to mRNA sites having only partial/imperfect sequence complementarity complicate purely computational analyses of microRNA-mRNA interactomes. Furthermore, computational microRNA target prediction programs typically ignore biological context, and therefore the principal determinants of microRNA-mRNA binding: the presence and quantity of each. To address these deficiencies we describe an empirical method, developed via studies of stressed and failing hearts, to determine disease-induced changes in microRNAs, mRNAs, and the mRNAs targeted to the RISC, without cross-linking mRNAs to RISC proteins. Deep sequencing methods are used to determine RNA abundances, delivering unbiased, quantitative RNA data limited only by their annotation in the genome of interest. We describe the laboratory bench steps required to perform these experiments, experimental design strategies to achieve an appropriate number of sequencing reads per biological replicate, and computer-based processing tools and procedures to convert large raw sequencing data files into gene expression measures useful for differential expression analyses.

  1. Testing genotyping strategies for ultra-deep sequencing of a co-amplifying gene family: MHC class I in a passerine bird.

    Science.gov (United States)

    Biedrzycka, Aleksandra; Sebastian, Alvaro; Migalska, Magdalena; Westerdahl, Helena; Radwan, Jacek

    2017-07-01

    Characterization of highly duplicated genes, such as genes of the major histocompatibility complex (MHC), where multiple loci often co-amplify, has until recently been hindered by insufficient read depths per amplicon. Here, we used ultra-deep Illumina sequencing to resolve genotypes at exon 3 of MHC class I genes in the sedge warbler (Acrocephalus schoenobaenus). We sequenced 24 individuals in two replicates and used this data, as well as a simulated data set, to test the effect of amplicon coverage (range: 500-20 000 reads per amplicon) on the repeatability of genotyping using four different genotyping approaches. A third replicate employed unique barcoding to assess the extent of tag jumping, that is swapping of individual tag identifiers, which may confound genotyping. The reliability of MHC genotyping increased with coverage and approached or exceeded 90% within-method repeatability of allele calling at coverages of >5000 reads per amplicon. We found generally high agreement between genotyping methods, especially at high coverages. High reliability of the tested genotyping approaches was further supported by our analysis of the simulated data set, although the genotyping approach relying primarily on replication of variants in independent amplicons proved sensitive to repeatable errors. According to the most repeatable genotyping method, the number of co-amplifying variants per individual ranged from 19 to 42. Tag jumping was detectable, but at such low frequencies that it did not affect the reliability of genotyping. We thus demonstrate that gene families with many co-amplifying genes can be reliably genotyped using HTS, provided that there is sufficient per amplicon coverage. © 2016 John Wiley & Sons Ltd.

  2. Identification of ribonucleotide reductase mutation causing temperature-sensitivity of herpes simplex virus isolates from whitlow by deep sequencing.

    Science.gov (United States)

    Daikoku, Tohru; Oyama, Yukari; Yajima, Misako; Sekizuka, Tsuyoshi; Kuroda, Makoto; Shimada, Yuka; Takehara, Kazuhiko; Miwa, Naoko; Okuda, Tomoko; Sata, Tetsutaro; Shiraki, Kimiyasu

    2015-06-01

    Herpes simplex virus 2 caused a genital ulcer, and a secondary herpetic whitlow appeared during acyclovir therapy. The secondary and recurrent whitlow isolates were acyclovir-resistant and temperature-sensitive in contrast to a genital isolate. We identified the ribonucleotide reductase mutation responsible for temperature-sensitivity by deep-sequencing analysis.

  3. A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network

    Directory of Open Access Journals (Sweden)

    Jianzheng Liu

    2016-01-01

    Full Text Available This paper presents a method for recognizing human faces with facial expression. In the proposed approach, a motion history image (MHI is employed to get the features in an expressive face. The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics. We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression. Then the fusion features were used to feed a 7-layer deep learning neural network. The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features. The last layer of the network can be seen as a softmax regression; we used it to get the identification decision. Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods.

  4. Fungal diversity in deep-sea sediments of a hydrothermal vent system in the Southwest Indian Ridge

    Science.gov (United States)

    Xu, Wei; Gong, Lin-feng; Pang, Ka-Lai; Luo, Zhu-Hua

    2018-01-01

    Deep-sea hydrothermal sediment is known to support remarkably diverse microbial consortia. In deep sea environments, fungal communities remain less studied despite their known taxonomic and functional diversity. High-throughput sequencing methods have augmented our capacity to assess eukaryotic diversity and their functions in microbial ecology. Here we provide the first description of the fungal community diversity found in deep sea sediments collected at the Southwest Indian Ridge (SWIR) using culture-dependent and high-throughput sequencing approaches. A total of 138 fungal isolates were cultured from seven different sediment samples using various nutrient media, and these isolates were identified to 14 fungal taxa, including 11 Ascomycota taxa (7 genera) and 3 Basidiomycota taxa (2 genera) based on internal transcribed spacers (ITS1, ITS2 and 5.8S) of rDNA. Using illumina HiSeq sequencing, a total of 757,467 fungal ITS2 tags were recovered from the samples and clustered into 723 operational taxonomic units (OTUs) belonging to 79 taxa (Ascomycota and Basidiomycota contributed to 99% of all samples) based on 97% sequence similarity. Results from both approaches suggest that there is a high fungal diversity in the deep-sea sediments collected in the SWIR and fungal communities were shown to be slightly different by location, although all were collected from adjacent sites at the SWIR. This study provides baseline data of the fungal diversity and biogeography, and a glimpse to the microbial ecology associated with the deep-sea sediments of the hydrothermal vent system of the Southwest Indian Ridge.

  5. Using Flipped Classroom Approach to Explore Deep Learning in Large Classrooms

    Science.gov (United States)

    Danker, Brenda

    2015-01-01

    This project used two Flipped Classroom approaches to stimulate deep learning in large classrooms during the teaching of a film module as part of a Diploma in Performing Arts course at Sunway University, Malaysia. The flipped classes utilized either a blended learning approach where students first watched online lectures as homework, and then…

  6. Rapid and Deep Proteomes by Faster Sequencing on a Benchtop Quadrupole Ultra-High-Field Orbitrap Mass Spectrometer

    DEFF Research Database (Denmark)

    Kelstrup, Christian D; Jersie-Christensen, Rosa R; Batth, Tanveer Singh

    2014-01-01

    per second or up to 600 new peptides sequenced per gradient minute. We identify 4400 proteins from one microgram of HeLa digest using a one hour gradient, which is an approximately 30% improvement compared to previous instrumentation. In addition, we show very deep proteome coverage can be achieved...... in less than 24 hours of analysis time by offline high pH reversed-phase peptide fractionation from which we identify more than 140,000 unique peptide sequences. This is comparable to state-of-the-art multi-day, multi-enzyme efforts. Finally the acquisition methods are evaluated for single...

  7. Integrated Risk-Capability Analysis under Deep Uncertainty : An ESDMA Approach

    NARCIS (Netherlands)

    Pruyt, E.; Kwakkel, J.H.

    2012-01-01

    Integrated risk-capability analysis methodologies for dealing with increasing degrees of complexity and deep uncertainty are urgently needed in an ever more complex and uncertain world. Although scenario approaches, risk assessment methods, and capability analysis methods are used, few organizations

  8. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  9. Order and correlations in genomic DNA sequences. The spectral approach

    International Nuclear Information System (INIS)

    Lobzin, Vasilii V; Chechetkin, Vladimir R

    2000-01-01

    The structural analysis of genomic DNA sequences is discussed in the framework of the spectral approach, which is sufficiently universal due to the reciprocal correspondence and mutual complementarity of Fourier transform length scales. The spectral characteristics of random sequences of the same nucleotide composition possess the property of self-averaging for relatively short sequences of length M≥100-300. Comparison with the characteristics of random sequences determines the statistical significance of the structural features observed. Apart from traditional applications to the search for hidden periodicities, spectral methods are also efficient in studying mutual correlations in DNA sequences. By combining spectra for structure factors and correlation functions, not only integral correlations can be estimated but also their origin identified. Using the structural spectral entropy approach, the regularity of a sequence can be quantitatively assessed. A brief introduction to the problem is also presented and other major methods of DNA sequence analysis described. (reviews of topical problems)

  10. Ultra Deep Sequencing of a Baculovirus Population Reveals Widespread Genomic Variations

    Directory of Open Access Journals (Sweden)

    Aurélien Chateigner

    2015-07-01

    Full Text Available Viruses rely on widespread genetic variation and large population size for adaptation. Large DNA virus populations are thought to harbor little variation though natural populations may be polymorphic. To measure the genetic variation present in a dsDNA virus population, we deep sequenced a natural strain of the baculovirus Autographa californica multiple nucleopolyhedrovirus. With 124,221X average genome coverage of our 133,926 bp long consensus, we could detect low frequency mutations (0.025%. K-means clustering was used to classify the mutations in four categories according to their frequency in the population. We found 60 high frequency non-synonymous mutations under balancing selection distributed in all functional classes. These mutants could alter viral adaptation dynamics, either through competitive or synergistic processes. Lastly, we developed a technique for the delimitation of large deletions in next generation sequencing data. We found that large deletions occur along the entire viral genome, with hotspots located in homologous repeat regions (hrs. Present in 25.4% of the genomes, these deletion mutants presumably require functional complementation to complete their infection cycle. They might thus have a large impact on the fitness of the baculovirus population. Altogether, we found a wide breadth of genomic variation in the baculovirus population, suggesting it has high adaptive potential.

  11. A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.

    Science.gov (United States)

    Anas, Emran Mohammad Abu; Mousavi, Parvin; Abolmaesumi, Purang

    2018-06-01

    Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10 mm and a mean Hausdorff distance error of 3.0 mm. A comparison of the reported results with those of a state-of-the-art technique indicates statistically significant improvement achieved by the proposed approach. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. Deep-sequencing protocols influence the results obtained in small-RNA sequencing.

    Directory of Open Access Journals (Sweden)

    Joern Toedling

    Full Text Available Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and protocols. We have analysed and compared the expression of microRNAs, as well as populations of small RNAs derived from repetitive elements. Despite the fact that different libraries display a good correlation between sequencing platforms, qualitative and quantitative variations in the results were found, depending on the protocol used. Thus, when comparing libraries from different biological samples, it is strongly recommended to use the same sequencing platform and protocol in order to ensure the biological relevance of the comparisons.

  13. Top tagging with deep neural networks [Vidyo

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Recent literature on deep neural networks for top tagging has focussed on image based techniques or multivariate approaches using high level jet substructure variables. Here, we take a sequential approach to this task by using anordered sequence of energy deposits as training inputs. Unlike previous approaches, this strategy does not result in a loss of information during pixelization or the calculation of high level features. We also propose new preprocessing methods that do not alter key physical quantities such as jet mass. We compare the performance of this approach to standard tagging techniques and present results evaluating the robustness of the neural network to pileup.

  14. Generic Amplicon Deep Sequencing to Determine Ilarvirus Species Diversity in Australian Prunus.

    Science.gov (United States)

    Kinoti, Wycliff M; Constable, Fiona E; Nancarrow, Narelle; Plummer, Kim M; Rodoni, Brendan

    2017-01-01

    The distribution of Ilarvirus species populations amongst 61 Australian Prunus trees was determined by next generation sequencing (NGS) of amplicons generated using a genus-based generic RT-PCR targeting a conserved region of the Ilarvirus RNA2 component that encodes the RNA dependent RNA polymerase (RdRp) gene. Presence of Ilarvirus sequences in each positive sample was further validated by Sanger sequencing of cloned amplicons of regions of each of RNA1, RNA2 and/or RNA3 that were generated by species specific PCRs and by metagenomic NGS. Prunus necrotic ringspot virus (PNRSV) was the most frequently detected Ilarvirus , occurring in 48 of the 61 Ilarvirus -positive trees and Prune dwarf virus (PDV) and Apple mosaic virus (ApMV) were detected in three trees and one tree, respectively. American plum line pattern virus (APLPV) was detected in three trees and represents the first report of APLPV detection in Australia. Two novel and distinct groups of Ilarvirus -like RNA2 amplicon sequences were also identified in several trees by the generic amplicon NGS approach. The high read depth from the amplicon NGS of the generic PCR products allowed the detection of distinct RNA2 RdRp sequence variant populations of PNRSV, PDV, ApMV, APLPV and the two novel Ilarvirus -like sequences. Mixed infections of ilarviruses were also detected in seven Prunus trees. Sanger sequencing of specific RNA1, RNA2, and/or RNA3 genome segments of each virus and total nucleic acid metagenomics NGS confirmed the presence of PNRSV, PDV, ApMV and APLPV detected by RNA2 generic amplicon NGS. However, the two novel groups of Ilarvirus -like RNA2 amplicon sequences detected by the generic amplicon NGS could not be associated to the presence of sequence from RNA1 or RNA3 genome segments or full Ilarvirus genomes, and their origin is unclear. This work highlights the sensitivity of genus-specific amplicon NGS in detection of virus sequences and their distinct populations in multiple samples, and the

  15. Generic Amplicon Deep Sequencing to Determine Ilarvirus Species Diversity in Australian Prunus

    Directory of Open Access Journals (Sweden)

    Wycliff M. Kinoti

    2017-06-01

    Full Text Available The distribution of Ilarvirus species populations amongst 61 Australian Prunus trees was determined by next generation sequencing (NGS of amplicons generated using a genus-based generic RT-PCR targeting a conserved region of the Ilarvirus RNA2 component that encodes the RNA dependent RNA polymerase (RdRp gene. Presence of Ilarvirus sequences in each positive sample was further validated by Sanger sequencing of cloned amplicons of regions of each of RNA1, RNA2 and/or RNA3 that were generated by species specific PCRs and by metagenomic NGS. Prunus necrotic ringspot virus (PNRSV was the most frequently detected Ilarvirus, occurring in 48 of the 61 Ilarvirus-positive trees and Prune dwarf virus (PDV and Apple mosaic virus (ApMV were detected in three trees and one tree, respectively. American plum line pattern virus (APLPV was detected in three trees and represents the first report of APLPV detection in Australia. Two novel and distinct groups of Ilarvirus-like RNA2 amplicon sequences were also identified in several trees by the generic amplicon NGS approach. The high read depth from the amplicon NGS of the generic PCR products allowed the detection of distinct RNA2 RdRp sequence variant populations of PNRSV, PDV, ApMV, APLPV and the two novel Ilarvirus-like sequences. Mixed infections of ilarviruses were also detected in seven Prunus trees. Sanger sequencing of specific RNA1, RNA2, and/or RNA3 genome segments of each virus and total nucleic acid metagenomics NGS confirmed the presence of PNRSV, PDV, ApMV and APLPV detected by RNA2 generic amplicon NGS. However, the two novel groups of Ilarvirus-like RNA2 amplicon sequences detected by the generic amplicon NGS could not be associated to the presence of sequence from RNA1 or RNA3 genome segments or full Ilarvirus genomes, and their origin is unclear. This work highlights the sensitivity of genus-specific amplicon NGS in detection of virus sequences and their distinct populations in multiple samples

  16. Superficial and deep learning approaches among medical students in an interdisciplinary integrated curriculum.

    Science.gov (United States)

    Mirghani, Hisham M; Ezimokhai, Mutairu; Shaban, Sami; van Berkel, Henk J M

    2014-01-01

    Students' learning approaches have a significant impact on the success of the educational experience, and a mismatch between instructional methods and the learning approach is very likely to create an obstacle to learning. Educational institutes' understanding of students' learning approaches allows those institutes to introduce changes in their curriculum content, instructional format, and assessment methods that will allow students to adopt deep learning techniques and critical thinking. The objective of this study was to determine and compare learning approaches among medical students following an interdisciplinary integrated curriculum. This was a cross-sectional study in which an electronic questionnaire using the Biggs two-factor Study Process Questionnaire (SPQ) with 20 questions was administered. Of a total of 402 students at the medical school, 214 (53.2%) completed the questionnaire. There was a significant difference in the mean score of superficial approach, motive and strategy between students in the six medical school years. However, no significant difference was observed in the mean score of deep approach, motive and strategy. The mean score for years 1 and 2 showed a significantly higher surface approach, surface motive and surface strategy when compared with students in years 4-6 in medical school. The superficial approach to learning was mostly preferred among first and second year medical students, and the least preferred among students in the final clinical years. These results may be useful in creating future teaching, learning and assessment strategies aiming to enhance a deep learning approach among medical students. Future studies are needed to investigate the reason for the preferred superficial approach among medical students in their early years of study.

  17. Mapping vaccinia virus DNA replication origins at nucleotide level by deep sequencing.

    Science.gov (United States)

    Senkevich, Tatiana G; Bruno, Daniel; Martens, Craig; Porcella, Stephen F; Wolf, Yuri I; Moss, Bernard

    2015-09-01

    Poxviruses reproduce in the host cytoplasm and encode most or all of the enzymes and factors needed for expression and synthesis of their double-stranded DNA genomes. Nevertheless, the mode of poxvirus DNA replication and the nature and location of the replication origins remain unknown. A current but unsubstantiated model posits only leading strand synthesis starting at a nick near one covalently closed end of the genome and continuing around the other end to generate a concatemer that is subsequently resolved into unit genomes. The existence of specific origins has been questioned because any plasmid can replicate in cells infected by vaccinia virus (VACV), the prototype poxvirus. We applied directional deep sequencing of short single-stranded DNA fragments enriched for RNA-primed nascent strands isolated from the cytoplasm of VACV-infected cells to pinpoint replication origins. The origins were identified as the switching points of the fragment directions, which correspond to the transition from continuous to discontinuous DNA synthesis. Origins containing a prominent initiation point mapped to a sequence within the hairpin loop at one end of the VACV genome and to the same sequence within the concatemeric junction of replication intermediates. These findings support a model for poxvirus genome replication that involves leading and lagging strand synthesis and is consistent with the requirements for primase and ligase activities as well as earlier electron microscopic and biochemical studies implicating a replication origin at the end of the VACV genome.

  18. Draft Genome Sequences of TwoThiomicrospiraStrains Isolated from the Brine-Seawater Interface of Kebrit Deep in the Red Sea

    KAUST Repository

    Zhang, Guishan

    2016-03-11

    Two Thiomicrospira strains, WB1 and XS5, were isolated from the Kebrit Deep brine-seawater interface in the Red Sea, Saudi Arabia. Here, we present the draft genome sequences of these gammaproteobacteria, which both produce sulfuric acid from thiosulfate in culture.

  19. Draft Genome Sequences of TwoThiomicrospiraStrains Isolated from the Brine-Seawater Interface of Kebrit Deep in the Red Sea

    KAUST Repository

    Zhang, Guishan; Haroon, Mohamed; Zhang, Ruifu; Hikmawan, Tyas I.; Stingl, Ulrich

    2016-01-01

    Two Thiomicrospira strains, WB1 and XS5, were isolated from the Kebrit Deep brine-seawater interface in the Red Sea, Saudi Arabia. Here, we present the draft genome sequences of these gammaproteobacteria, which both produce sulfuric acid from thiosulfate in culture.

  20. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    Science.gov (United States)

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  1. Approaches for in silico finishing of microbial genome sequences

    Directory of Open Access Journals (Sweden)

    Frederico Schmitt Kremer

    Full Text Available Abstract The introduction of next-generation sequencing (NGS had a significant effect on the availability of genomic information, leading to an increase in the number of sequenced genomes from a large spectrum of organisms. Unfortunately, due to the limitations implied by the short-read sequencing platforms, most of these newly sequenced genomes remained as “drafts”, incomplete representations of the whole genetic content. The previous genome sequencing studies indicated that finishing a genome sequenced by NGS, even bacteria, may require additional sequencing to fill the gaps, making the entire process very expensive. As such, several in silico approaches have been developed to optimize the genome assemblies and facilitate the finishing process. The present review aims to explore some free (open source, in many cases tools that are available to facilitate genome finishing.

  2. Approaches for in silico finishing of microbial genome sequences.

    Science.gov (United States)

    Kremer, Frederico Schmitt; McBride, Alan John Alexander; Pinto, Luciano da Silva

    The introduction of next-generation sequencing (NGS) had a significant effect on the availability of genomic information, leading to an increase in the number of sequenced genomes from a large spectrum of organisms. Unfortunately, due to the limitations implied by the short-read sequencing platforms, most of these newly sequenced genomes remained as "drafts", incomplete representations of the whole genetic content. The previous genome sequencing studies indicated that finishing a genome sequenced by NGS, even bacteria, may require additional sequencing to fill the gaps, making the entire process very expensive. As such, several in silico approaches have been developed to optimize the genome assemblies and facilitate the finishing process. The present review aims to explore some free (open source, in many cases) tools that are available to facilitate genome finishing.

  3. Deep sequencing of Salmonella RNA associated with heterologous Hfq proteins in vivo reveals small RNAs as a major target class and identifies RNA processing phenotypes.

    Science.gov (United States)

    Sittka, Alexandra; Sharma, Cynthia M; Rolle, Katarzyna; Vogel, Jörg

    2009-01-01

    The bacterial Sm-like protein, Hfq, is a key factor for the stability and function of small non-coding RNAs (sRNAs) in Escherichia coli. Homologues of this protein have been predicted in many distantly related organisms yet their functional conservation as sRNA-binding proteins has not entirely been clear. To address this, we expressed in Salmonella the Hfq proteins of two eubacteria (Neisseria meningitides, Aquifex aeolicus) and an archaeon (Methanocaldococcus jannaschii), and analyzed the associated RNA by deep sequencing. This in vivo approach identified endogenous Salmonella sRNAs as a major target of the foreign Hfq proteins. New Salmonella sRNA species were also identified, and some of these accumulated specifically in the presence of a foreign Hfq protein. In addition, we observed specific RNA processing defects, e.g., suppression of precursor processing of SraH sRNA by Methanocaldococcus Hfq, or aberrant accumulation of extracytoplasmic target mRNAs of the Salmonella GcvB, MicA or RybB sRNAs. Taken together, our study provides evidence of a conserved inherent sRNA-binding property of Hfq, which may facilitate the lateral transmission of regulatory sRNAs among distantly related species. It also suggests that the expression of heterologous RNA-binding proteins combined with deep sequencing analysis of RNA ligands can be used as a molecular tool to dissect individual steps of RNA metabolism in vivo.

  4. A comparative evaluation of deep and shallow approaches to the automatic detection of common grammatical errors

    OpenAIRE

    Wagner, Joachim; Foster, Jennifer; van Genabith, Josef

    2007-01-01

    This paper compares a deep and a shallow processing approach to the problem of classifying a sentence as grammatically wellformed or ill-formed. The deep processing approach uses the XLE LFG parser and English grammar: two versions are presented, one which uses the XLE directly to perform the classification, and another one which uses a decision tree trained on features consisting of the XLE’s output statistics. The shallow processing approach predicts grammaticality based on n-gram freque...

  5. Targeted amplicon sequencing (TAS): a scalable next-gen approach to multilocus, multitaxa phylogenetics.

    Science.gov (United States)

    Bybee, Seth M; Bracken-Grissom, Heather; Haynes, Benjamin D; Hermansen, Russell A; Byers, Robert L; Clement, Mark J; Udall, Joshua A; Wilcox, Edward R; Crandall, Keith A

    2011-01-01

    Next-gen sequencing technologies have revolutionized data collection in genetic studies and advanced genome biology to novel frontiers. However, to date, next-gen technologies have been used principally for whole genome sequencing and transcriptome sequencing. Yet many questions in population genetics and systematics rely on sequencing specific genes of known function or diversity levels. Here, we describe a targeted amplicon sequencing (TAS) approach capitalizing on next-gen capacity to sequence large numbers of targeted gene regions from a large number of samples. Our TAS approach is easily scalable, simple in execution, neither time-nor labor-intensive, relatively inexpensive, and can be applied to a broad diversity of organisms and/or genes. Our TAS approach includes a bioinformatic application, BarcodeCrucher, to take raw next-gen sequence reads and perform quality control checks and convert the data into FASTA format organized by gene and sample, ready for phylogenetic analyses. We demonstrate our approach by sequencing targeted genes of known phylogenetic utility to estimate a phylogeny for the Pancrustacea. We generated data from 44 taxa using 68 different 10-bp multiplexing identifiers. The overall quality of data produced was robust and was informative for phylogeny estimation. The potential for this method to produce copious amounts of data from a single 454 plate (e.g., 325 taxa for 24 loci) significantly reduces sequencing expenses incurred from traditional Sanger sequencing. We further discuss the advantages and disadvantages of this method, while offering suggestions to enhance the approach.

  6. Profile of microbial communities on carbonate stones of the medieval church of San Leonardo di Siponto (Italy) by Illumina-based deep sequencing.

    Science.gov (United States)

    Chimienti, Guglielmina; Piredda, Roberta; Pepe, Gabriella; van der Werf, Inez Dorothé; Sabbatini, Luigia; Crecchio, Carmine; Ricciuti, Patrizia; D'Erchia, Anna Maria; Manzari, Caterina; Pesole, Graziano

    2016-10-01

    Comprehensive studies of the biodiversity of the microbial epilithic community on monuments may provide critical insights for clarifying factors involved in the colonization processes. We carried out a high-throughput investigation of the communities colonizing the medieval church of San Leonardo di Siponto (Italy) by Illumina-based deep sequencing. The metagenomic analysis of sequences revealed the presence of Archaea, Bacteria, and Eukarya. Bacteria were Actinobacteria, Proteobacteria, Bacteroidetes, Cyanobacteria, Chloroflexi, Firmicutes and Candidatus Saccharibacteria. The predominant phylum was Actinobacteria, with the orders Actynomycetales and Rubrobacteriales, represented by the genera Pseudokineococcus, Sporichthya, Blastococcus, Arthrobacter, Geodermatophilus, Friedmanniella, Modestobacter, and Rubrobacter, respectively. Cyanobacteria sequences showing strong similarity with an uncultured bacterium sequence were identified. The presence of the green algae Oocystaceae and Trebuxiaceae was revealed. The microbial diversity was explored at qualitative and quantitative levels, evaluating the richness (the number of operational taxonomic units (OTUs)) and the abundance of reads associated with each OTU. The rarefaction curves approached saturation, suggesting that the majority of OTUs were recovered. The results highlighted a structured community, showing low diversity, made up of extremophile organisms adapted to desiccation and UV radiation. Notably, the microbiome appeared to be composed not only of microorganisms possibly involved in biodeterioration but also of carbonatogenic bacteria, such as those belonging to the genus Arthrobacter, which could be useful in bioconservation. Our investigation demonstrated that molecular tools, and in particular the easy-to-run next-generation sequencing, are powerful to perform a microbiological diagnosis in order to plan restoration and protection strategies.

  7. PENGEMBANGAN PENDIDIKAN KARAKTER DALAM MATA KULIAH EVALUASI PEMBELAJARAN MELALUI PENDEKATAN DEEP APPROACH TO LEARNING

    Directory of Open Access Journals (Sweden)

    Nanik Suryani

    2016-01-01

    Full Text Available The objectives of this study are to find and to test the model of characters education in Learning Evaluation Subject through deep approach to learning. The subject of the study is the class of Learning Evaluation of Office Administration Program, Economics Education Department, Economics Faculty, Semarang State University. The data are collected by a test, and then analyzed by qualitative descriptive. The result of this study showed that the model of characters education through deep approach to learning could improve students’ self awareness in learning the subject.

  8. PENGEMBANGAN PENDIDIKAN KARAKTER DALAM MATA KULIAH EVALUASI PEMBELAJARAN MELALUI PENDEKATAN DEEP APPROACH TO LEARNING

    Directory of Open Access Journals (Sweden)

    Nanik Suryani

    2012-12-01

    Full Text Available The objectives of this study are to find and to test the model of characters education in Learning Evaluation Subject through deep approach to learning. The subject of the study is the class of Learning Evaluation of Office Administration Program, Economics Education Department, Economics Faculty, Semarang State University. The data are collected by a test, and then analyzed by qualitative descriptive. The result of this study showed that the model of characters education through deep approach to learning could improve students’ self awareness in learning the subject.

  9. High-resolution deep sequencing reveals biodiversity, population structure, and persistence of HIV-1 quasispecies within host ecosystems

    Directory of Open Access Journals (Sweden)

    Yin Li

    2012-12-01

    Full Text Available Abstract Background Deep sequencing provides the basis for analysis of biodiversity of taxonomically similar organisms in an environment. While extensively applied to microbiome studies, population genetics studies of viruses are limited. To define the scope of HIV-1 population biodiversity within infected individuals, a suite of phylogenetic and population genetic algorithms was applied to HIV-1 envelope hypervariable domain 3 (Env V3 within peripheral blood mononuclear cells from a group of perinatally HIV-1 subtype B infected, therapy-naïve children. Results Biodiversity of HIV-1 Env V3 quasispecies ranged from about 70 to 270 unique sequence clusters across individuals. Viral population structure was organized into a limited number of clusters that included the dominant variants combined with multiple clusters of low frequency variants. Next generation viral quasispecies evolved from low frequency variants at earlier time points through multiple non-synonymous changes in lineages within the evolutionary landscape. Minor V3 variants detected as long as four years after infection co-localized in phylogenetic reconstructions with early transmitting viruses or with subsequent plasma virus circulating two years later. Conclusions Deep sequencing defines HIV-1 population complexity and structure, reveals the ebb and flow of dominant and rare viral variants in the host ecosystem, and identifies an evolutionary record of low-frequency cell-associated viral V3 variants that persist for years. Bioinformatics pipeline developed for HIV-1 can be applied for biodiversity studies of virome populations in human, animal, or plant ecosystems.

  10. Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing

    Science.gov (United States)

    Kannan, Kalpana; Wang, Liguo; Wang, Jianghua; Ittmann, Michael M.; Li, Wei; Yen, Laising

    2011-01-01

    Transcription-induced chimeric RNAs, possessing sequences from different genes, are expected to increase the proteomic diversity through chimeric proteins or altered regulation. Despite their importance, few studies have focused on chimeric RNAs especially regarding their presence/roles in human cancers. By deep sequencing the transcriptome of 20 human prostate cancer and 10 matched benign prostate tissues, we obtained 1.3 billion sequence reads, which led to the identification of 2,369 chimeric RNA candidates. Chimeric RNAs occurred in significantly higher frequency in cancer than in matched benign samples. Experimental investigation of a selected 46 set led to the confirmation of 32 chimeric RNAs, of which 27 were highly recurrent and previously undescribed in prostate cancer. Importantly, a subset of these chimeras was present in prostate cancer cell lines, but not detectable in primary human prostate epithelium cells, implying their associations with cancer. These chimeras contain discernable 5′ and 3′ splice sites at the RNA junction, indicating that their formation is mediated by splicing. Their presence is also largely independent of the expression of parental genes, suggesting that other factors are involved in their production and regulation. One chimera, TMEM79-SMG5, is highly differentially expressed in human cancer samples and therefore a potential biomarker. The prevalence of chimeric RNAs may allow the limited number of human genes to encode a substantially larger number of RNAs and proteins, forming an additional layer of cellular complexity. Together, our results suggest that chimeric RNAs are widespread, and increased chimeric RNA events could represent a unique class of molecular alteration in cancer. PMID:21571633

  11. Ultra-deep sequencing of mouse mitochondrial DNA: mutational patterns and their origins.

    Directory of Open Access Journals (Sweden)

    Adam Ameur

    2011-03-01

    Full Text Available Somatic mutations of mtDNA are implicated in the aging process, but there is no universally accepted method for their accurate quantification. We have used ultra-deep sequencing to study genome-wide mtDNA mutation load in the liver of normally- and prematurely-aging mice. Mice that are homozygous for an allele expressing a proof-reading-deficient mtDNA polymerase (mtDNA mutator mice have 10-times-higher point mutation loads than their wildtype siblings. In addition, the mtDNA mutator mice have increased levels of a truncated linear mtDNA molecule, resulting in decreased sequence coverage in the deleted region. In contrast, circular mtDNA molecules with large deletions occur at extremely low frequencies in mtDNA mutator mice and can therefore not drive the premature aging phenotype. Sequence analysis shows that the main proportion of the mutation load in heterozygous mtDNA mutator mice and their wildtype siblings is inherited from their heterozygous mothers consistent with germline transmission. We found no increase in levels of point mutations or deletions in wildtype C57Bl/6N mice with increasing age, thus questioning the causative role of these changes in aging. In addition, there was no increased frequency of transversion mutations with time in any of the studied genotypes, arguing against oxidative damage as a major cause of mtDNA mutations. Our results from studies of mice thus indicate that most somatic mtDNA mutations occur as replication errors during development and do not result from damage accumulation in adult life.

  12. Deep Sequencing of 71 Candidate Genes to Characterize Variation Associated with Alcohol Dependence.

    Science.gov (United States)

    Clark, Shaunna L; McClay, Joseph L; Adkins, Daniel E; Kumar, Gaurav; Aberg, Karolina A; Nerella, Srilaxmi; Xie, Linying; Collins, Ann L; Crowley, James J; Quackenbush, Corey R; Hilliard, Christopher E; Shabalin, Andrey A; Vrieze, Scott I; Peterson, Roseann E; Copeland, William E; Silberg, Judy L; McGue, Matt; Maes, Hermine; Iacono, William G; Sullivan, Patrick F; Costello, Elizabeth J; van den Oord, Edwin J

    2017-04-01

    Previous genomewide association studies (GWASs) have identified a number of putative risk loci for alcohol dependence (AD). However, only a few loci have replicated and these replicated variants only explain a small proportion of AD risk. Using an innovative approach, the goal of this study was to generate hypotheses about potentially causal variants for AD that can be explored further through functional studies. We employed targeted capture of 71 candidate loci and flanking regions followed by next-generation deep sequencing (mean coverage 78X) in 806 European Americans. Regions included in our targeted capture library were genes identified through published GWAS of alcohol, all human alcohol and aldehyde dehydrogenases, reward system genes including dopaminergic and opioid receptors, prioritized candidate genes based on previous associations, and genes involved in the absorption, distribution, metabolism, and excretion of drugs. We performed single-locus tests to determine if any single variant was associated with AD symptom count. Sets of variants that overlapped with biologically meaningful annotations were tested for association in aggregate. No single, common variant was significantly associated with AD in our study. We did, however, find evidence for association with several variant sets. Two variant sets were significant at the q-value <0.10 level: a genic enhancer for ADHFE1 (p = 1.47 × 10 -5 ; q = 0.019), an alcohol dehydrogenase, and ADORA1 (p = 5.29 × 10 -5 ; q = 0.035), an adenosine receptor that belongs to a G-protein-coupled receptor gene family. To our knowledge, this is the first sequencing study of AD to examine variants in entire genes, including flanking and regulatory regions. We found that in addition to protein coding variant sets, regulatory variant sets may play a role in AD. From these findings, we have generated initial functional hypotheses about how these sets may influence AD. Copyright © 2017 by the Research Society on

  13. Performance evaluation of 2D and 3D deep learning approaches for automatic segmentation of multiple organs on CT images

    Science.gov (United States)

    Zhou, Xiangrong; Yamada, Kazuma; Kojima, Takuya; Takayama, Ryosuke; Wang, Song; Zhou, Xinxin; Hara, Takeshi; Fujita, Hiroshi

    2018-02-01

    The purpose of this study is to evaluate and compare the performance of modern deep learning techniques for automatically recognizing and segmenting multiple organ regions on 3D CT images. CT image segmentation is one of the important task in medical image analysis and is still very challenging. Deep learning approaches have demonstrated the capability of scene recognition and semantic segmentation on nature images and have been used to address segmentation problems of medical images. Although several works showed promising results of CT image segmentation by using deep learning approaches, there is no comprehensive evaluation of segmentation performance of the deep learning on segmenting multiple organs on different portions of CT scans. In this paper, we evaluated and compared the segmentation performance of two different deep learning approaches that used 2D- and 3D deep convolutional neural networks (CNN) without- and with a pre-processing step. A conventional approach that presents the state-of-the-art performance of CT image segmentation without deep learning was also used for comparison. A dataset that includes 240 CT images scanned on different portions of human bodies was used for performance evaluation. The maximum number of 17 types of organ regions in each CT scan were segmented automatically and compared to the human annotations by using ratio of intersection over union (IU) as the criterion. The experimental results demonstrated the IUs of the segmentation results had a mean value of 79% and 67% by averaging 17 types of organs that segmented by a 3D- and 2D deep CNN, respectively. All the results of the deep learning approaches showed a better accuracy and robustness than the conventional segmentation method that used probabilistic atlas and graph-cut methods. The effectiveness and the usefulness of deep learning approaches were demonstrated for solving multiple organs segmentation problem on 3D CT images.

  14. Bioinformatics for whole-genome shotgun sequencing of microbial communities.

    Directory of Open Access Journals (Sweden)

    Kevin Chen

    2005-07-01

    Full Text Available The application of whole-genome shotgun sequencing to microbial communities represents a major development in metagenomics, the study of uncultured microbes via the tools of modern genomic analysis. In the past year, whole-genome shotgun sequencing projects of prokaryotic communities from an acid mine biofilm, the Sargasso Sea, Minnesota farm soil, three deep-sea whale falls, and deep-sea sediments have been reported, adding to previously published work on viral communities from marine and fecal samples. The interpretation of this new kind of data poses a wide variety of exciting and difficult bioinformatics problems. The aim of this review is to introduce the bioinformatics community to this emerging field by surveying existing techniques and promising new approaches for several of the most interesting of these computational problems.

  15. A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks

    Science.gov (United States)

    Mohan, Arvind; Gaitonde, Datta

    2017-11-01

    Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.

  16. DEEPre: sequence-based enzyme EC number prediction by deep learning

    KAUST Repository

    Li, Yu

    2017-10-20

    Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manuallycrafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre\\'s ability to capture the functional difference of enzyme isoforms.The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.

  17. DEEPre: sequence-based enzyme EC number prediction by deep learning

    KAUST Repository

    Li, Yu; Wang, Sheng; Umarov, Ramzan; Xie, Bingqing; Fan, Ming; Li, Lihua; Gao, Xin

    2017-01-01

    Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manuallycrafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms.The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.

  18. Characterization of GM events by insert knowledge adapted re-sequencing approaches.

    Science.gov (United States)

    Yang, Litao; Wang, Congmao; Holst-Jensen, Arne; Morisset, Dany; Lin, Yongjun; Zhang, Dabing

    2013-10-03

    Detection methods and data from molecular characterization of genetically modified (GM) events are needed by stakeholders of public risk assessors and regulators. Generally, the molecular characteristics of GM events are incomprehensively revealed by current approaches and biased towards detecting transformation vector derived sequences. GM events are classified based on available knowledge of the sequences of vectors and inserts (insert knowledge). Herein we present three insert knowledge-adapted approaches for characterization GM events (TT51-1 and T1c-19 rice as examples) based on paired-end re-sequencing with the advantages of comprehensiveness, accuracy, and automation. The comprehensive molecular characteristics of two rice events were revealed with additional unintended insertions comparing with the results from PCR and Southern blotting. Comprehensive transgene characterization of TT51-1 and T1c-19 is shown to be independent of a priori knowledge of the insert and vector sequences employing the developed approaches. This provides an opportunity to identify and characterize also unknown GM events.

  19. Analysis of Plasmodium falciparum diversity in natural infections by deep sequencing

    Science.gov (United States)

    Manske, Magnus; Miotto, Olivo; Campino, Susana; Auburn, Sarah; Almagro-Garcia, Jacob; Maslen, Gareth; O’Brien, Jack; Djimde, Abdoulaye; Doumbo, Ogobara; Zongo, Issaka; Ouedraogo, Jean-Bosco; Michon, Pascal; Mueller, Ivo; Siba, Peter; Nzila, Alexis; Borrmann, Steffen; Kiara, Steven M.; Marsh, Kevin; Jiang, Hongying; Su, Xin-Zhuan; Amaratunga, Chanaki; Fairhurst, Rick; Socheat, Duong; Nosten, Francois; Imwong, Mallika; White, Nicholas J.; Sanders, Mandy; Anastasi, Elisa; Alcock, Dan; Drury, Eleanor; Oyola, Samuel; Quail, Michael A.; Turner, Daniel J.; Rubio, Valentin Ruano; Jyothi, Dushyanth; Amenga-Etego, Lucas; Hubbart, Christina; Jeffreys, Anna; Rowlands, Kate; Sutherland, Colin; Roper, Cally; Mangano, Valentina; Modiano, David; Tan, John C.; Ferdig, Michael T.; Amambua-Ngwa, Alfred; Conway, David J.; Takala-Harrison, Shannon; Plowe, Christopher V.; Rayner, Julian C.; Rockett, Kirk A.; Clark, Taane G.; Newbold, Chris I.; Berriman, Matthew; MacInnis, Bronwyn; Kwiatkowski, Dominic P.

    2013-01-01

    Malaria elimination strategies require surveillance of the parasite population for genetic changes that demand a public health response, such as new forms of drug resistance. 1,2 Here we describe methods for large-scale analysis of genetic variation in Plasmodium falciparum by deep sequencing of parasite DNA obtained from the blood of patients with malaria, either directly or after short term culture. Analysis of 86,158 exonic SNPs that passed genotyping quality control in 227 samples from Africa, Asia and Oceania provides genome-wide estimates of allele frequency distribution, population structure and linkage disequilibrium. By comparing the genetic diversity of individual infections with that of the local parasite population, we derive a metric of within-host diversity that is related to the level of inbreeding in the population. An open-access web application has been established for exploration of regional differences in allele frequency and of highly differentiated loci in the P. falciparum genome. PMID:22722859

  20. Geochemical features and effects on deep-seated fluids during the May-June 2012 southern Po Valley seismic sequence

    Directory of Open Access Journals (Sweden)

    Francesco Italiano

    2012-10-01

    Full Text Available A periodic sampling of the groundwaters and dissolved and free gases in selected deep wells located in the area affected by the May-June 2012 southern Po Valley seismic sequence has provided insight into seismogenic-induced changes of the local aquifer systems. The results obtained show progressive changes in the fluid geochemistry, allowing it to be established that deep-seated fluids were mobilized during the seismic sequence and reached surface layers along faults and fractures, which generated significant geochemical anomalies. The May-June 2012 seismic swarm (mainshock on May 29, 2012, M 5.8; 7 shocks M >5, about 200 events 3 > M > 5 induced several modifications in the circulating fluids. This study reports the preliminary results obtained for the geochemical features of the waters and gases collected over the epicentral area from boreholes drilled at different depths, thus intercepting water and gases with different origins and circulation. The aim of the investigations was to improve our knowledge of the fluids circulating over the seismic area (e.g. origin, provenance, interactions, mixing of different components, temporal changes. This was achieved by collecting samples from both shallow and deep-drilled boreholes, and then, after the selection of the relevant sites, we looked for temporal changes with mid-to-long-term monitoring activity following a constant sampling rate. This allowed us to gain better insight into the relationships between the fluid circulation and the faulting activity. The sampling sites are listed in Table 1, along with the analytical results of the gas phase. […

  1. A deep learning approach to adherence detection for type 2 diabetics

    DEFF Research Database (Denmark)

    Mohebbi, Ali; Aradóttir, Tinna Björk; Johansen, Alexander Rosenberg

    2017-01-01

    Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2...

  2. Digging deeper on "deep" learning: A computational ecology approach.

    Science.gov (United States)

    Buscema, Massimo; Sacco, Pier Luigi

    2017-01-01

    We propose an alternative approach to "deep" learning that is based on computational ecologies of structurally diverse artificial neural networks, and on dynamic associative memory responses to stimuli. Rather than focusing on massive computation of many different examples of a single situation, we opt for model-based learning and adaptive flexibility. Cross-fertilization of learning processes across multiple domains is the fundamental feature of human intelligence that must inform "new" artificial intelligence.

  3. Deep learning? What deep learning? | Fourie | South African ...

    African Journals Online (AJOL)

    In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability ...

  4. Sequence protein identification by randomized sequence database and transcriptome mass spectrometry (SPIDER-TMS): from manual to automatic application of a 'de novo sequencing' approach.

    Science.gov (United States)

    Pascale, Raffaella; Grossi, Gerarda; Cruciani, Gabriele; Mecca, Giansalvatore; Santoro, Donatello; Sarli Calace, Renzo; Falabella, Patrizia; Bianco, Giuliana

    Sequence protein identification by a randomized sequence database and transcriptome mass spectrometry software package has been developed at the University of Basilicata in Potenza (Italy) and designed to facilitate the determination of the amino acid sequence of a peptide as well as an unequivocal identification of proteins in a high-throughput manner with enormous advantages of time, economical resource and expertise. The software package is a valid tool for the automation of a de novo sequencing approach, overcoming the main limits and a versatile platform useful in the proteomic field for an unequivocal identification of proteins, starting from tandem mass spectrometry data. The strength of this software is that it is a user-friendly and non-statistical approach, so protein identification can be considered unambiguous.

  5. Deep round window insertion versus standard approach in cochlear implant surgery.

    Science.gov (United States)

    Nordfalk, Karl Fredrik; Rasmussen, Kjell; Bunne, Marie; Jablonski, Greg Eigner

    2016-01-01

    The aim of this study was to compare the outcomes of vestibular tests and the residual hearing of patients who have undergone full insertion cochlear implant surgery using the round window approach with a hearing preservation protocol (RW-HP) or the standard cochleostomy approach (SCA) without hearing preservation. A prospective study of 34 adults who underwent unilateral cochlear implantation was carried out. One group was operated using the RW-HP (n = 17) approach with Med-El +Flex(SOFT) electrode array with full insertion, while the control group underwent a more conventional SCA surgery (n = 17) with shorter perimodiolar electrodes. Assessments of residual hearing, cervical vestibular-evoked myogenic potentials (cVEMP), videonystagmography, subjective visual vertical/horizontal (SVH/SVV) were performed before and after surgery. There was a significantly (p < 0.05) greater number of subjects who exhibited complete or partial hearing preservation in the deep insertion RW-HP group (9/17) compared to the SCA group (2/15). A higher degree of vestibular loss but a lower degree of vertigo symptoms could be seen in the RW-HP group, but the differences were not statistically significant. It is possible to preserve residual hearing to a certain extent also with deep insertion. Full insertion with hearing preservation was less harmful to residual hearing particularly at 125 Hz (p < 0.05), than was the standard cochleostomy approach.

  6. Deep learning

    CERN Document Server

    Goodfellow, Ian; Courville, Aaron

    2016-01-01

    Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

  7. A Critical Comparison of Transformation and Deep Approach Theories of Learning

    Science.gov (United States)

    Howie, Peter; Bagnall, Richard

    2015-01-01

    This paper reports a critical comparative analysis of two popular and significant theories of adult learning: the transformation and the deep approach theories of learning. These theories are operative in different educational sectors, are significant, respectively, in each, and they may be seen as both touching on similar concerns with learning…

  8. The Influence of Parents and Teachers on the Deep Learning Approach of Pupils in Norwegian Upper-Secondary Schools

    Science.gov (United States)

    Elstad, Eyvind; Christophersen, Knut-Andreas; Turmo, Are

    2012-01-01

    Introduction: The purpose of this article was to explore the influence of parents and teachers on the deep learning approach of pupils by estimating the strength of the relationships between these factors and the motivation, volition and deep learning approach of Norwegian 16-year-olds. Method: Structural equation modeling for cross-sectional…

  9. First-Year Students' Approaches to Learning, and Factors Related to Change or Stability in Their Deep Approach during a Pharmacy Course

    Science.gov (United States)

    Varunki, Maaret; Katajavuori, Nina; Postareff, Liisa

    2017-01-01

    Research shows that a surface approach to learning is more common among students in the natural sciences, while students representing the "soft" sciences are more likely to apply a deep approach. However, findings conflict concerning the stability of approaches to learning in general. This study explores the variation in students'…

  10. Identifying genomic changes associated with insecticide resistance in the dengue mosquito Aedes aegypti by deep targeted sequencing

    Science.gov (United States)

    Faucon, Frederic; Dusfour, Isabelle; Gaude, Thierry; Navratil, Vincent; Boyer, Frederic; Chandre, Fabrice; Sirisopa, Patcharawan; Thanispong, Kanutcharee; Juntarajumnong, Waraporn; Poupardin, Rodolphe; Chareonviriyaphap, Theeraphap; Girod, Romain; Corbel, Vincent; Reynaud, Stephane; David, Jean-Philippe

    2015-01-01

    The capacity of mosquitoes to resist insecticides threatens the control of diseases such as dengue and malaria. Until alternative control tools are implemented, characterizing resistance mechanisms is crucial for managing resistance in natural populations. Insecticide biodegradation by detoxification enzymes is a common resistance mechanism; however, the genomic changes underlying this mechanism have rarely been identified, precluding individual resistance genotyping. In particular, the role of copy number variations (CNVs) and polymorphisms of detoxification enzymes have never been investigated at the genome level, although they can represent robust markers of metabolic resistance. In this context, we combined target enrichment with high-throughput sequencing for conducting the first comprehensive screening of gene amplifications and polymorphisms associated with insecticide resistance in mosquitoes. More than 760 candidate genes were captured and deep sequenced in several populations of the dengue mosquito Ae. aegypti displaying distinct genetic backgrounds and contrasted resistance levels to the insecticide deltamethrin. CNV analysis identified 41 gene amplifications associated with resistance, most affecting cytochrome P450s overtranscribed in resistant populations. Polymorphism analysis detected more than 30,000 variants and strong selection footprints in specific genomic regions. Combining Bayesian and allele frequency filtering approaches identified 55 nonsynonymous variants strongly associated with resistance. Both CNVs and polymorphisms were conserved within regions but differed across continents, confirming that genomic changes underlying metabolic resistance to insecticides are not universal. By identifying novel DNA markers of insecticide resistance, this study opens the way for tracking down metabolic changes developed by mosquitoes to resist insecticides within and among populations. PMID:26206155

  11. Deep Echo State Network (DeepESN): A Brief Survey

    OpenAIRE

    Gallicchio, Claudio; Micheli, Alessio

    2017-01-01

    The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions ...

  12. Deep Learning and Its Applications in Biomedicine.

    Science.gov (United States)

    Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

    2018-02-01

    Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

  13. De Novo Deep Transcriptome Analysis of Medicinal Plants for Gene Discovery in Biosynthesis of Plant Natural Products.

    Science.gov (United States)

    Han, R; Rai, A; Nakamura, M; Suzuki, H; Takahashi, H; Yamazaki, M; Saito, K

    2016-01-01

    Study on transcriptome, the entire pool of transcripts in an organism or single cells at certain physiological or pathological stage, is indispensable in unraveling the connection and regulation between DNA and protein. Before the advent of deep sequencing, microarray was the main approach to handle transcripts. Despite obvious shortcomings, including limited dynamic range and difficulties to compare the results from distinct experiments, microarray was widely applied. During the past decade, next-generation sequencing (NGS) has revolutionized our understanding of genomics in a fast, high-throughput, cost-effective, and tractable manner. By adopting NGS, efficiency and fruitful outcomes concerning the efforts to elucidate genes responsible for producing active compounds in medicinal plants were profoundly enhanced. The whole process involves steps, from the plant material sampling, to cDNA library preparation, to deep sequencing, and then bioinformatics takes over to assemble enormous-yet fragmentary-data from which to comb and extract information. The unprecedentedly rapid development of such technologies provides so many choices to facilitate the task, which can cause confusion when choosing the suitable methodology for specific purposes. Here, we review the general approaches for deep transcriptome analysis and then focus on their application in discovering biosynthetic pathways of medicinal plants that produce important secondary metabolites. © 2016 Elsevier Inc. All rights reserved.

  14. Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

    Energy Technology Data Exchange (ETDEWEB)

    Shi, CY; Yang, H; Wei, CL; Yu, O; Zhang, ZZ; Sun, J; Wan, XC

    2011-01-01

    Tea is one of the most popular non-alcoholic beverages worldwide. However, the tea plant, Camellia sinensis, is difficult to culture in vitro, to transform, and has a large genome, rendering little genomic information available. Recent advances in large-scale RNA sequencing (RNA-seq) provide a fast, cost-effective, and reliable approach to generate large expression datasets for functional genomic analysis, which is especially suitable for non-model species with un-sequenced genomes. Using high-throughput Illumina RNA-seq, the transcriptome from poly (A){sup +} RNA of C. sinensis was analyzed at an unprecedented depth (2.59 gigabase pairs). Approximate 34.5 million reads were obtained, trimmed, and assembled into 127,094 unigenes, with an average length of 355 bp and an N50 of 506 bp, which consisted of 788 contig clusters and 126,306 singletons. This number of unigenes was 10-fold higher than existing C. sinensis sequences deposited in GenBank (as of August 2010). Sequence similarity analyses against six public databases (Uniprot, NR and COGs at NCBI, Pfam, InterPro and KEGG) found 55,088 unigenes that could be annotated with gene descriptions, conserved protein domains, or gene ontology terms. Some of the unigenes were assigned to putative metabolic pathways. Targeted searches using these annotations identified the majority of genes associated with several primary metabolic pathways and natural product pathways that are important to tea quality, such as flavonoid, theanine and caffeine biosynthesis pathways. Novel candidate genes of these secondary pathways were discovered. Comparisons with four previously prepared cDNA libraries revealed that this transcriptome dataset has both a high degree of consistency with previous EST data and an approximate 20 times increase in coverage. Thirteen unigenes related to theanine and flavonoid synthesis were validated. Their expression patterns in different organs of the tea plant were analyzed by RT-PCR and quantitative real

  15. Deep sequencing of the Camellia sinensis transcriptome revealed candidate genes for major metabolic pathways of tea-specific compounds

    Directory of Open Access Journals (Sweden)

    Chen Qi

    2011-02-01

    Full Text Available Abstract Background Tea is one of the most popular non-alcoholic beverages worldwide. However, the tea plant, Camellia sinensis, is difficult to culture in vitro, to transform, and has a large genome, rendering little genomic information available. Recent advances in large-scale RNA sequencing (RNA-seq provide a fast, cost-effective, and reliable approach to generate large expression datasets for functional genomic analysis, which is especially suitable for non-model species with un-sequenced genomes. Results Using high-throughput Illumina RNA-seq, the transcriptome from poly (A+ RNA of C. sinensis was analyzed at an unprecedented depth (2.59 gigabase pairs. Approximate 34.5 million reads were obtained, trimmed, and assembled into 127,094 unigenes, with an average length of 355 bp and an N50 of 506 bp, which consisted of 788 contig clusters and 126,306 singletons. This number of unigenes was 10-fold higher than existing C. sinensis sequences deposited in GenBank (as of August 2010. Sequence similarity analyses against six public databases (Uniprot, NR and COGs at NCBI, Pfam, InterPro and KEGG found 55,088 unigenes that could be annotated with gene descriptions, conserved protein domains, or gene ontology terms. Some of the unigenes were assigned to putative metabolic pathways. Targeted searches using these annotations identified the majority of genes associated with several primary metabolic pathways and natural product pathways that are important to tea quality, such as flavonoid, theanine and caffeine biosynthesis pathways. Novel candidate genes of these secondary pathways were discovered. Comparisons with four previously prepared cDNA libraries revealed that this transcriptome dataset has both a high degree of consistency with previous EST data and an approximate 20 times increase in coverage. Thirteen unigenes related to theanine and flavonoid synthesis were validated. Their expression patterns in different organs of the tea plant were

  16. Phylogenetic and genome-wide deep-sequencing analyses of canine parvovirus reveal co-infection with field variants and emergence of a recent recombinant strain.

    Directory of Open Access Journals (Sweden)

    Ruben Pérez

    Full Text Available Canine parvovirus (CPV, a fast-evolving single-stranded DNA virus, comprises three antigenic variants (2a, 2b, and 2c with different frequencies and genetic variability among countries. The contribution of co-infection and recombination to the genetic variability of CPV is far from being fully elucidated. Here we took advantage of a natural CPV population, recently formed by the convergence of divergent CPV-2c and CPV-2a strains, to study co-infection and recombination. Complete sequences of the viral coding region of CPV-2a and CPV-2c strains from 40 samples were generated and analyzed using phylogenetic tools. Two samples showed co-infection and were further analyzed by deep sequencing. The sequence profile of one of the samples revealed the presence of CPV-2c and CPV-2a strains that differed at 29 nucleotides. The other sample included a minor CPV-2a strain (13.3% of the viral population and a major recombinant strain (86.7%. The recombinant strain arose from inter-genotypic recombination between CPV-2c and CPV-2a strains within the VP1/VP2 gene boundary. Our findings highlight the importance of deep-sequencing analysis to provide a better understanding of CPV molecular diversity.

  17. Phylogenetic and Genome-Wide Deep-Sequencing Analyses of Canine Parvovirus Reveal Co-Infection with Field Variants and Emergence of a Recent Recombinant Strain

    Science.gov (United States)

    Pérez, Ruben; Calleros, Lucía; Marandino, Ana; Sarute, Nicolás; Iraola, Gregorio; Grecco, Sofia; Blanc, Hervé; Vignuzzi, Marco; Isakov, Ofer; Shomron, Noam; Carrau, Lucía; Hernández, Martín; Francia, Lourdes; Sosa, Katia; Tomás, Gonzalo; Panzera, Yanina

    2014-01-01

    Canine parvovirus (CPV), a fast-evolving single-stranded DNA virus, comprises three antigenic variants (2a, 2b, and 2c) with different frequencies and genetic variability among countries. The contribution of co-infection and recombination to the genetic variability of CPV is far from being fully elucidated. Here we took advantage of a natural CPV population, recently formed by the convergence of divergent CPV-2c and CPV-2a strains, to study co-infection and recombination. Complete sequences of the viral coding region of CPV-2a and CPV-2c strains from 40 samples were generated and analyzed using phylogenetic tools. Two samples showed co-infection and were further analyzed by deep sequencing. The sequence profile of one of the samples revealed the presence of CPV-2c and CPV-2a strains that differed at 29 nucleotides. The other sample included a minor CPV-2a strain (13.3% of the viral population) and a major recombinant strain (86.7%). The recombinant strain arose from inter-genotypic recombination between CPV-2c and CPV-2a strains within the VP1/VP2 gene boundary. Our findings highlight the importance of deep-sequencing analysis to provide a better understanding of CPV molecular diversity. PMID:25365348

  18. Draft Genome Sequence of Pseudoalteromonas sp. Strain XI10 Isolated from the Brine-Seawater Interface of Erba Deep in the Red Sea

    KAUST Repository

    Zhang, Guishan; Haroon, Mohamed; Zhang, Ruifu; Hikmawan, Tyas I.; Stingl, Ulrich

    2016-01-01

    Pseudoalteromonas sp. strain XI10 was isolated from the brine-seawater interface of Erba Deep in the Red Sea, Saudi Arabia. Here, we present the draft genome sequence of strain XI10, a gammaproteobacterium that synthesizes polysaccharides for biofilm formation when grown in liquid culture.

  19. Draft Genome Sequence of Pseudoalteromonas sp. Strain XI10 Isolated from the Brine-Seawater Interface of Erba Deep in the Red Sea

    KAUST Repository

    Zhang, Guishan

    2016-03-10

    Pseudoalteromonas sp. strain XI10 was isolated from the brine-seawater interface of Erba Deep in the Red Sea, Saudi Arabia. Here, we present the draft genome sequence of strain XI10, a gammaproteobacterium that synthesizes polysaccharides for biofilm formation when grown in liquid culture.

  20. Relationship between motivational goal orientations, perceptions of general education classroom learning environment, and deep approaches to learning

    OpenAIRE

    Chanut Poondej; Thanita Lerdpornkulrat

    2016-01-01

    Researchers have reported empirical evidence that the deep approaches to learning account for significant successful learning. The present study aimed to investigate the relationship between students' motivational goal orientation, their perceptions of the general education classroom learning environment, and deep approaches to learning strategies. Participants (N = 494) were first- and second-year college students enrolled in any of the general education courses in higher education in Thaila...

  1. Foundations of Sequence-to-Sequence Modeling for Time Series

    OpenAIRE

    Kuznetsov, Vitaly; Mariet, Zelda

    2018-01-01

    The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

  2. A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices.

    Science.gov (United States)

    Ravi, Daniele; Wong, Charence; Lo, Benny; Yang, Guang-Zhong

    2017-01-01

    The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning framework where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.

  3. Integrative analysis of deep sequencing data identifies estrogen receptor early response genes and links ATAD3B to poor survival in breast cancer.

    Directory of Open Access Journals (Sweden)

    Kristian Ovaska

    Full Text Available Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor α (ERα, H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 ERa early responsive genes and compared the biological functions of the genes having ERα binding within 20 kb of the transcription start site (TSS to genes without such binding site. Our results show that the non-genomic action of ERα via the MAPK pathway, instead of direct ERa binding, may be responsible for early cell responses to ERα activation. Our results also indicate that the ERα responsive genes triggered by the genomic pathway are transcribed faster than those without ERα binding sites. The survival analysis of the 777 ERα responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have ERα binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.

  4. Sequence of structures in fine-grained turbidites: Comparison of recent deep-sea and ancient flysch sediments

    Science.gov (United States)

    Stow, Dorrik A. V.; Shanmugam, Ganapathy

    1980-01-01

    A comparative study of the sequence of sedimentary structures in ancient and modern fine-grained turbidites is made in three contrasting areas. They are (1) Holocene and Pleistocene deep-sea muds of the Nova Scotian Slope and Rise, (2) Middle Ordovician Sevier Shale of the Valley and Ridge Province of the Southern Appalachians, and (3) Cambro-Ordovician Halifax Slate of the Meguma Group in Nova Scotia. A standard sequence of structures is proposed for fine-grained turbidites. The complete sequence has nine sub-divisions that are here termed T 0 to T 8. "The lower subdivision (T 0) comprises a silt lamina which has a sharp, scoured and load-cast base, internal parallel-lamination and cross-lamination, and a sharp current-lineated or wavy surface with 'fading-ripples' (= Type C etc. …)." (= Type C ripple-drift cross-lamination, Jopling and Walker, 1968). The overlying sequence shows textural and compositional grading through alternating silt and mud laminae. A convolute-laminated sub-division (T 1) is overlain by low-amplitude climbing ripples (T 2), thin regular laminae (T 3), thin indistinct laminae (T 4), and thin wipsy or convolute laminae (T 5). The topmost three divisions, graded mud (T 6), ungraded mud (T 7) and bioturbated mud (T 8), do not have silt laminae but rare patchy silt lenses and silt pseudonodules and a thin zone of micro-burrowing near the upper surface. The proposed sequence is analogous to the Bouma (1962) structural scheme for sandy turbidites and is approximately equivalent to Bouma's (C)DE divisions. The repetition of partial sequences characterizes different parts of the slope/base-of-slope/basin plain environment, and represents deposition from different stages of evolution of a large, muddy, turbidity flow. Microstructural detail and sequence are well preserved in ancient and even slightly metamorphosed sediments. Their recognition is important for determining depositional processes and for palaeoenvironmental interpretation.

  5. A deep learning approach for fetal QRS complex detection.

    Science.gov (United States)

    Zhong, Wei; Liao, Lijuan; Guo, Xuemei; Wang, Guoli

    2018-04-20

    Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

  6. A time warping approach to multiple sequence alignment.

    Science.gov (United States)

    Arribas-Gil, Ana; Matias, Catherine

    2017-04-25

    We propose an approach for multiple sequence alignment (MSA) derived from the dynamic time warping viewpoint and recent techniques of curve synchronization developed in the context of functional data analysis. Starting from pairwise alignments of all the sequences (viewed as paths in a certain space), we construct a median path that represents the MSA we are looking for. We establish a proof of concept that our method could be an interesting ingredient to include into refined MSA techniques. We present a simple synthetic experiment as well as the study of a benchmark dataset, together with comparisons with 2 widely used MSA softwares.

  7. DeepLoc: prediction of protein subcellular localization using deep learning

    DEFF Research Database (Denmark)

    Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    2017-01-01

    The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

  8. Deep ecology: A movement and a new approach to solving environmental problems

    Directory of Open Access Journals (Sweden)

    Mišković Milan M.

    2016-01-01

    Full Text Available In the industrial society, nature is conceived as a resource for unlimited exploitation, and the entropic effects of its pollution and depletion can be effectively controlled and resolved. Non-human entities are viewed as raw materials for technical manipulation and the increase in the standard of living for consumers in mass societies. Contrary to such utilitarian pragmatism, some new views on the relationship of man, society and nature are appearing, as well as different concepts of environmentally balanced development. According to these views, the transition to ecological society and ecological culture will not be possible without replacing the current anthropocentric ethics with the ecocentric or environmental ethics. Deep ecology arises in the spectrum of environmental ethics theories. It is considered as a movement and a new approach to solving environmental problems. Deep ecology is a type of ecosophy formed by Arne Nes, and it focuses on wisdom and ecological balance. It is based on ecological science, but it asks deeper questions about the causes of the ecological crisis and corresponds to the general discourse on sustainable development. The article discusses the platform of deep ecology movement and gives the basic principles of deep ecology. It gives explanations of the two basic norms of deep ecology (self-understanding and biospheric egalitarianism and criticism of these concepts.

  9. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

    Science.gov (United States)

    Liu, Jie; Hu, Youmin; Wang, Yan; Wu, Bo; Fan, Jikai; Hu, Zhongxu

    2018-05-01

    The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.

  10. DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

    Science.gov (United States)

    Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

    2016-09-01

    Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. A novel approach to tracking antigen-experienced CD4 T cells into functional compartments via tandem deep and shallow TCR clonotyping.

    Science.gov (United States)

    Estorninho, Megan; Gibson, Vivienne B; Kronenberg-Versteeg, Deborah; Liu, Yuk-Fun; Ni, Chester; Cerosaletti, Karen; Peakman, Mark

    2013-12-01

    Extensive diversity in the human repertoire of TCRs for Ag is both a cornerstone of effective adaptive immunity that enables host protection against a multiplicity of pathogens and a weakness that gives rise to potential pathological self-reactivity. The complexity arising from diversity makes detection and tracking of single Ag-specific CD4 T cells (ASTs) involved in these immune responses challenging. We report a tandem, multistep process to quantify rare TCRβ-chain variable sequences of ASTs in large polyclonal populations. The approach combines deep high-throughput sequencing (HTS) within functional CD4 T cell compartments, such as naive/memory cells, with shallow, multiple identifier-based HTS of ASTs identified by activation marker upregulation after short-term Ag stimulation in vitro. We find that clonotypes recognizing HLA class II-restricted epitopes of both pathogen-derived Ags and self-Ags are oligoclonal and typically private. Clonotype tracking within an individual reveals private AST clonotypes resident in the memory population, as would be expected, representing clonal expansions (identical nucleotide sequence; "ultraprivate"). Other AST clonotypes share CDR3β amino acid sequences through convergent recombination and are found in memory populations of multiple individuals. Tandem HTS-based clonotyping will facilitate studying AST dynamics, epitope spreading, and repertoire changes that arise postvaccination and following Ag-specific immunotherapies for cancer and autoimmune disease.

  12. Pengembangan Pendidikan Karakter Dalam Mata Kuliah Evaluasi Pembelajaran Melalui Pendekatan Deep Approach to Learning

    OpenAIRE

    Suryani, Nanik; Pramushinto, Hengky

    2012-01-01

    The objectives of this study are to find and to test the model of characters education in Learning Evaluation Subject through deep approach to learning. The subject of the study is the class of Learning Evaluation of Office Administration Program, Economics Education Department, Economics Faculty, Semarang State University. The data are collected by a test, and then analyzed by qualitative descriptive. The result of this study showed that the model of characters education through deep approac...

  13. Transcriptional Slippage and RNA Editing Increase the Diversity of Transcripts in Chloroplasts: Insight from Deep Sequencing of Vigna radiata Genome and Transcriptome.

    Directory of Open Access Journals (Sweden)

    Ching-Ping Lin

    Full Text Available We performed deep sequencing of the nuclear and organellar genomes of three mungbean genotypes: Vigna radiata ssp. sublobata TC1966, V. radiata var. radiata NM92 and the recombinant inbred line RIL59 derived from a cross between TC1966 and NM92. Moreover, we performed deep sequencing of the RIL59 transcriptome to investigate transcript variability. The mungbean chloroplast genome has a quadripartite structure including a pair of inverted repeats separated by two single copy regions. A total of 213 simple sequence repeats were identified in the chloroplast genomes of NM92 and RIL59; 78 single nucleotide variants and nine indels were discovered in comparing the chloroplast genomes of TC1966 and NM92. Analysis of the mungbean chloroplast transcriptome revealed mRNAs that were affected by transcriptional slippage and RNA editing. Transcriptional slippage frequency was positively correlated with the length of simple sequence repeats of the mungbean chloroplast genome (R2=0.9911. In total, 41 C-to-U editing sites were found in 23 chloroplast genes and in one intergenic spacer. No editing site that swapped U to C was found. A combination of bioinformatics and experimental methods revealed that the plastid-encoded RNA polymerase-transcribed genes psbF and ndhA are affected by transcriptional slippage in mungbean and in main lineages of land plants, including three dicots (Glycine max, Brassica rapa, and Nicotiana tabacum, two monocots (Oryza sativa and Zea mays, two gymnosperms (Pinus taeda and Ginkgo biloba and one moss (Physcomitrella patens. Transcript analysis of the rps2 gene showed that transcriptional slippage could affect transcripts at single sequence repeat regions with poly-A runs. It showed that transcriptional slippage together with incomplete RNA editing may cause sequence diversity of transcripts in chloroplasts of land plants.

  14. Molecular Characterization of Transgenic Events Using Next Generation Sequencing Approach.

    Science.gov (United States)

    Guttikonda, Satish K; Marri, Pradeep; Mammadov, Jafar; Ye, Liang; Soe, Khaing; Richey, Kimberly; Cruse, James; Zhuang, Meibao; Gao, Zhifang; Evans, Clive; Rounsley, Steve; Kumpatla, Siva P

    2016-01-01

    Demand for the commercial use of genetically modified (GM) crops has been increasing in light of the projected growth of world population to nine billion by 2050. A prerequisite of paramount importance for regulatory submissions is the rigorous safety assessment of GM crops. One of the components of safety assessment is molecular characterization at DNA level which helps to determine the copy number, integrity and stability of a transgene; characterize the integration site within a host genome; and confirm the absence of vector DNA. Historically, molecular characterization has been carried out using Southern blot analysis coupled with Sanger sequencing. While this is a robust approach to characterize the transgenic crops, it is both time- and resource-consuming. The emergence of next-generation sequencing (NGS) technologies has provided highly sensitive and cost- and labor-effective alternative for molecular characterization compared to traditional Southern blot analysis. Herein, we have demonstrated the successful application of both whole genome sequencing and target capture sequencing approaches for the characterization of single and stacked transgenic events and compared the results and inferences with traditional method with respect to key criteria required for regulatory submissions.

  15. Molecular Characterization of Transgenic Events Using Next Generation Sequencing Approach.

    Directory of Open Access Journals (Sweden)

    Satish K Guttikonda

    Full Text Available Demand for the commercial use of genetically modified (GM crops has been increasing in light of the projected growth of world population to nine billion by 2050. A prerequisite of paramount importance for regulatory submissions is the rigorous safety assessment of GM crops. One of the components of safety assessment is molecular characterization at DNA level which helps to determine the copy number, integrity and stability of a transgene; characterize the integration site within a host genome; and confirm the absence of vector DNA. Historically, molecular characterization has been carried out using Southern blot analysis coupled with Sanger sequencing. While this is a robust approach to characterize the transgenic crops, it is both time- and resource-consuming. The emergence of next-generation sequencing (NGS technologies has provided highly sensitive and cost- and labor-effective alternative for molecular characterization compared to traditional Southern blot analysis. Herein, we have demonstrated the successful application of both whole genome sequencing and target capture sequencing approaches for the characterization of single and stacked transgenic events and compared the results and inferences with traditional method with respect to key criteria required for regulatory submissions.

  16. Resolving the Complexity of Human Skin Metagenomes Using Single-Molecule Sequencing

    Directory of Open Access Journals (Sweden)

    Yu-Chih Tsai

    2016-02-01

    Full Text Available Deep metagenomic shotgun sequencing has emerged as a powerful tool to interrogate composition and function of complex microbial communities. Computational approaches to assemble genome fragments have been demonstrated to be an effective tool for de novo reconstruction of genomes from these communities. However, the resultant “genomes” are typically fragmented and incomplete due to the limited ability of short-read sequence data to assemble complex or low-coverage regions. Here, we use single-molecule, real-time (SMRT sequencing to reconstruct a high-quality, closed genome of a previously uncharacterized Corynebacterium simulans and its companion bacteriophage from a skin metagenomic sample. Considerable improvement in assembly quality occurs in hybrid approaches incorporating short-read data, with even relatively small amounts of long-read data being sufficient to improve metagenome reconstruction. Using short-read data to evaluate strain variation of this C. simulans in its skin community at single-nucleotide resolution, we observed a dominant C. simulans strain with moderate allelic heterozygosity throughout the population. We demonstrate the utility of SMRT sequencing and hybrid approaches in metagenome quantitation, reconstruction, and annotation.

  17. Resolving the Complexity of Human Skin Metagenomes Using Single-Molecule Sequencing

    Science.gov (United States)

    Tsai, Yu-Chih; Deming, Clayton; Segre, Julia A.; Kong, Heidi H.; Korlach, Jonas

    2016-01-01

    ABSTRACT Deep metagenomic shotgun sequencing has emerged as a powerful tool to interrogate composition and function of complex microbial communities. Computational approaches to assemble genome fragments have been demonstrated to be an effective tool for de novo reconstruction of genomes from these communities. However, the resultant “genomes” are typically fragmented and incomplete due to the limited ability of short-read sequence data to assemble complex or low-coverage regions. Here, we use single-molecule, real-time (SMRT) sequencing to reconstruct a high-quality, closed genome of a previously uncharacterized Corynebacterium simulans and its companion bacteriophage from a skin metagenomic sample. Considerable improvement in assembly quality occurs in hybrid approaches incorporating short-read data, with even relatively small amounts of long-read data being sufficient to improve metagenome reconstruction. Using short-read data to evaluate strain variation of this C. simulans in its skin community at single-nucleotide resolution, we observed a dominant C. simulans strain with moderate allelic heterozygosity throughout the population. We demonstrate the utility of SMRT sequencing and hybrid approaches in metagenome quantitation, reconstruction, and annotation. PMID:26861018

  18. A Stepwise Approach: Decreasing Infection in Deep Brain Stimulation for Childhood Dystonic Cerebral Palsy.

    Science.gov (United States)

    Johans, Stephen J; Swong, Kevin N; Hofler, Ryan C; Anderson, Douglas E

    2017-09-01

    Dystonia is a movement disorder characterized by involuntary muscle contractions, which cause twisting movements or abnormal postures. Deep brain stimulation has been used to improve the quality of life for secondary dystonia caused by cerebral palsy. Despite being a viable treatment option for childhood dystonic cerebral palsy, deep brain stimulation is associated with a high rate of infection in children. The authors present a small series of patients with dystonic cerebral palsy who underwent a stepwise approach for bilateral globus pallidus interna deep brain stimulation placement in order to decrease the rate of infection. Four children with dystonic cerebral palsy who underwent a total of 13 surgical procedures (electrode and battery placement) were identified via a retrospective review. There were zero postoperative infections. Using a multistaged surgical plan for pediatric patients with dystonic cerebral palsy undergoing deep brain stimulation may help to reduce the risk of infection.

  19. Revealing Holobiont Structure and Function of Three Red Sea Deep-Sea Corals

    KAUST Repository

    Yum, Lauren

    2014-12-01

    Deep-sea corals have long been regarded as cold-water coral; however a reevaluation of their habitat limitations has been suggested after the discovery of deep-sea coral in the Red Sea where temperatures exceed 20˚C. To gain further insight into the biology of deep-sea corals at these temperatures, the work in this PhD employed a holotranscriptomic approach, looking at coral animal host and bacterial symbiont gene expression in Dendrophyllia sp., Eguchipsammia fistula, and Rhizotrochus sp. sampled from the deep Red Sea. Bacterial community composition was analyzed via amplicon-based 16S surveys and cultured bacterial strains were subjected to bioprospecting in order to gauge the pharmaceutical potential of coralassociated microbes. Coral host transcriptome data suggest that coral can employ mitochondrial hypometabolism, anaerobic glycolysis, and surface cilia to enhance mass transport rates to manage the low oxygen and highly oligotrophic Red Sea waters. In the microbial community associated with these corals, ribokinases and retron-type reverse transcriptases are abundantly expressed. In its first application to deep-sea coral associated microbial communities, 16S-based next-generation sequencing found that a single operational taxonomic unit can comprise the majority of sequence reads and that a large number of low abundance populations are present, which cannot be visualized with first generation sequencing. Bioactivity testing of selected bacterial isolates was surveyed over 100 cytological parameters with high content screening, covering several major organelles and key proteins involved in a variety of signaling cascades. Some of these cytological profiles were similar to those of several reference pharmacologically active compounds, which suggest that the bacteria isolates produce compounds with similar mechanisms of action as the reference compounds. The sum of this work offers several mechanisms by which Red Sea deep-sea corals cope with environmental

  20. Identification of microRNAs from Amur grape (Vitis amurensis Rupr.) by deep sequencing and analysis of microRNA variations with bioinformatics.

    Science.gov (United States)

    Wang, Chen; Han, Jian; Liu, Chonghuai; Kibet, Korir Nicholas; Kayesh, Emrul; Shangguan, Lingfei; Li, Xiaoying; Fang, Jinggui

    2012-03-29

    MicroRNA (miRNA) is a class of functional non-coding small RNA with 19-25 nucleotides in length while Amur grape (Vitis amurensis Rupr.) is an important wild fruit crop with the strongest cold resistance among the Vitis species, is used as an excellent breeding parent for grapevine, and has elicited growing interest in wine production. To date, there is a relatively large number of grapevine miRNAs (vv-miRNAs) from cultivated grapevine varieties such as Vitis vinifera L. and hybrids of V. vinifera and V. labrusca, but there is no report on miRNAs from Vitis amurensis Rupr, a wild grapevine species. A small RNA library from Amur grape was constructed and Solexa technology used to perform deep sequencing of the library followed by subsequent bioinformatics analysis to identify new miRNAs. In total, 126 conserved miRNAs belonging to 27 miRNA families were identified, and 34 known but non-conserved miRNAs were also found. Significantly, 72 new potential Amur grape-specific miRNAs were discovered. The sequences of these new potential va-miRNAs were further validated through miR-RACE, and accumulation of 18 new va-miRNAs in seven tissues of grapevines confirmed by real time RT-PCR (qRT-PCR) analysis. The expression levels of va-miRNAs in flowers and berries were found to be basically consistent in identity to those from deep sequenced sRNAs libraries of combined corresponding tissues. We also describe the conservation and variation of va-miRNAs using miR-SNPs and miR-LDs during plant evolution based on comparison of orthologous sequences, and further reveal that the number and sites of miR-SNP in diverse miRNA families exhibit distinct divergence. Finally, 346 target genes for the new miRNAs were predicted and they include a number of Amur grape stress tolerance genes and many genes regulating anthocyanin synthesis and sugar metabolism. Deep sequencing of short RNAs from Amur grape flowers and berries identified 72 new potential miRNAs and 34 known but non-conserved mi

  1. Identification of microRNAs from Amur grape (vitis amurensis Rupr. by deep sequencing and analysis of microRNA variations with bioinformatics

    Directory of Open Access Journals (Sweden)

    Wang Chen

    2012-03-01

    Full Text Available Abstract Background MicroRNA (miRNA is a class of functional non-coding small RNA with 19-25 nucleotides in length while Amur grape (Vitis amurensis Rupr. is an important wild fruit crop with the strongest cold resistance among the Vitis species, is used as an excellent breeding parent for grapevine, and has elicited growing interest in wine production. To date, there is a relatively large number of grapevine miRNAs (vv-miRNAs from cultivated grapevine varieties such as Vitis vinifera L. and hybrids of V. vinifera and V. labrusca, but there is no report on miRNAs from Vitis amurensis Rupr, a wild grapevine species. Results A small RNA library from Amur grape was constructed and Solexa technology used to perform deep sequencing of the library followed by subsequent bioinformatics analysis to identify new miRNAs. In total, 126 conserved miRNAs belonging to 27 miRNA families were identified, and 34 known but non-conserved miRNAs were also found. Significantly, 72 new potential Amur grape-specific miRNAs were discovered. The sequences of these new potential va-miRNAs were further validated through miR-RACE, and accumulation of 18 new va-miRNAs in seven tissues of grapevines confirmed by real time RT-PCR (qRT-PCR analysis. The expression levels of va-miRNAs in flowers and berries were found to be basically consistent in identity to those from deep sequenced sRNAs libraries of combined corresponding tissues. We also describe the conservation and variation of va-miRNAs using miR-SNPs and miR-LDs during plant evolution based on comparison of orthologous sequences, and further reveal that the number and sites of miR-SNP in diverse miRNA families exhibit distinct divergence. Finally, 346 target genes for the new miRNAs were predicted and they include a number of Amur grape stress tolerance genes and many genes regulating anthocyanin synthesis and sugar metabolism. Conclusions Deep sequencing of short RNAs from Amur grape flowers and berries identified 72

  2. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  3. An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence Databases

    Directory of Open Access Journals (Sweden)

    Md. Rezaul Karim

    2012-03-01

    Full Text Available Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

  4. Genome-wide analyses of long noncoding RNA expression profiles correlated with radioresistance in nasopharyngeal carcinoma via next-generation deep sequencing.

    Science.gov (United States)

    Li, Guo; Liu, Yong; Liu, Chao; Su, Zhongwu; Ren, Shuling; Wang, Yunyun; Deng, Tengbo; Huang, Donghai; Tian, Yongquan; Qiu, Yuanzheng

    2016-09-06

    Radioresistance is one of the major factors limiting the therapeutic efficacy and prognosis of patients with nasopharyngeal carcinoma (NPC). Accumulating evidence has suggested that aberrant expression of long noncoding RNAs (lncRNAs) contributes to cancer progression. Therefore, here we identified lncRNAs associated with radioresistance in NPC. The differential expression profiles of lncRNAs associated with NPC radioresistance were constructed by next-generation deep sequencing by comparing radioresistant NPC cells with their parental cells. LncRNA-related mRNAs were predicted and analyzed using bioinformatics algorithms compared with the mRNA profiles related to radioresistance obtained in our previous study. Several lncRNAs and associated mRNAs were validated in established NPC radioresistant cell models and NPC tissues. By comparison between radioresistant CNE-2-Rs and parental CNE-2 cells by next-generation deep sequencing, a total of 781 known lncRNAs and 2054 novel lncRNAs were annotated. The top five upregulated and downregulated known/novel lncRNAs were detected using quantitative real-time reverse transcription-polymerase chain reaction, and 7/10 known lncRNAs and 3/10 novel lncRNAs were demonstrated to have significant differential expression trends that were the same as those predicted by deep sequencing. From the prediction process, 13 pairs of lncRNAs and their associated genes were acquired, and the prediction trends of three pairs were validated in both radioresistant CNE-2-Rs and 6-10B-Rs cell lines, including lncRNA n373932 and SLITRK5, n409627 and PRSS12, and n386034 and RIMKLB. LncRNA n373932 and its related SLITRK5 showed dramatic expression changes in post-irradiation radioresistant cells and a negative expression correlation in NPC tissues (R = -0.595, p < 0.05). Our study provides an overview of the expression profiles of radioresistant lncRNAs and potentially related mRNAs, which will facilitate future investigations into the

  5. Evolution of simeprevir-resistant variants over time by ultra-deep sequencing in HCV genotype 1b.

    Science.gov (United States)

    Akuta, Norio; Suzuki, Fumitaka; Sezaki, Hitomi; Suzuki, Yoshiyuki; Hosaka, Tetsuya; Kobayashi, Masahiro; Kobayashi, Mariko; Saitoh, Satoshi; Ikeda, Kenji; Kumada, Hiromitsu

    2014-08-01

    Using ultra-deep sequencing technology, the present study was designed to investigate the evolution of simeprevir-resistant variants (amino acid substitutions of aa80, aa155, aa156, and aa168 positions in HCV NS3 region) over time. In Toranomon Hospital, 18 Japanese patients infected with HCV genotype 1b, received triple therapy of simeprevir/PEG-IFN/ribavirin (DRAGON or CONCERT study). Sustained virological response rate was 67%, and that was significantly higher in patients with IL28B rs8099917 TT than in those with non-TT. Six patients, who did not achieve sustained virological response, were tested for resistant variants by ultra-deep sequencing, at the baseline, at the time of re-elevation of viral loads, and at 96 weeks after the completion of treatment. Twelve of 18 resistant variants, detected at re-elevation of viral load, were de novo resistant variants. Ten of 12 de novo resistant variants become undetectable over time, and that five of seven resistant variants, detected at baseline, persisted over time. In one patient, variants of Q80R at baseline (0.3%) increased at 96-week after the cessation of treatment (10.2%), and de novo resistant variants of D168E (0.3%) also increased at 96-week after the cessation of treatment (9.7%). In conclusion, the present study indicates that the emergence of simeprevir-resistant variants after the start of treatment could not be predicted at baseline, and the majority of de novo resistant variants become undetectable over time. Further large-scale prospective studies should be performed to investigate the clinical utility in detecting simeprevir-resistant variants. © 2014 Wiley Periodicals, Inc.

  6. Metaheuristic approaches to order sequencing on a unidirectional picking line

    Directory of Open Access Journals (Sweden)

    AP de Villiers

    2013-06-01

    Full Text Available In this paper the sequencing of orders on a unidirectional picking line is considered. The aim of the order sequencing is to minimise the number of cycles travelled by a picker within the picking line to complete all orders. A tabu search, simulated annealing, genetic algorithm, generalised extremal optimisation and a random local search are presented as possible solution approaches. Computational results based on real life data instances are presented for these metaheuristics and compared to the performance of a lower bound and the solutions used in practise. The random local search exhibits the best overall solution quality, however, the generalised extremal optimisation approach delivers comparable results in considerably shorter computational times.

  7. Solving the Water Jugs Problem by an Integer Sequence Approach

    Science.gov (United States)

    Man, Yiu-Kwong

    2012-01-01

    In this article, we present an integer sequence approach to solve the classic water jugs problem. The solution steps can be obtained easily by additions and subtractions only, which is suitable for manual calculation or programming by computer. This approach can be introduced to secondary and undergraduate students, and also to teachers and…

  8. Markov chains and entropy tests in genetic-based lithofacies analysis of deep-water clastic depositional systems

    Directory of Open Access Journals (Sweden)

    Borka Szabolcs

    2016-01-01

    Full Text Available The aim of this study was to examine the relationship between structural elements and the so-called genetic lithofacies in a clastic deep-water depositional system. Process-sedimentology has recently been gaining importance in the characterization of these systems. This way the recognized facies attributes can be associated with the depositional processes establishing the genetic lithofacies. In this paper this approach was presented through a case study of a Tertiary deep-water sequence of the Pannonian-basin.

  9. Ultra-deep sequencing reveals high prevalence and broad structural diversity of hepatitis B surface antigen mutations in a global population.

    Science.gov (United States)

    Gencay, Mikael; Hübner, Kirsten; Gohl, Peter; Seffner, Anja; Weizenegger, Michael; Neofytos, Dionysios; Batrla, Richard; Woeste, Andreas; Kim, Hyon-Suk; Westergaard, Gaston; Reinsch, Christine; Brill, Eva; Thu Thuy, Pham Thi; Hoang, Bui Huu; Sonderup, Mark; Spearman, C Wendy; Pabinger, Stephan; Gautier, Jérémie; Brancaccio, Giuseppina; Fasano, Massimo; Santantonio, Teresa; Gaeta, Giovanni B; Nauck, Markus; Kaminski, Wolfgang E

    2017-01-01

    The diversity of the hepatitis B surface antigen (HBsAg) has a significant impact on the performance of diagnostic screening tests and the clinical outcome of hepatitis B infection. Neutralizing or diagnostic antibodies against the HBsAg are directed towards its highly conserved major hydrophilic region (MHR), in particular towards its "a" determinant subdomain. Here, we explored, on a global scale, the genetic diversity of the HBsAg MHR in a large, multi-ethnic cohort of randomly selected subjects with HBV infection from four continents. A total of 1553 HBsAg positive blood samples of subjects originating from 20 different countries across Africa, America, Asia and central Europe were characterized for amino acid variation in the MHR. Using highly sensitive ultra-deep sequencing, we found 72.8% of the successfully sequenced subjects (n = 1391) demonstrated amino acid sequence variation in the HBsAg MHR. This indicates that the global variation frequency in the HBsAg MHR is threefold higher than previously reported. The majority of the amino acid mutations were found in the HBV genotypes B (28.9%) and C (25.4%). Collectively, we identified 345 distinct amino acid mutations in the MHR. Among these, we report 62 previously unknown mutations, which extends the worldwide pool of currently known HBsAg MHR mutations by 22%. Importantly, topological analysis identified the "a" determinant upstream flanking region as the structurally most diverse subdomain of the HBsAg MHR. The highest prevalence of "a" determinant region mutations was observed in subjects from Asia, followed by the African, American and European cohorts, respectively. Finally, we found that more than half (59.3%) of all HBV subjects investigated carried multiple MHR mutations. Together, this worldwide ultra-deep sequencing based genotyping study reveals that the global prevalence and structural complexity of variation in the hepatitis B surface antigen have, to date, been significantly underappreciated.

  10. Detection of eardrum abnormalities using ensemble deep learning approaches

    Science.gov (United States)

    Senaras, Caglar; Moberly, Aaron C.; Teknos, Theodoros; Essig, Garth; Elmaraghy, Charles; Taj-Schaal, Nazhat; Yua, Lianbo; Gurcan, Metin N.

    2018-02-01

    In this study, we proposed an approach to report the condition of the eardrum as "normal" or "abnormal" by ensembling two different deep learning architectures. In the first network (Network 1), we applied transfer learning to the Inception V3 network by using 409 labeled samples. As a second network (Network 2), we designed a convolutional neural network to take advantage of auto-encoders by using additional 673 unlabeled eardrum samples. The individual classification accuracies of the Network 1 and Network 2 were calculated as 84.4%(+/- 12.1%) and 82.6% (+/- 11.3%), respectively. Only 32% of the errors of the two networks were the same, making it possible to combine two approaches to achieve better classification accuracy. The proposed ensemble method allows us to achieve robust classification because it has high accuracy (84.4%) with the lowest standard deviation (+/- 10.3%).

  11. Advancing Eucalyptus genomics: identification and sequencing of lignin biosynthesis genes from deep-coverage BAC libraries

    Directory of Open Access Journals (Sweden)

    Kudrna David

    2011-03-01

    Full Text Available Abstract Background Eucalyptus species are among the most planted hardwoods in the world because of their rapid growth, adaptability and valuable wood properties. The development and integration of genomic resources into breeding practice will be increasingly important in the decades to come. Bacterial artificial chromosome (BAC libraries are key genomic tools that enable positional cloning of important traits, synteny evaluation, and the development of genome framework physical maps for genetic linkage and genome sequencing. Results We describe the construction and characterization of two deep-coverage BAC libraries EG_Ba and EG_Bb obtained from nuclear DNA fragments of E. grandis (clone BRASUZ1 digested with HindIII and BstYI, respectively. Genome coverages of 17 and 15 haploid genome equivalents were estimated for EG_Ba and EG_Bb, respectively. Both libraries contained large inserts, with average sizes ranging from 135 Kb (Eg_Bb to 157 Kb (Eg_Ba, very low extra-nuclear genome contamination providing a probability of finding a single copy gene ≥ 99.99%. Libraries were screened for the presence of several genes of interest via hybridizations to high-density BAC filters followed by PCR validation. Five selected BAC clones were sequenced and assembled using the Roche GS FLX technology providing the whole sequence of the E. grandis chloroplast genome, and complete genomic sequences of important lignin biosynthesis genes. Conclusions The two E. grandis BAC libraries described in this study represent an important milestone for the advancement of Eucalyptus genomics and forest tree research. These BAC resources have a highly redundant genome coverage (> 15×, contain large average inserts and have a very low percentage of clones with organellar DNA or empty vectors. These publicly available BAC libraries are thus suitable for a broad range of applications in genetic and genomic research in Eucalyptus and possibly in related species of Myrtaceae

  12. MinION™ nanopore sequencing of environmental metagenomes: a synthetic approach.

    Science.gov (United States)

    Brown, Bonnie L; Watson, Mick; Minot, Samuel S; Rivera, Maria C; Franklin, Rima B

    2017-03-01

    Environmental metagenomic analysis is typically accomplished by assigning taxonomy and/or function from whole genome sequencing or 16S amplicon sequences. Both of these approaches are limited, however, by read length, among other technical and biological factors. A nanopore-based sequencing platform, MinION™, produces reads that are ≥1 × 104 bp in length, potentially providing for more precise assignment, thereby alleviating some of the limitations inherent in determining metagenome composition from short reads. We tested the ability of sequence data produced by MinION (R7.3 flow cells) to correctly assign taxonomy in single bacterial species runs and in three types of low-complexity synthetic communities: a mixture of DNA using equal mass from four species, a community with one relatively rare (1%) and three abundant (33% each) components, and a mixture of genomic DNA from 20 bacterial strains of staggered representation. Taxonomic composition of the low-complexity communities was assessed by analyzing the MinION sequence data with three different bioinformatic approaches: Kraken, MG-RAST, and One Codex. Results: Long read sequences generated from libraries prepared from single strains using the version 5 kit and chemistry, run on the original MinION device, yielded as few as 224 to as many as 3497 bidirectional high-quality (2D) reads with an average overall study length of 6000 bp. For the single-strain analyses, assignment of reads to the correct genus by different methods ranged from 53.1% to 99.5%, assignment to the correct species ranged from 23.9% to 99.5%, and the majority of misassigned reads were to closely related organisms. A synthetic metagenome sequenced with the same setup yielded 714 high quality 2D reads of approximately 5500 bp that were up to 98% correctly assigned to the species level. Synthetic metagenome MinION libraries generated using version 6 kit and chemistry yielded from 899 to 3497 2D reads with lengths averaging 5700 bp with up

  13. A Deep Learning Approach to Neuroanatomical Characterisation of Alzheimer's Disease.

    Science.gov (United States)

    Ambastha, Abhinit Kumar; Leong, Tze-Yun

    2017-01-01

    Alzheimer's disease (AD) is a neurological degenerative disorder that leads to progressive mental deterioration. This work introduces a computational approach to improve our understanding of the progression of AD. We use ensemble learning methods and deep neural networks to identify salient structural correlations among brain regions that degenerate together in AD; this provides an understanding of how AD progresses in the brain. The proposed technique has a classification accuracy of 81.79% for AD against healthy subjects using a single modality imaging dataset.

  14. Simultaneous identification of DNA and RNA viruses present in pig faeces using process-controlled deep sequencing.

    Directory of Open Access Journals (Sweden)

    Jana Sachsenröder

    Full Text Available BACKGROUND: Animal faeces comprise a community of many different microorganisms including bacteria and viruses. Only scarce information is available about the diversity of viruses present in the faeces of pigs. Here we describe a protocol, which was optimized for the purification of the total fraction of viral particles from pig faeces. The genomes of the purified DNA and RNA viruses were simultaneously amplified by PCR and subjected to deep sequencing followed by bioinformatic analyses. The efficiency of the method was monitored using a process control consisting of three bacteriophages (T4, M13 and MS2 with different morphology and genome types. Defined amounts of the bacteriophages were added to the sample and their abundance was assessed by quantitative PCR during the preparation procedure. RESULTS: The procedure was applied to a pooled faecal sample of five pigs. From this sample, 69,613 sequence reads were generated. All of the added bacteriophages were identified by sequence analysis of the reads. In total, 7.7% of the reads showed significant sequence identities with published viral sequences. They mainly originated from bacteriophages (73.9% and mammalian viruses (23.9%; 0.8% of the sequences showed identities to plant viruses. The most abundant detected porcine viruses were kobuvirus, rotavirus C, astrovirus, enterovirus B, sapovirus and picobirnavirus. In addition, sequences with identities to the chimpanzee stool-associated circular ssDNA virus were identified. Whole genome analysis indicates that this virus, tentatively designated as pig stool-associated circular ssDNA virus (PigSCV, represents a novel pig virus. CONCLUSION: The established protocol enables the simultaneous detection of DNA and RNA viruses in pig faeces including the identification of so far unknown viruses. It may be applied in studies investigating aetiology, epidemiology and ecology of diseases. The implemented process control serves as quality control, ensures

  15. Genetic diversity of archaea in deep-sea hydrothermal vent environments.

    Science.gov (United States)

    Takai, K; Horikoshi, K

    1999-08-01

    Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the phylogenetic analysis, a number of rDNA sequences obtained from deep-sea hydrothermal vents were placed in deep lineages of the crenarchaeotic phylum prior to the divergence of cultivated thermophilic members of the crenarchaeota or between thermophilic members of the euryarchaeota and members of the methanogen-halophile clade. Whole cell in situ hybridization analysis suggested that some microorganisms of novel phylotypes predicted by molecular phylogenetic analysis were likely present in deep-sea hydrothermal vent environments. These findings expand our view of the genetic diversity of archaea in deep-sea hydrothermal vent environments and of the phylogenetic organization of archaea.

  16. Transcriptome analysis of the model protozoan, Tetrahymena thermophila, using Deep RNA sequencing.

    Directory of Open Access Journals (Sweden)

    Jie Xiong

    Full Text Available BACKGROUND: The ciliated protozoan Tetrahymena thermophila is a well-studied single-celled eukaryote model organism for cellular and molecular biology. However, the lack of extensive T. thermophila cDNA libraries or a large expressed sequence tag (EST database limited the quality of the original genome annotation. METHODOLOGY/PRINCIPAL FINDINGS: This RNA-seq study describes the first deep sequencing analysis of the T. thermophila transcriptome during the three major stages of the life cycle: growth, starvation and conjugation. Uniquely mapped reads covered more than 96% of the 24,725 predicted gene models in the somatic genome. More than 1,000 new transcribed regions were identified. The great dynamic range of RNA-seq allowed detection of a nearly six order-of-magnitude range of measurable gene expression orchestrated by this cell. RNA-seq also allowed the first prediction of transcript untranslated regions (UTRs and an updated (larger size estimate of the T. thermophila transcriptome: 57 Mb, or about 55% of the somatic genome. Our study identified nearly 1,500 alternative splicing (AS events distributed over 5.2% of T. thermophila genes. This percentage represents a two order-of-magnitude increase over previous EST-based estimates in Tetrahymena. Evidence of stage-specific regulation of alternative splicing was also obtained. Finally, our study allowed us to completely confirm about 26.8% of the genes originally predicted by the gene finder, to correct coding sequence boundaries and intron-exon junctions for about a third, and to reassign microarray probes and correct earlier microarray data. CONCLUSIONS/SIGNIFICANCE: RNA-seq data significantly improve the genome annotation and provide a fully comprehensive view of the global transcriptome of T. thermophila. To our knowledge, 5.2% of T. thermophila genes with AS is the highest percentage of genes showing AS reported in a unicellular eukaryote. Tetrahymena thus becomes an excellent unicellular

  17. Deep sequencing-based analysis of the anaerobic stimulon in Neisseria gonorrhoeae

    Directory of Open Access Journals (Sweden)

    Clark Virginia L

    2011-01-01

    Full Text Available Abstract Background Maintenance of an anaerobic denitrification system in the obligate human pathogen, Neisseria gonorrhoeae, suggests that an anaerobic lifestyle may be important during the course of infection. Furthermore, mounting evidence suggests that reduction of host-produced nitric oxide has several immunomodulary effects on the host. However, at this point there have been no studies analyzing the complete gonococcal transcriptome response to anaerobiosis. Here we performed deep sequencing to compare the gonococcal transcriptomes of aerobically and anaerobically grown cells. Using the information derived from this sequencing, we discuss the implications of the robust transcriptional response to anaerobic growth. Results We determined that 198 chromosomal genes were differentially expressed (~10% of the genome in response to anaerobic conditions. We also observed a large induction of genes encoded within the cryptic plasmid, pJD1. Validation of RNA-seq data using translational-lacZ fusions or RT-PCR demonstrated the RNA-seq results to be very reproducible. Surprisingly, many genes of prophage origin were induced anaerobically, as well as several transcriptional regulators previously unknown to be involved in anaerobic growth. We also confirmed expression and regulation of a small RNA, likely a functional equivalent of fnrS in the Enterobacteriaceae family. We also determined that many genes found to be responsive to anaerobiosis have also been shown to be responsive to iron and/or oxidative stress. Conclusions Gonococci will be subject to many forms of environmental stress, including oxygen-limitation, during the course of infection. Here we determined that the anaerobic stimulon in gonococci was larger than previous studies would suggest. Many new targets for future research have been uncovered, and the results derived from this study may have helped to elucidate factors or mechanisms of virulence that may have otherwise been overlooked.

  18. Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE

    DEFF Research Database (Denmark)

    Valen, Eivind; Pascarella, Giovanni; Chalk, Alistair

    2009-01-01

    in a given tissue. Here, we present a new method for high-throughput sequencing of 5' cDNA tags-DeepCAGE: merging the Cap Analysis of Gene Expression method with ultra-high-throughput sequence technology. We apply DeepCAGE to characterize 1.4 million sequenced TSS from mouse hippocampus and reveal a wealth...

  19. A deep learning approach for pose estimation from volumetric OCT data.

    Science.gov (United States)

    Gessert, Nils; Schlüter, Matthias; Schlaefer, Alexander

    2018-05-01

    Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

    Science.gov (United States)

    Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas

    2007-02-01

    The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

  1. DeepPVP: phenotype-based prioritization of causative variants using deep learning

    KAUST Repository

    Boudellioua, Imene

    2018-05-02

    Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

  2. [Efficacy of the program "Testas's (mis)adventures" to promote the deep approach to learning].

    Science.gov (United States)

    Rosário, Pedro; González-Pienda, Julio Antonio; Cerezo, Rebeca; Pinto, Ricardo; Ferreira, Pedro; Abilio, Lourenço; Paiva, Olimpia

    2010-11-01

    This paper provides information about the efficacy of a tutorial training program intended to enhance elementary fifth graders' study processes and foster their deep approaches to learning. The program "Testas's (mis)adventures" consists of a set of books in which Testas, a typical student, reveals and reflects upon his life experiences during school years. These life stories are nothing but an opportunity to present and train a wide range of learning strategies and self-regulatory processes, designed to insure students' deeper preparation for present and future learning challenges. The program has been developed along a school year, in a one hour weekly tutorial sessions. The training program had a semi-experimental design, included an experimental group (n=50) and a control one (n=50), and used pre- and posttest measures (learning strategies' declarative knowledge, learning approaches and academic achievement). Data suggest that the students enrolled in the training program, comparing with students in the control group, showed a significant improvement in their declarative knowledge of learning strategies and in their deep approach to learning, consequently lowering their use of a surface approach. In spite of this, in what concerns to academic achievement, no statistically significant differences have been found.

  3. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  4. Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus.

    Science.gov (United States)

    Sobel Leonard, Ashley; Weissman, Daniel B; Greenbaum, Benjamin; Ghedin, Elodie; Koelle, Katia

    2017-07-15

    The bottleneck governing infectious disease transmission describes the size of the pathogen population transferred from the donor to the recipient host. Accurate quantification of the bottleneck size is particularly important for rapidly evolving pathogens such as influenza virus, as narrow bottlenecks reduce the amount of transferred viral genetic diversity and, thus, may decrease the rate of viral adaptation. Previous studies have estimated bottleneck sizes governing viral transmission by using statistical analyses of variants identified in pathogen sequencing data. These analyses, however, did not account for variant calling thresholds and stochastic viral replication dynamics within recipient hosts. Because these factors can skew bottleneck size estimates, we introduce a new method for inferring bottleneck sizes that accounts for these factors. Through the use of a simulated data set, we first show that our method, based on beta-binomial sampling, accurately recovers transmission bottleneck sizes, whereas other methods fail to do so. We then apply our method to a data set of influenza A virus (IAV) infections for which viral deep-sequencing data from transmission pairs are available. We find that the IAV transmission bottleneck size estimates in this study are highly variable across transmission pairs, while the mean bottleneck size of 196 virions is consistent with a previous estimate for this data set. Furthermore, regression analysis shows a positive association between estimated bottleneck size and donor infection severity, as measured by temperature. These results support findings from experimental transmission studies showing that bottleneck sizes across transmission events can be variable and influenced in part by epidemiological factors. IMPORTANCE The transmission bottleneck size describes the size of the pathogen population transferred from the donor to the recipient host and may affect the rate of pathogen adaptation within host populations. Recent

  5. Association of Kinesthetic and Read-Write Learner with Deep Approach Learning and Academic Achievement

    Directory of Open Access Journals (Sweden)

    Latha Rajendra Kumar

    2011-06-01

    Full Text Available Background: The main purpose of the present study was to further investigate study processes, learning styles, and academic achievement in medical students. Methods: A total of 214 (mean age 22.5 years first and second year students - preclinical years - at the Asian Institute of Medical Science and Technology (AIMST University School of Medicine, in Malaysia participated.  There were 119 women (55.6% and 95 men (44.4%.   Biggs questionnaire for determining learning approaches and the VARK questionnaire for determining learning styles were used.  These were compared to the student’s performance in the assessment examinations. Results: The major findings were 1 the majority of students prefer to study alone, 2 most students employ a superficial study approach, and 3 students with high kinesthetic and read-write scores performed better on examinations and approached the subject by deep approach method compared to students with low scores.  Furthermore, there was a correlation between superficial approach scores and visual learner’s scores. Discussion: Read-write and kinesthetic learners who adopt a deep approach learning strategy perform better academically than do the auditory, visual learners that employ superficial study strategies.   Perhaps visual and auditory learners can be encouraged to adopt kinesthetic and read-write styles to enhance their performance in the exams.

  6. Personalized mapping of the deep brain with a white matter attenuated inversion recovery (WAIR) sequence at 1.5-tesla: Experience based on a series of 156 patients.

    Science.gov (United States)

    Zerroug, A; Gabrillargues, J; Coll, G; Vassal, F; Jean, B; Chabert, E; Claise, B; Khalil, T; Sakka, L; Feschet, F; Durif, F; Boyer, L; Coste, J; Lemaire, J-J

    2016-08-01

    Deep brain mapping has been proposed for direct targeting in stereotactic functional surgery, aiming to personalize electrode implantation according to individual MRI anatomy without atlas or statistical template. We report our clinical experience of direct targeting in a series of 156 patients operated on using a dedicated Inversion Recovery Turbo Spin Echo sequence at 1.5-tesla, called White Matter Attenuated Inversion Recovery (WAIR). After manual contouring of all pertinent structures and 3D planning of trajectories, 312 DBS electrodes were implanted. Detailed anatomy of close neighbouring structures, whether gray nuclei or white matter regions, was identified during each planning procedure. We gathered the experience of these 312 deep brain mappings and elaborated consistent procedures of anatomical MRI mapping for pallidal, subthalamic and ventral thalamic regions. We studied the number of times the central track anatomically optimized was selected for implantation of definitive electrodes. WAIR sequence provided high-quality images of most common functional targets, successfully used for pure direct stereotactic targeting: the central track corresponding to the optimized primary anatomical trajectory was chosen for implantation of definitive electrodes in 90.38%. WAIR sequence is anatomically reliable, enabling precise deep brain mapping and direct stereotactic targeting under routine clinical conditions. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  7. Deep Recurrent Neural Networks for Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Abdulmajid Murad

    2017-11-01

    Full Text Available Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM and k-nearest neighbors (KNN. Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs and CNNs.

  8. Deep Recurrent Neural Networks for Human Activity Recognition.

    Science.gov (United States)

    Murad, Abdulmajid; Pyun, Jae-Young

    2017-11-06

    Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

  9. AMID: Accurate Magnetic Indoor Localization Using Deep Learning

    Directory of Open Access Journals (Sweden)

    Namkyoung Lee

    2018-05-01

    Full Text Available Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID, an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment.

  10. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Science.gov (United States)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-11

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  11. Deep sequencing of the oral microbiome reveals signatures of periodontal disease.

    Directory of Open Access Journals (Sweden)

    Bo Liu

    Full Text Available The oral microbiome, the complex ecosystem of microbes inhabiting the human mouth, harbors several thousands of bacterial types. The proliferation of pathogenic bacteria within the mouth gives rise to periodontitis, an inflammatory disease known to also constitute a risk factor for cardiovascular disease. While much is known about individual species associated with pathogenesis, the system-level mechanisms underlying the transition from health to disease are still poorly understood. Through the sequencing of the 16S rRNA gene and of whole community DNA we provide a glimpse at the global genetic, metabolic, and ecological changes associated with periodontitis in 15 subgingival plaque samples, four from each of two periodontitis patients, and the remaining samples from three healthy individuals. We also demonstrate the power of whole-metagenome sequencing approaches in characterizing the genomes of key players in the oral microbiome, including an unculturable TM7 organism. We reveal the disease microbiome to be enriched in virulence factors, and adapted to a parasitic lifestyle that takes advantage of the disrupted host homeostasis. Furthermore, diseased samples share a common structure that was not found in completely healthy samples, suggesting that the disease state may occupy a narrow region within the space of possible configurations of the oral microbiome. Our pilot study demonstrates the power of high-throughput sequencing as a tool for understanding the role of the oral microbiome in periodontal disease. Despite a modest level of sequencing (~2 lanes Illumina 76 bp PE and high human DNA contamination (up to ~90% we were able to partially reconstruct several oral microbes and to preliminarily characterize some systems-level differences between the healthy and diseased oral microbiomes.

  12. microRNA expression profiling in fetal single ventricle malformation identified by deep sequencing.

    Science.gov (United States)

    Yu, Zhang-Bin; Han, Shu-Ping; Bai, Yun-Fei; Zhu, Chun; Pan, Ya; Guo, Xi-Rong

    2012-01-01

    microRNAs (miRNAs) have emerged as key regulators in many biological processes, particularly cardiac growth and development, although the specific miRNA expression profile associated with this process remains to be elucidated. This study aimed to characterize the cellular microRNA profile involved in the development of congenital heart malformation, through the investigation of single ventricle (SV) defects. Comprehensive miRNA profiling in human fetal SV cardiac tissue was performed by deep sequencing. Differential expression of 48 miRNAs was revealed by sequencing by oligonucleotide ligation and detection (SOLiD) analysis. Of these, 38 were down-regulated and 10 were up-regulated in differentiated SV cardiac tissue, compared to control cardiac tissue. This was confirmed by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis. Predicted target genes of the 48 differentially expressed miRNAs were analyzed by gene ontology and categorized according to cellular process, regulation of biological process and metabolic process. Pathway-Express analysis identified the WNT and mTOR signaling pathways as the most significant processes putatively affected by the differential expression of these miRNAs. The candidate genes involved in cardiac development were identified as potential targets for these differentially expressed microRNAs and the collaborative network of microRNAs and cardiac development related-mRNAs was constructed. These data provide the basis for future investigation of the mechanism of the occurrence and development of fetal SV malformations.

  13. Adaptive template generation for amyloid PET using a deep learning approach.

    Science.gov (United States)

    Kang, Seung Kwan; Seo, Seongho; Shin, Seong A; Byun, Min Soo; Lee, Dong Young; Kim, Yu Kyeong; Lee, Dong Soo; Lee, Jae Sung

    2018-05-11

    Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired 11 C-PIB PET and T1-weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto-encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI-based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI-less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research. © 2018 Wiley Periodicals, Inc.

  14. Ordered Pooling of Optical Flow Sequences for Action Recognition

    OpenAIRE

    Wang, Jue; Cherian, Anoop; Porikli, Fatih

    2017-01-01

    Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB fram...

  15. Deep Incremental Boosting

    OpenAIRE

    Mosca, Alan; Magoulas, George D

    2017-01-01

    This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep In...

  16. Characterization of GM events by insert knowledge adapted re-sequencing approaches

    OpenAIRE

    Yang, Litao; Wang, Congmao; Holst-Jensen, Arne; Morisset, Dany; Lin, Yongjun; Zhang, Dabing

    2013-01-01

    Detection methods and data from molecular characterization of genetically modified (GM) events are needed by stakeholders of public risk assessors and regulators. Generally, the molecular characteristics of GM events are incomprehensively revealed by current approaches and biased towards detecting transformation vector derived sequences. GM events are classified based on available knowledge of the sequences of vectors and inserts (insert knowledge). Herein we present three insert knowledge-ad...

  17. Characterization and Development of EST-SSRs by Deep Transcriptome Sequencing in Chinese Cabbage (Brassica rapa L. ssp. pekinensis

    Directory of Open Access Journals (Sweden)

    Qian Ding

    2015-01-01

    Full Text Available Simple sequence repeats (SSRs are among the most important markers for population analysis and have been widely used in plant genetic mapping and molecular breeding. Expressed sequence tag-SSR (EST-SSR markers, located in the coding regions, are potentially more efficient for QTL mapping, gene targeting, and marker-assisted breeding. In this study, we investigated 51,694 nonredundant unigenes, assembled from clean reads from deep transcriptome sequencing with a Solexa/Illumina platform, for identification and development of EST-SSRs in Chinese cabbage. In total, 10,420 EST-SSRs with over 12 bp were identified and characterized, among which 2744 EST-SSRs are new and 2317 are known ones showing polymorphism with previously reported SSRs. A total of 7877 PCR primer pairs for 1561 EST-SSR loci were designed, and primer pairs for twenty-four EST-SSRs were selected for primer evaluation. In nineteen EST-SSR loci (79.2%, amplicons were successfully generated with high quality. Seventeen (89.5% showed polymorphism in twenty-four cultivars of Chinese cabbage. The polymorphic alleles of each polymorphic locus were sequenced, and the results showed that most polymorphisms were due to variations of SSR repeat motifs. The EST-SSRs identified and characterized in this study have important implications for developing new tools for genetics and molecular breeding in Chinese cabbage.

  18. SHARAKU: an algorithm for aligning and clustering read mapping profiles of deep sequencing in non-coding RNA processing.

    Science.gov (United States)

    Tsuchiya, Mariko; Amano, Kojiro; Abe, Masaya; Seki, Misato; Hase, Sumitaka; Sato, Kengo; Sakakibara, Yasubumi

    2016-06-15

    Deep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs. We developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5'-end processing and 3'-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain. The source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502. yasu@bio.keio.ac.jp Supplementary data are available

  19. MOOC Design – Dissemination to the Masses or Facilitation of Social Learning and a Deep Approach to Learning?

    DEFF Research Database (Denmark)

    Christensen, Inger-Marie F.; Dam Laursen, Mette; Bøggild, Jacob

    2016-01-01

    This article accounts for the design of the massive open online course (MOOC) Hans Christian Andersen’s Fairy tales on FutureLearn and reports on the effectiveness of this design in terms of engaging learners in social learning and encouraging a deep approach to learning. A learning pathway...... and increased educator feedback. Course data show that that some learners use the space provided for social interaction and mutual support. A learning pathway that engages learners in discussion and progression from week to week facilitates a deep approach to learning. However, this requires more support from...

  20. Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach.

    Science.gov (United States)

    Yang, Hao-Fan; Dillon, Tharam S; Chen, Yi-Ping Phoebe

    2017-10-01

    Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

  1. Deep sequencing-based transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus reveals insight into the immune-relevant genes in marine fish

    Directory of Open Access Journals (Sweden)

    Xiang Li-xin

    2010-08-01

    Full Text Available Abstract Background Systematic research on fish immunogenetics is indispensable in understanding the origin and evolution of immune systems. This has long been a challenging task because of the limited number of deep sequencing technologies and genome backgrounds of non-model fish available. The newly developed Solexa/Illumina RNA-seq and Digital gene expression (DGE are high-throughput sequencing approaches and are powerful tools for genomic studies at the transcriptome level. This study reports the transcriptome profiling analysis of bacteria-challenged Lateolabrax japonicus using RNA-seq and DGE in an attempt to gain insights into the immunogenetics of marine fish. Results RNA-seq analysis generated 169,950 non-redundant consensus sequences, among which 48,987 functional transcripts with complete or various length encoding regions were identified. More than 52% of these transcripts are possibly involved in approximately 219 known metabolic or signalling pathways, while 2,673 transcripts were associated with immune-relevant genes. In addition, approximately 8% of the transcripts appeared to be fish-specific genes that have never been described before. DGE analysis revealed that the host transcriptome profile of Vibrio harveyi-challenged L. japonicus is considerably altered, as indicated by the significant up- or down-regulation of 1,224 strong infection-responsive transcripts. Results indicated an overall conservation of the components and transcriptome alterations underlying innate and adaptive immunity in fish and other vertebrate models. Analysis suggested the acquisition of numerous fish-specific immune system components during early vertebrate evolution. Conclusion This study provided a global survey of host defence gene activities against bacterial challenge in a non-model marine fish. Results can contribute to the in-depth study of candidate genes in marine fish immunity, and help improve current understanding of host

  2. Key roles for freshwater Actinobacteria revealed by deep metagenomic sequencing.

    Science.gov (United States)

    Ghai, Rohit; Mizuno, Carolina Megumi; Picazo, Antonio; Camacho, Antonio; Rodriguez-Valera, Francisco

    2014-12-01

    Freshwater ecosystems are critical but fragile environments directly affecting society and its welfare. However, our understanding of genuinely freshwater microbial communities, constrained by our capacity to manipulate its prokaryotic participants in axenic cultures, remains very rudimentary. Even the most abundant components, freshwater Actinobacteria, remain largely unknown. Here, applying deep metagenomic sequencing to the microbial community of a freshwater reservoir, we were able to circumvent this traditional bottleneck and reconstruct de novo seven distinct streamlined actinobacterial genomes. These genomes represent three new groups of photoheterotrophic, planktonic Actinobacteria. We describe for the first time genomes of two novel clades, acMicro (Micrococcineae, related to Luna2,) and acAMD (Actinomycetales, related to acTH1). Besides, an aggregate of contigs belonged to a new branch of the Acidimicrobiales. All are estimated to have small genomes (approximately 1.2 Mb), and their GC content varied from 40 to 61%. One of the Micrococcineae genomes encodes a proteorhodopsin, a rhodopsin type reported for the first time in Actinobacteria. The remarkable potential capacity of some of these genomes to transform recalcitrant plant detrital material, particularly lignin-derived compounds, suggests close linkages between the terrestrial and aquatic realms. Moreover, abundances of Actinobacteria correlate inversely to those of Cyanobacteria that are responsible for prolonged and frequently irretrievable damage to freshwater ecosystems. This suggests that they might serve as sentinels of impending ecological catastrophes. © 2014 John Wiley & Sons Ltd.

  3. An approach for identification of unknown viruses using sequencing-by-hybridization.

    Science.gov (United States)

    Katoski, Sarah E; Meyer, Hermann; Ibrahim, Sofi

    2015-09-01

    Accurate identification of biological threat agents, especially RNA viruses, in clinical or environmental samples can be challenging because the concentration of viral genomic material in a given sample is usually low, viral genomic RNA is liable to degradation, and RNA viruses are extremely diverse. A two-tiered approach was used for initial identification, then full genomic characterization of 199 RNA viruses belonging to virus families Arenaviridae, Bunyaviridae, Filoviridae, Flaviviridae, and Togaviridae. A Sequencing-by-hybridization (SBH) microarray was used to tentatively identify a viral pathogen then, the identity is confirmed by guided next-generation sequencing (NGS). After optimization and evaluation of the SBH and NGS methodologies with various virus species and strains, the approach was used to test the ability to identify viruses in blinded samples. The SBH correctly identified two Ebola viruses in the blinded samples within 24 hr, and by using guided amplicon sequencing with 454 GS FLX, the identities of the viruses in both samples were confirmed. SBH provides at relatively low-cost screening of biological samples against a panel of viral pathogens that can be custom-designed on a microarray. Once the identity of virus is deduced from the highest hybridization signal on the SBH microarray, guided (amplicon) NGS sequencing can be used not only to confirm the identity of the virus but also to provide further information about the strain or isolate, including a potential genetic manipulation. This approach can be useful in situations where natural or deliberate biological threat incidents might occur and a rapid response is required. © 2015 Wiley Periodicals, Inc.

  4. Sequence comparison alignment-free approach based on suffix tree and L-words frequency.

    Science.gov (United States)

    Soares, Inês; Goios, Ana; Amorim, António

    2012-01-01

    The vast majority of methods available for sequence comparison rely on a first sequence alignment step, which requires a number of assumptions on evolutionary history and is sometimes very difficult or impossible to perform due to the abundance of gaps (insertions/deletions). In such cases, an alternative alignment-free method would prove valuable. Our method starts by a computation of a generalized suffix tree of all sequences, which is completed in linear time. Using this tree, the frequency of all possible words with a preset length L-L-words--in each sequence is rapidly calculated. Based on the L-words frequency profile of each sequence, a pairwise standard Euclidean distance is then computed producing a symmetric genetic distance matrix, which can be used to generate a neighbor joining dendrogram or a multidimensional scaling graph. We present an improvement to word counting alignment-free approaches for sequence comparison, by determining a single optimal word length and combining suffix tree structures to the word counting tasks. Our approach is, thus, a fast and simple application that proved to be efficient and powerful when applied to mitochondrial genomes. The algorithm was implemented in Python language and is freely available on the web.

  5. Automatic feature extraction in large fusion databases by using deep learning approach

    Energy Technology Data Exchange (ETDEWEB)

    Farias, Gonzalo, E-mail: gonzalo.farias@ucv.cl [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile); Dormido-Canto, Sebastián [Departamento de Informática y Automática, UNED, Madrid (Spain); Vega, Jesús; Rattá, Giuseppe [Asociación EURATOM/CIEMAT Para Fusión, CIEMAT, Madrid (Spain); Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín [Pontificia Universidad Católica de Valparaíso, Valparaíso (Chile)

    2016-11-15

    Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

  6. Automatic feature extraction in large fusion databases by using deep learning approach

    International Nuclear Information System (INIS)

    Farias, Gonzalo; Dormido-Canto, Sebastián; Vega, Jesús; Rattá, Giuseppe; Vargas, Héctor; Hermosilla, Gabriel; Alfaro, Luis; Valencia, Agustín

    2016-01-01

    Highlights: • Feature extraction is a very critical stage in any machine learning algorithm. • The problem dimensionality can be reduced enormously when selecting suitable attributes. • Despite the importance of feature extraction, the process is commonly done manually by trial and error. • Fortunately, recent advances in deep learning approach have proposed an encouraging way to find a good feature representation automatically. • In this article, deep learning is applied to the TJ-II fusion database to get more robust and accurate classifiers in comparison to previous work. - Abstract: Feature extraction is one of the most important machine learning issues. Finding suitable attributes of datasets can enormously reduce the dimensionality of the input space, and from a computational point of view can help all of the following steps of pattern recognition problems, such as classification or information retrieval. However, the feature extraction step is usually performed manually. Moreover, depending on the type of data, we can face a wide range of methods to extract features. In this sense, the process to select appropriate techniques normally takes a long time. This work describes the use of recent advances in deep learning approach in order to find a good feature representation automatically. The implementation of a special neural network called sparse autoencoder and its application to two classification problems of the TJ-II fusion database is shown in detail. Results have shown that it is possible to get robust classifiers with a high successful rate, in spite of the fact that the feature space is reduced to less than 0.02% from the original one.

  7. Identification and characterization of novel serum microRNA candidates from deep sequencing in cervical cancer patients.

    Science.gov (United States)

    Juan, Li; Tong, Hong-li; Zhang, Pengjun; Guo, Guanghong; Wang, Zi; Wen, Xinyu; Dong, Zhennan; Tian, Ya-ping

    2014-09-03

    Small non-coding microRNAs (miRNAs) are involved in cancer development and progression, and serum profiles of cervical cancer patients may be useful for identifying novel miRNAs. We performed deep sequencing on serum pools of cervical cancer patients and healthy controls with 3 replicates and constructed a small RNA library. We used MIREAP to predict novel miRNAs and identified 2 putative novel miRNAs between serum pools of cervical cancer patients and healthy controls after filtering out pseudo-pre-miRNAs using Triplet-SVM analysis. The 2 putative novel miRNAs were validated by real time PCR and were significantly decreased in cervical cancer patients compared with healthy controls. One novel miRNA had an area under curve (AUC) of 0.921 (95% CI: 0.883, 0.959) with a sensitivity of 85.7% and a specificity of 88.2% when discriminating between cervical cancer patients and healthy controls. Our results suggest that characterizing serum profiles of cervical cancers by Solexa sequencing may be a good method for identifying novel miRNAs and that the validated novel miRNAs described here may be cervical cancer-associated biomarkers.

  8. A Deep Learning Approach to Drone Monitoring

    OpenAIRE

    Chen, Yueru; Aggarwal, Pranav; Choi, Jongmoo; Kuo, C. -C. Jay

    2017-01-01

    A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. To track a small flying drone, we utilize the residual information between consecutive i...

  9. Insights into the genetic structure and diversity of 38 South Asian Indians from deep whole-genome sequencing.

    Science.gov (United States)

    Wong, Lai-Ping; Lai, Jason Kuan-Han; Saw, Woei-Yuh; Ong, Rick Twee-Hee; Cheng, Anthony Youzhi; Pillai, Nisha Esakimuthu; Liu, Xuanyao; Xu, Wenting; Chen, Peng; Foo, Jia-Nee; Tan, Linda Wei-Lin; Koo, Seok-Hwee; Soong, Richie; Wenk, Markus Rene; Lim, Wei-Yen; Khor, Chiea-Chuen; Little, Peter; Chia, Kee-Seng; Teo, Yik-Ying

    2014-05-01

    South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language-speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP). The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP). SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal) identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

  10. Population-genomic variation within RNA viruses of the Western honey bee, Apis mellifera, inferred from deep sequencing.

    Science.gov (United States)

    Cornman, Robert Scott; Boncristiani, Humberto; Dainat, Benjamin; Chen, Yanping; vanEngelsdorp, Dennis; Weaver, Daniel; Evans, Jay D

    2013-03-07

    Deep sequencing of viruses isolated from infected hosts is an efficient way to measure population-genetic variation and can reveal patterns of dispersal and natural selection. In this study, we mined existing Illumina sequence reads to investigate single-nucleotide polymorphisms (SNPs) within two RNA viruses of the Western honey bee (Apis mellifera), deformed wing virus (DWV) and Israel acute paralysis virus (IAPV). All viral RNA was extracted from North American samples of honey bees or, in one case, the ectoparasitic mite Varroa destructor. Coverage depth was generally lower for IAPV than DWV, and marked gaps in coverage occurred in several narrow regions (selection. The Kakugo strain of DWV fell outside of all other DWV sequences at 100% bootstrap support. IAPV consensus sequences supported the existence of multiple clades as had been previously reported, and Fu and Li's D was closer to neutral expectation overall, although a sliding-window analysis identified a significantly positive D within the protease region, suggesting selection maintains diversity in that region. Within-sample mean diversity was comparable between the two viruses on average, although for both viruses there was substantial variation among samples in mean diversity at third codon positions and in the number of high-diversity sites. FST values were bimodal for DWV, likely reflecting neutral divergence in two low-diversity populations, whereas IAPV had several sites that were strong outliers with very low FST. This initial survey of genetic variation within honey bee RNA viruses suggests future directions for studies examining the underlying causes of population-genetic structure in these economically important pathogens.

  11. A symbolic dynamics approach for the complexity analysis of chaotic pseudo-random sequences

    International Nuclear Information System (INIS)

    Xiao Fanghong

    2004-01-01

    By considering a chaotic pseudo-random sequence as a symbolic sequence, authors present a symbolic dynamics approach for the complexity analysis of chaotic pseudo-random sequences. The method is applied to the cases of Logistic map and one-way coupled map lattice to demonstrate how it works, and a comparison is made between it and the approximate entropy method. The results show that this method is applicable to distinguish the complexities of different chaotic pseudo-random sequences, and it is superior to the approximate entropy method

  12. A deep learning method for lincRNA detection using auto-encoder algorithm.

    Science.gov (United States)

    Yu, Ning; Yu, Zeng; Pan, Yi

    2017-12-06

    RNA sequencing technique (RNA-seq) enables scientists to develop novel data-driven methods for discovering more unidentified lincRNAs. Meantime, knowledge-based technologies are experiencing a potential revolution ignited by the new deep learning methods. By scanning the newly found data set from RNA-seq, scientists have found that: (1) the expression of lincRNAs appears to be regulated, that is, the relevance exists along the DNA sequences; (2) lincRNAs contain some conversed patterns/motifs tethered together by non-conserved regions. The two evidences give the reasoning for adopting knowledge-based deep learning methods in lincRNA detection. Similar to coding region transcription, non-coding regions are split at transcriptional sites. However, regulatory RNAs rather than message RNAs are generated. That is, the transcribed RNAs participate the biological process as regulatory units instead of generating proteins. Identifying these transcriptional regions from non-coding regions is the first step towards lincRNA recognition. The auto-encoder method achieves 100% and 92.4% prediction accuracy on transcription sites over the putative data sets. The experimental results also show the excellent performance of predictive deep neural network on the lincRNA data sets compared with support vector machine and traditional neural network. In addition, it is validated through the newly discovered lincRNA data set and one unreported transcription site is found by feeding the whole annotated sequences through the deep learning machine, which indicates that deep learning method has the extensive ability for lincRNA prediction. The transcriptional sequences of lincRNAs are collected from the annotated human DNA genome data. Subsequently, a two-layer deep neural network is developed for the lincRNA detection, which adopts the auto-encoder algorithm and utilizes different encoding schemes to obtain the best performance over intergenic DNA sequence data. Driven by those newly

  13. Complete genome sequence of the aerobic, heterotroph Marinithermus hydrothermalis type strain (T1T) from a deep-sea hydrothermal vent chimney

    Energy Technology Data Exchange (ETDEWEB)

    Copeland, A [U.S. Department of Energy, Joint Genome Institute; Gu, Wei [U.S. Department of Energy, Joint Genome Institute; Yasawong, Montri [HZI - Helmholtz Centre for Infection Research, Braunschweig, Germany; Lapidus, Alla L. [U.S. Department of Energy, Joint Genome Institute; Lucas, Susan [U.S. Department of Energy, Joint Genome Institute; Deshpande, Shweta [U.S. Department of Energy, Joint Genome Institute; Pagani, Ioanna [U.S. Department of Energy, Joint Genome Institute; Tapia, Roxanne [Los Alamos National Laboratory (LANL); Cheng, Jan-Fang [U.S. Department of Energy, Joint Genome Institute; Goodwin, Lynne A. [Los Alamos National Laboratory (LANL); Pitluck, Sam [U.S. Department of Energy, Joint Genome Institute; Liolios, Konstantinos [U.S. Department of Energy, Joint Genome Institute; Ivanova, N [U.S. Department of Energy, Joint Genome Institute; Mavromatis, K [U.S. Department of Energy, Joint Genome Institute; Mikhailova, Natalia [U.S. Department of Energy, Joint Genome Institute; Pati, Amrita [U.S. Department of Energy, Joint Genome Institute; Chen, Amy [U.S. Department of Energy, Joint Genome Institute; Palaniappan, Krishna [U.S. Department of Energy, Joint Genome Institute; Land, Miriam L [ORNL; Pan, Chongle [ORNL; Brambilla, Evelyne-Marie [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Rohde, Manfred [HZI - Helmholtz Centre for Infection Research, Braunschweig, Germany; Tindall, Brian [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Sikorski, Johannes [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Goker, Markus [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Detter, J. Chris [U.S. Department of Energy, Joint Genome Institute; Bristow, James [U.S. Department of Energy, Joint Genome Institute; Eisen, Jonathan [U.S. Department of Energy, Joint Genome Institute; Markowitz, Victor [U.S. Department of Energy, Joint Genome Institute; Hugenholtz, Philip [U.S. Department of Energy, Joint Genome Institute; Kyrpides, Nikos C [U.S. Department of Energy, Joint Genome Institute; Klenk, Hans-Peter [DSMZ - German Collection of Microorganisms and Cell Cultures GmbH, Braunschweig, Germany; Woyke, Tanja [U.S. Department of Energy, Joint Genome Institute

    2012-01-01

    Marinithermus hydrothermalis Sako et al. 2003 is the type species of the monotypic genus Marinithermus. M. hydrothermalis T1 T was the first isolate within the phylum ThermusDeinococcus to exhibit optimal growth under a salinity equivalent to that of sea water and to have an absolute requirement for NaCl for growth. M. hydrothermalis T1 T is of interest because it may provide a new insight into the ecological significance of the aerobic, thermophilic decomposers in the circulation of organic compounds in deep-sea hydrothermal vent ecosystems. This is the first completed genome sequence of a member of the genus Marinithermus and the seventh sequence from the family Thermaceae. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 2,269,167 bp long genome with its 2,251 protein-coding and 59 RNA genes is a part of the Genomic Encyclopedia of Bacteria and Archaea project.

  14. Complete genome sequence of the aerobic, heterotroph Marinithermus hydrothermalis type strain (T1(T)) from a deep-sea hydrothermal vent chimney.

    Science.gov (United States)

    Copeland, Alex; Gu, Wei; Yasawong, Montri; Lapidus, Alla; Lucas, Susan; Deshpande, Shweta; Pagani, Ioanna; Tapia, Roxanne; Cheng, Jan-Fang; Goodwin, Lynne A; Pitluck, Sam; Liolios, Konstantinos; Ivanova, Natalia; Mavromatis, Konstantinos; Mikhailova, Natalia; Pati, Amrita; Chen, Amy; Palaniappan, Krishna; Land, Miriam; Pan, Chongle; Brambilla, Evelyne-Marie; Rohde, Manfred; Tindall, Brian J; Sikorski, Johannes; Göker, Markus; Detter, John C; Bristow, James; Eisen, Jonathan A; Markowitz, Victor; Hugenholtz, Philip; Kyrpides, Nikos C; Klenk, Hans-Peter; Woyke, Tanja

    2012-03-19

    Marinithermus hydrothermalis Sako et al. 2003 is the type species of the monotypic genus Marinithermus. M. hydrothermalis T1(T) was the first isolate within the phylum "Thermus-Deinococcus" to exhibit optimal growth under a salinity equivalent to that of sea water and to have an absolute requirement for NaCl for growth. M. hydrothermalis T1(T) is of interest because it may provide a new insight into the ecological significance of the aerobic, thermophilic decomposers in the circulation of organic compounds in deep-sea hydrothermal vent ecosystems. This is the first completed genome sequence of a member of the genus Marinithermus and the seventh sequence from the family Thermaceae. Here we describe the features of this organism, together with the complete genome sequence and annotation. The 2,269,167 bp long genome with its 2,251 protein-coding and 59 RNA genes is a part of the Genomic Encyclopedia of Bacteria and Archaea project.

  15. Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

    Science.gov (United States)

    Han, Hu; K Jain, Anil; Shan, Shiguang; Chen, Xilin

    2017-08-10

    Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal vs. nominal and holistic vs. local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

  16. A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

    Science.gov (United States)

    Zhu, Yanan; Ouyang, Qi; Mao, Youdong

    2017-07-21

    Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

  17. An introduction to Deep learning on biological sequence data - Examples and solutions

    DEFF Research Database (Denmark)

    Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten

    2017-01-01

    Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use....... Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively...

  18. Sequence Comparison Alignment-Free Approach Based on Suffix Tree and L-Words Frequency

    Directory of Open Access Journals (Sweden)

    Inês Soares

    2012-01-01

    Full Text Available The vast majority of methods available for sequence comparison rely on a first sequence alignment step, which requires a number of assumptions on evolutionary history and is sometimes very difficult or impossible to perform due to the abundance of gaps (insertions/deletions. In such cases, an alternative alignment-free method would prove valuable. Our method starts by a computation of a generalized suffix tree of all sequences, which is completed in linear time. Using this tree, the frequency of all possible words with a preset length L—L-words—in each sequence is rapidly calculated. Based on the L-words frequency profile of each sequence, a pairwise standard Euclidean distance is then computed producing a symmetric genetic distance matrix, which can be used to generate a neighbor joining dendrogram or a multidimensional scaling graph. We present an improvement to word counting alignment-free approaches for sequence comparison, by determining a single optimal word length and combining suffix tree structures to the word counting tasks. Our approach is, thus, a fast and simple application that proved to be efficient and powerful when applied to mitochondrial genomes. The algorithm was implemented in Python language and is freely available on the web.

  19. Deep learning evaluation using deep linguistic processing

    OpenAIRE

    Kuhnle, Alexander; Copestake, Ann

    2017-01-01

    We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

  20. Deep sequencing and flow cytometric characterization of expanded effector memory CD8+CD57+ T cells frequently reveals T-cell receptor Vβ oligoclonality and CDR3 homology in acquired aplastic anemia.

    Science.gov (United States)

    Giudice, Valentina; Feng, Xingmin; Lin, Zenghua; Hu, Wei; Zhang, Fanmao; Qiao, Wangmin; Ibanez, Maria Del Pilar Fernandez; Rios, Olga; Young, Neal S

    2018-05-01

    Oligoclonal expansion of CD8 + CD28 - lymphocytes has been considered indirect evidence for a pathogenic immune response in acquired aplastic anemia. A subset of CD8 + CD28 - cells with CD57 expression, termed effector memory cells, is expanded in several immune-mediated diseases and may have a role in immune surveillance. We hypothesized that effector memory CD8 + CD28 - CD57 + cells may drive aberrant oligoclonal expansion in aplastic anemia. We found CD8 + CD57 + cells frequently expanded in the blood of aplastic anemia patients, with oligoclonal characteristics by flow cytometric Vβ usage analysis: skewing in 1-5 Vβ families and frequencies of immunodominant clones ranging from 1.98% to 66.5%. Oligoclonal characteristics were also observed in total CD8 + cells from aplastic anemia patients with CD8 + CD57 + cell expansion by T-cell receptor deep sequencing, as well as the presence of 1-3 immunodominant clones. Oligoclonality was confirmed by T-cell receptor repertoire deep sequencing of enriched CD8 + CD57 + cells, which also showed decreased diversity compared to total CD4 + and CD8 + cell pools. From analysis of complementarity-determining region 3 sequences in the CD8 + cell pool, a total of 29 sequences were shared between patients and controls, but these sequences were highly expressed in aplastic anemia subjects and also present in their immunodominant clones. In summary, expansion of effector memory CD8 + T cells is frequent in aplastic anemia and mirrors Vβ oligoclonal expansion. Flow cytometric Vβ usage analysis combined with deep sequencing technologies allows high resolution characterization of the T-cell receptor repertoire, and might represent a useful tool in the diagnosis and periodic evaluation of aplastic anemia patients. (Registered at clinicaltrials.gov identifiers: 00001620, 01623167, 00001397, 00071045, 00081523, 00961064 ). Copyright © 2018 Ferrata Storti Foundation.

  1. Using Flipped Classroom Approach to Explore Deep Learning in Large Classrooms

    Directory of Open Access Journals (Sweden)

    Brenda Danker

    2015-01-01

    Full Text Available This project used two Flipped Classroom approaches to stimulate deep learning in large classrooms during the teaching of a film module as part of a Diploma in Performing Arts course at Sunway University, Malaysia. The flipped classes utilized either a blended learning approach where students first watched online lectures as homework, and then completed their assignments and practical work in class; or utilized a guided inquiry approach at the beginning of class using this same process. During the class the lecturers were present to help the students, and in addition, the students were advantaged by being able to help one another. The in-class learning activities also included inquiry-based learning, active learning, and peer-learning. This project used an action research approach to improve the in-class instructional design progressively to achieve its impact of deep learning among the students. The in-class learning activities that was included in the later flipped classes merged aspects of blended learning with an inquiry-based learning cycle which focused on the exploration of concepts. Data was gathered from questionnaires filled out by the students and from short interviews with the students, as well as from the teacher’s reflective journals. The findings verified that the flipped classrooms were able to remodel large lecture classes into active-learning classes. The results also support the possibility of individualised learning for the students as being high as a result of the teacher’s ability to provide one-on-one tutoring through technology-infused lessons. It is imperative that the in-class learning activities are purposefully designed as the inclusion of the exploratory learning through guided inquiry-based activities in the flipped classes was a successful way to engage students on a deeper level and increased the students’ curiosity and engaged them to develop higher-order thinking skills. This project also concluded that

  2. A robust, simple genotyping-by-sequencing (GBS approach for high diversity species.

    Directory of Open Access Journals (Sweden)

    Robert J Elshire

    Full Text Available Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs. This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM and barley (Oregon Wolfe Barley recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.

  3. Detecting false positive sequence homology: a machine learning approach.

    Science.gov (United States)

    Fujimoto, M Stanley; Suvorov, Anton; Jensen, Nicholas O; Clement, Mark J; Bybee, Seth M

    2016-02-24

    Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging from building phylogenies to predicting functional gene annotations. There are many existing heuristic tools, most commonly based on bidirectional BLAST searches that are used to identify homologous genes and combine them into two fundamentally distinct classes: orthologs and paralogs. Due to only using heuristic filtering based on significance score cutoffs and having no cluster post-processing tools available, these methods can often produce multiple clusters constituting unrelated (non-homologous) sequences. Therefore sequencing data extracted from incomplete genome/transcriptome assemblies originated from low coverage sequencing or produced by de novo processes without a reference genome are susceptible to high false positive rates of homology detection. In this paper we develop biologically informative features that can be extracted from multiple sequence alignments of putative homologous genes (orthologs and paralogs) and further utilized in context of guided experimentation to verify false positive outcomes. We demonstrate that our machine learning method trained on both known homology clusters obtained from OrthoDB and randomly generated sequence alignments (non-homologs), successfully determines apparent false positives inferred by heuristic algorithms especially among proteomes recovered from low-coverage RNA-seq data. Almost ~42 % and ~25 % of predicted putative homologies by InParanoid and HaMStR respectively were classified as false positives on experimental data set. Our process increases the quality of output from other clustering algorithms by providing a novel post-processing method that is both fast and efficient at removing low quality clusters of putative homologous genes recovered by heuristic-based approaches.

  4. DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier

    KAUST Repository

    Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert

    2017-01-01

    A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often

  5. Invited talk: Deep Learning Meets Physics

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

  6. Analysis of deep learning methods for blind protein contact prediction in CASP12.

    Science.gov (United States)

    Wang, Sheng; Sun, Siqi; Xu, Jinbo

    2018-03-01

    Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median family size of around 58 effective sequences, our server obtained an average top L/5 long- and medium-range contact accuracy of 47% and 44%, respectively (L = length). A complete implementation has an average accuracy of 59% and 57%, respectively. Our deep learning method formulates contact prediction as a pixel-level image labeling problem and simultaneously predicts all residue pairs of a protein using a combination of two deep residual neural networks, taking as input the residue conservation information, predicted secondary structure and solvent accessibility, contact potential, and coevolution information. Our approach differs from existing methods mainly in (1) formulating contact prediction as a pixel-level image labeling problem instead of an image-level classification problem; (2) simultaneously predicting all contacts of an individual protein to make effective use of contact occurrence patterns; and (3) integrating both one-dimensional and two-dimensional deep convolutional neural networks to effectively learn complex sequence-structure relationship including high-order residue correlation. This paper discusses the RaptorX-Contact pipeline, both contact prediction and contact-based folding results, and finally the strength and weakness of our method. © 2017 Wiley Periodicals, Inc.

  7. Insights into the genetic structure and diversity of 38 South Asian Indians from deep whole-genome sequencing.

    Directory of Open Access Journals (Sweden)

    Lai-Ping Wong

    2014-05-01

    Full Text Available South Asia possesses a significant amount of genetic diversity due to considerable intergroup differences in culture and language. There have been numerous reports on the genetic structure of Asian Indians, although these have mostly relied on genotyping microarrays or targeted sequencing of the mitochondria and Y chromosomes. Asian Indians in Singapore are primarily descendants of immigrants from Dravidian-language-speaking states in south India, and 38 individuals from the general population underwent deep whole-genome sequencing with a target coverage of 30X as part of the Singapore Sequencing Indian Project (SSIP. The genetic structure and diversity of these samples were compared against samples from the Singapore Sequencing Malay Project and populations in Phase 1 of the 1,000 Genomes Project (1 KGP. SSIP samples exhibited greater intra-population genetic diversity and possessed higher heterozygous-to-homozygous genotype ratio than other Asian populations. When compared against a panel of well-defined Asian Indians, the genetic makeup of the SSIP samples was closely related to South Indians. However, even though the SSIP samples clustered distinctly from the Europeans in the global population structure analysis with autosomal SNPs, eight samples were assigned to mitochondrial haplogroups that were predominantly present in Europeans and possessed higher European admixture than the remaining samples. An analysis of the relative relatedness between SSIP with two archaic hominins (Denisovan, Neanderthal identified higher ancient admixture in East Asian populations than in SSIP. The data resource for these samples is publicly available and is expected to serve as a valuable complement to the South Asian samples in Phase 3 of 1 KGP.

  8. An ensemble deep learning based approach for red lesion detection in fundus images.

    Science.gov (United States)

    Orlando, José Ignacio; Prokofyeva, Elena; Del Fresno, Mariana; Blaschko, Matthew B

    2018-01-01

    Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms (MAs) and hemorrhages (HEs). In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Moreover, it provides comprehensive feedback that is easy to assess by the physicians. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a convolutional neural network (CNN) are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our

  9. Genetic diversity of archaea in deep-sea hydrothermal vent environments.

    OpenAIRE

    Takai, K; Horikoshi, K

    1999-01-01

    Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the...

  10. A Novel Approach Based on MEMS-Gyro's Data Deep Coupling for Determining the Centroid of Star Spot

    Directory of Open Access Journals (Sweden)

    Xing Fei

    2012-01-01

    Full Text Available The traditional approach of star tracker for determining the centroid of spot requires enough energy and good shape, so a relatively long exposure time and stable three-axis state become necessary conditions to maintain high accuracy, these limit its update rate and dynamic performance. In view of these issues, this paper presents an approach for determining the centroid of star spot which based on MEMS-Gyro's data deep coupling, it achieves the deep fusion of the data of star tracker and MEMS-Gyro at star map level through the introduction of EKF. The trajectory predicted by using the angular velocity of three axes can be used to set the extraction window, this enhances the dynamic performance because of the accurate extraction when the satellite has angular speed. The optimal estimations of the centroid position and the drift in the output signal of MEMS-Gyro through this approach reduce the influence of noise of the detector on accuracy of the traditional approach for determining the centroid and effectively correct the output signal of MEMS-Gyro. At the end of this paper, feasibility of this approach is verified by simulation.

  11. Next-generation phylogeography: a targeted approach for multilocus sequencing of non-model organisms.

    Directory of Open Access Journals (Sweden)

    Jonathan B Puritz

    Full Text Available The field of phylogeography has long since realized the need and utility of incorporating nuclear DNA (nDNA sequences into analyses. However, the use of nDNA sequence data, at the population level, has been hindered by technical laboratory difficulty, sequencing costs, and problematic analytical methods dealing with genotypic sequence data, especially in non-model organisms. Here, we present a method utilizing the 454 GS-FLX Titanium pyrosequencing platform with the capacity to simultaneously sequence two species of sea star (Meridiastra calcar and Parvulastra exigua at five different nDNA loci across 16 different populations of 20 individuals each per species. We compare results from 3 populations with traditional Sanger sequencing based methods, and demonstrate that this next-generation sequencing platform is more time and cost effective and more sensitive to rare variants than Sanger based sequencing. A crucial advantage is that the high coverage of clonally amplified sequences simplifies haplotype determination, even in highly polymorphic species. This targeted next-generation approach can greatly increase the use of nDNA sequence loci in phylogeographic and population genetic studies by mitigating many of the time, cost, and analytical issues associated with highly polymorphic, diploid sequence markers.

  12. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

    Science.gov (United States)

    Li, Yifeng; Shi, Wenqiang; Wasserman, Wyeth W

    2018-05-31

    In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations.

  13. Arthropod phylogenetics in light of three novel millipede (myriapoda: diplopoda mitochondrial genomes with comments on the appropriateness of mitochondrial genome sequence data for inferring deep level relationships.

    Directory of Open Access Journals (Sweden)

    Michael S Brewer

    Full Text Available BACKGROUND: Arthropods are the most diverse group of eukaryotic organisms, but their phylogenetic relationships are poorly understood. Herein, we describe three mitochondrial genomes representing orders of millipedes for which complete genomes had not been characterized. Newly sequenced genomes are combined with existing data to characterize the protein coding regions of myriapods and to attempt to reconstruct the evolutionary relationships within the Myriapoda and Arthropoda. RESULTS: The newly sequenced genomes are similar to previously characterized millipede sequences in terms of synteny and length. Unique translocations occurred within the newly sequenced taxa, including one half of the Appalachioria falcifera genome, which is inverted with respect to other millipede genomes. Across myriapods, amino acid conservation levels are highly dependent on the gene region. Additionally, individual loci varied in the level of amino acid conservation. Overall, most gene regions showed low levels of conservation at many sites. Attempts to reconstruct the evolutionary relationships suffered from questionable relationships and low support values. Analyses of phylogenetic informativeness show the lack of signal deep in the trees (i.e., genes evolve too quickly. As a result, the myriapod tree resembles previously published results but lacks convincing support, and, within the arthropod tree, well established groups were recovered as polyphyletic. CONCLUSIONS: The novel genome sequences described herein provide useful genomic information concerning millipede groups that had not been investigated. Taken together with existing sequences, the variety of compositions and evolution of myriapod mitochondrial genomes are shown to be more complex than previously thought. Unfortunately, the use of mitochondrial protein-coding regions in deep arthropod phylogenetics appears problematic, a result consistent with previously published studies. Lack of phylogenetic

  14. Arthropod phylogenetics in light of three novel millipede (myriapoda: diplopoda) mitochondrial genomes with comments on the appropriateness of mitochondrial genome sequence data for inferring deep level relationships.

    Science.gov (United States)

    Brewer, Michael S; Swafford, Lynn; Spruill, Chad L; Bond, Jason E

    2013-01-01

    Arthropods are the most diverse group of eukaryotic organisms, but their phylogenetic relationships are poorly understood. Herein, we describe three mitochondrial genomes representing orders of millipedes for which complete genomes had not been characterized. Newly sequenced genomes are combined with existing data to characterize the protein coding regions of myriapods and to attempt to reconstruct the evolutionary relationships within the Myriapoda and Arthropoda. The newly sequenced genomes are similar to previously characterized millipede sequences in terms of synteny and length. Unique translocations occurred within the newly sequenced taxa, including one half of the Appalachioria falcifera genome, which is inverted with respect to other millipede genomes. Across myriapods, amino acid conservation levels are highly dependent on the gene region. Additionally, individual loci varied in the level of amino acid conservation. Overall, most gene regions showed low levels of conservation at many sites. Attempts to reconstruct the evolutionary relationships suffered from questionable relationships and low support values. Analyses of phylogenetic informativeness show the lack of signal deep in the trees (i.e., genes evolve too quickly). As a result, the myriapod tree resembles previously published results but lacks convincing support, and, within the arthropod tree, well established groups were recovered as polyphyletic. The novel genome sequences described herein provide useful genomic information concerning millipede groups that had not been investigated. Taken together with existing sequences, the variety of compositions and evolution of myriapod mitochondrial genomes are shown to be more complex than previously thought. Unfortunately, the use of mitochondrial protein-coding regions in deep arthropod phylogenetics appears problematic, a result consistent with previously published studies. Lack of phylogenetic signal renders the resulting tree topologies as suspect

  15. Probing the Rare Biosphere of the North-West Mediterranean Sea: An Experiment with High Sequencing Effort.

    Directory of Open Access Journals (Sweden)

    Bibiana G Crespo

    Full Text Available High-throughput sequencing (HTS techniques have suggested the existence of a wealth of species with very low relative abundance: the rare biosphere. We attempted to exhaustively map this rare biosphere in two water samples by performing an exceptionally deep pyrosequencing analysis (~500,000 final reads per sample. Species data were derived by a 97% identity criterion and various parametric distributions were fitted to the observed counts. Using the best-fitting Sichel distribution we estimate a total species richness of 1,568-1,669 (95% Credible Interval and 5,027-5,196 for surface and deep water samples respectively, implying that 84-89% of the total richness in those two samples was sequenced, and we predict that a quadrupling of the present sequencing effort would suffice to observe 90% of the total richness in both samples. Comparing the HTS results with a culturing approach we found that most of the cultured taxa were not obtained by HTS, despite the high sequencing effort. Culturing therefore remains a useful tool for uncovering marine bacterial diversity, in addition to its other uses for studying the ecology of marine bacteria.

  16. Probing the Rare Biosphere of the North-West Mediterranean Sea: An Experiment with High Sequencing Effort.

    Science.gov (United States)

    Crespo, Bibiana G; Wallhead, Philip J; Logares, Ramiro; Pedrós-Alió, Carlos

    2016-01-01

    High-throughput sequencing (HTS) techniques have suggested the existence of a wealth of species with very low relative abundance: the rare biosphere. We attempted to exhaustively map this rare biosphere in two water samples by performing an exceptionally deep pyrosequencing analysis (~500,000 final reads per sample). Species data were derived by a 97% identity criterion and various parametric distributions were fitted to the observed counts. Using the best-fitting Sichel distribution we estimate a total species richness of 1,568-1,669 (95% Credible Interval) and 5,027-5,196 for surface and deep water samples respectively, implying that 84-89% of the total richness in those two samples was sequenced, and we predict that a quadrupling of the present sequencing effort would suffice to observe 90% of the total richness in both samples. Comparing the HTS results with a culturing approach we found that most of the cultured taxa were not obtained by HTS, despite the high sequencing effort. Culturing therefore remains a useful tool for uncovering marine bacterial diversity, in addition to its other uses for studying the ecology of marine bacteria.

  17. Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture

    OpenAIRE

    Park, Seong Hyeon; Kim, ByeongDo; Kang, Chang Mook; Chung, Chung Choo; Choi, Jun Won

    2018-01-01

    In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the $K$ most likely trajectory candidates over occupancy grid ma...

  18. PCR amplification of repetitive sequences as a possible approach in relative species quantification

    DEFF Research Database (Denmark)

    Ballin, Nicolai Zederkopff; Vogensen, Finn Kvist; Karlsson, Anders H

    2012-01-01

    Abstract Both relative and absolute quantifications are possible in species quantification when single copy genomic DNA is used. However, amplification of single copy genomic DNA does not allow a limit of detection as low as one obtained from amplification of repetitive sequences. Amplification...... of repetitive sequences is therefore frequently used in absolute quantification but problems occur in relative quantification as the number of repetitive sequences is unknown. A promising approach was developed where data from amplification of repetitive sequences were used in relative quantification of species...... to relatively quantify the amount of chicken DNA in a binary mixture of chicken DNA and pig DNA. However, the designed PCR primers lack the specificity required for regulatory species control....

  19. Prevalence of Hepatitis C Virus Subgenotypes 1a and 1b in Japanese Patients: Ultra-Deep Sequencing Analysis of HCV NS5B Genotype-Specific Region

    Science.gov (United States)

    Wu, Shuang; Kanda, Tatsuo; Nakamoto, Shingo; Jiang, Xia; Miyamura, Tatsuo; Nakatani, Sueli M.; Ono, Suzane Kioko; Takahashi-Nakaguchi, Azusa; Gonoi, Tohru; Yokosuka, Osamu

    2013-01-01

    Background Hepatitis C virus (HCV) subgenotypes 1a and 1b have different impacts on the treatment response to peginterferon plus ribavirin with direct-acting antivirals (DAAs) against patients infected with HCV genotype 1, as the emergence rates of resistance mutations are different between these two subgenotypes. In Japan, almost all of HCV genotype 1 belongs to subgenotype 1b. Methods and Findings To determine HCV subgenotype 1a or 1b in Japanese patients infected with HCV genotype 1, real-time PCR-based method and Sanger method were used for the HCV NS5B region. HCV subgenotypes were determined in 90% by real-time PCR-based method. We also analyzed the specific probe regions for HCV subgenotypes 1a and 1b using ultra-deep sequencing, and uncovered mutations that could not be revealed using direct-sequencing by Sanger method. We estimated the prevalence of HCV subgenotype 1a as 1.2-2.5% of HCV genotype 1 patients in Japan. Conclusions Although real-time PCR-based HCV subgenotyping method seems fair for differentiating HCV subgenotypes 1a and 1b, it may not be sufficient for clinical practice. Ultra-deep sequencing is useful for revealing the resistant strain(s) of HCV before DAA treatment as well as mixed infection with different genotypes or subgenotypes of HCV. PMID:24069214

  20. Revisiting bovine pyometra-New insights into the disease using a culture-independent deep sequencing approach

    DEFF Research Database (Denmark)

    Knudsen, Lif Rødtness Vesterby; Karstrup, Cecilia Christensen; Pedersen, Hanne Gervi

    2015-01-01

    -independent studies have demonstrated that the bacterial diversity in most environments is underestimated in culture-based studies. Consequently, fastidious pyometra-associated pathogens may have been overlooked. Therefore, the primary purpose of this study was to investigate the diversity of bacteria in the uterus......The bacteria present in the uterus during pyometra have previously been studied using bacteriological culturing. These studies identified Fusobacterium necrophorum and Trueperella pyogenes as the major contributors to the pathogenesis of pyometra. However, an increasing number of culture...... of cows with pyometra by using culture-independent 16S rRNA PCR combined with next generation sequencing. We investigated the microbial composition in the uterus of 21 cows with pyometra, which were obtained from a Danish slaughterhouse. Similar to the observations from the culture studies...

  1. Comparison of different deep learning approaches for parotid gland segmentation from CT images

    Science.gov (United States)

    Hänsch, Annika; Schwier, Michael; Gass, Tobias; Morgas, Tomasz; Haas, Benjamin; Klein, Jan; Hahn, Horst K.

    2018-02-01

    The segmentation of target structures and organs at risk is a crucial and very time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and often low contrast to surrounding structures, segmentation of the parotid gland is especially challenging. Motivated by the recent success of deep learning, we study different deep learning approaches for parotid gland segmentation. Particularly, we compare 2D, 2D ensemble and 3D U-Net approaches and find that the 2D U-Net ensemble yields the best results with a mean Dice score of 0.817 on our test data. The ensemble approach reduces false positives without the need for an automatic region of interest detection. We also apply our trained 2D U-Net ensemble to segment the test data of the 2015 MICCAI head and neck auto-segmentation challenge. With a mean Dice score of 0.861, our classifier exceeds the highest mean score in the challenge. This shows that the method generalizes well onto data from independent sites. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed to properly train a neural network. We evaluate the classifier performance after training with differently sized training sets (50-450) and find that 250 cases (without using extensive data augmentation) are sufficient to obtain good results with the 2D ensemble. Adding more samples does not significantly improve the Dice score of the segmentations.

  2. Biogeography of Persephonella in deep-sea hydrothermal vents of the Western Pacific.

    Directory of Open Access Journals (Sweden)

    Sayaka eMino

    2013-04-01

    Full Text Available Deep-sea hydrothermal vent fields are areas on the seafloor with high biological productivity fueled by microbial chemosynthesis. Members of the Aquificales genus Persephonella are obligately chemosynthetic bacteria, and appear to be key players in carbon, sulfur, and nitrogen cycles in high temperature habitats at deep-sea vents. Although this group of bacteria has cosmopolitan distribution in deep-sea hydrothermal ecosystem around the world, little is known about their population structure such as intraspecific genomic diversity, distribution pattern, and phenotypic diversity. We developed the multi-locus sequence analysis (MLSA scheme for their genomic characterization. Sequence variation was determined in five housekeeping genes and one functional gene of 36 P. hydrogeniphila strains originated from the Okinawa Trough and the South Mariana Trough. Although the strains share > 98.7% similarities in 16S rRNA gene sequences, MLSA revealed 35 different sequence types, indicating their extensive genomic diversity. A phylogenetic tree inferred from all concatenated gene sequences revealed the clustering of isolates according to the geographic origin. In addition, the phenotypic clustering pattern inferred from whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF/MS analysis can be correlated to their MLSA clustering pattern. This study represents the first MLSA combined with phenotypic analysis indicative of allopatric speciation of deep-sea hydrothermal vent bacteria.

  3. The sequence coding and search system: an approach for constructing and analyzing event sequences at commercial nuclear power plants

    International Nuclear Information System (INIS)

    Mays, G.T.

    1990-01-01

    The U.S. Nuclear Regulatory Commission (NRC) has recognized the importance of the collection, assessment, and feedback of operating experience data from commercial nuclear power plants and has centralized these activities in the Office for Analysis and Evaluation of Operational Data (AEOD). Such data is essential for performing safety and reliability analyses, especially analyses of trends and patterns to identify undesirable changes in plant performance at the earliest opportunity to implement corrective measures to preclude the occurrence of a more serious event. One of NRC's principal tools for collecting and evaluating operating experience data is the Sequence Coding and Search System (SCSS). The SCSS consists of a methodology for structuring event sequences and the requisite computer system to store and search the data. The source information for SCSS is the Licensee Event Report (LER), which is a legally required document. This paper describes the objectives of SCSS, the information it contains, and the format and approach for constructing SCSS event sequences. Examples are presented demonstrating the use of SCSS to support the analysis of LER data. The SCSS contains over 30,000 LERs describing events from 1980 through the present. Insights gained from working with a complex data system from the initial developmental stage to the point of a mature operating system are highlighted. Considerable experience has been gained in the areas of evolving and changing data requirements, staffing requirements, and quality control and quality assurance procedures for addressing consistency, software/hardware considerations for developing and maintaining a complex system, documentation requirements, and end-user needs. Two other approaches for constructing and evaluating event sequences are examined including the Accident Precursor Program (ASP) where sequences having the potential for core damage are identified and analyzed, and the Significant Event Compilation Tree

  4. A Generic Deep-Learning-Based Approach for Automated Surface Inspection.

    Science.gov (United States)

    Ren, Ruoxu; Hung, Terence; Tan, Kay Chen

    2018-03-01

    Automated surface inspection (ASI) is a challenging task in industry, as collecting training dataset is usually costly and related methods are highly dataset-dependent. In this paper, a generic approach that requires small training data for ASI is proposed. First, this approach builds classifier on the features of image patches, where the features are transferred from a pretrained deep learning network. Next, pixel-wise prediction is obtained by convolving the trained classifier over input image. An experiment on three public and one industrial data set is carried out. The experiment involves two tasks: 1) image classification and 2) defect segmentation. The results of proposed algorithm are compared against several best benchmarks in literature. In the classification tasks, the proposed method improves accuracy by 0.66%-25.50%. In the segmentation tasks, the proposed method reduces error escape rates by 6.00%-19.00% in three defect types and improves accuracies by 2.29%-9.86% in all seven defect types. In addition, the proposed method achieves 0.0% error escape rate in the segmentation task of industrial data.

  5. College Seniors' Plans for Graduate School: Do Deep Approaches Learning and Holland Academic Environments Matter?

    Science.gov (United States)

    Rocconi, Louis M.; Ribera, Amy K.; Nelson Laird, Thomas F.

    2015-01-01

    This study examines the extent to which college seniors' plans for graduate school are related to their tendency to engage in deep approaches to learning (DAL) and their academic environments (majors) as classified by Holland type. Using data from the National Survey of Student Engagement, we analyzed responses from over 116,000 seniors attending…

  6. Problematic Smartphone Use, Deep and Surface Approaches to Learning, and Social Media Use in Lectures

    Directory of Open Access Journals (Sweden)

    Dmitri Rozgonjuk

    2018-01-01

    Full Text Available Several studies have shown that problematic smartphone use (PSU is related to detrimental outcomes, such as worse psychological well-being, higher cognitive distraction, and poorer academic outcomes. In addition, many studies have shown that PSU is strongly related to social media use. Despite this, the relationships between PSU, as well as the frequency of social media use in lectures, and different approaches to learning have not been previously studied. In our study, we hypothesized that both PSU and the frequency of social media use in lectures are negatively correlated with a deep approach to learning (defined as learning for understanding and positively correlated with a surface approach to learning (defined as superficial learning. The study participants were 415 Estonian university students aged 19–46 years (78.8% females; age M = 23.37, SD = 4.19; the effective sample comprised 405 participants aged 19–46 years (79.0% females; age M = 23.33, SD = 4.21. In addition to basic socio-demographics, participants were asked about the frequency of their social media use in lectures, and they filled out the Estonian Smartphone Addiction Proneness Scale and the Estonian Revised Study Process Questionnaire. Bivariate correlation analysis showed that PSU and the frequency of social media use in lectures were negatively correlated with a deep approach to learning and positively correlated with a surface approach to learning. Mediation analysis showed that social media use in lectures completely mediates the relationship between PSU and approaches to learning. These results indicate that the frequency of social media use in lectures might explain the relationships between poorer academic outcomes and PSU.

  7. Problematic Smartphone Use, Deep and Surface Approaches to Learning, and Social Media Use in Lectures.

    Science.gov (United States)

    Rozgonjuk, Dmitri; Saal, Kristiina; Täht, Karin

    2018-01-08

    Several studies have shown that problematic smartphone use (PSU) is related to detrimental outcomes, such as worse psychological well-being, higher cognitive distraction, and poorer academic outcomes. In addition, many studies have shown that PSU is strongly related to social media use. Despite this, the relationships between PSU, as well as the frequency of social media use in lectures, and different approaches to learning have not been previously studied. In our study, we hypothesized that both PSU and the frequency of social media use in lectures are negatively correlated with a deep approach to learning (defined as learning for understanding) and positively correlated with a surface approach to learning (defined as superficial learning). The study participants were 415 Estonian university students aged 19-46 years (78.8% females; age M = 23.37, SD = 4.19); the effective sample comprised 405 participants aged 19-46 years (79.0% females; age M = 23.33, SD = 4.21). In addition to basic socio-demographics, participants were asked about the frequency of their social media use in lectures, and they filled out the Estonian Smartphone Addiction Proneness Scale and the Estonian Revised Study Process Questionnaire. Bivariate correlation analysis showed that PSU and the frequency of social media use in lectures were negatively correlated with a deep approach to learning and positively correlated with a surface approach to learning. Mediation analysis showed that social media use in lectures completely mediates the relationship between PSU and approaches to learning. These results indicate that the frequency of social media use in lectures might explain the relationships between poorer academic outcomes and PSU.

  8. Deciphering KRAS and NRAS mutated clone dynamics in MLL-AF4 paediatric leukaemia by ultra deep sequencing analysis.

    Science.gov (United States)

    Trentin, Luca; Bresolin, Silvia; Giarin, Emanuela; Bardini, Michela; Serafin, Valentina; Accordi, Benedetta; Fais, Franco; Tenca, Claudya; De Lorenzo, Paola; Valsecchi, Maria Grazia; Cazzaniga, Giovanni; Kronnie, Geertruy Te; Basso, Giuseppe

    2016-10-04

    To induce and sustain the leukaemogenic process, MLL-AF4+ leukaemia seems to require very few genetic alterations in addition to the fusion gene itself. Studies of infant and paediatric patients with MLL-AF4+ B cell precursor acute lymphoblastic leukaemia (BCP-ALL) have reported mutations in KRAS and NRAS with incidences ranging from 25 to 50%. Whereas previous studies employed Sanger sequencing, here we used next generation amplicon deep sequencing for in depth evaluation of RAS mutations in 36 paediatric patients at diagnosis of MLL-AF4+ leukaemia. RAS mutations including those in small sub-clones were detected in 63.9% of patients. Furthermore, the mutational analysis of 17 paired samples at diagnosis and relapse revealed complex RAS clone dynamics and showed that the mutated clones present at relapse were almost all originated from clones that were already detectable at diagnosis and survived to the initial therapy. Finally, we showed that mutated patients were indeed characterized by a RAS related signature at both transcriptional and protein levels and that the targeting of the RAS pathway could be of beneficial for treatment of MLL-AF4+ BCP-ALL clones carrying somatic RAS mutations.

  9. Predicting healthcare trajectories from medical records: A deep learning approach.

    Science.gov (United States)

    Pham, Trang; Tran, Truyen; Phung, Dinh; Venkatesh, Svetha

    2017-05-01

    Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. A Bac Library and Paired-PCR Approach to Mapping and Completing the Genome Sequence of Sulfolobus Solfataricus P2

    DEFF Research Database (Denmark)

    She, Qunxin; Confalonieri, F.; Zivanovic, Y.

    2000-01-01

    The original strategy used in the Sulfolobus solfatnricus genome project was to sequence non overlapping, or minimally overlapping, cosmid or lambda inserts without constructing a physical map. However, after only about two thirds of the genome sequence was completed, this approach became counter......-productive because there was a high sequence bias in the cosmid and lambda libraries. Therefore, a new approach was devised for linking the sequenced regions which may be generally applicable. BAC libraries were constructed and terminal sequences of the clones were determined and used for both end mapping and PCR...

  11. Inspecting Targeted Deep Sequencing of Whole Genome Amplified DNA Versus Fresh DNA for Somatic Mutation Detection: A Genetic Study in Myelodysplastic Syndrome Patients.

    Science.gov (United States)

    Palomo, Laura; Fuster-Tormo, Francisco; Alvira, Daniel; Ademà, Vera; Armengol, María Pilar; Gómez-Marzo, Paula; de Haro, Nuri; Mallo, Mar; Xicoy, Blanca; Zamora, Lurdes; Solé, Francesc

    2017-08-01

    Whole genome amplification (WGA) has become an invaluable method for preserving limited samples of precious stock material and has been used during the past years as an alternative tool to increase the amount of DNA before library preparation for next-generation sequencing. Myelodysplastic syndromes (MDS) are a group of clonal hematopoietic stem cell disorders characterized by presenting somatic mutations in several myeloid-related genes. In this work, targeted deep sequencing has been performed on four paired fresh DNA and WGA DNA samples from bone marrow of MDS patients, to assess the feasibility of using WGA DNA for detecting somatic mutations. The results of this study highlighted that, in general, the sequencing and alignment statistics of fresh DNA and WGA DNA samples were similar. However, after variant calling and when considering variants detected at all frequencies, there was a high level of discordance between fresh DNA and WGA DNA (overall, a higher number of variants was detected in WGA DNA). After proper filtering, a total of three somatic mutations were detected in the cohort. All somatic mutations detected in fresh DNA were also identified in WGA DNA and validated by whole exome sequencing.

  12. The deep, hot biosphere: Twenty-five years of retrospection.

    Science.gov (United States)

    Colman, Daniel R; Poudel, Saroj; Stamps, Blake W; Boyd, Eric S; Spear, John R

    2017-07-03

    Twenty-five years ago this month, Thomas Gold published a seminal manuscript suggesting the presence of a "deep, hot biosphere" in the Earth's crust. Since this publication, a considerable amount of attention has been given to the study of deep biospheres, their role in geochemical cycles, and their potential to inform on the origin of life and its potential outside of Earth. Overwhelming evidence now supports the presence of a deep biosphere ubiquitously distributed on Earth in both terrestrial and marine settings. Furthermore, it has become apparent that much of this life is dependent on lithogenically sourced high-energy compounds to sustain productivity. A vast diversity of uncultivated microorganisms has been detected in subsurface environments, and we show that H 2 , CH 4 , and CO feature prominently in many of their predicted metabolisms. Despite 25 years of intense study, key questions remain on life in the deep subsurface, including whether it is endemic and the extent of its involvement in the anaerobic formation and degradation of hydrocarbons. Emergent data from cultivation and next-generation sequencing approaches continue to provide promising new hints to answer these questions. As Gold suggested, and as has become increasingly evident, to better understand the subsurface is critical to further understanding the Earth, life, the evolution of life, and the potential for life elsewhere. To this end, we suggest the need to develop a robust network of interdisciplinary scientists and accessible field sites for long-term monitoring of the Earth's subsurface in the form of a deep subsurface microbiome initiative.

  13. Developing Critical Understanding in HRM Students: Using Innovative Teaching Methods to Encourage Deep Approaches to Study

    Science.gov (United States)

    Butler, Michael J. R.; Reddy, Peter

    2010-01-01

    Purpose: This paper aims to focus on developing critical understanding in human resource management (HRM) students in Aston Business School, UK. The paper reveals that innovative teaching methods encourage deep approaches to study, an indicator of students reaching their own understanding of material and ideas. This improves student employability…

  14. Deep sequencing as a method of typing bluetongue virus isolates.

    Science.gov (United States)

    Rao, Pavuluri Panduranga; Reddy, Yella Narasimha; Ganesh, Kapila; Nair, Shreeja G; Niranjan, Vidya; Hegde, Nagendra R

    2013-11-01

    Bluetongue (BT) is an economically important endemic disease of livestock in tropics and subtropics. In addition, its recent spread to temperate regions like North America and Northern Europe is of serious concern. Rapid serotyping and characterization of BT virus (BTV) is an essential step in the identification of origin of the virus and for controlling the disease. Serotyping of BTV is typically performed by serum neutralization, and of late by nucleotide sequencing. This report describes the near complete genome sequencing and typing of two isolates of BTV using Illumina next generation sequencing platform. Two of the BTV RNAs were multiplexed with ten other unknown samples. Viral RNA was isolated and fragmented, reverse transcribed, the cDNA ends were repaired and ligated with a multiplex oligo. The genome library was amplified using primers complementary to the ligated oligo and subjected to single and paired end sequencing. The raw reads were assembled using a de novo method and reference-based assembly was performed based on the contig data. Near complete sequences of all segments of BTV were obtained with more than 20× coverage, and single read sequencing method was sufficient to identify the genotype and serotype of the virus. The two viruses used in this study were typed as BTV-1 and BTV-9E. Copyright © 2013 Elsevier B.V. All rights reserved.

  15. Cultivating the Deep Subsurface Microbiome

    Science.gov (United States)

    Casar, C. P.; Osburn, M. R.; Flynn, T. M.; Masterson, A.; Kruger, B.

    2017-12-01

    Subterranean ecosystems are poorly understood because many microbes detected in metagenomic surveys are only distantly related to characterized isolates. Cultivating microorganisms from the deep subsurface is challenging due to its inaccessibility and potential for contamination. The Deep Mine Microbial Observatory (DeMMO) in Lead, SD however, offers access to deep microbial life via pristine fracture fluids in bedrock to a depth of 1478 m. The metabolic landscape of DeMMO was previously characterized via thermodynamic modeling coupled with genomic data, illustrating the potential for microbial inhabitants of DeMMO to utilize mineral substrates as energy sources. Here, we employ field and lab based cultivation approaches with pure minerals to link phylogeny to metabolism at DeMMO. Fracture fluids were directed through reactors filled with Fe3O4, Fe2O3, FeS2, MnO2, and FeCO3 at two sites (610 m and 1478 m) for 2 months prior to harvesting for subsequent analyses. We examined mineralogical, geochemical, and microbiological composition of the reactors via DNA sequencing, microscopy, lipid biomarker characterization, and bulk C and N isotope ratios to determine the influence of mineralogy on biofilm community development. Pre-characterized mineral chips were imaged via SEM to assay microbial growth; preliminary results suggest MnO2, Fe3O4, and Fe2O3 were most conducive to colonization. Solid materials from reactors were used as inoculum for batch cultivation experiments. Media designed to mimic fracture fluid chemistry was supplemented with mineral substrates targeting metal reducers. DNA sequences and microscopy of iron oxide-rich biofilms and fracture fluids suggest iron oxidation is a major energy source at redox transition zones where anaerobic fluids meet more oxidizing conditions. We utilized these biofilms and fluids as inoculum in gradient cultivation experiments targeting microaerophilic iron oxidizers. Cultivation of microbes endemic to DeMMO, a system

  16. Using a combination of binning strategies and taxonomic approaches to unravel the anaerobic digestion microbiome

    DEFF Research Database (Denmark)

    Campanaro, Stefano; Treu, Laura; Kougias, Panagiotis

    of scaffolds comprehensive of thousands genome sequences, but the binning of these scaffolds into OTUs representative of microbial genomes is still challenging. In the attempt to obtain a deep characterization of the anaerobic digestion microbiome, different metagenomic binning approaches were integrated...

  17. Deeply Affecting First-Year Students' Thinking: Deep Approaches to Learning and Three Dimensions of Cognitive Development

    Science.gov (United States)

    Laird, Thomas F. Nelson; Seifert, Tricia A.; Pascarella, Ernest T.; Mayhew, Matthew J.; Blaich, Charles F.

    2014-01-01

    This study estimates the effects of a deep approaches to learning scale and its subscales on measures of students' critical thinking, need for cognition, and positive attitudes toward literacy, controlling for pre-college scores for the outcomes and other covariates. Results suggest reflection is critical to making gains across the outcomes.

  18. Sedimentology, tephrostratigraphy, and chronology of the DEEP site sediment record, Lake Ohrid (Albania, FYROM)

    Science.gov (United States)

    Leicher, Niklas; Wagner, Bernd; Francke, Alexander; Just, Janna; Zanchetta, Giovanni; Sulpizio, Roberto; Giaccio, Biagio; Nomade, Sebastien

    2017-04-01

    biogeochemical proxy data to orbital parameters in order to develop an age depth model for the last 637 kyr. This dating approach for the upper part will be further extended for the lower sequence below 247.8 m and combined with paleomagnetic information. The Brunhes/Matuyama boundary and the Jaramillo subchron are evident in the DEEP site sequence and will be further confined by higher resolution paleomagnetic measurements. The high-resolution data will also enable the reconstruction of the dynamic of the Earth's Magnetic Field during polarity transitions. This mulit-method dating approach will provide a robust chronology of the core, which is the backbone to fulfil the major aims of the SCOPSCO project.

  19. Evolutionary process of deep-sea bathymodiolus mussels.

    Science.gov (United States)

    Miyazaki, Jun-Ichi; de Oliveira Martins, Leonardo; Fujita, Yuko; Matsumoto, Hiroto; Fujiwara, Yoshihiro

    2010-04-27

    Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4) genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of symbiosis in that nutritional adaptation to the deep sea proceeded from extracellular

  20. Application of Tandem Two-Dimensional Mass Spectrometry for Top-Down Deep Sequencing of Calmodulin.

    Science.gov (United States)

    Floris, Federico; Chiron, Lionel; Lynch, Alice M; Barrow, Mark P; Delsuc, Marc-André; O'Connor, Peter B

    2018-06-04

    Two-dimensional mass spectrometry (2DMS) involves simultaneous acquisition of the fragmentation patterns of all the analytes in a mixture by correlating their precursor and fragment ions by modulating precursor ions systematically through a fragmentation zone. Tandem two-dimensional mass spectrometry (MS/2DMS) unites the ultra-high accuracy of Fourier transform ion cyclotron resonance (FT-ICR) MS/MS and the simultaneous data-independent fragmentation of 2DMS to achieve extensive inter-residue fragmentation of entire proteins. 2DMS was recently developed for top-down proteomics (TDP), and applied to the analysis of calmodulin (CaM), reporting a cleavage coverage of about ~23% using infrared multiphoton dissociation (IRMPD) as fragmentation technique. The goal of this work is to expand the utility of top-down protein analysis using MS/2DMS in order to extend the cleavage coverage in top-down proteomics further into the interior regions of the protein. In this case, using MS/2DMS, the cleavage coverage of CaM increased from ~23% to ~42%. Graphical Abstract Two-dimensional mass spectrometry, when applied to primary fragment ions from the source, allows deep-sequencing of the protein calmodulin.

  1. Mitochondrial genome sequences reveal deep divergences among Anopheles punctulatus sibling species in Papua New Guinea

    Directory of Open Access Journals (Sweden)

    Logue Kyle

    2013-02-01

    Full Text Available Abstract Background Members of the Anopheles punctulatus group (AP group are the primary vectors of human malaria in Papua New Guinea. The AP group includes 13 sibling species, most of them morphologically indistinguishable. Understanding why only certain species are able to transmit malaria requires a better comprehension of their evolutionary history. In particular, understanding relationships and divergence times among Anopheles species may enable assessing how malaria-related traits (e.g. blood feeding behaviours, vector competence have evolved. Methods DNA sequences of 14 mitochondrial (mt genomes from five AP sibling species and two species of the Anopheles dirus complex of Southeast Asia were sequenced. DNA sequences from all concatenated protein coding genes (10,770 bp were then analysed using a Bayesian approach to reconstruct phylogenetic relationships and date the divergence of the AP sibling species. Results Phylogenetic reconstruction using the concatenated DNA sequence of all mitochondrial protein coding genes indicates that the ancestors of the AP group arrived in Papua New Guinea 25 to 54 million years ago and rapidly diverged to form the current sibling species. Conclusion Through evaluation of newly described mt genome sequences, this study has revealed a divergence among members of the AP group in Papua New Guinea that would significantly predate the arrival of humans in this region, 50 thousand years ago. The divergence observed among the mtDNA sequences studied here may have resulted from reproductive isolation during historical changes in sea-level through glacial minima and maxima. This leads to a hypothesis that the AP sibling species have evolved independently for potentially thousands of generations. This suggests that the evolution of many phenotypes, such as insecticide resistance will arise independently in each of the AP sibling species studied here.

  2. Magnetic resonance imaging (MRI) of articular cartilage of the knee using ultrashort echo time (uTE) sequences with spiral acquisition

    International Nuclear Information System (INIS)

    Goto, Hajimu; Fujii, Masahiko; Iwama, Yuki; Aoyama, Nobukazu; Ohno, Yoshiharu; Sugimura, Kazuro

    2012-01-01

    The objective of this study was to evaluate the sensitivity of ultrashort echo time (uTE) sequence for visualisation of calcified deep layers of articular cartilage. MRI with a uTE sequence was performed on five healthy volunteers. Signals from the calcified deep layers of the articular knee cartilage were evaluated on uTE subtraction images and computed tomography images. The calcified deep layers of the articular cartilage changed from having a low to a high signal when imaged with a uTE sequence. The reported uTE sequence was effective in imaging the deep layers of the knee cartilage.

  3. Superresolution restoration of an image sequence: adaptive filtering approach.

    Science.gov (United States)

    Elad, M; Feuer, A

    1999-01-01

    This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.

  4. Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

    Science.gov (United States)

    Lyons, James; Dehzangi, Abdollah; Heffernan, Rhys; Sharma, Alok; Paliwal, Kuldip; Sattar, Abdul; Zhou, Yaoqi; Yang, Yuedong

    2014-10-30

    Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org. Copyright © 2014 Wiley Periodicals, Inc.

  5. Sequenced Integration and the Identification of a Problem-Solving Approach through a Learning Process

    Science.gov (United States)

    Cormas, Peter C.

    2016-01-01

    Preservice teachers (N = 27) in two sections of a sequenced, methodological and process integrated mathematics/science course solved a levers problem with three similar learning processes and a problem-solving approach, and identified a problem-solving approach through one different learning process. Similar learning processes used included:…

  6. Trace maps for arbitrary substitution sequences

    International Nuclear Information System (INIS)

    Avishai, Y.

    1993-01-01

    The discovery of quasi-crystals and their 1-dimensional modeling have led to a deep mathematical study of Schroedinger operators with an arbitrary deterministic potential sequence. In this work we address this problem and find trace maps for an arbitrary substitution sequence. our trace maps have lower dimensionality than those of Kolar and Nori, which make them quite attractive for actual applications. (authors)

  7. A comparative experimental approach to ecotoxicology in shallow-water and deep-sea holothurians suggests similar behavioural responses.

    Science.gov (United States)

    Brown, Alastair; Wright, Roseanna; Mevenkamp, Lisa; Hauton, Chris

    2017-10-01

    Exploration of deep-sea mineral resources is burgeoning, raising concerns regarding ecotoxicological impacts on deep-sea fauna. Assessing toxicity in deep-sea species is technologically challenging, which promotes interest in establishing shallow-water ecotoxicological proxy species. However, the effects of temperature and hydrostatic pressure on toxicity, and how adaptation to deep-sea environmental conditions might moderate these effects, are unknown. To address these uncertainties we assessed behavioural and physiological (antioxidant enzyme activity) responses to exposure to copper-spiked artificial sediments in a laboratory experiment using a shallow-water holothurian (Holothuria forskali), and in an in situ experiment using a deep-sea holothurian (Amperima sp.). Both species demonstrated sustained avoidance behaviour, evading contact with contaminated artificial sediment. However, A. sp. demonstrated sustained avoidance of 5mgl -1 copper-contaminated artificial sediment whereas H. forskali demonstrated only temporary avoidance of 5mgl -1 copper-contaminated artificial sediment, suggesting that H. forskali may be more tolerant of metal exposure over 96h. Nonetheless, the acute behavioural response appears consistent between the shallow-water species and the deep-sea species, suggesting that H. forskali may be a suitable ecotoxicological proxy for A. sp. in acute (≤24h) exposures, which may be representative of deep-sea mining impacts. No antioxidant response was observed in either species, which was interpreted to be the consequence of avoiding copper exposure. Although these data suggest that shallow-water taxa may be suitable ecotoxicological proxies for deep-sea taxa, differences in methodological and analytical approaches, and in sex and reproductive stage of experimental subjects, require caution in assessing the suitability of H. forskali as an ecotoxicological proxy for A. sp. Nonetheless, avoidance behaviour may have bioenergetic consequences that

  8. High throughput sequencing and proteomics to identify immunogenic proteins of a new pathogen: the dirty genome approach.

    Science.gov (United States)

    Greub, Gilbert; Kebbi-Beghdadi, Carole; Bertelli, Claire; Collyn, François; Riederer, Beat M; Yersin, Camille; Croxatto, Antony; Raoult, Didier

    2009-12-23

    With the availability of new generation sequencing technologies, bacterial genome projects have undergone a major boost. Still, chromosome completion needs a costly and time-consuming gap closure, especially when containing highly repetitive elements. However, incomplete genome data may be sufficiently informative to derive the pursued information. For emerging pathogens, i.e. newly identified pathogens, lack of release of genome data during gap closure stage is clearly medically counterproductive. We thus investigated the feasibility of a dirty genome approach, i.e. the release of unfinished genome sequences to develop serological diagnostic tools. We showed that almost the whole genome sequence of the emerging pathogen Parachlamydia acanthamoebae was retrieved even with relatively short reads from Genome Sequencer 20 and Solexa. The bacterial proteome was analyzed to select immunogenic proteins, which were then expressed and used to elaborate the first steps of an ELISA. This work constitutes the proof of principle for a dirty genome approach, i.e. the use of unfinished genome sequences of pathogenic bacteria, coupled with proteomics to rapidly identify new immunogenic proteins useful to develop in the future specific diagnostic tests such as ELISA, immunohistochemistry and direct antigen detection. Although applied here to an emerging pathogen, this combined dirty genome sequencing/proteomic approach may be used for any pathogen for which better diagnostics are needed. These genome sequences may also be very useful to develop DNA based diagnostic tests. All these diagnostic tools will allow further evaluations of the pathogenic potential of this obligate intracellular bacterium.

  9. Problematic Smartphone Use, Deep and Surface Approaches to Learning, and Social Media Use in Lectures †

    Science.gov (United States)

    Rozgonjuk, Dmitri; Saal, Kristiina

    2018-01-01

    Several studies have shown that problematic smartphone use (PSU) is related to detrimental outcomes, such as worse psychological well-being, higher cognitive distraction, and poorer academic outcomes. In addition, many studies have shown that PSU is strongly related to social media use. Despite this, the relationships between PSU, as well as the frequency of social media use in lectures, and different approaches to learning have not been previously studied. In our study, we hypothesized that both PSU and the frequency of social media use in lectures are negatively correlated with a deep approach to learning (defined as learning for understanding) and positively correlated with a surface approach to learning (defined as superficial learning). The study participants were 415 Estonian university students aged 19–46 years (78.8% females; age M = 23.37, SD = 4.19); the effective sample comprised 405 participants aged 19–46 years (79.0% females; age M = 23.33, SD = 4.21). In addition to basic socio-demographics, participants were asked about the frequency of their social media use in lectures, and they filled out the Estonian Smartphone Addiction Proneness Scale and the Estonian Revised Study Process Questionnaire. Bivariate correlation analysis showed that PSU and the frequency of social media use in lectures were negatively correlated with a deep approach to learning and positively correlated with a surface approach to learning. Mediation analysis showed that social media use in lectures completely mediates the relationship between PSU and approaches to learning. These results indicate that the frequency of social media use in lectures might explain the relationships between poorer academic outcomes and PSU. PMID:29316697

  10. Whole-genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations.

    Science.gov (United States)

    Fuentes-Pardo, Angela P; Ruzzante, Daniel E

    2017-10-01

    Whole-genome resequencing (WGR) is a powerful method for addressing fundamental evolutionary biology questions that have not been fully resolved using traditional methods. WGR includes four approaches: the sequencing of individuals to a high depth of coverage with either unresolved or resolved haplotypes, the sequencing of population genomes to a high depth by mixing equimolar amounts of unlabelled-individual DNA (Pool-seq) and the sequencing of multiple individuals from a population to a low depth (lcWGR). These techniques require the availability of a reference genome. This, along with the still high cost of shotgun sequencing and the large demand for computing resources and storage, has limited their implementation in nonmodel species with scarce genomic resources and in fields such as conservation biology. Our goal here is to describe the various WGR methods, their pros and cons and potential applications in conservation biology. WGR offers an unprecedented marker density and surveys a wide diversity of genetic variations not limited to single nucleotide polymorphisms (e.g., structural variants and mutations in regulatory elements), increasing their power for the detection of signatures of selection and local adaptation as well as for the identification of the genetic basis of phenotypic traits and diseases. Currently, though, no single WGR approach fulfils all requirements of conservation genetics, and each method has its own limitations and sources of potential bias. We discuss proposed ways to minimize such biases. We envision a not distant future where the analysis of whole genomes becomes a routine task in many nonmodel species and fields including conservation biology. © 2017 John Wiley & Sons Ltd.

  11. Deep Packet/Flow Analysis using GPUs

    Energy Technology Data Exchange (ETDEWEB)

    Gong, Qian [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Wu, Wenji [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); DeMar, Phil [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

    2017-11-12

    Deep packet inspection (DPI) faces severe performance challenges in high-speed networks (40/100 GE) as it requires a large amount of raw computing power and high I/O throughputs. Recently, researchers have tentatively used GPUs to address the above issues and boost the performance of DPI. Typically, DPI applications involve highly complex operations in both per-packet and per-flow data level, often in real-time. The parallel architecture of GPUs fits exceptionally well for per-packet network traffic processing. However, for stateful network protocols such as TCP, their data stream need to be reconstructed in a per-flow level to deliver a consistent content analysis. Since the flow-centric operations are naturally antiparallel and often require large memory space for buffering out-of-sequence packets, they can be problematic for GPUs, whose memory is normally limited to several gigabytes. In this work, we present a highly efficient GPU-based deep packet/flow analysis framework. The proposed design includes a purely GPU-implemented flow tracking and TCP stream reassembly. Instead of buffering and waiting for TCP packets to become in sequence, our framework process the packets in batch and uses a deterministic finite automaton (DFA) with prefix-/suffix- tree method to detect patterns across out-of-sequence packets that happen to be located in different batches. In conclusion, evaluation shows that our code can reassemble and forward tens of millions of packets per second and conduct a stateful signature-based deep packet inspection at 55 Gbit/s using an NVIDIA K40 GPU.

  12. Localization techniques in resection of deep seated cavernous angiomas - review and reevaluation of frame based stereotactic approaches.

    Science.gov (United States)

    Slotty, P J; Ewelt, C; Sarikaya-Seiwert, S; Steiger, H-J; Vesper, J; Hänggi, D

    2013-04-01

    Providing high accuracy is crucial in neurosurgery especially for resection of deep seated small cerebral pathologies such as cavernous angiomas. The goal of the present series was to reevaluate the feasibility, accuracy, efficacy and safety of frame-based, stereotactically guided resection for patients suffering from small deep-seated cavernous angiomas. Additionally a review of the literature on navigational tools in cavernoma surgery is provided comparing different navigation strategies. Ten patients with deep-seated, small intracranial, cavernous angiomas being subject to frame-based, stereotactically aided resection are included in this survey. Based on the stereotactic-fused image, set entry and target point aimed at the rim of the cavernoma were calculated. A minicraniotomy (Assets and drawbacks of the stereotactic-aided approach were evaluated, patients were analyzed for surgery-related neurological deficits and completeness of resection. Complete resection was achieved in all ten patients verified by post-surgery MRI imaging. The surgical procedure itself was only slightly aggravated by the stereotactic equipment. No adverse events such as bleedings or infections were observed in our series. Stereotactically guided, minimally invasive resection of deep seated and small cavernous angiomas is accurate and effective. The frame-based stereotactic guidance requires some additional time and effort which seems justified only for deep seated and small cavernous angiomas. Frameless neuronavigation is a common tool in cavernoma surgery and its spatial resolution is sufficient for the majority of cases.

  13. Uniform, optimal signal processing of mapped deep-sequencing data.

    Science.gov (United States)

    Kumar, Vibhor; Muratani, Masafumi; Rayan, Nirmala Arul; Kraus, Petra; Lufkin, Thomas; Ng, Huck Hui; Prabhakar, Shyam

    2013-07-01

    Despite their apparent diversity, many problems in the analysis of high-throughput sequencing data are merely special cases of two general problems, signal detection and signal estimation. Here we adapt formally optimal solutions from signal processing theory to analyze signals of DNA sequence reads mapped to a genome. We describe DFilter, a detection algorithm that identifies regulatory features in ChIP-seq, DNase-seq and FAIRE-seq data more accurately than assay-specific algorithms. We also describe EFilter, an estimation algorithm that accurately predicts mRNA levels from as few as 1-2 histone profiles (R ∼0.9). Notably, the presence of regulatory motifs in promoters correlates more with histone modifications than with mRNA levels, suggesting that histone profiles are more predictive of cis-regulatory mechanisms. We show by applying DFilter and EFilter to embryonic forebrain ChIP-seq data that regulatory protein identification and functional annotation are feasible despite tissue heterogeneity. The mathematical formalism underlying our tools facilitates integrative analysis of data from virtually any sequencing-based functional profile.

  14. Sequence and phylogenetic analyses of novel totivirus-like double-stranded RNAs from field-collected powdery mildew fungi.

    Science.gov (United States)

    Kondo, Hideki; Hisano, Sakae; Chiba, Sotaro; Maruyama, Kazuyuki; Andika, Ida Bagus; Toyoda, Kazuhiro; Fujimori, Fumihiro; Suzuki, Nobuhiro

    2016-02-02

    The identification of mycoviruses contributes greatly to understanding of the diversity and evolutionary aspects of viruses. Powdery mildew fungi are important and widely studied obligate phytopathogenic agents, but there has been no report on mycoviruses infecting these fungi. In this study, we used a deep sequencing approach to analyze the double-stranded RNA (dsRNA) segments isolated from field-collected samples of powdery mildew fungus-infected red clover plants in Japan. Database searches identified the presence of at least ten totivirus (genus Totivirus)-like sequences, termed red clover powdery mildew-associated totiviruses (RPaTVs). The majority of these sequences shared moderate amino acid sequence identity with each other (powdery mildew fungus populations infecting red clover plants in the field. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Deep Unfolding for Topic Models.

    Science.gov (United States)

    Chien, Jen-Tzung; Lee, Chao-Hsi

    2018-02-01

    Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

  16. Deep Web Search Interface Identification: A Semi-Supervised Ensemble Approach

    Directory of Open Access Journals (Sweden)

    Hong Wang

    2014-12-01

    Full Text Available To surface the Deep Web, one crucial task is to predict whether a given web page has a search interface (searchable HyperText Markup Language (HTML form or not. Previous studies have focused on supervised classification with labeled examples. However, labeled data are scarce, hard to get and requires tediousmanual work, while unlabeled HTML forms are abundant and easy to obtain. In this research, we consider the plausibility of using both labeled and unlabeled data to train better models to identify search interfaces more effectively. We present a semi-supervised co-training ensemble learning approach using both neural networks and decision trees to deal with the search interface identification problem. We show that the proposed model outperforms previous methods using only labeled data. We also show that adding unlabeled data improves the effectiveness of the proposed model.

  17. MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

    Science.gov (United States)

    Fang, Chao; Shang, Yi; Xu, Dong

    2018-05-01

    Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

  18. The Bouma Sequence and the turbidite mind set

    Science.gov (United States)

    Shanmugam, G.

    1997-11-01

    Conventionally, the Bouma Sequence [Bouma, A.H., 1962. Sedimentology of some Flysch Deposits: A Graphic Approach to Facies Interpretation. Elsevier, Amsterdam, 168 pp.], composed of T a, T b, T c, T d, and T e divisions, is interpreted to be the product of a turbidity current. However, recent core and outcrop studies show that the complete and partial Bouma sequences can also be interpreted to be deposits formed by processes other than turbidity currents, such as sandy debris flows and bottom-current reworking. Many published examples of turbidites, most of them hydrocarbon-bearing sands, in the North Sea, the Norwegian Sea, offshore Nigeria, offshore Gabon, Gulf of Mexico, and the Ouachita Mountains, are being reinterpreted by the present author as dominantly deposits of sandy debris flows and bottom-current reworking with only a minor percentage of true turbidites (i.e., deposits of turbidity currents with fluidal or Newtonian rheology in which sediment is suspended by fluid turbulence). This reinterpretation is based on detailed description of 21,000 ft (6402 m) of conventional cores and 1200 ft (365 m) of outcrop sections. The predominance of interpreted turbidites in these areas by other workers can be attributed to the following: (1) loose applications of turbidity-current concepts without regard for fluid rheology, flow state, and sediment-support mechanism that result in a category of 'turbidity currents' that includes debris flows and bottom currents; (2) field description of deep-water sands using the Bouma Sequence (an interpretive model) that invariably leads to a model-driven turbidite interpretation; (3) the prevailing turbidite mind set that subconsciously forces one to routinely interpret most deep-water sands as some kind of turbidites; (4) the use of our inability to interpret transport mechanism from the depositional record as an excuse for assuming deep-water sands as deposits of turbidity currents; (5) the flawed concept of high

  19. Sequencing genes in silico using single nucleotide polymorphisms

    Directory of Open Access Journals (Sweden)

    Zhang Xinyi

    2012-01-01

    Full Text Available Abstract Background The advent of high throughput sequencing technology has enabled the 1000 Genomes Project Pilot 3 to generate complete sequence data for more than 906 genes and 8,140 exons representing 697 subjects. The 1000 Genomes database provides a critical opportunity for further interpreting disease associations with single nucleotide polymorphisms (SNPs discovered from genetic association studies. Currently, direct sequencing of candidate genes or regions on a large number of subjects remains both cost- and time-prohibitive. Results To accelerate the translation from discovery to functional studies, we propose an in silico gene sequencing method (ISS, which predicts phased sequences of intragenic regions, using SNPs. The key underlying idea of our method is to infer diploid sequences (a pair of phased sequences/alleles at every functional locus utilizing the deep sequencing data from the 1000 Genomes Project and SNP data from the HapMap Project, and to build prediction models using flanking SNPs. Using this method, we have developed a database of prediction models for 611 known genes. Sequence prediction accuracy for these genes is 96.26% on average (ranges 79%-100%. This database of prediction models can be enhanced and scaled up to include new genes as the 1000 Genomes Project sequences additional genes on additional individuals. Applying our predictive model for the KCNJ11 gene to the Wellcome Trust Case Control Consortium (WTCCC Type 2 diabetes cohort, we demonstrate how the prediction of phased sequences inferred from GWAS SNP genotype data can be used to facilitate interpretation and identify a probable functional mechanism such as protein changes. Conclusions Prior to the general availability of routine sequencing of all subjects, the ISS method proposed here provides a time- and cost-effective approach to broadening the characterization of disease associated SNPs and regions, and facilitating the prioritization of candidate

  20. Genomic DNA Enrichment Using Sequence Capture Microarrays: a Novel Approach to Discover Sequence Nucleotide Polymorphisms (SNP) in Brassica napus L

    Science.gov (United States)

    Clarke, Wayne E.; Parkin, Isobel A.; Gajardo, Humberto A.; Gerhardt, Daniel J.; Higgins, Erin; Sidebottom, Christine; Sharpe, Andrew G.; Snowdon, Rod J.; Federico, Maria L.; Iniguez-Luy, Federico L.

    2013-01-01

    Targeted genomic selection methodologies, or sequence capture, allow for DNA enrichment and large-scale resequencing and characterization of natural genetic variation in species with complex genomes, such as rapeseed canola (Brassica napus L., AACC, 2n=38). The main goal of this project was to combine sequence capture with next generation sequencing (NGS) to discover single nucleotide polymorphisms (SNPs) in specific areas of the B. napus genome historically associated (via quantitative trait loci –QTL– analysis) to traits of agronomical and nutritional importance. A 2.1 million feature sequence capture platform was designed to interrogate DNA sequence variation across 47 specific genomic regions, representing 51.2 Mb of the Brassica A and C genomes, in ten diverse rapeseed genotypes. All ten genotypes were sequenced using the 454 Life Sciences chemistry and to assess the effect of increased sequence depth, two genotypes were also sequenced using Illumina HiSeq chemistry. As a result, 589,367 potentially useful SNPs were identified. Analysis of sequence coverage indicated a four-fold increased representation of target regions, with 57% of the filtered SNPs falling within these regions. Sixty percent of discovered SNPs corresponded to transitions while 40% were transversions. Interestingly, fifty eight percent of the SNPs were found in genic regions while 42% were found in intergenic regions. Further, a high percentage of genic SNPs was found in exons (65% and 64% for the A and C genomes, respectively). Two different genotyping assays were used to validate the discovered SNPs. Validation rates ranged from 61.5% to 84% of tested SNPs, underpinning the effectiveness of this SNP discovery approach. Most importantly, the discovered SNPs were associated with agronomically important regions of the B. napus genome generating a novel data resource for research and breeding this crop species. PMID:24312619

  1. Life Support for Deep Space and Mars

    Science.gov (United States)

    Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

    2014-01-01

    How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

  2. Deep sequencing reveals different compositions of mRNA transcribed from the F8 gene in a panel of FVIII-producing CHO cell lines

    DEFF Research Database (Denmark)

    Kaas, Christian Schrøder; Bolt, Gert; Hansen, Jens J

    2015-01-01

    orders of magnitude lower than for antibodies. In the present study we investigated CHO DXB11 cells transfected with a plasmid encoding human coagulation factor VIII. Single cell clones were isolated from the pool of transfectants and a panel of 14 clones representing a dynamic range of FVIII...... FVIII productivity. It was found that three MTX resistant, nonproducing clones had different truncations of the F8 transcripts. We find that by using deep sequencing, in contrast to microarray technology, for determining the transcriptome from CHO transfectants, we are able to accurately deduce...

  3. High throughput sequencing and proteomics to identify immunogenic proteins of a new pathogen: the dirty genome approach.

    Directory of Open Access Journals (Sweden)

    Gilbert Greub

    Full Text Available BACKGROUND: With the availability of new generation sequencing technologies, bacterial genome projects have undergone a major boost. Still, chromosome completion needs a costly and time-consuming gap closure, especially when containing highly repetitive elements. However, incomplete genome data may be sufficiently informative to derive the pursued information. For emerging pathogens, i.e. newly identified pathogens, lack of release of genome data during gap closure stage is clearly medically counterproductive. METHODS/PRINCIPAL FINDINGS: We thus investigated the feasibility of a dirty genome approach, i.e. the release of unfinished genome sequences to develop serological diagnostic tools. We showed that almost the whole genome sequence of the emerging pathogen Parachlamydia acanthamoebae was retrieved even with relatively short reads from Genome Sequencer 20 and Solexa. The bacterial proteome was analyzed to select immunogenic proteins, which were then expressed and used to elaborate the first steps of an ELISA. CONCLUSIONS/SIGNIFICANCE: This work constitutes the proof of principle for a dirty genome approach, i.e. the use of unfinished genome sequences of pathogenic bacteria, coupled with proteomics to rapidly identify new immunogenic proteins useful to develop in the future specific diagnostic tests such as ELISA, immunohistochemistry and direct antigen detection. Although applied here to an emerging pathogen, this combined dirty genome sequencing/proteomic approach may be used for any pathogen for which better diagnostics are needed. These genome sequences may also be very useful to develop DNA based diagnostic tests. All these diagnostic tools will allow further evaluations of the pathogenic potential of this obligate intracellular bacterium.

  4. Structural Approaches to Sequence Evolution Molecules, Networks, Populations

    CERN Document Server

    Bastolla, Ugo; Roman, H. Eduardo; Vendruscolo, Michele

    2007-01-01

    Structural requirements constrain the evolution of biological entities at all levels, from macromolecules to their networks, right up to populations of biological organisms. Classical models of molecular evolution, however, are focused at the level of the symbols - the biological sequence - rather than that of their resulting structure. Now recent advances in understanding the thermodynamics of macromolecules, the topological properties of gene networks, the organization and mutation capabilities of genomes, and the structure of populations make it possible to incorporate these key elements into a broader and deeply interdisciplinary view of molecular evolution. This book gives an account of such a new approach, through clear tutorial contributions by leading scientists specializing in the different fields involved.

  5. A Systematic Approach to Analyse Critical Tribological Parameters in an Industrial Case Study of Progressive Die Sequence Production

    DEFF Research Database (Denmark)

    Üstünyagiz, Esmeray; Nielsen, Chris V.; Bay, Niels

    the tribologically critical parameters in an industrial production line in which a progressive tool sequence is used. The current industrial case is based on multistage deep drawing followed by an ironing operation. Severe reduction in the ironing stage leads to high interface temperature and pressure. As a result......, subsequent lubricant film breakdown in the production line occurs. The methodology combines finite element simulations and experimental measurements to determine tribological parameters which will later be used in laboratory testing of possible tribology systems....

  6. Accelerating Deep Learning with Shrinkage and Recall

    OpenAIRE

    Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

    2016-01-01

    Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

  7. Deep Sequencing of Plant and Animal DNA Contained within Traditional Chinese Medicines Reveals Legality Issues and Health Safety Concerns

    Science.gov (United States)

    Coghlan, Megan L.; Haile, James; Houston, Jayne; Murray, Dáithí C.; White, Nicole E.; Moolhuijzen, Paula; Bellgard, Matthew I.; Bunce, Michael

    2012-01-01

    Traditional Chinese medicine (TCM) has been practiced for thousands of years, but only within the last few decades has its use become more widespread outside of Asia. Concerns continue to be raised about the efficacy, legality, and safety of many popular complementary alternative medicines, including TCMs. Ingredients of some TCMs are known to include derivatives of endangered, trade-restricted species of plants and animals, and therefore contravene the Convention on International Trade in Endangered Species (CITES) legislation. Chromatographic studies have detected the presence of heavy metals and plant toxins within some TCMs, and there are numerous cases of adverse reactions. It is in the interests of both biodiversity conservation and public safety that techniques are developed to screen medicinals like TCMs. Targeting both the p-loop region of the plastid trnL gene and the mitochondrial 16S ribosomal RNA gene, over 49,000 amplicon sequence reads were generated from 15 TCM samples presented in the form of powders, tablets, capsules, bile flakes, and herbal teas. Here we show that second-generation, high-throughput sequencing (HTS) of DNA represents an effective means to genetically audit organic ingredients within complex TCMs. Comparison of DNA sequence data to reference databases revealed the presence of 68 different plant families and included genera, such as Ephedra and Asarum, that are potentially toxic. Similarly, animal families were identified that include genera that are classified as vulnerable, endangered, or critically endangered, including Asiatic black bear (Ursus thibetanus) and Saiga antelope (Saiga tatarica). Bovidae, Cervidae, and Bufonidae DNA were also detected in many of the TCM samples and were rarely declared on the product packaging. This study demonstrates that deep sequencing via HTS is an efficient and cost-effective way to audit highly processed TCM products and will assist in monitoring their legality and safety especially when

  8. A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

    Science.gov (United States)

    Deng, Lei; Fan, Chao; Zeng, Zhiwen

    2017-12-28

    Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

  9. An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences.

    Science.gov (United States)

    Ye, Kai; Kosters, Walter A; Ijzerman, Adriaan P

    2007-03-15

    Pattern discovery in protein sequences is often based on multiple sequence alignments (MSA). The procedure can be computationally intensive and often requires manual adjustment, which may be particularly difficult for a set of deviating sequences. In contrast, two algorithms, PRATT2 (http//www.ebi.ac.uk/pratt/) and TEIRESIAS (http://cbcsrv.watson.ibm.com/) are used to directly identify frequent patterns from unaligned biological sequences without an attempt to align them. Here we propose a new algorithm with more efficiency and more functionality than both PRATT2 and TEIRESIAS, and discuss some of its applications to G protein-coupled receptors, a protein family of important drug targets. In this study, we designed and implemented six algorithms to mine three different pattern types from either one or two datasets using a pattern growth approach. We compared our approach to PRATT2 and TEIRESIAS in efficiency, completeness and the diversity of pattern types. Compared to PRATT2, our approach is faster, capable of processing large datasets and able to identify the so-called type III patterns. Our approach is comparable to TEIRESIAS in the discovery of the so-called type I patterns but has additional functionality such as mining the so-called type II and type III patterns and finding discriminating patterns between two datasets. The source code for pattern growth algorithms and their pseudo-code are available at http://www.liacs.nl/home/kosters/pg/.

  10. Establishing and communicating confidence in the safety of deep geologic disposal. Approaches and arguments

    International Nuclear Information System (INIS)

    2002-01-01

    Confidence among both technical experts and the public in the safety of deep geologic repositories for radioactive waste is a key element in the successful development of the repositories. This report presents the approaches and arguments that are currently used in OECD countries to establish and communicate confidence in their safety. It evaluates the state of the art for obtaining, presenting and demonstrating confidence in long-term safety, and makes recommendations on future directions and initiatives to be taken for improving confidence. (author)

  11. Two-Stage Approach to Image Classification by Deep Neural Networks

    Science.gov (United States)

    Ososkov, Gennady; Goncharov, Pavel

    2018-02-01

    The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations.

  12. Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

    Directory of Open Access Journals (Sweden)

    Nouar AlDahoul

    2018-01-01

    Full Text Available Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN, pretrained CNN feature extractor, and hierarchical extreme learning machine for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running. Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM. H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU, H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU.

  13. Evolutionary process of deep-sea bathymodiolus mussels.

    Directory of Open Access Journals (Sweden)

    Jun-Ichi Miyazaki

    Full Text Available BACKGROUND: Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. METHODOLOGY/PRINCIPAL FINDING: We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI and NADH dehydrogenase subunit 4 (ND4 genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. CONCLUSIONS/SIGNIFICANCE: The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of

  14. A bioinformatics approach for identifying transgene insertion sites using whole genome sequencing data.

    Science.gov (United States)

    Park, Doori; Park, Su-Hyun; Ban, Yong Wook; Kim, Youn Shic; Park, Kyoung-Cheul; Kim, Nam-Soo; Kim, Ju-Kon; Choi, Ik-Young

    2017-08-15

    Genetically modified crops (GM crops) have been developed to improve the agricultural traits of modern crop cultivars. Safety assessments of GM crops are of paramount importance in research at developmental stages and before releasing transgenic plants into the marketplace. Sequencing technology is developing rapidly, with higher output and labor efficiencies, and will eventually replace existing methods for the molecular characterization of genetically modified organisms. To detect the transgenic insertion locations in the three GM rice gnomes, Illumina sequencing reads are mapped and classified to the rice genome and plasmid sequence. The both mapped reads are classified to characterize the junction site between plant and transgene sequence by sequence alignment. Herein, we present a next generation sequencing (NGS)-based molecular characterization method, using transgenic rice plants SNU-Bt9-5, SNU-Bt9-30, and SNU-Bt9-109. Specifically, using bioinformatics tools, we detected the precise insertion locations and copy numbers of transfer DNA, genetic rearrangements, and the absence of backbone sequences, which were equivalent to results obtained from Southern blot analyses. NGS methods have been suggested as an effective means of characterizing and detecting transgenic insertion locations in genomes. Our results demonstrate the use of a combination of NGS technology and bioinformatics approaches that offers cost- and time-effective methods for assessing the safety of transgenic plants.

  15. Deep sequencing of the viral phoH gene reveals temporal variation, depth-specific composition, and persistent dominance of the same viral phoH genes in the Sargasso Sea

    Directory of Open Access Journals (Sweden)

    Dawn B. Goldsmith

    2015-06-01

    Full Text Available Deep sequencing of the viral phoH gene, a host-derived auxiliary metabolic gene, was used to track viral diversity throughout the water column at the Bermuda Atlantic Time-series Study (BATS site in the summer (September and winter (March of three years. Viral phoH sequences reveal differences in the viral communities throughout a depth profile and between seasons in the same year. Variation was also detected between the same seasons in subsequent years, though these differences were not as great as the summer/winter distinctions. Over 3,600 phoH operational taxonomic units (OTUs; 97% sequence identity were identified. Despite high richness, most phoH sequences belong to a few large, common OTUs whereas the majority of the OTUs are small and rare. While many OTUs make sporadic appearances at just a few times or depths, a small number of OTUs dominate the community throughout the seasons, depths, and years.

  16. Congruent Deep Relationships in the Grape Family (Vitaceae) Based on Sequences of Chloroplast Genomes and Mitochondrial Genes via Genome Skimming.

    Science.gov (United States)

    Zhang, Ning; Wen, Jun; Zimmer, Elizabeth A

    2015-01-01

    Vitaceae is well-known for having one of the most economically important fruits, i.e., the grape (Vitis vinifera). The deep phylogeny of the grape family was not resolved until a recent phylogenomic analysis of 417 nuclear genes from transcriptome data. However, it has been reported extensively that topologies based on nuclear and organellar genes may be incongruent due to differences in their evolutionary histories. Therefore, it is important to reconstruct a backbone phylogeny of the grape family using plastomes and mitochondrial genes. In this study,next-generation sequencing data sets of 27 species were obtained using genome skimming with total DNAs from silica-gel preserved tissue samples on an Illumina NextSeq 500 instrument [corrected]. Plastomes were assembled using the combination of de novo and reference genome (of V. vinifera) methods. Sixteen mitochondrial genes were also obtained via genome skimming using the reference genome of V. vinifera. Extensive phylogenetic analyses were performed using maximum likelihood and Bayesian methods. The topology based on either plastome data or mitochondrial genes is congruent with the one using hundreds of nuclear genes, indicating that the grape family did not exhibit significant reticulation at the deep level. The results showcase the power of genome skimming in capturing extensive phylogenetic data: especially from chloroplast and mitochondrial DNAs.

  17. Congruent Deep Relationships in the Grape Family (Vitaceae Based on Sequences of Chloroplast Genomes and Mitochondrial Genes via Genome Skimming.

    Directory of Open Access Journals (Sweden)

    Ning Zhang

    Full Text Available Vitaceae is well-known for having one of the most economically important fruits, i.e., the grape (Vitis vinifera. The deep phylogeny of the grape family was not resolved until a recent phylogenomic analysis of 417 nuclear genes from transcriptome data. However, it has been reported extensively that topologies based on nuclear and organellar genes may be incongruent due to differences in their evolutionary histories. Therefore, it is important to reconstruct a backbone phylogeny of the grape family using plastomes and mitochondrial genes. In this study,next-generation sequencing data sets of 27 species were obtained using genome skimming with total DNAs from silica-gel preserved tissue samples on an Illumina NextSeq 500 instrument [corrected]. Plastomes were assembled using the combination of de novo and reference genome (of V. vinifera methods. Sixteen mitochondrial genes were also obtained via genome skimming using the reference genome of V. vinifera. Extensive phylogenetic analyses were performed using maximum likelihood and Bayesian methods. The topology based on either plastome data or mitochondrial genes is congruent with the one using hundreds of nuclear genes, indicating that the grape family did not exhibit significant reticulation at the deep level. The results showcase the power of genome skimming in capturing extensive phylogenetic data: especially from chloroplast and mitochondrial DNAs.

  18. Adaptive metric learning with deep neural networks for video-based facial expression recognition

    Science.gov (United States)

    Liu, Xiaofeng; Ge, Yubin; Yang, Chao; Jia, Ping

    2018-01-01

    Video-based facial expression recognition has become increasingly important for plenty of applications in the real world. Despite that numerous efforts have been made for the single sequence, how to balance the complex distribution of intra- and interclass variations well between sequences has remained a great difficulty in this area. We propose the adaptive (N+M)-tuplet clusters loss function and optimize it with the softmax loss simultaneously in the training phrase. The variations introduced by personal attributes are alleviated using the similarity measurements of multiple samples in the feature space with many fewer comparison times as conventional deep metric learning approaches, which enables the metric calculations for large data applications (e.g., videos). Both the spatial and temporal relations are well explored by a unified framework that consists of an Inception-ResNet network with long short term memory and the two fully connected layer branches structure. Our proposed method has been evaluated with three well-known databases, and the experimental results show that our method outperforms many state-of-the-art approaches.

  19. Assessing the Diversity of Rodent-Borne Viruses: Exploring of High-Throughput Sequencing and Classical Amplification/Sequencing Approaches.

    Science.gov (United States)

    Drewes, Stephan; Straková, Petra; Drexler, Jan F; Jacob, Jens; Ulrich, Rainer G

    2017-01-01

    Rodents are distributed throughout the world and interact with humans in many ways. They provide vital ecosystem services, some species are useful models in biomedical research and some are held as pet animals. However, many rodent species can have adverse effects such as damage to crops and stored produce, and they are of health concern because of the transmission of pathogens to humans and livestock. The first rodent viruses were discovered by isolation approaches and resulted in break-through knowledge in immunology, molecular and cell biology, and cancer research. In addition to rodent-specific viruses, rodent-borne viruses are causing a large number of zoonotic diseases. Most prominent examples are reemerging outbreaks of human hemorrhagic fever disease cases caused by arena- and hantaviruses. In addition, rodents are reservoirs for vector-borne pathogens, such as tick-borne encephalitis virus and Borrelia spp., and may carry human pathogenic agents, but likely are not involved in their transmission to human. In our days, next-generation sequencing or high-throughput sequencing (HTS) is revolutionizing the speed of the discovery of novel viruses, but other molecular approaches, such as generic RT-PCR/PCR and rolling circle amplification techniques, contribute significantly to the rapidly ongoing process. However, the current knowledge still represents only the tip of the iceberg, when comparing the known human viruses to those known for rodents, the mammalian taxon with the largest species number. The diagnostic potential of HTS-based metagenomic approaches is illustrated by their use in the discovery and complete genome determination of novel borna- and adenoviruses as causative disease agents in squirrels. In conclusion, HTS, in combination with conventional RT-PCR/PCR-based approaches, resulted in a drastically increased knowledge of the diversity of rodent viruses. Future improvements of the used workflows, including bioinformatics analysis, will further

  20. MicroRNAs in Amoebozoa: deep sequencing of the small RNA population in the social amoeba Dictyostelium discoideum reveals developmentally regulated microRNAs.

    Science.gov (United States)

    Avesson, Lotta; Reimegård, Johan; Wagner, E Gerhart H; Söderbom, Fredrik

    2012-10-01

    The RNA interference machinery has served as a guardian of eukaryotic genomes since the divergence from prokaryotes. Although the basic components have a shared origin, silencing pathways directed by small RNAs have evolved in diverse directions in different eukaryotic lineages. Micro (mi)RNAs regulate protein-coding genes and play vital roles in plants and animals, but less is known about their functions in other organisms. Here, we report, for the first time, deep sequencing of small RNAs from the social amoeba Dictyostelium discoideum. RNA from growing single-cell amoebae as well as from two multicellular developmental stages was sequenced. Computational analyses combined with experimental data reveal the expression of miRNAs, several of them exhibiting distinct expression patterns during development. To our knowledge, this is the first report of miRNAs in the Amoebozoa supergroup. We also show that overexpressed miRNA precursors generate miRNAs and, in most cases, miRNA* sequences, whose biogenesis is dependent on the Dicer-like protein DrnB, further supporting the presence of miRNAs in D. discoideum. In addition, we find miRNAs processed from hairpin structures originating from an intron as well as from a class of repetitive elements. We believe that these repetitive elements are sources for newly evolved miRNAs.

  1. De novo transcriptome assembly and positive selection analysis of an individual deep-sea fish.

    Science.gov (United States)

    Lan, Yi; Sun, Jin; Xu, Ting; Chen, Chong; Tian, Renmao; Qiu, Jian-Wen; Qian, Pei-Yuan

    2018-05-24

    High hydrostatic pressure and low temperatures make the deep sea a harsh environment for life forms. Actin organization and microtubules assembly, which are essential for intracellular transport and cell motility, can be disrupted by high hydrostatic pressure. High hydrostatic pressure can also damage DNA. Nucleic acids exposed to low temperatures can form secondary structures that hinder genetic information processing. To study how deep-sea creatures adapt to such a hostile environment, one of the most straightforward ways is to sequence and compare their genes with those of their shallow-water relatives. We captured an individual of the fish species Aldrovandia affinis, which is a typical deep-sea inhabitant, from the Okinawa Trough at a depth of 1550 m using a remotely operated vehicle (ROV). We sequenced its transcriptome and analyzed its molecular adaptation. We obtained 27,633 protein coding sequences using an Illumina platform and compared them with those of several shallow-water fish species. Analysis of 4918 single-copy orthologs identified 138 positively selected genes in A. affinis, including genes involved in microtubule regulation. Particularly, functional domains related to cold shock as well as DNA repair are exposed to positive selection pressure in both deep-sea fish and hadal amphipod. Overall, we have identified a set of positively selected genes related to cytoskeleton structures, DNA repair and genetic information processing, which shed light on molecular adaptation to the deep sea. These results suggest that amino acid substitutions of these positively selected genes may contribute crucially to the adaptation of deep-sea animals. Additionally, we provide a high-quality transcriptome of a deep-sea fish for future deep-sea studies.

  2. Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach.

    Science.gov (United States)

    Shahnazian, Danesh; Holroyd, Clay B

    2018-02-01

    Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.

  3. Two-Stage Approach to Image Classification by Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Ososkov Gennady

    2018-01-01

    Full Text Available The paper demonstrates the advantages of the deep learning networks over the ordinary neural networks on their comparative applications to image classifying. An autoassociative neural network is used as a standalone autoencoder for prior extraction of the most informative features of the input data for neural networks to be compared further as classifiers. The main efforts to deal with deep learning networks are spent for a quite painstaking work of optimizing the structures of those networks and their components, as activation functions, weights, as well as the procedures of minimizing their loss function to improve their performances and speed up their learning time. It is also shown that the deep autoencoders develop the remarkable ability for denoising images after being specially trained. Convolutional Neural Networks are also used to solve a quite actual problem of protein genetics on the example of the durum wheat classification. Results of our comparative study demonstrate the undoubted advantage of the deep networks, as well as the denoising power of the autoencoders. In our work we use both GPU and cloud services to speed up the calculations.

  4. Deep learning in bioinformatics.

    Science.gov (United States)

    Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

    2017-09-01

    In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  5. Deep sequencing-based identification of small regulatory RNAs in Synechocystis sp. PCC 6803.

    Directory of Open Access Journals (Sweden)

    Wen Xu

    Full Text Available Synechocystis sp. PCC 6803 is a genetically tractable model organism for photosynthesis research. The genome of Synechocystis sp. PCC 6803 consists of a circular chromosome and seven plasmids. The importance of small regulatory RNAs (sRNAs as mediators of a number of cellular processes in bacteria has begun to be recognized. However, little is known regarding sRNAs in Synechocystis sp. PCC 6803. To provide a comprehensive overview of sRNAs in this model organism, the sRNAs of Synechocystis sp. PCC 6803 were analyzed using deep sequencing, and 7,951,189 reads were obtained. High quality mapping reads (6,127,890 were mapped onto the genome and assembled into 16,192 transcribed regions (clusters based on read overlap. A total number of 5211 putative sRNAs were revealed from the genome and the 4 megaplasmids, and 27 of these molecules, including four from plasmids, were confirmed by RT-PCR. In addition, possible target genes regulated by all of the putative sRNAs identified in this study were predicted by IntaRNA and analyzed for functional categorization and biological pathways, which provided evidence that sRNAs are indeed involved in many different metabolic pathways, including basic metabolic pathways, such as glycolysis/gluconeogenesis, the citrate cycle, fatty acid metabolism and adaptations to environmentally stress-induced changes. The information from this study provides a valuable reservoir for understanding the sRNA-mediated regulation of the complex physiology and metabolic processes of cyanobacteria.

  6. Deep Sequence Analysis of AgoshRNA Processing Reveals 3' A Addition and Trimming.

    Science.gov (United States)

    Harwig, Alex; Herrera-Carrillo, Elena; Jongejan, Aldo; van Kampen, Antonius Hubertus; Berkhout, Ben

    2015-07-14

    The RNA interference (RNAi) pathway, in which microprocessor and Dicer collaborate to process microRNAs (miRNA), was recently expanded by the description of alternative processing routes. In one of these noncanonical pathways, Dicer action is replaced by the Argonaute2 (Ago2) slicer function. It was recently shown that the stem-length of precursor-miRNA or short hairpin RNA (shRNA) molecules is a major determinant for Dicer versus Ago2 processing. Here we present the results of a deep sequence study on the processing of shRNAs with different stem length and a top G·U wobble base pair (bp). This analysis revealed some unexpected properties of these so-called AgoshRNA molecules that are processed by Ago2 instead of Dicer. First, we confirmed the gradual shift from Dicer to Ago2 processing upon shortening of the hairpin length. Second, hairpins with a stem larger than 19 base pair are inefficiently cleaved by Ago2 and we noticed a shift in the cleavage site. Third, the introduction of a top G·U bp in a regular shRNA can promote Ago2-cleavage, which coincides with a loss of Ago2-loading of the Dicer-cleaved 3' strand. Fourth, the Ago2-processed AgoshRNAs acquire a short 3' tail of 1-3 A-nucleotides (nt) and we present evidence that this product is subsequently trimmed by the poly(A)-specific ribonuclease (PARN).

  7. MicroRNA discovery and analysis of pinewood nematode Bursaphelenchus xylophilus by deep sequencing.

    Directory of Open Access Journals (Sweden)

    Qi-Xing Huang

    Full Text Available BACKGROUND: MicroRNAs (miRNAs are considered to be very important in regulating the growth, development, behavior and stress response in animals and plants in post-transcriptional gene regulation. Pinewood nematode, Bursaphelenchus xylophilus, is an important invasive plant parasitic nematode in Asia. To have a comprehensive knowledge about miRNAs of the nematode is necessary for further in-depth study on roles of miRNAs in the ecological adaptation of the invasive species. METHODS AND FINDINGS: Five small RNA libraries were constructed and sequenced by Illumina/Solexa deep-sequencing technology. A total of 810 miRNA candidates (49 conserved and 761 novel were predicted by a computational pipeline, of which 57 miRNAs (20 conserved and 37 novel encoded by 53 miRNA precursors were identified by experimental methods. Ten novel miRNAs were considered to be species-specific miRNAs of B. xylophilus. Comparison of expression profiles of miRNAs in the five small RNA libraries showed that many miRNAs exhibited obviously different expression levels in the third-stage dispersal juvenile and at a cold-stressed status. Most of the miRNAs exhibited obviously down-regulated expression in the dispersal stage. But differences among the three geographic libraries were not prominent. A total of 979 genes were predicted to be targets of these authentic miRNAs. Among them, seven heat shock protein genes were targeted by 14 miRNAs, and six FMRFamide-like neuropeptides genes were targeted by 17 miRNAs. A real-time quantitative polymerase chain reaction was used to quantify the mRNA expression levels of target genes. CONCLUSIONS: Basing on the fact that a negative correlation existed between the expression profiles of miRNAs and the mRNA expression profiles of their target genes (hsp, flp by comparing those of the nematodes at a cold stressed status and a normal status, we suggested that miRNAs might participate in ecological adaptation and behavior regulation of the

  8. Reprint of "Sequence and phylogenetic analyses of novel totivirus-like double-stranded RNAs from field-collected powdery mildew fungi".

    Science.gov (United States)

    Kondo, Hideki; Hisano, Sakae; Chiba, Sotaro; Maruyama, Kazuyuki; Andika, Ida Bagus; Toyoda, Kazuhiro; Fujimori, Fumihiro; Suzuki, Nobuhiro

    2016-07-02

    The identification of mycoviruses contributes greatly to understanding of the diversity and evolutionary aspects of viruses. Powdery mildew fungi are important and widely studied obligate phytopathogenic agents, but there has been no report on mycoviruses infecting these fungi. In this study, we used a deep sequencing approach to analyze the double-stranded RNA (dsRNA) segments isolated from field-collected samples of powdery mildew fungus-infected red clover plants in Japan. Database searches identified the presence of at least ten totivirus (genus Totivirus)-like sequences, termed red clover powdery mildew-associated totiviruses (RPaTVs). The majority of these sequences shared moderate amino acid sequence identity with each other (powdery mildew fungus populations infecting red clover plants in the field. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

    Science.gov (United States)

    Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

    2017-03-01

    Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

  10. A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.

    Science.gov (United States)

    Liang, Liang; Liu, Minliang; Sun, Wei

    2017-11-01

    Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estimate collagenous tissue elastic properties directly from microscopy images using Machine Learning (ML) techniques. Glutaraldehyde-treated bovine pericardium (GLBP) tissue, widely used in the fabrication of bioprosthetic heart valves and vascular patches, was chosen to develop a representative application. A Deep Learning model was designed and trained to process second harmonic generation (SHG) images of collagen networks in GLBP tissue samples, and directly predict the tissue elastic mechanical properties. The trained model is capable of identifying the overall tissue stiffness with a classification accuracy of 84%, and predicting the nonlinear anisotropic stress-strain curves with average regression errors of 0.021 and 0.031. Thus, this study demonstrates the feasibility and great potential of using the Deep Learning approach for fast and noninvasive assessment of collagenous tissue elastic properties from microstructural images. In this study, we developed, to our best knowledge, the first Deep Learning-based approach to estimate the elastic properties of collagenous tissues directly from noninvasive second harmonic generation images. The success of this study holds promise for the use of Machine Learning techniques to noninvasively and efficiently estimate the mechanical properties of many structure-based biological materials, and it also enables many potential applications such as serving as a quality control tool to select tissue for the manufacturing of medical devices (e.g. bioprosthetic heart valves). Copyright © 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

  11. Deep Complementary Bottleneck Features for Visual Speech Recognition

    NARCIS (Netherlands)

    Petridis, Stavros; Pantic, Maja

    Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

  12. Micropathogen Community Analysis in Hyalomma rufipes via High-Throughput Sequencing of Small RNAs

    Science.gov (United States)

    Luo, Jin; Liu, Min-Xuan; Ren, Qiao-Yun; Chen, Ze; Tian, Zhan-Cheng; Hao, Jia-Wei; Wu, Feng; Liu, Xiao-Cui; Luo, Jian-Xun; Yin, Hong; Wang, Hui; Liu, Guang-Yuan

    2017-01-01

    Ticks are important vectors in the transmission of a broad range of micropathogens to vertebrates, including humans. Because of the role of ticks in disease transmission, identifying and characterizing the micropathogen profiles of tick populations have become increasingly important. The objective of this study was to survey the micropathogens of Hyalomma rufipes ticks. Illumina HiSeq2000 technology was utilized to perform deep sequencing of small RNAs (sRNAs) extracted from field-collected H. rufipes ticks in Gansu Province, China. The resultant sRNA library data revealed that the surveyed tick populations produced reads that were homologous to St. Croix River Virus (SCRV) sequences. We also observed many reads that were homologous to microbial and/or pathogenic isolates, including bacteria, protozoa, and fungi. As part of this analysis, a phylogenetic tree was constructed to display the relationships among the homologous sequences that were identified. The study offered a unique opportunity to gain insight into the micropathogens of H. rufipes ticks. The effective control of arthropod vectors in the future will require knowledge of the micropathogen composition of vectors harboring infectious agents. Understanding the ecological factors that regulate vector propagation in association with the prevalence and persistence of micropathogen lineages is also imperative. These interactions may affect the evolution of micropathogen lineages, especially if the micropathogens rely on the vector or host for dispersal. The sRNA deep-sequencing approach used in this analysis provides an intuitive method to survey micropathogen prevalence in ticks and other vector species. PMID:28861401

  13. A safe transoral surgical approach to parapharyngeal tumor arising from deep lobe of parotid gland

    Directory of Open Access Journals (Sweden)

    Manuele Casale

    2016-12-01

    Full Text Available The management of parapharyngeal tumor is surgical, but the approach remains a challenge. Attention should be paid to avoidance intra-operative bleeding or cranial nerves damage. We report a case of a 67-year-old male complaining of left-ear fullness. A submucosal mass arising from the lateral wall of oropharynx on the left side was observed. Magnetic resonance imaging detected a mass arising from the parotid gland, in particular from the deep lobe, and a fine needle biopsy was compatible with “Warthin tumor.” We performed a mini-invasive transoral approach under magnification, previous isolation of homolateral vessels. The decision on which surgical approach to be used is determined by site, size vascularity, and histology of the tumor. A literature review of the main surgical approaches was performed. We performed a combined transoral dissection under magnification with cervicotomic exposure of the neck vascular bundle allowing to dissect the tumor and manage any intra-operative complications.

  14. Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus.

    Science.gov (United States)

    Zhang, Yan; An, Lin; Xu, Jie; Zhang, Bo; Zheng, W Jim; Hu, Ming; Tang, Jijun; Yue, Feng

    2018-02-21

    Although Hi-C technology is one of the most popular tools for studying 3D genome organization, due to sequencing cost, the resolution of most Hi-C datasets are coarse and cannot be used to link distal regulatory elements to their target genes. Here we develop HiCPlus, a computational approach based on deep convolutional neural network, to infer high-resolution Hi-C interaction matrices from low-resolution Hi-C data. We demonstrate that HiCPlus can impute interaction matrices highly similar to the original ones, while only using 1/16 of the original sequencing reads. We show that the models learned from one cell type can be applied to make predictions in other cell or tissue types. Our work not only provides a computational framework to enhance Hi-C data resolution but also reveals features underlying the formation of 3D chromatin interactions.

  15. Deep sequencing of the Trypanosoma cruzi GP63 surface proteases reveals diversity and diversifying selection among chronic and congenital Chagas disease patients.

    Science.gov (United States)

    Llewellyn, Martin S; Messenger, Louisa A; Luquetti, Alejandro O; Garcia, Lineth; Torrico, Faustino; Tavares, Suelene B N; Cheaib, Bachar; Derome, Nicolas; Delepine, Marc; Baulard, Céline; Deleuze, Jean-Francois; Sauer, Sascha; Miles, Michael A

    2015-04-01

    Chagas disease results from infection with the diploid protozoan parasite Trypanosoma cruzi. T. cruzi is highly genetically diverse, and multiclonal infections in individual hosts are common, but little studied. In this study, we explore T. cruzi infection multiclonality in the context of age, sex and clinical profile among a cohort of chronic patients, as well as paired congenital cases from Cochabamba, Bolivia and Goias, Brazil using amplicon deep sequencing technology. A 450bp fragment of the trypomastigote TcGP63I surface protease gene was amplified and sequenced across 70 chronic and 22 congenital cases on the Illumina MiSeq platform. In addition, a second, mitochondrial target--ND5--was sequenced across the same cohort of cases. Several million reads were generated, and sequencing read depths were normalized within patient cohorts (Goias chronic, n = 43, Goias congenital n = 2, Bolivia chronic, n = 27; Bolivia congenital, n = 20), Among chronic cases, analyses of variance indicated no clear correlation between intra-host sequence diversity and age, sex or symptoms, while principal coordinate analyses showed no clustering by symptoms between patients. Between congenital pairs, we found evidence for the transmission of multiple sequence types from mother to infant, as well as widespread instances of novel genotypes in infants. Finally, non-synonymous to synonymous (dn:ds) nucleotide substitution ratios among sequences of TcGP63Ia and TcGP63Ib subfamilies within each cohort provided powerful evidence of strong diversifying selection at this locus. Our results shed light on the diversity of parasite DTUs within each patient, as well as the extent to which parasite strains pass between mother and foetus in congenital cases. Although we were unable to find any evidence that parasite diversity accumulates with age in our study cohorts, putative diversifying selection within members of the TcGP63I gene family suggests a link between genetic diversity within this gene

  16. Deep sequencing of ESTs from nacreous and prismatic layer producing tissues and a screen for novel shell formation-related genes in the pearl oyster.

    Directory of Open Access Journals (Sweden)

    Shigeharu Kinoshita

    Full Text Available BACKGROUND: Despite its economic importance, we have a limited understanding of the molecular mechanisms underlying shell formation in pearl oysters, wherein the calcium carbonate crystals, nacre and prism, are formed in a highly controlled manner. We constructed comprehensive expressed gene profiles in the shell-forming tissues of the pearl oyster Pinctada fucata and identified novel shell formation-related genes candidates. PRINCIPAL FINDINGS: We employed the GS FLX 454 system and constructed transcriptome data sets from pallial mantle and pearl sac, which form the nacreous layer, and from the mantle edge, which forms the prismatic layer in P. fucata. We sequenced 260477 reads and obtained 29682 unique sequences. We also screened novel nacreous and prismatic gene candidates by a combined analysis of sequence and expression data sets, and identified various genes encoding lectin, protease, protease inhibitors, lysine-rich matrix protein, and secreting calcium-binding proteins. We also examined the expression of known nacreous and prismatic genes in our EST library and identified novel isoforms with tissue-specific expressions. CONCLUSIONS: We constructed EST data sets from the nacre- and prism-producing tissues in P. fucata and found 29682 unique sequences containing novel gene candidates for nacreous and prismatic layer formation. This is the first report of deep sequencing of ESTs in the shell-forming tissues of P. fucata and our data provide a powerful tool for a comprehensive understanding of the molecular mechanisms of molluscan biomineralization.

  17. A deep belief network approach using VDRAS data for nowcasting

    Science.gov (United States)

    Han, Lei; Dai, Jie; Zhang, Wei; Zhang, Changjiang; Feng, Hanlei

    2018-04-01

    Nowcasting or very short-term forecasting convective storms is still a challenging problem due to the high nonlinearity and insufficient observation of convective weather. As the understanding of the physical mechanism of convective weather is also insufficient, the numerical weather model cannot predict convective storms well. Machine learning approaches provide a potential way to nowcast convective storms using various meteorological data. In this study, a deep belief network (DBN) is proposed to nowcast convective storms using the real-time re-analysis meteorological data. The nowcasting problem is formulated as a classification problem. The 3D meteorological variables are fed directly to the DBN with dimension of input layer 6*6*80. Three hidden layers are used in the DBN and the dimension of output layer is two. A box-moving method is presented to provide the input features containing the temporal and spatial information. The results show that the DNB can generate reasonable prediction results of the movement and growth of convective storms.

  18. A deep learning-based multi-model ensemble method for cancer prediction.

    Science.gov (United States)

    Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

    2018-01-01

    Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Deep Illumina-based shotgun sequencing reveals dietary effects on the structure and function of the fecal microbiome of growing kittens.

    Directory of Open Access Journals (Sweden)

    Oliver Deusch

    Full Text Available Previously, we demonstrated that dietary protein:carbohydrate ratio dramatically affects the fecal microbial taxonomic structure of kittens using targeted 16S gene sequencing. The present study, using the same fecal samples, applied deep Illumina shotgun sequencing to identify the diet-associated functional potential and analyze taxonomic changes of the feline fecal microbiome.Fecal samples from kittens fed one of two diets differing in protein and carbohydrate content (high-protein, low-carbohydrate, HPLC; and moderate-protein, moderate-carbohydrate, MPMC were collected at 8, 12 and 16 weeks of age (n = 6 per group. A total of 345.3 gigabases of sequence were generated from 36 samples, with 99.75% of annotated sequences identified as bacterial. At the genus level, 26% and 39% of reads were annotated for HPLC- and MPMC-fed kittens, with HPLC-fed cats showing greater species richness and microbial diversity. Two phyla, ten families and fifteen genera were responsible for more than 80% of the sequences at each taxonomic level for both diet groups, consistent with the previous taxonomic study. Significantly different abundances between diet groups were observed for 324 genera (56% of all genera identified demonstrating widespread diet-induced changes in microbial taxonomic structure. Diversity was not affected over time. Functional analysis identified 2,013 putative enzyme function groups were different (p<0.000007 between the two dietary groups and were associated to 194 pathways, which formed five discrete clusters based on average relative abundance. Of those, ten contained more (p<0.022 enzyme functions with significant diet effects than expected by chance. Six pathways were related to amino acid biosynthesis and metabolism linking changes in dietary protein with functional differences of the gut microbiome.These data indicate that feline feces-derived microbiomes have large structural and functional differences relating to the dietary

  20. New Approaches to Attenuated Hepatitis a Vaccine Development: Cloning and Sequencing of Cell-Culture Adapted Viral cDNA.

    Science.gov (United States)

    1987-10-13

    after multiple passages in vivo and in vitro. J. Gen. Virol. 67, 1741- 1744. Sabin , A.B. (1985). Oral poliovirus vaccine : history of its development...IN (N NEW APPROACHES TO ATTENUATED HEPATITIS A VACCINE DEVELOPMENT: Q) CLONING AND SEQUENCING OF CELL-CULTURE ADAPTED VIRAL cDNA I ANNUAL REPORT...6ll02Bsl0 A 055 11. TITLE (Include Security Classification) New Approaches to Attenuated Hepatitis A Vaccine Development: Cloning and Sequencing of Cell

  1. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity.

    Science.gov (United States)

    Kim, Hui Kwon; Min, Seonwoo; Song, Myungjae; Jung, Soobin; Choi, Jae Woo; Kim, Younggwang; Lee, Sangeun; Yoon, Sungroh; Kim, Hyongbum Henry

    2018-03-01

    We present two algorithms to predict the activity of AsCpf1 guide RNAs. Indel frequencies for 15,000 target sequences were used in a deep-learning framework based on a convolutional neural network to train Seq-deepCpf1. We then incorporated chromatin accessibility information to create the better-performing DeepCpf1 algorithm for cell lines for which such information is available and show that both algorithms outperform previous machine learning algorithms on our own and published data sets.

  2. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

    KAUST Repository

    Umarov, Ramzan

    2017-02-03

    Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.

  3. Stratification-Based Outlier Detection over the Deep Web.

    Science.gov (United States)

    Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

    2016-01-01

    For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

  4. Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty

    Directory of Open Access Journals (Sweden)

    Jan H. Kwakkel

    2013-03-01

    Full Text Available There is increasing interest in long-term plans that can adapt to changing situations under conditions of deep uncertainty. We argue that a sustainable plan should not only achieve economic, environmental, and social objectives, but should be robust and able to be adapted over time to (unforeseen future conditions. Large numbers of papers dealing with robustness and adaptive plans have begun to appear, but the literature is fragmented. The papers appear in disparate journals, and deal with a wide variety of policy domains. This paper (1 describes and compares a family of related conceptual approaches to designing a sustainable plan, and (2 describes several computational tools supporting these approaches. The conceptual approaches all have their roots in an approach to long-term planning called Assumption-Based Planning. Guiding principles for the design of a sustainable adaptive plan are: explore a wide variety of relevant uncertainties, connect short-term targets to long-term goals over time, commit to short-term actions while keeping options open, and continuously monitor the world and take actions if necessary. A key computational tool across the conceptual approaches is a fast, simple (policy analysis model that is used to make large numbers of runs, in order to explore the full range of uncertainties and to identify situations in which the plan would fail.

  5. Geotechnical properties of deep-ocean sediments: a critical state approach

    International Nuclear Information System (INIS)

    Ho, E.W.L.

    1988-11-01

    The possible disposal of high-level radioactive waste using the sediments of the deep-ocean floor as repositories has initiated research to establish an understanding of the fundamental behaviour of deep-ocean sediments. The work described in this thesis consisted of a series of triaxial stress path tests using microcomputer controlled hydraulic triaxial cells to investigate the strength and stress-strain behaviour for mainly anisotropically (K o ) consolidated 'undisturbed' (tubed) and reconstituted specimens of deep-ocean sediments taken from two study areas in the North Atlantic Ocean. The test results have been analysed within the framework of critical state soil mechanics to investigate sediment characteristics such as the state boundary surface, drained and undrained strength and stress-strain behaviour. While marked anisotropic behaviour is found in a number of respects, the results indicate that analysis in a critical state framework is as valid as for terrestrial sediments. Differences in behaviour between tubed and reconstituted specimens have been observed and the effect of the presence of carbonate has been investigated. An attempt has been made to develop an elasto-plastic constitutive K o model based on critical state concepts. This model has been found to agree reasonably well with experimental data for kaolin and deep-ocean sediments. (author)

  6. Neuromorphic Deep Learning Machines

    OpenAIRE

    Neftci, E; Augustine, C; Paul, S; Detorakis, G

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

  7. A deep learning / neuroevolution hybrid for visual control

    DEFF Research Database (Denmark)

    Poulsen, Andreas Precht; Thorhauge, Mark; Funch, Mikkel Hvilshj

    2017-01-01

    This paper presents a deep learning / neuroevolution hybrid approach called DLNE, which allows FPS bots to learn to aim & shoot based only on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature...... representations, while the evolving network takes those features as inputs to infer actions. The results suggest that combining deep learning and neuroevolution in a hybrid approach is a promising research direction that could make complex visual domains directly accessible to networks trained through evolution....

  8. On the Role of Discipline-Related Self-Concept in Deep and Surface Approaches to Learning among University Students

    Science.gov (United States)

    Platow, Michael J.; Mavor, Kenneth I.; Grace, Diana M.

    2013-01-01

    The current research examined the role that students' discipline-related self-concepts may play in their deep and surface approaches to learning, their overall learning outcomes, and continued engagement in the discipline itself. Using a cross-lagged panel design of first-year university psychology students, a causal path was observed in which…

  9. Microbial investigations of deep geological compartments

    International Nuclear Information System (INIS)

    Barsotti, V.; Sergeant, C.; Vesvres, M.H.; Joulian, C.; Coulon, S.; Le Marrec, C.; Garrido, F.

    2010-01-01

    Document available in extended abstract form only. Deep sedimentary rocks are now considered to contain a significant part of the total bacterial population, but are microbiologically unexplored. The drilling down to the base of the Triassic (1980 meters deep) in the geological formations of the eastern Paris Basin performed by ANDRA (EST433) in 2008 provides us a good opportunity to explore the deep biosphere. We conditioned and sub-sampled on the coring site, in as aseptic conditions as possible, the nine cores: two in the Callovo-Oxfordian clay, two in the Dogger, five in the Triassic compartments. In addition to storage at atmospheric pressure, a portion of the five Triassic samples was placed in a 190 bars pressurized bars chamber to investigate the influence of the conservation pressure factor on the found microflora. In parallel, in order to evaluate a potential bacterial contamination of the core by the drilling fluids, samples of mud just before each sample drilling were taken and analysed. The microbial exploration we started can be divided in two parts: - A cultural approach in different culture media for six metabolic groups to try to find microbial cells still viable. This type of experiment is difficult because of the small proportion of cultivable species, especially in these extreme environmental samples. - A molecular approach by direct extraction of genomic DNA from the geological samples to explore a larger biodiversity. Here, the limits are the difficulties to extract DNA from these low biomass containing rocks. The five Triassic samples were partly crushed in powder and inoculated in the six culture media with four NaCl concentrations, because this type of rock is known as saline or hyper-saline, and incubated at three temperatures: 30 deg. C, 55 deg. C under agitation and 70 deg. C. First results will be presented. The direct extraction of DNA needs a complete method optimisation to adapt existent procedures (using commercial kit and classical

  10. The induced earthquake sequence related to the St. Gallen deep geothermal project (Switzerland): Fault reactivation and fluid interactions imaged by microseismicity

    Science.gov (United States)

    Diehl, T.; Kraft, T.; Kissling, E.; Wiemer, S.

    2017-09-01

    In July 2013, a sequence of more than 340 earthquakes was induced by reservoir stimulations and well-control procedures following a gas kick at a deep geothermal drilling project close to the city of St. Gallen, Switzerland. The sequence culminated in an ML 3.5 earthquake, which was felt within 10-15 km from the epicenter. High-quality earthquake locations and 3-D reflection seismic data acquired in the St. Gallen project provide a unique data set, which allows high-resolution studies of earthquake triggering related to the injection of fluids into macroscopic fault zones. In this study, we present a high-precision earthquake catalog of the induced sequence. Absolute locations are constrained by a coupled hypocenter-velocity inversion, and subsequent double-difference relocations image the geometry of the ML 3.5 rupture and resolve the spatiotemporal evolution of seismicity. A joint interpretation of earthquake and seismic data shows that the majority of the seismicity occurred in the pre-Mesozoic basement, hundreds of meters below the borehole and the targeted Mesozoic sequence. We propose a hydraulic connectivity between the reactivated fault and the borehole, likely through faults mapped by seismic data. Despite the excellent quality of the seismic data, the association of seismicity with mapped faults remains ambiguous. In summary, our results document that the actual hydraulic properties of a fault system and hydraulic connections between its fault segments are complex and may not be predictable upfront. Incomplete knowledge of fault structures and stress heterogeneities within highly complex fault systems additionally challenge the degree of predictability of induced seismicity related to underground fluid injections.

  11. The 2007 Nazko, British Columbia, earthquake sequence: Injection of magma deep in the crust beneath the Anahim volcanic belt

    Science.gov (United States)

    Cassidy, J.F.; Balfour, N.; Hickson, C.; Kao, H.; White, Rickie; Caplan-Auerbach, J.; Mazzotti, S.; Rogers, Gary C.; Al-Khoubbi, I.; Bird, A.L.; Esteban, L.; Kelman, M.; Hutchinson, J.; McCormack, D.

    2011-01-01

    On 9 October 2007, an unusual sequence of earthquakes began in central British Columbia about 20 km west of the Nazko cone, the most recent (circa 7200 yr) volcanic center in the Anahim volcanic belt. Within 25 hr, eight earthquakes of magnitude 2.3-2.9 occurred in a region where no earthquakes had previously been recorded. During the next three weeks, more than 800 microearthquakes were located (and many more detected), most at a depth of 25-31 km and within a radius of about 5 km. After about two months, almost all activity ceased. The clear P- and S-wave arrivals indicated that these were high-frequency (volcanic-tectonic) earthquakes and the b value of 1.9 that we calculated is anomalous for crustal earthquakes but consistent with volcanic-related events. Analysis of receiver functions at a station immediately above the seismicity indicated a Moho near 30 km depth. Precise relocation of the seismicity using a double-difference method suggested a horizontal migration at the rate of about 0:5 km=d, with almost all events within the lowermost crust. Neither harmonic tremor nor long-period events were observed; however, some spasmodic bursts were recorded and determined to be colocated with the earthquake hypocenters. These observations are all very similar to a deep earthquake sequence recorded beneath Lake Tahoe, California, in 2003-2004. Based on these remarkable similarities, we interpret the Nazko sequence as an indication of an injection of magma into the lower crust beneath the Anahim volcanic belt. This magma injection fractures rock, producing high-frequency, volcanic-tectonic earthquakes and spasmodic bursts.

  12. Deep Mapping and Spatial Anthropology

    Directory of Open Access Journals (Sweden)

    Les Roberts

    2016-01-01

    Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

  13. Mimvec: a deep learning approach for analyzing the human phenome.

    Science.gov (United States)

    Gan, Mingxin; Li, Wenran; Zeng, Wanwen; Wang, Xiaojian; Jiang, Rui

    2017-09-21

    The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing. We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones. Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

  14. What time is it? Deep learning approaches for circadian rhythms.

    Science.gov (United States)

    Agostinelli, Forest; Ceglia, Nicholas; Shahbaba, Babak; Sassone-Corsi, Paolo; Baldi, Pierre

    2016-06-15

    Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ fagostin@uci.edu or pfbaldi@uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  15. Large-Scale Genotyping-by-Sequencing Indicates High Levels of Gene Flow in the Deep-Sea Octocoral Swiftia simplex (Nutting 1909 on the West Coast of the United States.

    Directory of Open Access Journals (Sweden)

    Meredith V Everett

    Full Text Available Deep-sea corals are a critical component of habitat in the deep-sea, existing as regional hotspots for biodiversity, and are associated with increased assemblages of fish, including commercially important species. Because sampling these species is so difficult, little is known about the connectivity and life history of deep-sea octocoral populations. This study evaluates the genetic connectivity among 23 individuals of the deep-sea octocoral Swiftia simplex collected from Eastern Pacific waters along the west coast of the United States. We utilized high-throughput restriction-site associated DNA (RAD-tag sequencing to develop the first molecular genetic resource for the deep-sea octocoral, Swiftia simplex. Using this technique we discovered thousands of putative genome-wide SNPs in this species, and after quality control, successfully genotyped 1,145 SNPs across individuals sampled from California to Washington. These SNPs were used to assess putative population structure across the region. A STRUCTURE analysis as well as a principal coordinates analysis both failed to detect any population differentiation across all geographic areas in these collections. Additionally, after assigning individuals to putative population groups geographically, no significant FST values could be detected (FST for the full data set 0.0056, and no significant isolation by distance could be detected (p = 0.999. Taken together, these results indicate a high degree of connectivity and potential panmixia in S. simplex along this portion of the continental shelf.

  16. Deep Learning Microscopy

    KAUST Repository

    Rivenson, Yair; Gorocs, Zoltan; Gunaydin, Harun; Zhang, Yibo; Wang, Hongda; Ozcan, Aydogan

    2017-01-01

    regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably

  17. Compositional Bias in Naïve and Chemically-modified Phage-Displayed Libraries uncovered by Paired-end Deep Sequencing.

    Science.gov (United States)

    He, Bifang; Tjhung, Katrina F; Bennett, Nicholas J; Chou, Ying; Rau, Andrea; Huang, Jian; Derda, Ratmir

    2018-01-19

    Understanding the composition of a genetically-encoded (GE) library is instrumental to the success of ligand discovery. In this manuscript, we investigate the bias in GE-libraries of linear, macrocyclic and chemically post-translationally modified (cPTM) tetrapeptides displayed on the M13KE platform, which are produced via trinucleotide cassette synthesis (19 codons) and NNK-randomized codon. Differential enrichment of synthetic DNA {S}, ligated vector {L} (extension and ligation of synthetic DNA into the vector), naïve libraries {N} (transformation of the ligated vector into the bacteria followed by expression of the library for 4.5 hours to yield a "naïve" library), and libraries chemically modified by aldehyde ligation and cysteine macrocyclization {M} characterized by paired-end deep sequencing, detected a significant drop in diversity in {L} → {N}, but only a minor compositional difference in {S} → {L} and {N} → {M}. Libraries expressed at the N-terminus of phage protein pIII censored positively charged amino acids Arg and Lys; libraries expressed between pIII domains N1 and N2 overcame Arg/Lys-censorship but introduced new bias towards Gly and Ser. Interrogation of biases arising from cPTM by aldehyde ligation and cysteine macrocyclization unveiled censorship of sequences with Ser/Phe. Analogous analysis can be used to explore library diversity in new display platforms and optimize cPTM of these libraries.

  18. Insights into bacterioplankton community structure from Sundarbans mangrove ecoregion using Sanger and Illumina MiSeq sequencing approaches: A comparative analysis

    Directory of Open Access Journals (Sweden)

    Anwesha Ghosh

    2017-03-01

    Full Text Available Next generation sequencing using platforms such as Illumina MiSeq provides a deeper insight into the structure and function of bacterioplankton communities in coastal ecosystems compared to traditional molecular techniques such as clone library approach which incorporates Sanger sequencing. In this study, structure of bacterioplankton communities was investigated from two stations of Sundarbans mangrove ecoregion using both Sanger and Illumina MiSeq sequencing approaches. The Illumina MiSeq data is available under the BioProject ID PRJNA35180 and Sanger sequencing data under accession numbers KX014101-KX014140 (Stn1 and KX014372-KX014410 (Stn3. Proteobacteria-, Firmicutes- and Bacteroidetes-like sequences retrieved from both approaches appeared to be abundant in the studied ecosystem. The Illumina MiSeq data (2.1 GB provided a deeper insight into the structure of bacterioplankton communities and revealed the presence of bacterial phyla such as Actinobacteria, Cyanobacteria, Tenericutes, Verrucomicrobia which were not recovered based on Sanger sequencing. A comparative analysis of bacterioplankton communities from both stations highlighted the presence of genera that appear in both stations and genera that occur exclusively in either station. However, both the Sanger sequencing and Illumina MiSeq data were coherent at broader taxonomic levels. Pseudomonas, Devosia, Hyphomonas and Erythrobacter-like sequences were the abundant bacterial genera found in the studied ecosystem. Both the sequencing methods showed broad coherence although as expected the Illumina MiSeq data helped identify rarer bacterioplankton groups and also showed the presence of unassigned OTUs indicating possible presence of novel bacterioplankton from the studied mangrove ecosystem.

  19. A theoretical justification for single molecule peptide sequencing.

    Directory of Open Access Journals (Sweden)

    Jagannath Swaminathan

    2015-02-01

    Full Text Available The proteomes of cells, tissues, and organisms reflect active cellular processes and change continuously in response to intracellular and extracellular cues. Deep, quantitative profiling of the proteome, especially if combined with mRNA and metabolite measurements, should provide an unprecedented view of cell state, better revealing functions and interactions of cell components. Molecular diagnostics and biomarker discovery should benefit particularly from the accurate quantification of proteomes, since complex diseases like cancer change protein abundances and modifications. Currently, shotgun mass spectrometry is the primary technology for high-throughput protein identification and quantification; while powerful, it lacks high sensitivity and coverage. We draw parallels with next-generation DNA sequencing and propose a strategy, termed fluorosequencing, for sequencing peptides in a complex protein sample at the level of single molecules. In the proposed approach, millions of individual fluorescently labeled peptides are visualized in parallel, monitoring changing patterns of fluorescence intensity as N-terminal amino acids are sequentially removed, and using the resulting fluorescence signatures (fluorosequences to uniquely identify individual peptides. We introduce a theoretical foundation for fluorosequencing and, by using Monte Carlo computer simulations, we explore its feasibility, anticipate the most likely experimental errors, quantify their potential impact, and discuss the broad potential utility offered by a high-throughput peptide sequencing technology.

  20. Post-contrast T1-weighted sequences in pediatric abdominal imaging: comparative analysis of three different sequences and imaging approach

    Energy Technology Data Exchange (ETDEWEB)

    Roque, Andreia; Ramalho, Miguel; AlObaidy, Mamdoh; Heredia, Vasco; Burke, Lauren M.; De Campos, Rafael O.P.; Semelka, Richard C. [University of North Carolina at Chapel Hill, Department of Radiology, Chapel Hill, NC (United States)

    2014-10-15

    Post-contrast T1-weighted imaging is an essential component of a comprehensive pediatric abdominopelvic MR examination. However, consistent good image quality is challenging, as respiratory motion in sedated children can substantially degrade the image quality. To compare the image quality of three different post-contrast T1-weighted imaging techniques - standard three-dimensional gradient-echo (3-D-GRE), magnetization-prepared gradient-recall echo (MP-GRE) and 3-D-GRE with radial data sampling (radial 3-D-GRE) - acquired in pediatric patients younger than 5 years of age. Sixty consecutive exams performed in 51 patients (23 females, 28 males; mean age 2.5 ± 1.4 years) constituted the final study population. Thirty-nine scans were performed at 3 T and 21 scans were performed at 1.5 T. Two different reviewers independently and blindly qualitatively evaluated all sequences to determine image quality and extent of artifacts. MP-GRE and radial 3-D-GRE sequences had the least respiratory motion (P < 0.0001). Standard 3-D-GRE sequences displayed the lowest average score ratings in hepatic and pancreatic edge definition, hepatic vessel clarity and overall image quality. Radial 3-D-GRE sequences showed the highest scores ratings in overall image quality. Our preliminary results support the preference of fat-suppressed radial 3-D-GRE as the best post-contrast T1-weighted imaging approach for patients under the age of 5 years, when dynamic imaging is not essential. (orig.)

  1. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

    Science.gov (United States)

    Wachinger, Christian; Reuter, Martin; Klein, Tassilo

    2018-04-15

    We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

  2. Differential genomic arrangements in Caryophyllales through deep transcriptome sequencing of A. hypochondriacus.

    Directory of Open Access Journals (Sweden)

    Meeta Sunil

    Full Text Available Genome duplication event in edible dicots under the orders Rosid and Asterid, common during the oligocene period, is missing for species under the order Caryophyllales. Despite this, grain amaranths not only survived this period but display many desirable traits missing in species under rosids and asterids. For example, grain amaranths display traits like C4 photosynthesis, high-lysine seeds, high-yield, drought resistance, tolerance to infection and resilience to stress. It is, therefore, of interest to look for minor genome rearrangements with potential functional implications that are unique to grain amaranths. Here, by deep sequencing and assembly of 16 transcriptomes (86.8 billion bases we have interrogated differential genome rearrangement unique to Amaranthus hypochondriacus with potential links to these phenotypes. We have predicted 125,581 non-redundant transcripts including 44,529 protein coding transcripts identified based on homology to known proteins and 13,529 predicted as novel/amaranth specific coding transcripts. Of the protein coding de novo assembled transcripts, we have identified 1810 chimeric transcripts. More than 30% and 19% of the gene pairs within the chimeric transcripts are found within the same loci in the genomes of A. hypochondriacus and Beta vulgaris respectively and are considered real positives. Interestingly, one of the chimeric transcripts comprises two important genes, namely DHDPS1, a key enzyme implicated in the biosynthesis of lysine, and alpha-glucosidase, an enzyme involved in sucrose catabolism, in close proximity to each other separated by a distance of 612 bases in the genome of A. hypochondriacus in a convergent configuration. We have experimentally validated that transcripts of these two genes are also overlapping in the 3' UTR with their expression negatively correlated from bud to mature seed, suggesting a potential link between the high seed lysine trait and unique genome organization.

  3. A methodological approach for designing and sequencing product families in Reconfigurable Disassembly Systems

    Directory of Open Access Journals (Sweden)

    Ignacio Eguia

    2011-10-01

    Full Text Available Purpose: A Reconfigurable Disassembly System (RDS represents a new paradigm of automated disassembly system that uses reconfigurable manufacturing technology for fast adaptation to changes in the quantity and mix of products to disassemble. This paper deals with a methodology for designing and sequencing product families in RDS. Design/methodology/approach: The methodology is developed in a two-phase approach, where products are first grouped into families and then families are sequenced through the RDS, computing the required machines and modules configuration for each family. Products are grouped into families based on their common features using a Hierarchical Clustering Algorithm. The optimal sequence of the product families is calculated using a Mixed-Integer Linear Programming model minimizing reconfigurability and operational costs. Findings: This paper is focused to enable reconfigurable manufacturing technologies to attain some degree of adaptability during disassembly automation design using modular machine tools. Research limitations/implications: The MILP model proposed for the second phase is similar to the well-known Travelling Salesman Problem (TSP and therefore its complexity grows exponentially with the number of products to disassemble. In real-world problems, which a higher number of products, it may be advisable to solve the model approximately with heuristics. Practical implications: The importance of industrial recycling and remanufacturing is growing due to increasing environmental and economic pressures. Disassembly is an important part of remanufacturing systems for reuse and recycling purposes. Automatic disassembly techniques have a growing number of applications in the area of electronics, aerospace, construction and industrial equipment. In this paper, a design and scheduling approach is proposed to apply in this area. Originality/value: This paper presents a new concept called Reconfigurable Disassembly System

  4. Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network

    Directory of Open Access Journals (Sweden)

    Buzhong Zhang

    2018-05-01

    Full Text Available Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson’s correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

  5. Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

    Science.gov (United States)

    Zhang, Buzhong; Li, Linqing; Lü, Qiang

    2018-05-25

    Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson's correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

  6. Reactive Sequencing for Autonomous Navigation Evolving from Phoenix Entry, Descent, and Landing

    Science.gov (United States)

    Grasso, Christopher A.; Riedel, Joseph E.; Vaughan, Andrew T.

    2010-01-01

    guidance, navigation and control scenarios, work began three years ago on substantial upgrades to VML that are now being exercised in scenarios for lunar landing and comet/asteroid rendezvous. The advanced state-based approach includes coordinated state transition machines with distributed decision-making logic. These state machines are not merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. Combined with the JPL's AutoNav software used on Deep Space 1 and Deep Impact, the system allows spacecraft to autonomously navigate to an unmapped surface, soft-contact, and either land or ascend. The state machine architecture enabled by VML 2.1 has successfully performed sampling missions and lunar descent missions in a simulated environment, and is progressing toward flight capability. The authors are also investigating using the VML 2.1 flight director architecture to perform autonomous activities like rendezvous with a passive hypothetical Mars sample return capsule. The approach being pursued is similar to the touch-and-go sampling state machines, with the added complications associated with the search for, physical capture of, and securing of a separate spacecraft. Complications include optically finding and tracking the Orbiting Sample Capsule (OSC), keeping the OSC illuminated, making orbital adjustments, and physically capturing the OSC. Other applications could include autonomous science collection and fault compensation.

  7. Deep inelastic processes and the parton model

    International Nuclear Information System (INIS)

    Altarelli, G.

    The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

  8. Two comments to utilization of structure function approach in deep inelastic scattering experiments

    International Nuclear Information System (INIS)

    Kuraev, E.; Galynskij, M.; Il'ichev, A.

    2002-01-01

    The 'returning to resonance' mechanism can be used to obtain the simple procedure of taking radiative corrections (RC) to deep inelastic scattering (DIS) cross sections into account in the framework of the Drell-Yan picture. Iteration procedure is proposed. Kinematical region y→1 can be described in the framework of the Drell-Yan picture using the structure function approach. The large RC in the lowest order reflect the Sudakov form factor suppression, which can be taken into account in all orders of the perturbation theory. Based on explicit calculation in two lowest orders of the perturbation theory, we construct the cross section in the y→1 region obeying renormalization group equations and including the Sudakov-like form factor suppression

  9. Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

    Science.gov (United States)

    Zheng, Yu-Jun; Sheng, Wei-Guo; Sun, Xing-Ming; Chen, Sheng-Yong

    2017-12-01

    Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

  10. Analysis of hepatitis C NS5A resistance associated polymorphisms using ultra deep single molecule real time (SMRT) sequencing.

    Science.gov (United States)

    Bergfors, Assar; Leenheer, Daniël; Bergqvist, Anders; Ameur, Adam; Lennerstrand, Johan

    2016-02-01

    Development of Hepatitis C virus (HCV) resistance against direct-acting antivirals (DAAs), including NS5A inhibitors, is an obstacle to successful treatment of HCV when DAAs are used in sub-optimal combinations. Furthermore, it has been shown that baseline (pre-existing) resistance against DAAs is present in treatment naïve-patients and this will potentially complicate future treatment strategies in different HCV genotypes (GTs). Thus the aim was to detect low levels of NS5A resistant associated variants (RAVs) in a limited sample set of treatment-naïve patients of HCV GT1a and 3a, since such polymorphisms can display in vitro resistance as high as 60000 fold. Ultra-deep single molecule real time (SMRT) sequencing with the Pacific Biosciences (PacBio) RSII instrument was used to detect these RAVs. The SMRT sequencing was conducted on ten samples; three of them positive with Sanger sequencing (GT1a Q30H and Y93N, and GT3a Y93H), five GT1a samples, and two GT3a non-positive samples. The same methods were applied to the HCV GT1a H77-plasmid in a dilution series, in order to determine the error rates of replication, which in turn was used to determine the limit of detection (LOD), as defined by mean + 3SD, of minority variants down to 0.24%. We found important baseline NS5A RAVs at levels between 0.24 and 0.5%, which could potentially have clinical relevance. This new method with low level detection of baseline RAVs could be useful in predicting the most cost-efficient combination of DAA treatment, and reduce the treatment duration for an HCV infected individual. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Shotgun protein sequencing.

    Energy Technology Data Exchange (ETDEWEB)

    Faulon, Jean-Loup Michel; Heffelfinger, Grant S.

    2009-06-01

    A novel experimental and computational technique based on multiple enzymatic digestion of a protein or protein mixture that reconstructs protein sequences from sequences of overlapping peptides is described in this SAND report. This approach, analogous to shotgun sequencing of DNA, is to be used to sequence alternative spliced proteins, to identify post-translational modifications, and to sequence genetically engineered proteins.

  12. Gene expression in the deep biosphere.

    Science.gov (United States)

    Orsi, William D; Edgcomb, Virginia P; Christman, Glenn D; Biddle, Jennifer F

    2013-07-11

    Scientific ocean drilling has revealed a deep biosphere of widespread microbial life in sub-seafloor sediment. Microbial metabolism in the marine subsurface probably has an important role in global biogeochemical cycles, but deep biosphere activities are not well understood. Here we describe and analyse the first sub-seafloor metatranscriptomes from anaerobic Peru Margin sediment up to 159 metres below the sea floor, represented by over 1 billion complementary DNA (cDNA) sequence reads. Anaerobic metabolism of amino acids, carbohydrates and lipids seem to be the dominant metabolic processes, and profiles of dissimilatory sulfite reductase (dsr) transcripts are consistent with pore-water sulphate concentration profiles. Moreover, transcripts involved in cell division increase as a function of microbial cell concentration, indicating that increases in sub-seafloor microbial abundance are a function of cell division across all three domains of life. These data support calculations and models of sub-seafloor microbial metabolism and represent the first holistic picture of deep biosphere activities.

  13. Deep Sequence Analysis of AgoshRNA Processing Reveals 3’ A Addition and Trimming

    Directory of Open Access Journals (Sweden)

    Alex Harwig

    2015-01-01

    Full Text Available The RNA interference (RNAi pathway, in which microprocessor and Dicer collaborate to process microRNAs (miRNA, was recently expanded by the description of alternative processing routes. In one of these noncanonical pathways, Dicer action is replaced by the Argonaute2 (Ago2 slicer function. It was recently shown that the stem-length of precursor-miRNA or short hairpin RNA (shRNA molecules is a major determinant for Dicer versus Ago2 processing. Here we present the results of a deep sequence study on the processing of shRNAs with different stem length and a top G·U wobble base pair (bp. This analysis revealed some unexpected properties of these so-called AgoshRNA molecules that are processed by Ago2 instead of Dicer. First, we confirmed the gradual shift from Dicer to Ago2 processing upon shortening of the hairpin length. Second, hairpins with a stem larger than 19 base pair are inefficiently cleaved by Ago2 and we noticed a shift in the cleavage site. Third, the introduction of a top G·U bp in a regular shRNA can promote Ago2-cleavage, which coincides with a loss of Ago2-loading of the Dicer-cleaved 3’ strand. Fourth, the Ago2-processed AgoshRNAs acquire a short 3’ tail of 1–3 A-nucleotides (nt and we present evidence that this product is subsequently trimmed by the poly(A-specific ribonuclease (PARN.

  14. Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

    Science.gov (United States)

    Fang, Shih-Hau; Tsao, Yu; Hsiao, Min-Jing; Chen, Ji-Ying; Lai, Ying-Hui; Lin, Feng-Chuan; Wang, Chi-Te

    2018-03-19

    Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

  15. The sequence coding and search system: An approach for constructing and analyzing event sequences at commercial nuclear power plants

    International Nuclear Information System (INIS)

    Mays, G.T.

    1989-04-01

    The US Nuclear Regulatory Commission (NRC) has recognized the importance of the collection, assessment, and feedstock of operating experience data from commercial nuclear power plants and has centralized these activities in the Office for Analysis and Evaluation of Operational Data (AEOD). Such data is essential for performing safety and reliability analyses, especially analyses of trends and patterns to identify undesirable changes in plant performance at the earliest opportunity to implement corrective measures to preclude the occurrences of a more serious event. One of NRC's principal tools for collecting and evaluating operating experience data is the Sequence Coding and Search System (SCSS). The SCSS consists of a methodology for structuring event sequences and the requisite computer system to store and search the data. The source information for SCSS is the Licensee Event Report (LER), which is a legally required document. This paper describes the objective SCSS, the information it contains, and the format and approach for constructuring SCSS event sequences. Examples are presented demonstrating the use SCSS to support the analysis of LER data. The SCSS contains over 30,000 LERs describing events from 1980 through the present. Insights gained from working with a complex data system from the initial developmental stage to the point of a mature operating system are highlighted

  16. Metagenomic approach for discovering new pathogens in infection disease outbreaks

    Directory of Open Access Journals (Sweden)

    Emanuela Giombini

    2011-09-01

    Full Text Available Viruses represent the most abundant biological components on earth.They can be found in every environment, from deep layers of oceans to animal bodies.Although several viruses have been isolated and sequenced, in each environment there are millions of different types of viruses that have not been identified yet.The advent of nextgeneration sequencing technologies with their high throughput capabilities make possible to study in a single experiment all the community of microorganisms present in a particular sample “microbioma”.They made more feasible the application of the metagenomic approach, by which it is also possible to discover and identify new pathogens, that may pose a threat to public health.This paper summarizes the most recent applications of nextgeneration sequencing to discover new viral pathogens during the occurrence of infection disease outbreaks.

  17. Zseq: An Approach for Preprocessing Next-Generation Sequencing Data.

    Science.gov (United States)

    Alkhateeb, Abedalrhman; Rueda, Luis

    2017-08-01

    Next-generation sequencing technology generates a huge number of reads (short sequences), which contain a vast amount of genomic data. The sequencing process, however, comes with artifacts. Preprocessing of sequences is mandatory for further downstream analysis. We present Zseq, a linear method that identifies the most informative genomic sequences and reduces the number of biased sequences, sequence duplications, and ambiguous nucleotides. Zseq finds the complexity of the sequences by counting the number of unique k-mers in each sequence as its corresponding score and also takes into the account other factors such as ambiguous nucleotides or high GC-content percentage in k-mers. Based on a z-score threshold, Zseq sweeps through the sequences again and filters those with a z-score less than the user-defined threshold. Zseq algorithm is able to provide a better mapping rate; it reduces the number of ambiguous bases significantly in comparison with other methods. Evaluation of the filtered reads has been conducted by aligning the reads and assembling the transcripts using the reference genome as well as de novo assembly. The assembled transcripts show a better discriminative ability to separate cancer and normal samples in comparison with another state-of-the-art method. Moreover, de novo assembled transcripts from the reads filtered by Zseq have longer genomic sequences than other tested methods. Estimating the threshold of the cutoff point is introduced using labeling rules with optimistic results.

  18. Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

    Science.gov (United States)

    Liu, Fang; Jang, Hyungseok; Kijowski, Richard; Bradshaw, Tyler; McMillan, Alan B

    2018-02-01

    Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared

  19. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

    Directory of Open Access Journals (Sweden)

    Sheng Wang

    2015-07-01

    Full Text Available Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields, to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors.

  20. Deep sequencing reveals a novel closterovirus associated with wild rose leaf rosette disease.

    Science.gov (United States)

    He, Yan; Yang, Zuokun; Hong, Ni; Wang, Guoping; Ning, Guogui; Xu, Wenxing

    2015-06-01

    A bizarre virus-like symptom of a leaf rosette formed by dense small leaves on branches of wild roses (Rosa multiflora Thunb.), designated as 'wild rose leaf rosette disease' (WRLRD), was observed in China. To investigate the presumed causal virus, a wild rose sample affected by WRLRD was subjected to deep sequencing of small interfering RNAs (siRNAs) for a complete survey of the infecting viruses and viroids. The assembly of siRNAs led to the reconstruction of the complete genomes of three known viruses, namely Apple stem grooving virus (ASGV), Blackberry chlorotic ringspot virus (BCRV) and Prunus necrotic ringspot virus (PNRSV), and of a novel virus provisionally named 'rose leaf rosette-associated virus' (RLRaV). Phylogenetic analysis clearly placed RLRaV alongside members of the genus Closterovirus, family Closteroviridae. Genome organization of RLRaV RNA (17,653 nucleotides) showed 13 open reading frames (ORFs), except ORF1 and the quintuple gene block, most of which showed no significant similarities with known viral proteins, but, instead, had detectable identities to fungal or bacterial proteins. Additional novel molecular features indicated that RLRaV seems to be the most complex virus among the known genus members. To our knowledge, this is the first report of WRLRD and its associated closterovirus, as well as two ilarviruses and one capilovirus, infecting wild roses. Our findings present novel information about the closterovirus and the aetiology of this rose disease which should facilitate its control. More importantly, the novel features of RLRaV help to clarify the molecular and evolutionary features of the closterovirus. © 2014 BSPP AND JOHN WILEY & SONS LTD.

  1. Insertion sequences enrichment in extreme Red sea brine pool vent

    KAUST Repository

    Elbehery, Ali H. A.

    2016-12-03

    Mobile genetic elements are major agents of genome diversification and evolution. Limited studies addressed their characteristics, including abundance, and role in extreme habitats. One of the rare natural habitats exposed to multiple-extreme conditions, including high temperature, salinity and concentration of heavy metals, are the Red Sea brine pools. We assessed the abundance and distribution of different mobile genetic elements in four Red Sea brine pools including the world’s largest known multiple-extreme deep-sea environment, the Red Sea Atlantis II Deep. We report a gradient in the abundance of mobile genetic elements, dramatically increasing in the harshest environment of the pool. Additionally, we identified a strong association between the abundance of insertion sequences and extreme conditions, being highest in the harshest and deepest layer of the Red Sea Atlantis II Deep. Our comparative analyses of mobile genetic elements in secluded, extreme and relatively non-extreme environments, suggest that insertion sequences predominantly contribute to polyextremophiles genome plasticity.

  2. Stable architectures for deep neural networks

    Science.gov (United States)

    Haber, Eldad; Ruthotto, Lars

    2018-01-01

    Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

  3. Human-associated fungi in deep subseafloor sediment?

    Science.gov (United States)

    Fulfer, V. M.; Kirkpatrick, J. B.; D'Hondt, S.

    2015-12-01

    Recent studies have reported fungi in marine sediment samples from depths as great as 1740 meters below seafloor (mbsf) (Rédou et al., 2014). Such studies have utilized a variety of techniques to identify fungi, including cultivation of isolates, amplicon sequencing, and metagenomics. Six recent studies of marine sediment collectively identify nearly 100 fungal taxa at the genus and species levels (Damare et al., 2006; Lai et al., 2007; Edgcomb et al., 2010; Singh et al., 2010; Orsi et al., 2013; Rédou et al., 2014). Known marine taxa are rarely identified by these studies. For individual studies with more than two taxa, between 16% and 57% of the fungal taxa are human microflora or associated with human environments (e.g., human skin or indoor air). For example, three of the six studies identified Malassezia species that are common skin inhabitants of humans and dogs. Although human-associated taxa have been identified in both shallow and deep sediment, they pose a particularly acute problem for deep subseafloor samples, where claims of a eukaryotic deep biosphere are most striking; depending on the study, 25% to 38% of species identified in sediment taken at depths greater than 40 meters are human-associated. Only one to three species have been reported from each of the four samples taken at depths greater than one km (eight species total; Rédou et al., 2014). Of these eight species, three are human-associated. This ubiquity of human-associated microflora is very problematic for interpretations of an indigenous deep subseafloor fungal community; either human-associated taxa comprise a large fraction of marine sedimentary fungi, or sample and analytical contamination is so widespread that the extent and ubiquity of a deep subseafloor fungal community remains uncertain. This highlights the need for stringent quality control measures throughout coring, sampling, and recovery of marine sediment, and when cultivating, extracting, and/or sequencing fungi from

  4. Deep sequencing of uveal melanoma identifies a recurrent mutation in PLCB4

    DEFF Research Database (Denmark)

    Johansson, Peter; Aoude, Lauren G; Wadt, Karin

    2016-01-01

    Next generation sequencing of uveal melanoma (UM) samples has identified a number of recurrent oncogenic or loss-of-function mutations in key driver genes including: GNAQ, GNA11, EIF1AX, SF3B1 and BAP1. To search for additional driver mutations in this tumor type we carried out whole......, instead, a BRCA mutation signature predominated. In addition to mutations in the known UM driver genes, we found a recurrent mutation in PLCB4 (c.G1888T, p.D630Y, NM_000933), which was validated using Sanger sequencing. The identical mutation was also found in published UM sequence data (1 of 56 tumors......-genome or whole-exome sequencing of 28 tumors or primary cell lines. These samples have a low mutation burden, with a mean of 10.6 protein changing mutations per sample (range 0 to 53). As expected for these sun-shielded melanomas the mutation spectrum was not consistent with an ultraviolet radiation signature...

  5. Complete genome sequence of a tomato infecting tomato mottle mosaic virus in New York

    Science.gov (United States)

    Complete genome sequence of an emerging isolate of tomato mottle mosaic virus (ToMMV) infecting experimental nicotianan benthamiana plants in up-state New York was obtained using small RNA deep sequencing. ToMMV_NY-13 shared 99% sequence identity to ToMMV isolates from Mexico and Florida. Broader d...

  6. Pro deep learning with TensorFlow a mathematical approach to advanced artificial intelligence in Python

    CERN Document Server

    Pattanayak, Santanu

    2017-01-01

    Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.

  7. The complete mitochondrial genome of the deep-sea sponge Poecillastra laminaris (Astrophorida, Vulcanellidae).

    Science.gov (United States)

    Zeng, Cong; Thomas, Leighton J; Kelly, Michelle; Gardner, Jonathan P A

    2016-05-01

    The complete mitochondrial genome of a New Zealand specimen of the deep-sea sponge Poecillastra laminaris (Sollas, 1886) (Astrophorida, Vulcanellidae), from the Colville Ridge, New Zealand, was sequenced using the 454 Life Science pyrosequencing system. To identify homologous mitochondrial sequences, the 454 reads were mapped to the complete mitochondrial genome sequence of Geodia neptuni (GeneBank No. NC_006990). The P. laminaris genome is 18,413 bp in length and includes 14 protein-coding genes, 24 transfer RNA genes and 2 ribosomal RNA genes. Gene order resembled that of other demosponges. The base composition of the genome is A (29.1%), T (35.2%), C (14.0%) and G (21.7%). This is the second published mitogenome for a sponge of the order Astrophorida and will be useful in future phylogenetic analysis of deep-sea sponges.

  8. Recalcitrant deep and shallow nodes in Aristolochia (Aristolochiaceae) illuminated using anchored hybrid enrichment.

    Science.gov (United States)

    Wanke, Stefan; Granados Mendoza, Carolina; Müller, Sebastian; Paizanni Guillén, Anna; Neinhuis, Christoph; Lemmon, Alan R; Lemmon, Emily Moriarty; Samain, Marie-Stéphanie

    2017-12-01

    Recalcitrant relationships are characterized by very short internodes that can be found among shallow and deep phylogenetic scales all over the tree of life. Adding large amounts of presumably informative sequences, while decreasing systematic error, has been suggested as a possible approach to increase phylogenetic resolution. The development of enrichment strategies, coupled with next generation sequencing, resulted in a cost-effective way to facilitate the reconstruction of recalcitrant relationships. By applying the anchored hybrid enrichment (AHE) genome partitioning strategy to Aristolochia using an universal angiosperm probe set, we obtained 231-233 out of 517 single or low copy nuclear loci originally contained in the enrichment kit, resulting in a total alignment length of 154,756bp to 160,150bp. Since Aristolochia (Piperales; magnoliids) is distantly related to any angiosperm species whose genome has been used for the plant AHE probe design (Amborella trichopoda being the closest), it serves as a proof of universality for this probe set. Aristolochia comprises approximately 500 species grouped in several clades (OTUs), whose relationships to each other are partially unknown. Previous phylogenetic studies have shown that these lineages branched deep in time and in quick succession, seen as short-deep internodes. Short-shallow internodes are also characteristic of some Aristolochia lineages such as Aristolochia subsection Pentandrae, a clade of presumably recent diversification. This subsection is here included to test the performance of AHE at species level. Filtering and subsampling loci using the phylogenetic informativeness method resolves several recalcitrant phylogenetic relationships within Aristolochia. By assuming different ploidy levels during bioinformatics processing of raw data, first hints are obtained that polyploidization contributed to the evolution of Aristolochia. Phylogenetic results are discussed in the light of current systematics and

  9. Comparison of illumina and 454 deep sequencing in participants failing raltegravir-based antiretroviral therapy.

    Directory of Open Access Journals (Sweden)

    Jonathan Z Li

    Full Text Available The impact of raltegravir-resistant HIV-1 minority variants (MVs on raltegravir treatment failure is unknown. Illumina sequencing offers greater throughput than 454, but sequence analysis tools for viral sequencing are needed. We evaluated Illumina and 454 for the detection of HIV-1 raltegravir-resistant MVs.A5262 was a single-arm study of raltegravir and darunavir/ritonavir in treatment-naïve patients. Pre-treatment plasma was obtained from 5 participants with raltegravir resistance at the time of virologic failure. A control library was created by pooling integrase clones at predefined proportions. Multiplexed sequencing was performed with Illumina and 454 platforms at comparable costs. Illumina sequence analysis was performed with the novel snp-assess tool and 454 sequencing was analyzed with V-Phaser.Illumina sequencing resulted in significantly higher sequence coverage and a 0.095% limit of detection. Illumina accurately detected all MVs in the control library at ≥0.5% and 7/10 MVs expected at 0.1%. 454 sequencing failed to detect any MVs at 0.1% with 5 false positive calls. For MVs detected in the patient samples by both 454 and Illumina, the correlation in the detected variant frequencies was high (R2 = 0.92, P<0.001. Illumina sequencing detected 2.4-fold greater nucleotide MVs and 2.9-fold greater amino acid MVs compared to 454. The only raltegravir-resistant MV detected was an E138K mutation in one participant by Illumina sequencing, but not by 454.In participants of A5262 with raltegravir resistance at virologic failure, baseline raltegravir-resistant MVs were rarely detected. At comparable costs to 454 sequencing, Illumina demonstrated greater depth of coverage, increased sensitivity for detecting HIV MVs, and fewer false positive variant calls.

  10. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

    Science.gov (United States)

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-22

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

  11. Effects of hydrostatic pressure on yeasts isolated from deep-sea hydrothermal vents.

    Science.gov (United States)

    Burgaud, Gaëtan; Hué, Nguyen Thi Minh; Arzur, Danielle; Coton, Monika; Perrier-Cornet, Jean-Marie; Jebbar, Mohamed; Barbier, Georges

    2015-11-01

    Hydrostatic pressure plays a significant role in the distribution of life in the biosphere. Knowledge of deep-sea piezotolerant and (hyper)piezophilic bacteria and archaea diversity has been well documented, along with their specific adaptations to cope with high hydrostatic pressure (HHP). Recent investigations of deep-sea microbial community compositions have shown unexpected micro-eukaryotic communities, mainly dominated by fungi. Molecular methods such as next-generation sequencing have been used for SSU rRNA gene sequencing to reveal fungal taxa. Currently, a difficult but fascinating challenge for marine mycologists is to create deep-sea marine fungus culture collections and assess their ability to cope with pressure. Indeed, although there is no universal genetic marker for piezoresistance, physiological analyses provide concrete relevant data for estimating their adaptations and understanding the role of fungal communities in the abyss. The present study investigated morphological and physiological responses of fungi to HHP using a collection of deep-sea yeasts as a model. The aim was to determine whether deep-sea yeasts were able to tolerate different HHP and if they were metabolically active. Here we report an unexpected taxonomic-based dichotomic response to pressure with piezosensitve ascomycetes and piezotolerant basidiomycetes, and distinct morphological switches triggered by pressure for certain strains. Copyright © 2015 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  12. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules.

    Science.gov (United States)

    Lusci, Alessandro; Pollastri, Gianluca; Baldi, Pierre

    2013-07-22

    Shallow machine learning methods have been applied to chemoinformatics problems with some success. As more data becomes available and more complex problems are tackled, deep machine learning methods may also become useful. Here, we present a brief overview of deep learning methods and show in particular how recursive neural network approaches can be applied to the problem of predicting molecular properties. However, molecules are typically described by undirected cyclic graphs, while recursive approaches typically use directed acyclic graphs. Thus, we develop methods to address this discrepancy, essentially by considering an ensemble of recursive neural networks associated with all possible vertex-centered acyclic orientations of the molecular graph. One advantage of this approach is that it relies only minimally on the identification of suitable molecular descriptors because suitable representations are learned automatically from the data. Several variants of this approach are applied to the problem of predicting aqueous solubility and tested on four benchmark data sets. Experimental results show that the performance of the deep learning methods matches or exceeds the performance of other state-of-the-art methods according to several evaluation metrics and expose the fundamental limitations arising from training sets that are too small or too noisy. A Web-based predictor, AquaSol, is available online through the ChemDB portal ( cdb.ics.uci.edu ) together with additional material.

  13. DeepSAT: A Deep Learning Approach to Tree-cover Delineation in 1-m NAIP Imagery for the Continental United States

    Science.gov (United States)

    Ganguly, S.; Basu, S.; Nemani, R. R.; Mukhopadhyay, S.; Michaelis, A.; Votava, P.

    2016-12-01

    High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with 60 million pixels) and has a total size of 65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to 8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the

  14. DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

    Science.gov (United States)

    Ganguly, Sangram; Basu, Saikat; Nemani, Ramakrishna R.; Mukhopadhyay, Supratik; Michaelis, Andrew; Votava, Petr

    2016-01-01

    High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with 60 million pixels) and has a total size of 65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to 8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the

  15. Evolving Deep Networks Using HPC

    Energy Technology Data Exchange (ETDEWEB)

    Young, Steven R. [ORNL, Oak Ridge; Rose, Derek C. [ORNL, Oak Ridge; Johnston, Travis [ORNL, Oak Ridge; Heller, William T. [ORNL, Oak Ridge; Karnowski, thomas P. [ORNL, Oak Ridge; Potok, Thomas E. [ORNL, Oak Ridge; Patton, Robert M. [ORNL, Oak Ridge; Perdue, Gabriel [Fermilab; Miller, Jonathan [Santa Maria U., Valparaiso

    2017-01-01

    While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus often a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.

  16. Deep subsurface microbial processes

    Science.gov (United States)

    Lovley, D.R.; Chapelle, F.H.

    1995-01-01

    Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of

  17. Sequencing of a patient with balanced chromosome abnormalities and neurodevelopmental disease identifies disruption of multiple high risk loci by structural variation.

    Directory of Open Access Journals (Sweden)

    Jonathon Blake

    Full Text Available Balanced chromosome abnormalities (BCAs occur at a high frequency in healthy and diseased individuals, but cost-efficient strategies to identify BCAs and evaluate whether they contribute to a phenotype have not yet become widespread. Here we apply genome-wide mate-pair library sequencing to characterize structural variation in a patient with unclear neurodevelopmental disease (NDD and complex de novo BCAs at the karyotype level. Nucleotide-level characterization of the clinically described BCA breakpoints revealed disruption of at least three NDD candidate genes (LINC00299, NUP205, PSMD14 that gave rise to abnormal mRNAs and could be assumed as disease-causing. However, unbiased genome-wide analysis of the sequencing data for cryptic structural variation was key to reveal an additional submicroscopic inversion that truncates the schizophrenia- and bipolar disorder-associated brain transcription factor ZNF804A as an equally likely NDD-driving gene. Deep sequencing of fluorescent-sorted wild-type and derivative chromosomes confirmed the clinically undetected BCA. Moreover, deep sequencing further validated a high accuracy of mate-pair library sequencing to detect structural variants larger than 10 kB, proposing that this approach is powerful for clinical-grade genome-wide structural variant detection. Our study supports previous evidence for a role of ZNF804A in NDD and highlights the need for a more comprehensive assessment of structural variation in karyotypically abnormal individuals and patients with neurocognitive disease to avoid diagnostic deception.

  18. Sequencing of a Patient with Balanced Chromosome Abnormalities and Neurodevelopmental Disease Identifies Disruption of Multiple High Risk Loci by Structural Variation

    Science.gov (United States)

    Blake, Jonathon; Riddell, Andrew; Theiss, Susanne; Gonzalez, Alexis Perez; Haase, Bettina; Jauch, Anna; Janssen, Johannes W. G.; Ibberson, David; Pavlinic, Dinko; Moog, Ute; Benes, Vladimir; Runz, Heiko

    2014-01-01

    Balanced chromosome abnormalities (BCAs) occur at a high frequency in healthy and diseased individuals, but cost-efficient strategies to identify BCAs and evaluate whether they contribute to a phenotype have not yet become widespread. Here we apply genome-wide mate-pair library sequencing to characterize structural variation in a patient with unclear neurodevelopmental disease (NDD) and complex de novo BCAs at the karyotype level. Nucleotide-level characterization of the clinically described BCA breakpoints revealed disruption of at least three NDD candidate genes (LINC00299, NUP205, PSMD14) that gave rise to abnormal mRNAs and could be assumed as disease-causing. However, unbiased genome-wide analysis of the sequencing data for cryptic structural variation was key to reveal an additional submicroscopic inversion that truncates the schizophrenia- and bipolar disorder-associated brain transcription factor ZNF804A as an equally likely NDD-driving gene. Deep sequencing of fluorescent-sorted wild-type and derivative chromosomes confirmed the clinically undetected BCA. Moreover, deep sequencing further validated a high accuracy of mate-pair library sequencing to detect structural variants larger than 10 kB, proposing that this approach is powerful for clinical-grade genome-wide structural variant detection. Our study supports previous evidence for a role of ZNF804A in NDD and highlights the need for a more comprehensive assessment of structural variation in karyotypically abnormal individuals and patients with neurocognitive disease to avoid diagnostic deception. PMID:24625750

  19. Fungal diversity in deep-sea sediments associated with asphalt seeps at the Sao Paulo Plateau

    Science.gov (United States)

    Nagano, Yuriko; Miura, Toshiko; Nishi, Shinro; Lima, Andre O.; Nakayama, Cristina; Pellizari, Vivian H.; Fujikura, Katsunori

    2017-12-01

    We investigated the fungal diversity in a total of 20 deep-sea sediment samples (of which 14 samples were associated with natural asphalt seeps and 6 samples were not associated) collected from two different sites at the Sao Paulo Plateau off Brazil by Ion Torrent PGM targeting ITS region of ribosomal RNA. Our results suggest that diverse fungi (113 operational taxonomic units (OTUs) based on clustering at 97% sequence similarity assigned into 9 classes and 31 genus) are present in deep-sea sediment samples collected at the Sao Paulo Plateau, dominated by Ascomycota (74.3%), followed by Basidiomycota (11.5%), unidentified fungi (7.1%), and sequences with no affiliation to any organisms in the public database (7.1%). However, it was revealed that only three species, namely Penicillium sp., Cadophora malorum and Rhodosporidium diobovatum, were dominant, with the majority of OTUs remaining a minor community. Unexpectedly, there was no significant difference in major fungal community structure between the asphalt seep and non-asphalt seep sites, despite the presence of mass hydrocarbon deposits and the high amount of macro organisms surrounding the asphalt seeps. However, there were some differences in the minor fungal communities, with possible asphalt degrading fungi present specifically in the asphalt seep sites. In contrast, some differences were found between the two different sampling sites. Classification of OTUs revealed that only 47 (41.6%) fungal OTUs exhibited >97% sequence similarity, in comparison with pre-existing ITS sequences in public databases, indicating that a majority of deep-sea inhabiting fungal taxa still remain undescribed. Although our knowledge on fungi and their role in deep-sea environments is still limited and scarce, this study increases our understanding of fungal diversity and community structure in deep-sea environments.

  20. Toolkits and Libraries for Deep Learning.

    Science.gov (United States)

    Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

    2017-08-01

    Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

  1. INTEGRATED APPROACH TO GENERATION OF PRECEDENCE RELATIONS AND PRECEDENCE GRAPHS FOR ASSEMBLY SEQUENCE PLANNING

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    An integrated approach to generation of precedence relations and precedence graphs for assembly sequence planning is presented, which contains more assembly flexibility. The approach involves two stages. Based on the assembly model, the components in the assembly can be divided into partially constrained components and completely constrained components in the first stage, and then geometric precedence relation for every component is generated automatically. According to the result of the first stage, the second stage determines and constructs all precedence graphs. The algorithms of these two stages proposed are verified by two assembly examples.

  2. Bacterial Diversity in Bentonites, Engineered Barrier for Deep Geological Disposal of Radioactive Wastes.

    Science.gov (United States)

    Lopez-Fernandez, Margarita; Cherkouk, Andrea; Vilchez-Vargas, Ramiro; Jauregui, Ruy; Pieper, Dietmar; Boon, Nico; Sanchez-Castro, Ivan; Merroun, Mohamed L

    2015-11-01

    The long-term disposal of radioactive wastes in a deep geological repository is the accepted international solution for the treatment and management of these special residues. The microbial community of the selected host rocks and engineered barriers for the deep geological repository may affect the performance and the safety of the radioactive waste disposal. In this work, the bacterial population of bentonite formations of Almeria (Spain), selected as a reference material for bentonite-engineered barriers in the disposal of radioactive wastes, was studied. 16S ribosomal RNA (rRNA) gene-based approaches were used to study the bacterial community of the bentonite samples by traditional clone libraries and Illumina sequencing. Using both techniques, the bacterial diversity analysis revealed similar results, with phylotypes belonging to 14 different bacterial phyla: Acidobacteria, Actinobacteria, Armatimonadetes, Bacteroidetes, Chloroflexi, Cyanobacteria, Deinococcus-Thermus, Firmicutes, Gemmatimonadetes, Planctomycetes, Proteobacteria, Nitrospirae, Verrucomicrobia and an unknown phylum. The dominant groups of the community were represented by Proteobacteria and Bacteroidetes. A high diversity was found in three of the studied samples. However, two samples were less diverse and dominated by Betaproteobacteria.

  3. Deep developmental transcriptome sequencing uncovers numerous new genes and enhances gene annotation in the sponge Amphimedon queenslandica.

    Science.gov (United States)

    Fernandez-Valverde, Selene L; Calcino, Andrew D; Degnan, Bernard M

    2015-05-15

    The demosponge Amphimedon queenslandica is amongst the few early-branching metazoans with an assembled and annotated draft genome, making it an important species in the study of the origin and early evolution of animals. Current gene models in this species are largely based on in silico predictions and low coverage expressed sequence tag (EST) evidence. Amphimedon queenslandica protein-coding gene models are improved using deep RNA-Seq data from four developmental stages and CEL-Seq data from 82 developmental samples. Over 86% of previously predicted genes are retained in the new gene models, although 24% have additional exons; there is also a marked increase in the total number of annotated 3' and 5' untranslated regions (UTRs). Importantly, these new developmental transcriptome data reveal numerous previously unannotated protein-coding genes in the Amphimedon genome, increasing the total gene number by 25%, from 30,060 to 40,122. In general, Amphimedon genes have introns that are markedly smaller than those in other animals and most of the alternatively spliced genes in Amphimedon undergo intron-retention; exon-skipping is the least common mode of alternative splicing. Finally, in addition to canonical polyadenylation signal sequences, Amphimedon genes are enriched in a number of unique AT-rich motifs in their 3' UTRs. The inclusion of developmental transcriptome data has substantially improved the structure and composition of protein-coding gene models in Amphimedon queenslandica, providing a more accurate and comprehensive set of genes for functional and comparative studies. These improvements reveal the Amphimedon genome is comprised of a remarkably high number of tightly packed genes. These genes have small introns and there is pervasive intron retention amongst alternatively spliced transcripts. These aspects of the sponge genome are more similar unicellular opisthokont genomes than to other animal genomes.

  4. Comparison of two approaches for the classification of 16S rRNA gene sequences.

    Science.gov (United States)

    Chatellier, Sonia; Mugnier, Nathalie; Allard, Françoise; Bonnaud, Bertrand; Collin, Valérie; van Belkum, Alex; Veyrieras, Jean-Baptiste; Emler, Stefan

    2014-10-01

    The use of 16S rRNA gene sequences for microbial identification in clinical microbiology is accepted widely, and requires databases and algorithms. We compared a new research database containing curated 16S rRNA gene sequences in combination with the lca (lowest common ancestor) algorithm (RDB-LCA) to a commercially available 16S rDNA Centroid approach. We used 1025 bacterial isolates characterized by biochemistry, matrix-assisted laser desorption/ionization time-of-flight MS and 16S rDNA sequencing. Nearly 80 % of isolates were identified unambiguously at the species level by both classification platforms used. The remaining isolates were mostly identified correctly at the genus level due to the limited resolution of 16S rDNA sequencing. Discrepancies between both 16S rDNA platforms were due to differences in database content and the algorithm used, and could amount to up to 10.5 %. Up to 1.4 % of the analyses were found to be inconclusive. It is important to realize that despite the overall good performance of the pipelines for analysis, some inconclusive results remain that require additional in-depth analysis performed using supplementary methods. © 2014 The Authors.

  5. Discrepancy between Hepatitis C Virus Genotypes and NS4-Based Serotypes: Association with Their Subgenomic Sequences

    Directory of Open Access Journals (Sweden)

    Nan Nwe Win

    2017-01-01

    Full Text Available Determination of hepatitis C virus (HCV genotypes plays an important role in the direct-acting agent era. Discrepancies between HCV genotyping and serotyping assays are occasionally observed. Eighteen samples with discrepant results between genotyping and serotyping methods were analyzed. HCV serotyping and genotyping were based on the HCV nonstructural 4 (NS4 region and 5′-untranslated region (5′-UTR, respectively. HCV core and NS4 regions were chosen to be sequenced and were compared with the genotyping and serotyping results. Deep sequencing was also performed for the corresponding HCV NS4 regions. Seventeen out of 18 discrepant samples could be sequenced by the Sanger method. Both HCV core and NS4 sequences were concordant with that of genotyping in the 5′-UTR in all 17 samples. In cloning analysis of the HCV NS4 region, there were several amino acid variations, but each sequence was much closer to the peptide with the same genotype. Deep sequencing revealed that minor clones with different subgenotypes existed in two of the 17 samples. Genotyping by genome amplification showed high consistency, while several false reactions were detected by serotyping. The deep sequencing method also provides accurate genotyping results and may be useful for analyzing discrepant cases. HCV genotyping should be correctly determined before antiviral treatment.

  6. A comparison of parallel pyrosequencing and sanger clone-based sequencing and its impact on the characterization of the genetic diversity of HIV-1.

    Directory of Open Access Journals (Sweden)

    Binhua Liang

    Full Text Available BACKGROUND: Pyrosequencing technology has the potential to rapidly sequence HIV-1 viral quasispecies without requiring the traditional approach of cloning. In this study, we investigated the utility of ultra-deep pyrosequencing to characterize genetic diversity of the HIV-1 gag quasispecies and assessed the possible contribution of pyrosequencing technology in studying HIV-1 biology and evolution. METHODOLOGY/PRINCIPAL FINDINGS: HIV-1 gag gene was amplified from 96 patients using nested PCR. The PCR products were cloned and sequenced using capillary based Sanger fluorescent dideoxy termination sequencing. The same PCR products were also directly sequenced using the 454 pyrosequencing technology. The two sequencing methods were evaluated for their ability to characterize quasispecies variation, and to reveal sites under host immune pressure for their putative functional significance. A total of 14,034 variations were identified by 454 pyrosequencing versus 3,632 variations by Sanger clone-based (SCB sequencing. 11,050 of these variations were detected only by pyrosequencing. These undetected variations were located in the HIV-1 Gag region which is known to contain putative cytotoxic T lymphocyte (CTL and neutralizing antibody epitopes, and sites related to virus assembly and packaging. Analysis of the positively selected sites derived by the two sequencing methods identified several differences. All of them were located within the CTL epitope regions. CONCLUSIONS/SIGNIFICANCE: Ultra-deep pyrosequencing has proven to be a powerful tool for characterization of HIV-1 genetic diversity with enhanced sensitivity, efficiency, and accuracy. It also improved reliability of downstream evolutionary and functional analysis of HIV-1 quasispecies.

  7. Deep-Elaborative Learning of Introductory Management Accounting for Business Students

    Science.gov (United States)

    Choo, Freddie; Tan, Kim B.

    2005-01-01

    Research by Choo and Tan (1990; 1995) suggests that accounting students, who engage in deep-elaborative learning, have a better understanding of the course materials. The purposes of this paper are: (1) to describe a deep-elaborative instructional approach (hereafter DEIA) that promotes deep-elaborative learning of introductory management…

  8. A novel approach reveals high zooplankton standing stock deep in the sea

    Directory of Open Access Journals (Sweden)

    A. Vereshchaka

    2016-11-01

    Full Text Available In a changing ocean there is a critical need to understand global biogeochemical cycling, particularly regarding carbon. We have made strides in understanding upper ocean dynamics, but the deep ocean interior (> 1000 m is still largely unknown, despite representing the overwhelming majority of Earth's biosphere. Here we present a method for estimating deep-pelagic zooplankton biomass on an ocean-basin scale. We have made several new discoveries about the Atlantic, which likely apply to the world ocean. First, multivariate analysis showed that depth and Chl were the basic factors affecting the wet biomass of the main plankton groups. Wet biomass of all major groups was significantly correlated with Chl. Second, zooplankton biomass in the upper bathypelagic domain is higher than expected. Third, the majority of this biomass comprises macroplanktonic shrimps, which have been historically underestimated. These findings, coupled with recent findings of increased global deep-pelagic fish biomass, suggest that the contribution of the deep-ocean pelagic fauna for biogeochemical cycles may be more important than previously thought.

  9. A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data

    KAUST Repository

    Sepúlveda, Nuno

    2013-02-26

    Background: The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model.Results: Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates.Conclusions: In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data. 2013 Seplveda et al.; licensee BioMed Central Ltd.

  10. A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data.

    Science.gov (United States)

    Sepúlveda, Nuno; Campino, Susana G; Assefa, Samuel A; Sutherland, Colin J; Pain, Arnab; Clark, Taane G

    2013-02-26

    The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model. Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates. In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data.

  11. A Poisson hierarchical modelling approach to detecting copy number variation in sequence coverage data

    KAUST Repository

    Sepú lveda, Nuno; Campino, Susana G; Assefa, Samuel A; Sutherland, Colin J; Pain, Arnab; Clark, Taane G

    2013-01-01

    Background: The advent of next generation sequencing technology has accelerated efforts to map and catalogue copy number variation (CNV) in genomes of important micro-organisms for public health. A typical analysis of the sequence data involves mapping reads onto a reference genome, calculating the respective coverage, and detecting regions with too-low or too-high coverage (deletions and amplifications, respectively). Current CNV detection methods rely on statistical assumptions (e.g., a Poisson model) that may not hold in general, or require fine-tuning the underlying algorithms to detect known hits. We propose a new CNV detection methodology based on two Poisson hierarchical models, the Poisson-Gamma and Poisson-Lognormal, with the advantage of being sufficiently flexible to describe different data patterns, whilst robust against deviations from the often assumed Poisson model.Results: Using sequence coverage data of 7 Plasmodium falciparum malaria genomes (3D7 reference strain, HB3, DD2, 7G8, GB4, OX005, and OX006), we showed that empirical coverage distributions are intrinsically asymmetric and overdispersed in relation to the Poisson model. We also demonstrated a low baseline false positive rate for the proposed methodology using 3D7 resequencing data and simulation. When applied to the non-reference isolate data, our approach detected known CNV hits, including an amplification of the PfMDR1 locus in DD2 and a large deletion in the CLAG3.2 gene in GB4, and putative novel CNV regions. When compared to the recently available FREEC and cn.MOPS approaches, our findings were more concordant with putative hits from the highest quality array data for the 7G8 and GB4 isolates.Conclusions: In summary, the proposed methodology brings an increase in flexibility, robustness, accuracy and statistical rigour to CNV detection using sequence coverage data. 2013 Seplveda et al.; licensee BioMed Central Ltd.

  12. A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head.

    Science.gov (United States)

    Devalla, Sripad Krishna; Chin, Khai Sing; Mari, Jean-Martial; Tun, Tin A; Strouthidis, Nicholas G; Aung, Tin; Thiéry, Alexandre H; Girard, Michaël J A

    2018-01-01

    To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH). A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally stain (i.e., highlight) six tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the dice coefficient, sensitivity, specificity, intersection over union (IU), and accuracy. We studied the effect of compensation, number of training images, and performance comparison between glaucoma and healthy subjects. For images it had not yet assessed, our algorithm was able to digitally stain the retinal nerve fiber layer + prelamina, the RPE, all other retinal layers, the choroid, and the peripapillary sclera and lamina cribrosa. For all tissues, the dice coefficient, sensitivity, specificity, IU, and accuracy (mean) were 0.84 ± 0.03, 0.92 ± 0.03, 0.99 ± 0.00, 0.89 ± 0.03, and 0.94 ± 0.02, respectively. Our algorithm performed significantly better when compensated images were used for training (P deep learning algorithm can simultaneously stain the neural and connective tissues of the ONH, offering a framework to automatically measure multiple key structural parameters of the ONH that may be critical to improve glaucoma management.

  13. Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

    Science.gov (United States)

    Lim, Chun Shen; Brown, Chris M.

    2018-01-01

    Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community. PMID:29354101

  14. A domain sequence approach to pangenomics: applications to Escherichia coli [v2; ref status: indexed, http://f1000r.es/ul

    Directory of Open Access Journals (Sweden)

    Lars-Gustav Snipen

    2013-05-01

    Full Text Available The study of microbial pangenomes relies on the computation of gene families, i.e. the clustering of coding sequences into groups of essentially similar genes. There is no standard approach to obtain such gene families. Ideally, the gene family computations should be robust against errors in the annotation of genes in various genomes. In an attempt to achieve this robustness, we propose to cluster sequences by their domain sequence, i.e. the ordered sequence of domains in their protein sequence. In a study of 347 genomes from Escherichia coli we find on average around 4500 proteins having hits in Pfam-A in every genome, clustering into around 2500 distinct domain sequence families in each genome. Across all genomes we find a total of 5724 such families. A binomial mixture model approach indicates this is around 95% of all domain sequences we would expect to see in E. coli in the future. A Heaps law analysis indicates the population of domain sequences is larger, but this analysis is also very sensitive to smaller changes in the computation procedure. The resolution between strains is good despite the coarse grouping obtained by domain sequence families. Clustering sequences by their ordered domain content give us domain sequence families, who are robust to errors in the gene prediction step. The computational load of the procedure scales linearly with the number of genomes, which is needed for the future explosion in the number of re-sequenced strains. The use of domain sequence families for a functional classification of strains clearly has some potential to be explored.

  15. U.V. repair in deep-sea bacteria

    International Nuclear Information System (INIS)

    Lutz, L.; Yayanos, A.A.

    1986-01-01

    Exposure of cells to light of less than 320 nanometers wavelengths may lead to lethal lesions and perhaps carcinogenesis. Many organisms have evolved mechanisms to repair U.V. light-induced damage. Organisms such as deep-sea bacteria are presumably never exposed to U.V. light and perhaps occasionally to visible from bioluminescence. Thus, the repair of U.V. damage in deep-sea bacterial DNA might be inefficient and repair by photoreactivation unlikely. The bacteria utilized in this investigation are temperature sensitive and barophilic. Four deep-sea isolates were chosen for this study: PE-36 from 3584 m, CNPT-3 from 5782 m, HS-34 from 5682 m, and MT-41 from 10,476 m, all are from the North Pacific ocean. The deep-sea extends from 1100 m to depths greater than 7000 m. It is a region of relatively uniform conditions. The temperature ranges from 5 to -1 0 C. There is no solar light in the deep-sea. Deep-sea bacteria are sensitive to U.V. light; in fact more sensitive than a variety of terrestrial and sea-surface bacteria. All four isolates demonstrate thymine dimer repair. Photoreactivation was observed in only MT-41. The other strains from shallower depths displayed no photoreactivation. The presence of DNA sequences homologous to the rec A, uvr A, B, and C and phr genes of E. coli have been examined by Southern hybridization techniques

  16. Deep Visual Attention Prediction

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing

    2018-05-01

    In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

  17. ADDRESS SEQUENCES FOR MULTI RUN RAM TESTING

    Directory of Open Access Journals (Sweden)

    V. N. Yarmolik

    2014-01-01

    Full Text Available A universal approach for generation of address sequences with specified properties is proposed and analyzed. A modified version of the Antonov and Saleev algorithm for Sobol sequences genera-tion is chosen as a mathematical description of the proposed method. Within the framework of the proposed universal approach, the Sobol sequences form a subset of the address sequences. Other sub-sets are also formed, which are Gray sequences, anti-Gray sequences, counter sequences and sequenc-es with specified properties.

  18. Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach.

    Science.gov (United States)

    Nemati, Shamim; Ghassemi, Mohammad M; Clifford, Gari D

    2016-08-01

    Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep reinforcement learning algorithm that learns an optimal heparin dosing policy from sample dosing trails and their associated outcomes in large electronic medical records. Using separate training and testing datasets, our model was observed to be effective in proposing heparin doses that resulted in better expected outcomes than the clinical guidelines. Our results demonstrate that a sequential modeling approach, learned from retrospective data, could potentially be used at the bedside to derive individualized patient dosing policies.

  19. Abnormality Detection in Mammography using Deep Convolutional Neural Networks

    OpenAIRE

    Xi, Pengcheng; Shu, Chang; Goubran, Rafik

    2018-01-01

    Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...

  20. Development of an Electrochemistry Teaching Sequence using a Phenomenographic Approach

    Science.gov (United States)

    Rodriguez-Velazquez, Sorangel

    the core concepts from discipline-specific models and theories serve as visual tools to describe reversible redox half-reactions at equilibrium, predict the spontaneity of the electrochemical process and explain interfacial equilibrium between redox species and electrodes in solution. The integration of physics concepts into electrochemistry instruction facilitated describing the interactions between the chemical system (e.g., redox species) and the external circuit (e.g., voltmeter). The "Two worlds" theoretical framework was chosen to anchor a robust educational design where the world of objects and events is deliberately connected to the world of theories and models. The core concepts in Marcus theory and density of states (DOS) provided the scientific foundations to connect both worlds. The design of this teaching sequence involved three phases; the selection of the content to be taught, the determination of a coherent and explicit connection among concepts and the development of educational activities to engage students in the learning process. The reduction-oxidation and electrochemistry chapters of three of the most popular general chemistry textbooks were revised in order to identify potential gaps during instruction, taking into consideration learning and teaching difficulties. The electrochemistry curriculum was decomposed into manageable sections contained in modules. Thirteen modules were developed and each module addresses specific conceptions with regard to terminology, redox reactions in electrochemical cells, and the function of the external circuit in electrochemical process. The electrochemistry teaching sequence was evaluated using a phenomenographic approach. This approach allows describing the qualitative variation in instructors' consciousness about the teaching of electrochemistry. A phenomenographic analysis revealed that the most relevant aspect of variation came from instructors' expertise. Participant A expertise (electrochemist) promoted in

  1. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

    Science.gov (United States)

    Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

    2017-08-01

    Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

  2. A Physics-Based Deep Learning Approach to Shadow Invariant Representations of Hyperspectral Images.

    Science.gov (United States)

    Windrim, Lloyd; Ramakrishnan, Rishi; Melkumyan, Arman; Murphy, Richard J

    2018-02-01

    This paper proposes the Relit Spectral Angle-Stacked Autoencoder, a novel unsupervised feature learning approach for mapping pixel reflectances to illumination invariant encodings. This work extends the Spectral Angle-Stacked Autoencoder so that it can learn a shadow-invariant mapping. The method is inspired by a deep learning technique, Denoising Autoencoders, with the incorporation of a physics-based model for illumination such that the algorithm learns a shadow invariant mapping without the need for any labelled training data, additional sensors, a priori knowledge of the scene or the assumption of Planckian illumination. The method is evaluated using datasets captured from several different cameras, with experiments to demonstrate the illumination invariance of the features and how they can be used practically to improve the performance of high-level perception algorithms that operate on images acquired outdoors.

  3. DEEP: a general computational framework for predicting enhancers

    KAUST Repository

    Kleftogiannis, Dimitrios A.

    2014-11-05

    Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

  4. DEEP: a general computational framework for predicting enhancers

    KAUST Repository

    Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

    2014-01-01

    Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

  5. A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs.

    Science.gov (United States)

    Wu, Zifeng; Huang, Yongzhen; Wang, Liang; Wang, Xiaogang; Tan, Tieniu

    2017-02-01

    This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications.

  6. DeepPy: Pythonic deep learning

    DEFF Research Database (Denmark)

    Larsen, Anders Boesen Lindbo

    This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

  7. Identification of somatic mutations in postmortem human brains by whole genome sequencing and their implications for psychiatric disorders.

    Science.gov (United States)

    Nishioka, Masaki; Bundo, Miki; Ueda, Junko; Katsuoka, Fumiki; Sato, Yukuto; Kuroki, Yoko; Ishii, Takao; Ukai, Wataru; Murayama, Shigeo; Hashimoto, Eri; Nagasaki, Masao; Yasuda, Jun; Kasai, Kiyoto; Kato, Tadafumi; Iwamoto, Kazuya

    2018-04-01

    Somatic mutations in the human brain are hypothesized to contribute to the functional diversity of brain cells as well as the pathophysiology of neuropsychiatric diseases. However, there are still few reports on somatic mutations in non-neoplastic human brain tissues. This study attempted to unveil the landscape of somatic mutations in the human brain. We explored the landscape of somatic mutations in human brain tissues derived from three individuals with no neuropsychiatric diseases by whole-genome deep sequencing at a depth of around 100. The candidate mutations underwent multi-layered filtering, and were validated by ultra-deep target amplicon sequencing at a depth of around 200 000. Thirty-one somatic mutations were identified in the human brain, demonstrating the utility of whole-genome sequencing of bulk brain tissue. The mutations were enriched in neuron-expressed genes, and two-thirds of the identified somatic single nucleotide variants in the brain tissues were cytosine-to-thymine transitions, half of which were in CpG dinucleotides. Our developed filtering and validation approaches will be useful to identify somatic mutations in the human brain. The vulnerability of neuron-expressed genes to mutational events suggests their potential relevance to neuropsychiatric diseases. © 2017 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

  8. A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome

    Directory of Open Access Journals (Sweden)

    Scoté-Blachon Céline

    2008-09-01

    Full Text Available Abstract Background "Open" transcriptome analysis methods allow to study gene expression without a priori knowledge of the transcript sequences. As of now, SAGE (Serial Analysis of Gene Expression, LongSAGE and MPSS (Massively Parallel Signature Sequencing are the mostly used methods for "open" transcriptome analysis. Both LongSAGE and MPSS rely on the isolation of 21 pb tag sequences from each transcript. In contrast to LongSAGE, the high throughput sequencing method used in MPSS enables the rapid sequencing of very large libraries containing several millions of tags, allowing deep transcriptome analysis. However, a bias in the complexity of the transcriptome representation obtained by MPSS was recently uncovered. Results In order to make a deep analysis of mouse hypothalamus transcriptome avoiding the limitation introduced by MPSS, we combined LongSAGE with the Solexa sequencing technology and obtained a library of more than 11 millions of tags. We then compared it to a LongSAGE library of mouse hypothalamus sequenced with the Sanger method. Conclusion We found that Solexa sequencing technology combined with LongSAGE is perfectly suited for deep transcriptome analysis. In contrast to MPSS, it gives a complex representation of transcriptome as reliable as a LongSAGE library sequenced by the Sanger method.

  9. Deep ART Neural Model for Biologically Inspired Episodic Memory and Its Application to Task Performance of Robots.

    Science.gov (United States)

    Park, Gyeong-Moon; Yoo, Yong-Ho; Kim, Deok-Hwa; Kim, Jong-Hwan

    2017-06-26

    Robots are expected to perform smart services and to undertake various troublesome or difficult tasks in the place of humans. Since these human-scale tasks consist of a temporal sequence of events, robots need episodic memory to store and retrieve the sequences to perform the tasks autonomously in similar situations. As episodic memory, in this paper we propose a novel Deep adaptive resonance theory (ART) neural model and apply it to the task performance of the humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory at KAIST. Deep ART has a deep structure to learn events, episodes, and even more like daily episodes. Moreover, it can retrieve the correct episode from partial input cues robustly. To demonstrate the effectiveness and applicability of the proposed Deep ART, experiments are conducted with the humanoid robot, Mybot, for performing the three tasks of arranging toys, making cereal, and disposing of garbage.

  10. Distributed deep learning networks among institutions for medical imaging.

    Science.gov (United States)

    Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

    2018-03-29

    Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

  11. Deep Learning Microscopy

    KAUST Repository

    Rivenson, Yair

    2017-05-12

    We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably better resolution, matching the performance of higher numerical aperture lenses, also significantly surpassing their limited field-of-view and depth-of-field. These results are transformative for various fields that use microscopy tools, including e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, our presented approach is broadly applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging.

  12. Investigation of the Effect of Finite Pulse Errors on BABA Pulse Sequence Using Floquet-Magnus Expansion Approach.

    Science.gov (United States)

    Mananga, Eugene S; Reid, Alicia E

    This paper presents the study of finite pulse widths for the BABA pulse sequence using the Floquet-Magnus expansion (FME) approach. In the FME scheme, the first order F 1 is identical to its counterparts in average Hamiltonian theory (AHT) and Floquet theory (FT). However, the timing part in the FME approach is introduced via the Λ 1 ( t ) function not present in other schemes. This function provides an easy way for evaluating the spin evolution during "the time in between" through the Magnus expansion of the operator connected to the timing part of the evolution. The evaluation of Λ 1 ( t ) is useful especially for the analysis of the non-stroboscopic evolution. Here, the importance of the boundary conditions, which provides a natural choice of Λ 1 (0) is ignored. This work uses the Λ 1 ( t ) function to compare the efficiency of the BABA pulse sequence with δ - pulses and the BABA pulse sequence with finite pulses. Calculations of Λ 1 ( t ) and F 1 are presented.

  13. Laser Capture and Deep Sequencing Reveals the Transcriptomic Programmes Regulating the Onset of Pancreas and Liver Differentiation in Human Embryos

    Directory of Open Access Journals (Sweden)

    Rachel E. Jennings

    2017-11-01

    Full Text Available To interrogate the alternative fates of pancreas and liver in the earliest stages of human organogenesis, we developed laser capture, RNA amplification, and computational analysis of deep sequencing. Pancreas-enriched gene expression was less conserved between human and mouse than for liver. The dorsal pancreatic bud was enriched for components of Notch, Wnt, BMP, and FGF signaling, almost all genes known to cause pancreatic agenesis or hypoplasia, and over 30 unexplored transcription factors. SOX9 and RORA were imputed as key regulators in pancreas compared with EP300, HNF4A, and FOXA family members in liver. Analyses implied that current in vitro human stem cell differentiation follows a dorsal rather than a ventral pancreatic program and pointed to additional factors for hepatic differentiation. In summary, we provide the transcriptional codes regulating the start of human liver and pancreas development to facilitate stem cell research and clinical interpretation without inter-species extrapolation.

  14. HIV-1 transmission patterns in antiretroviral therapy-naive, HIV-infected North Americans based on phylogenetic analysis by population level and ultra-deep DNA sequencing.

    Directory of Open Access Journals (Sweden)

    Lisa L Ross

    Full Text Available Factors that contribute to the transmission of human immunodeficiency virus type 1 (HIV-1, especially drug-resistant HIV-1 variants remain a significant public health concern. In-depth phylogenetic analyses of viral sequences obtained in the screening phase from antiretroviral-naïve HIV-infected patients seeking enrollment in EPZ108859, a large open-label study in the USA, Canada and Puerto Rico (ClinicalTrials.gov NCT00440947 were examined for insights into the roles of drug resistance and epidemiological factors that could impact disease dissemination. Viral transmission clusters (VTCs were initially predicted from a phylogenetic analysis of population level HIV-1 pol sequences obtained from 690 antiretroviral-naïve subjects in 2007. Subsequently, the predicted VTCs were tested for robustness by ultra deep sequencing (UDS using pyrosequencing technology and further phylogenetic analyses. The demographic characteristics of clustered and non-clustered subjects were then compared. From 690 subjects, 69 were assigned to 1 of 30 VTCs, each containing 2 to 5 subjects. Race composition of VTCs were significantly more likely to be white (72% vs. 60%; p = 0.04. VTCs had fewer reverse transcriptase and major PI resistance mutations (9% vs. 24%; p = 0.002 than non-clustered sequences. Both men-who-have-sex-with-men (MSM (68% vs. 48%; p = 0.001 and Canadians (29% vs. 14%; p = 0.03 were significantly more frequent in VTCs than non-clustered sequences. Of the 515 subjects who initiated antiretroviral therapy, 33 experienced confirmed virologic failure through 144 weeks while only 3/33 were from VTCs. Fewer VTCs subjects (as compared to those with non-clustering virus had HIV-1 with resistance-associated mutations or experienced virologic failure during the course of the study. Our analysis shows specific geographical and drug resistance trends that correlate well with transmission clusters defined by HIV sequences of similarity

  15. Minimally invasive trans-portal resection of deep intracranial lesions.

    NARCIS (Netherlands)

    Raza, S.M.; Recinos, P.F.; Avendano, J.; Adams, H.; Jallo, G.I.; Quinones-Hinojosa, A.

    2011-01-01

    BACKGROUND: The surgical management of deep intra-axial lesions still requires microsurgical approaches that utilize retraction of deep white matter to obtain adequate visualization. We report our experience with a new tubular retractor system, designed specifically for intracranial applications,

  16. A facile approach for decorating quantum dots deep inside of anodically grown self-organized TiO{sub 2} nanotubes

    Energy Technology Data Exchange (ETDEWEB)

    Shrestha, Nabeen K.; Yoon, Seog Joon; Lee, Deok Yeon; Lee, Misun; Lim, Iseul; Sung, MyungMo; Han, Sung-Hwan [Department of Chemistry, Hanyang University, Haengdang-dong 17, Sungdong-ku, Seoul 133-791 (Korea, Republic of); Ahn, Heejoon [Department of Fiber and Polymer Engineering, Hanyang University, Haengdang-dong 17, Sungdong-ku, Seoul 133-791 (Korea, Republic of)

    2011-04-15

    We demonstrate here a simple but very effective approach to decorate anodically grown TiO{sub 2} nanotubes (NTs) uniformly with CdS and PbS quantum dots (QDs) deep inside the NT walls. This approach is based on SILAR (successive ionic layer adsorption and reaction) technique assisted with evacuation of the NTs. The basic idea of evacuation is to remove air pockets trapped inside the NTs so as to clear the passage for the penetration of QD precursors down the bottom of the NTs. (copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)

  17. Single-strand conformation polymorphism (SSCP)-based mutation scanning approaches to fingerprint sequence variation in ribosomal DNA of ascaridoid nematodes.

    Science.gov (United States)

    Zhu, X Q; Gasser, R B

    1998-06-01

    In this study, we assessed single-strand conformation polymorphism (SSCP)-based approaches for their capacity to fingerprint sequence variation in ribosomal DNA (rDNA) of ascaridoid nematodes of veterinary and/or human health significance. The second internal transcribed spacer region (ITS-2) of rDNA was utilised as the target region because it is known to provide species-specific markers for this group of parasites. ITS-2 was amplified by PCR from genomic DNA derived from individual parasites and subjected to analysis. Direct SSCP analysis of amplicons from seven taxa (Toxocara vitulorum, Toxocara cati, Toxocara canis, Toxascaris leonina, Baylisascaris procyonis, Ascaris suum and Parascaris equorum) showed that the single-strand (ss) ITS-2 patterns produced allowed their unequivocal identification to species. While no variation in SSCP patterns was detected in the ITS-2 within four species for which multiple samples were available, the method allowed the direct display of four distinct sequence types of ITS-2 among individual worms of T. cati. Comparison of SSCP/sequencing with the methods of dideoxy fingerprinting (ddF) and restriction endonuclease fingerprinting (REF) revealed that also ddF allowed the definition of the four sequence types, whereas REF displayed three of four. The findings indicate the usefulness of the SSCP-based approaches for the identification of ascaridoid nematodes to species, the direct display of sequence variation in rDNA and the detection of population variation. The ability to fingerprint microheterogeneity in ITS-2 rDNA using such approaches also has implications for studying fundamental aspects relating to mutational change in rDNA.

  18. Process-based approach for the detection of deep gas invading the surface

    Science.gov (United States)

    Romanak, Katherine; Bennett, Philip C.

    2017-05-09

    The present invention includes a method for determining the level of deep gas in a near surface formation that includes: measuring CO.sub.2, O.sub.2, CH.sub.4, and N.sub.2 levels in percent by volume from one or more surface or near surface geological samples; adding the water vapor content to the measured CO.sub.2, O.sub.2, CH.sub.4, and N.sub.2 levels in percent by volume; normalizing the gas mixture to 100% by volume or 1 atmospheric total pressure; and determining the ratios of: O.sub.2 versus CO.sub.2 to distinguish in-situ vadose zone CO.sub.2 from exogenous deep leakage CO.sub.2; CO.sub.2 versus N.sub.2 to distinguish whether CO.sub.2 is being removed from the near surface formation or CO.sub.2 is added from an exogenous deep leakage input; or CO.sub.2 versus N.sub.2/O.sub.2 to determine the degree of oxygen influx, consumption, or both; wherein the ratios are indicative of natural in situ CO.sub.2 or CO.sub.2 from the exogenous deep leakage input.

  19. Oral Microbiome of Deep and Shallow Dental Pockets In Chronic Periodontitis

    Science.gov (United States)

    Ge, Xiuchun; Rodriguez, Rafael; Trinh, My; Gunsolley, John; Xu, Ping

    2013-01-01

    We examined the subgingival bacterial biodiversity in untreated chronic periodontitis patients by sequencing 16S rRNA genes. The primary purpose of the study was to compare the oral microbiome in deep (diseased) and shallow (healthy) sites. A secondary purpose was to evaluate the influences of smoking, race and dental caries on this relationship. A total of 88 subjects from two clinics were recruited. Paired subgingival plaque samples were taken from each subject, one from a probing site depth >5 mm (deep site) and the other from a probing site depth ≤3mm (shallow site). A universal primer set was designed to amplify the V4–V6 region for oral microbial 16S rRNA sequences. Differences in genera and species attributable to deep and shallow sites were determined by statistical analysis using a two-part model and false discovery rate. Fifty-one of 170 genera and 200 of 746 species were found significantly different in abundances between shallow and deep sites. Besides previously identified periodontal disease-associated bacterial species, additional species were found markedly changed in diseased sites. Cluster analysis revealed that the microbiome difference between deep and shallow sites was influenced by patient-level effects such as clinic location, race and smoking. The differences between clinic locations may be influenced by racial distribution, in that all of the African Americans subjects were seen at the same clinic. Our results suggested that there were influences from the microbiome for caries and periodontal disease and these influences are independent. PMID:23762384

  20. A deep learning pipeline for Indian dance style classification

    Science.gov (United States)

    Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

    2018-04-01

    In this paper, we address the problem of dance style classification to classify Indian dance or any dance in general. We propose a 3-step deep learning pipeline. First, we extract 14 essential joint locations of the dancer from each video frame, this helps us to derive any body region location within the frame, we use this in the second step which forms the main part of our pipeline. Here, we divide the dancer into regions of important motion in each video frame. We then extract patches centered at these regions. Main discriminative motion is captured in these patches. We stack the features from all such patches of a frame into a single vector and form our hierarchical dance pose descriptor. Finally, in the third step, we build a high level representation of the dance video using the hierarchical descriptors and train it using a Recurrent Neural Network (RNN) for classification. Our novelty also lies in the way we use multiple representations for a single video. This helps us to: (1) Overcome the RNN limitation of learning small sequences over big sequences such as dance; (2) Extract more data from the available dataset for effective deep learning by training multiple representations. Our contributions in this paper are three-folds: (1) We provide a deep learning pipeline for classification of any form of dance; (2) We prove that a segmented representation of a dance video works well with sequence learning techniques for recognition purposes; (3) We extend and refine the ICD dataset and provide a new dataset for evaluation of dance. Our model performs comparable or better in some cases than the state-of-the-art on action recognition benchmarks.

  1. The application of bilateral femoral vein approaches in interventional treatment of deep venous thrombosis of left lower extremity

    International Nuclear Information System (INIS)

    Wang Xiuping; Liu Jian; Wang Bin; Yao Zhongqiang; Zhang Yan; Li Chen

    2011-01-01

    Objective: To investigate the feasibility of interventional catheterization with bilateral femoral vein approaches for performing the thrombolytic treatment of acute deep venous thrombosis of left lower extremity. Methods: Antegrade puncturing into the left femoral vein was carried out in eighteen patients with acute deep vein thrombosis in the left lower extremity after left iliac-femoral vein catheterization via the right femoral vein or the right jugular vein access failed. When the puncturing of the left femoral vein was successfully done and was confirmed by angiography, the guide wire was inserted into the inferior vena cava and was pulled out through the right femoral vein or right jugular vein, and a wire track was thus established. Then, retrograde insertion of the catheter was conducted along the wire from the right to the left until the catheter was placed into the left iliac-femoral vein for thrombolysis. Results: Of 18 cases,successful puncturing into the left femoral vein was achieved in 16, and an effective wire track was established between the left and right femoral veins, based on which the catheter was smoothly inserted into the left iliac-femoral vein via the right femoral vein or jugular vein. Catheter thrombolysis was employed for 3 to 14 days, the thrombus was completely dissolved and the lower extremity swelling subsided. During the course of thrombolysis, no obvious congestion or hematoma occurred at the puncturing site of the left femoral vein. Conclusion: For patients with deep vein thrombosis of left lower extremity, when left iliac-femoral vein catheterization via the right femoral vein or the right jugular vein access failed, the establishment of wire track by using bilateral femoral vein approaches for further catheterization of left iliac-femoral vein and subsequent thrombolysis is feasible in clinical practice. This technique is safe and minimally-invasive with higher success rate. (authors)

  2. Automated analysis of high-content microscopy data with deep learning.

    Science.gov (United States)

    Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

    2017-04-18

    Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

  3. Time and space resolved deep metagenomics to investigate selection pressures on low abundant species in complex environments

    DEFF Research Database (Denmark)

    Albertsen, Mads; Saunders, Aaron Marc; Nielsen, Kåre Lehmann

    and between EBPR plants we sequenced a total of 10 samples from 3 different plants over a 3 year period at a depth of 25 Gb each. In addition, one time point was selected for deep sequencing, generating 200 Gb of sequence divided between replicates. Quantitative FISH analysis using >30 oligonucleotide probes...

  4. A deep learning framework for causal shape transformation.

    Science.gov (United States)

    Lore, Kin Gwn; Stoecklein, Daniel; Davies, Michael; Ganapathysubramanian, Baskar; Sarkar, Soumik

    2018-02-01

    Recurrent neural network (RNN) and Long Short-term Memory (LSTM) networks are the common go-to architecture for exploiting sequential information where the output is dependent on a sequence of inputs. However, in most considered problems, the dependencies typically lie in the latent domain which may not be suitable for applications involving the prediction of a step-wise transformation sequence that is dependent on the previous states only in the visible domain with a known terminal state. We propose a hybrid architecture of convolution neural networks (CNN) and stacked autoencoders (SAE) to learn a sequence of causal actions that nonlinearly transform an input visual pattern or distribution into a target visual pattern or distribution with the same support and demonstrated its practicality in a real-world engineering problem involving the physics of fluids. We solved a high-dimensional one-to-many inverse mapping problem concerning microfluidic flow sculpting, where the use of deep learning methods as an inverse map is very seldom explored. This work serves as a fruitful use-case to applied scientists and engineers in how deep learning can be beneficial as a solution for high-dimensional physical problems, and potentially opening doors to impactful advance in fields such as material sciences and medical biology where multistep topological transformations is a key element. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. A novel, privacy-preserving cryptographic approach for sharing sequencing data

    Science.gov (United States)

    Cassa, Christopher A; Miller, Rachel A; Mandl, Kenneth D

    2013-01-01

    Objective DNA samples are often processed and sequenced in facilities external to the point of collection. These samples are routinely labeled with patient identifiers or pseudonyms, allowing for potential linkage to identity and private clinical information if intercepted during transmission. We present a cryptographic scheme to securely transmit externally generated sequence data which does not require any patient identifiers, public key infrastructure, or the transmission of passwords. Materials and methods This novel encryption scheme cryptographically protects participant sequence data using a shared secret key that is derived from a unique subset of an individual’s genetic sequence. This scheme requires access to a subset of an individual’s genetic sequence to acquire full access to the transmitted sequence data, which helps to prevent sample mismatch. Results We validate that the proposed encryption scheme is robust to sequencing errors, population uniqueness, and sibling disambiguation, and provides sufficient cryptographic key space. Discussion Access to a set of an individual’s genotypes and a mutually agreed cryptographic seed is needed to unlock the full sequence, which provides additional sample authentication and authorization security. We present modest fixed and marginal costs to implement this transmission architecture. Conclusions It is possible for genomics researchers who sequence participant samples externally to protect the transmission of sequence data using unique features of an individual’s genetic sequence. PMID:23125421

  6. Bacterial diversity and biogeography in deep-sea sediments of the South Atlantic Ocean

    DEFF Research Database (Denmark)

    Schauer, Regina; Bienhold, Christina; Ramette, Alban

    2010-01-01

    in 1051 sequences. Phylotypes affiliated with Gammaproteobacteria, Deltaproteobacteria and Acidobacteria were present in all three basins. The distribution of these shared phylotypes seemed to be influenced neither by the Walvis Ridge nor by different deep water masses, suggesting a high dispersal......Microbial biogeographic patterns in the deep sea depend on the ability of microorganisms to disperse. One possible limitation to microbial dispersal may be the Walvis Ridge that separates the Antarctic Lower Circumpolar Deep Water from the North Atlantic Deep Water. We examined bacterial...... communities in three basins of the eastern South Atlantic Ocean to determine diversity and biogeography of bacterial communities in deep-sea surface sediments. The analysis of 16S ribosomal RNA (rRNA) gene clone libraries in each basin revealed a high diversity, representing 521 phylotypes with 98% identity...

  7. Gene Prediction in Metagenomic Fragments with Deep Learning

    Directory of Open Access Journals (Sweden)

    Shao-Wu Zhang

    2017-01-01

    Full Text Available Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features and using deep stacking networks learning model, we present a novel method (called Meta-MFDL to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

  8. Metagenomic Signatures of Microbial Communities in Deep-Sea Hydrothermal Sediments of Azores Vent Fields.

    Science.gov (United States)

    Cerqueira, Teresa; Barroso, Cristina; Froufe, Hugo; Egas, Conceição; Bettencourt, Raul

    2018-01-21

    The organisms inhabiting the deep-seafloor are known to play a crucial role in global biogeochemical cycles. Chemolithoautotrophic prokaryotes, which produce biomass from single carbon molecules, constitute the primary source of nutrition for the higher organisms, being critical for the sustainability of food webs and overall life in the deep-sea hydrothermal ecosystems. The present study investigates the metabolic profiles of chemolithoautotrophs inhabiting the sediments of Menez Gwen and Rainbow deep-sea vent fields, in the Mid-Atlantic Ridge. Differences in the microbial community structure might be reflecting the distinct depth, geology, and distance from vent of the studied sediments. A metagenomic sequencing approach was conducted to characterize the microbiome of the deep-sea hydrothermal sediments and the relevant metabolic pathways used by microbes. Both Menez Gwen and Rainbow metagenomes contained a significant number of genes involved in carbon fixation, revealing the largely autotrophic communities thriving in both sites. Carbon fixation at Menez Gwen site was predicted to occur mainly via the reductive tricarboxylic acid cycle, likely reflecting the dominance of sulfur-oxidizing Epsilonproteobacteria at this site, while different autotrophic pathways were identified at Rainbow site, in particular the Calvin-Benson-Bassham cycle. Chemolithotrophy appeared to be primarily driven by the oxidation of reduced sulfur compounds, whether through the SOX-dependent pathway at Menez Gwen site or through reverse sulfate reduction at Rainbow site. Other energy-yielding processes, such as methane, nitrite, or ammonia oxidation, were also detected but presumably contributing less to chemolithoautotrophy. This work furthers our knowledge of the microbial ecology of deep-sea hydrothermal sediments and represents an important repository of novel genes with potential biotechnological interest.

  9. Microbial profiling of cpn60 universal target sequences in artificial mixtures of vaginal bacteria sampled by nylon swabs or self-sampling devices under different storage conditions.

    Science.gov (United States)

    Schellenberg, John J; Oh, Angela Yena; Hill, Janet E

    2017-05-01

    The vaginal microbiome is increasingly characterized by deep sequencing of universal genes. However, there are relatively few studies of how different specimen collection and sample storage and processing influence these molecular profiles. Here, we evaluate molecular microbial community profiles of samples collected using the HerSwab™ self-sampling device, compared to nylon swabs and under different storage conditions. In order to minimize technical variation, mixtures of 11 common vaginal bacteria in simulated vaginal fluid medium were sampled and DNA extracts prepared for massively parallel sequencing of the cpn60 universal target (UT). Three artificial mixtures imitating commonly observed vaginal microbiome profiles were easily distinguished and proportion of sequence reads correlated with the estimated proportion of the organism added to the artificial mixtures. Our results indicate that cpn60 UT amplicon sequencing quantifies the proportional abundance of member organisms in these artificial communities regardless of swab type or storage conditions, although some significant differences were observed between samples that were stored frozen and thawed prior to DNA extraction, compared to extractions from samples stored at room temperature for up to 7days. Our results indicate that an on-the-market device developed for infectious disease diagnostics may be appropriate for vaginal microbiome profiling, an approach that is increasingly facilitated by rapidly dropping deep sequencing costs. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Review on chemical processes around the facilities in deep underground and study on numerical approach to evaluate them

    International Nuclear Information System (INIS)

    Sawada, Masataka

    2003-01-01

    The facilities for radioactive waste repositories are constructed in deep underground. Various chemical reactions including microbial activities may affect the long-term performance of the barrier system. An advancement of the evaluation method for the long-term behavior of barrier materials is desired. One of the efficient approaches is numerical simulation based on modeling of chemical processes. In the first part of this report, chemical processes and microbial reactions that can affect the performance of facilities in deep underground are reviewed. For example, dissolution and precipitation of minerals composing bentonite and rock are caused by highly alkaline water from cementitious materials. Numerical approaches to the chemical processes are also studied. Most chemical processes are reactions between groundwater (or solutes in it) and minerals composing barrier materials. So they can be simulated by coupled reaction rate transport analyses. Some analysis codes are developed and applied to problems in radioactive waste disposal. Microbial reaction rate can be modeled using the growth equation of microorganisms. In order to evaluate the performance of the barrier system after altered by chemical processes, not only the change in composition but also properties of altered materials is required to be obtained as output of numerical simulation. If the relationships between reaction rate and material properties are obtained, time history and spatial distribution of material properties can also be obtained by the coupled reaction rate transport analysis. At present, modeling study on the relationships between them is not sufficient, and obtaining such relationships using both theoretical and experimental approaches are also an important research target. (author)

  11. Addressing the Concerns Surrounding Continuous Deep Sedation in Singapore and Southeast Asia: A Palliative Care Approach.

    Science.gov (United States)

    Krishna, Lalit Kumar Radha

    2015-09-01

    The application of continuous deep sedation (CDS) in the treatment of intractable suffering at the end of life continues to be tied to a number of concerns that have negated its use in palliative care. Part of the resistance towards use of this treatment option of last resort has been the continued association of CDS with physician-associated suicide and/or euthanasia (PAS/E), which is compounded by a lack clinical guidelines and a failure to cite this treatment under the aegis of a palliative care approach. I argue that reinstituting a palliative care-inspired approach that includes a holistic review of the patient's situation and the engagement of a multidisciplinary team (MDT) guided by clearly defined practice requirements that have been lacking amongst many prevailing guidelines will overcome prevailing objections to this practice and allow for the legitimization of this process.

  12. Deep Learning Policy Quantization

    NARCIS (Netherlands)

    van de Wolfshaar, Jos; Wiering, Marco; Schomaker, Lambertus

    2018-01-01

    We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on learning vector quantization. We replace the softmax operator of the policy with a more general and more flexible operator that is similar to the robust soft learning vector quantization algorithm.

  13. Use of deep whole-genome sequencing data to identify structure risk variants in breast cancer susceptibility genes.

    Science.gov (United States)

    Guo, Xingyi; Shi, Jiajun; Cai, Qiuyin; Shu, Xiao-Ou; He, Jing; Wen, Wanqing; Allen, Jamie; Pharoah, Paul; Dunning, Alison; Hunter, David J; Kraft, Peter; Easton, Douglas F; Zheng, Wei; Long, Jirong

    2018-03-01

    Functional disruptions of susceptibility genes by large genomic structure variant (SV) deletions in germlines are known to be associated with cancer risk. However, few studies have been conducted to systematically search for SV deletions in breast cancer susceptibility genes. We analysed deep (> 30x) whole-genome sequencing (WGS) data generated in blood samples from 128 breast cancer patients of Asian and European descent with either a strong family history of breast cancer or early cancer onset disease. To identify SV deletions in known or suspected breast cancer susceptibility genes, we used multiple SV calling tools including Genome STRiP, Delly, Manta, BreakDancer and Pindel. SV deletions were detected by at least three of these bioinformatics tools in five genes. Specifically, we identified heterozygous deletions covering a fraction of the coding regions of BRCA1 (with approximately 80kb in two patients), and TP53 genes (with ∼1.6 kb in two patients), and of intronic regions (∼1 kb) of the PALB2 (one patient), PTEN (three patients) and RAD51C genes (one patient). We confirmed the presence of these deletions using real-time quantitative PCR (qPCR). Our study identified novel SV deletions in breast cancer susceptibility genes and the identification of such SV deletions may improve clinical testing.

  14. Application of the whole-transcriptome shotgun sequencing approach to the study of Philadelphia-positive acute lymphoblastic leukemia

    International Nuclear Information System (INIS)

    Iacobucci, I; Ferrarini, A; Sazzini, M; Giacomelli, E; Lonetti, A; Xumerle, L; Ferrari, A; Papayannidis, C; Malerba, G; Luiselli, D; Boattini, A; Garagnani, P; Vitale, A; Soverini, S; Pane, F; Baccarani, M; Delledonne, M; Martinelli, G

    2012-01-01

    Although the pathogenesis of BCR–ABL1-positive acute lymphoblastic leukemia (ALL) is mainly related to the expression of the BCR–ABL1 fusion transcript, additional cooperating genetic lesions are supposed to be involved in its development and progression. Therefore, in an attempt to investigate the complex landscape of mutations, changes in expression profiles and alternative splicing (AS) events that can be observed in such disease, the leukemia transcriptome of a BCR–ABL1-positive ALL patient at diagnosis and at relapse was sequenced using a whole-transcriptome shotgun sequencing (RNA-Seq) approach. A total of 13.9 and 15.8 million sequence reads was generated from de novo and relapsed samples, respectively, and aligned to the human genome reference sequence. This led to the identification of five validated missense mutations in genes involved in metabolic processes (DPEP1, TMEM46), transport (MVP), cell cycle regulation (ABL1) and catalytic activity (CTSZ), two of which resulted in acquired relapse variants. In all, 6390 and 4671 putative AS events were also detected, as well as expression levels for 18 315 and 18 795 genes, 28% of which were differentially expressed in the two disease phases. These data demonstrate that RNA-Seq is a suitable approach for identifying a wide spectrum of genetic alterations potentially involved in ALL

  15. Detecting atypical examples of known domain types by sequence similarity searching: the SBASE domain library approach.

    Science.gov (United States)

    Dhir, Somdutta; Pacurar, Mircea; Franklin, Dino; Gáspári, Zoltán; Kertész-Farkas, Attila; Kocsor, András; Eisenhaber, Frank; Pongor, Sándor

    2010-11-01

    SBASE is a project initiated to detect known domain types and predicting domain architectures using sequence similarity searching (Simon et al., Protein Seq Data Anal, 5: 39-42, 1992, Pongor et al, Nucl. Acids. Res. 21:3111-3115, 1992). The current approach uses a curated collection of domain sequences - the SBASE domain library - and standard similarity search algorithms, followed by postprocessing which is based on a simple statistics of the domain similarity network (http://hydra.icgeb.trieste.it/sbase/). It is especially useful in detecting rare, atypical examples of known domain types which are sometimes missed even by more sophisticated methodologies. This approach does not require multiple alignment or machine learning techniques, and can be a useful complement to other domain detection methodologies. This article gives an overview of the project history as well as of the concepts and principles developed within this the project.

  16. Forecasting spot electricity prices : Deep learning approaches and empirical comparison of traditional algorithms

    NARCIS (Netherlands)

    Lago Garcia, J.; De Ridder, Fjo; De Schutter, B.H.K.

    2018-01-01

    In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning

  17. Magnetism Teaching Sequences Based on an Inductive Approach for First-Year Thai University Science Students

    Science.gov (United States)

    Narjaikaew, Pattawan; Emarat, Narumon; Arayathanitkul, Kwan; Cowie, Bronwen

    2010-01-01

    The study investigated the impact on student motivation and understanding of magnetism of teaching sequences based on an inductive approach. The study was conducted in large lecture classes. A pre- and post-Conceptual Survey of Electricity and Magnetism was conducted with just fewer than 700 Thai undergraduate science students, before and after…

  18. Bacterial community diversity of the deep-sea octocoral Paramuricea placomus

    Directory of Open Access Journals (Sweden)

    Christina A. Kellogg

    2016-09-01

    Full Text Available Compared to tropical corals, much less is known about deep-sea coral biology and ecology. Although the microbial communities of some deep-sea corals have been described, this is the first study to characterize the bacterial community associated with the deep-sea octocoral, Paramuricea placomus. Samples from five colonies of P. placomus were collected from Baltimore Canyon (379–382 m depth in the Atlantic Ocean off the east coast of the United States of America. DNA was extracted from the coral samples and 16S rRNA gene amplicons were pyrosequenced using V4-V5 primers. Three samples sequenced deeply (>4,000 sequences each and were further analyzed. The dominant microbial phylum was Proteobacteria, but other major phyla included Firmicutes and Planctomycetes. A conserved community of bacterial taxa held in common across the three P. placomus colonies was identified, comprising 68–90% of the total bacterial community depending on the coral individual. The bacterial community of P. placomus does not appear to include the genus Endozoicomonas, which has been found previously to be the dominant bacterial associate in several temperate and tropical gorgonians. Inferred functionality suggests the possibility of nitrogen cycling by the core bacterial community.

  19. Three-dimensional fluid-attenuated inversion recovery sequence for visualisation of subthalamic nucleus for deep brain stimulation in Parkinson's disease

    Energy Technology Data Exchange (ETDEWEB)

    Heo, Young Jin [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology, Research Institute of Radiology, Seoul (Korea, Republic of); Inje University, Department of Radiology, Busan Paik Hospital, Busan (Korea, Republic of); Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Chai [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology, Research Institute of Radiology, Seoul (Korea, Republic of); Lee, Jung Kyo [University of Ulsan College of Medicine, Asan Medical Center, Department of Neurosurgery, Seoul (Korea, Republic of); Lee, Chong Sik; Chung, Sun J. [University of Ulsan College of Medicine, Asan Medical Center, Department of Neurology, Seoul (Korea, Republic of); Cho, So Hyun [Department of Radiology, Busan (Korea, Republic of); Lee, Gyoung Ro [Philips HealthCare Korea, Seoul (Korea, Republic of)

    2015-09-15

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an accepted treatment for advanced Parkinson's disease (PD). However, targeting the STN is difficult due to its relatively small size and variable location. The purpose of this study was to assess which of the following sequences obtained with the 3.0 T MR system can accurately delineate the STN: coronal 3D fluid-attenuated inversion recovery (FLAIR), 2D T2*-weighted fast-field echo (T2*-FFE) and 2D T2-weighted turbo spin-echo (TSE) sequences. We included 20 consecutive patients with PD who underwent 3.0 T MR for DBS targeting. 3D FLAIR, 2D T2*-FFE and T2-TSE images were obtained for all study patients. Image quality and demarcation of the STN were analysed using 4-point scales, and contrast ratio (CR) of the STN and normal white matter was calculated. The Friedman test was used to compare the three sequences. In qualitative analysis, the 2D T2*-FFE image showed more artefacts than 3D FLAIR or 2D T2-TSE, but the difference did not reach statistical significance. 3D FLAIR images showed significantly superior demarcation of the STN compared with 2D T2*-FFE and T2-TSE images (P < 0.001, respectively). The CR of 3D FLAIR was significantly higher than that of 2D T2*-FFE or T2-TSE images in multiple comparison correction (P < 0.001), but there was no significant difference in the CR between 2D T2*-FFE and T2-TSE images. Coronal 3D FLAIR images showed the most accurate demarcation of the STN for DBS targeting among coronal 3D FLAIR, 2D T2*-FFE and T2-TSE images. (orig.)

  20. Three-dimensional fluid-attenuated inversion recovery sequence for visualisation of subthalamic nucleus for deep brain stimulation in Parkinson's disease

    International Nuclear Information System (INIS)

    Heo, Young Jin; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Chai; Lee, Jung Kyo; Lee, Chong Sik; Chung, Sun J.; Cho, So Hyun; Lee, Gyoung Ro

    2015-01-01

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an accepted treatment for advanced Parkinson's disease (PD). However, targeting the STN is difficult due to its relatively small size and variable location. The purpose of this study was to assess which of the following sequences obtained with the 3.0 T MR system can accurately delineate the STN: coronal 3D fluid-attenuated inversion recovery (FLAIR), 2D T2*-weighted fast-field echo (T2*-FFE) and 2D T2-weighted turbo spin-echo (TSE) sequences. We included 20 consecutive patients with PD who underwent 3.0 T MR for DBS targeting. 3D FLAIR, 2D T2*-FFE and T2-TSE images were obtained for all study patients. Image quality and demarcation of the STN were analysed using 4-point scales, and contrast ratio (CR) of the STN and normal white matter was calculated. The Friedman test was used to compare the three sequences. In qualitative analysis, the 2D T2*-FFE image showed more artefacts than 3D FLAIR or 2D T2-TSE, but the difference did not reach statistical significance. 3D FLAIR images showed significantly superior demarcation of the STN compared with 2D T2*-FFE and T2-TSE images (P < 0.001, respectively). The CR of 3D FLAIR was significantly higher than that of 2D T2*-FFE or T2-TSE images in multiple comparison correction (P < 0.001), but there was no significant difference in the CR between 2D T2*-FFE and T2-TSE images. Coronal 3D FLAIR images showed the most accurate demarcation of the STN for DBS targeting among coronal 3D FLAIR, 2D T2*-FFE and T2-TSE images. (orig.)

  1. DRREP: deep ridge regressed epitope predictor.

    Science.gov (United States)

    Sher, Gene; Zhi, Degui; Zhang, Shaojie

    2017-10-03

    The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

  2. mPUMA: a computational approach to microbiota analysis by de novo assembly of operational taxonomic units based on protein-coding barcode sequences.

    Science.gov (United States)

    Links, Matthew G; Chaban, Bonnie; Hemmingsen, Sean M; Muirhead, Kevin; Hill, Janet E

    2013-08-15

    Formation of operational taxonomic units (OTU) is a common approach to data aggregation in microbial ecology studies based on amplification and sequencing of individual gene targets. The de novo assembly of OTU sequences has been recently demonstrated as an alternative to widely used clustering methods, providing robust information from experimental data alone, without any reliance on an external reference database. Here we introduce mPUMA (microbial Profiling Using Metagenomic Assembly, http://mpuma.sourceforge.net), a software package for identification and analysis of protein-coding barcode sequence data. It was developed originally for Cpn60 universal target sequences (also known as GroEL or Hsp60). Using an unattended process that is independent of external reference sequences, mPUMA forms OTUs by DNA sequence assembly and is capable of tracking OTU abundance. mPUMA processes microbial profiles both in terms of the direct DNA sequence as well as in the translated amino acid sequence for protein coding barcodes. By forming OTUs and calculating abundance through an assembly approach, mPUMA is capable of generating inputs for several popular microbiota analysis tools. Using SFF data from sequencing of a synthetic community of Cpn60 sequences derived from the human vaginal microbiome, we demonstrate that mPUMA can faithfully reconstruct all expected OTU sequences and produce compositional profiles consistent with actual community structure. mPUMA enables analysis of microbial communities while empowering the discovery of novel organisms through OTU assembly.

  3. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

    Science.gov (United States)

    Jiang, Hongkai; Li, Xingqiu; Shao, Haidong; Zhao, Ke

    2018-06-01

    Traditional intelligent fault diagnosis methods for rolling bearings heavily depend on manual feature extraction and feature selection. For this purpose, an intelligent deep learning method, named the improved deep recurrent neural network (DRNN), is proposed in this paper. Firstly, frequency spectrum sequences are used as inputs to reduce the input size and ensure good robustness. Secondly, DRNN is constructed by the stacks of the recurrent hidden layer to automatically extract the features from the input spectrum sequences. Thirdly, an adaptive learning rate is adopted to improve the training performance of the constructed DRNN. The proposed method is verified with experimental rolling bearing data, and the results confirm that the proposed method is more effective than traditional intelligent fault diagnosis methods.

  4. Correct approach to consideration of experimental resolution in parametric analysis of scaling violation in deep inelastic lepton-nucleon interaction

    International Nuclear Information System (INIS)

    Ammosov, V.V.; Usubov, Z.U.; Zhigunov, V.P.

    1990-01-01

    A problem of parametric analysis of the scaling violation in deep inelastic lepton-nucleon interactions in the framework of quantum chromodynamics (QCD) is considered. For a correct consideration of the experimental resolution we use the χ 2 -method, which is demonstrated by numeric experiments and analysis of the 15-foot bubble chamber neutrino experimental data. The model parameters obtained in this approach differ noticeably from those obtained earlier. (orig.)

  5. Investigation of the effect of finite pulse errors on the BABA pulse sequence using the Floquet-Magnus expansion approach

    Science.gov (United States)

    Mananga, Eugene S.; Reid, Alicia E.

    2013-01-01

    This paper presents a study of finite pulse widths for the BABA pulse sequence using the Floquet-Magnus expansion (FME) approach. In the FME scheme, the first order ? is identical to its counterparts in average Hamiltonian theory (AHT) and Floquet theory (FT). However, the timing part in the FME approach is introduced via the ? function not present in other schemes. This function provides an easy way for evaluating the spin evolution during the time in between' through the Magnus expansion of the operator connected to the timing part of the evolution. The evaluation of ? is particularly useful for the analysis of the non-stroboscopic evolution. Here, the importance of the boundary conditions, which provide a natural choice of ? , is ignored. This work uses the ? function to compare the efficiency of the BABA pulse sequence with ? and the BABA pulse sequence with finite pulses. Calculations of ? and ? are presented.

  6. An Advanced Deep Learning Approach for Ki-67 Stained Hotspot Detection and Proliferation Rate Scoring for Prognostic Evaluation of Breast Cancer.

    Science.gov (United States)

    Saha, Monjoy; Chakraborty, Chandan; Arun, Indu; Ahmed, Rosina; Chatterjee, Sanjoy

    2017-06-12

    Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring. In this article, we suggest an advanced deep learning model for computerized recognition of candidate hotspots and subsequent proliferation rate scoring by quantifying Ki-67 appearance in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology uses Gamma mixture model (GMM) with Expectation-Maximization for seed point detection and patch selection and deep learning, comprises with decision layer, for hotspots detection and proliferation scoring. Experimental results provide 93% precision, 0.88% recall and 0.91% F-score value. The model performance has also been compared with the pathologists' manual annotations and recently published articles. In future, the proposed deep learning framework will be highly reliable and beneficial to the junior and senior pathologists for fast and efficient Ki-67 scoring.

  7. Predicting Process Behaviour using Deep Learning

    OpenAIRE

    Evermann, Joerg; Rehse, Jana-Rebecca; Fettke, Peter

    2016-01-01

    Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real da...

  8. Deep Sea Coral voucher sequence dataset - Identification of deep-sea corals collected during the 2009 - 2014 West Coast Groundfish Bottom Trawl Survey

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Data for this project resides in the West Coast Groundfish Bottom Trawl Survey Database. Deep-sea corals are often components of trawling bycatch, though their...

  9. Unveiling the Biodiversity of Deep-Sea Nematodes through Metabarcoding: Are We Ready to Bypass the Classical Taxonomy?

    Science.gov (United States)

    Dell'Anno, Antonio; Carugati, Laura; Corinaldesi, Cinzia; Riccioni, Giulia; Danovaro, Roberto

    2015-01-01

    Nematodes inhabiting benthic deep-sea ecosystems account for >90% of the total metazoan abundances and they have been hypothesised to be hyper-diverse, but their biodiversity is still largely unknown. Metabarcoding could facilitate the census of biodiversity, especially for those tiny metazoans for which morphological identification is difficult. We compared, for the first time, different DNA extraction procedures based on the use of two commercial kits and a previously published laboratory protocol and tested their suitability for sequencing analyses of 18S rDNA of marine nematodes. We also investigated the reliability of Roche 454 sequencing analyses for assessing the biodiversity of deep-sea nematode assemblages previously morphologically identified. Finally, intra-genomic variation in 18S rRNA gene repeats was investigated by Illumina MiSeq in different deep-sea nematode morphospecies to assess the influence of polymorphisms on nematode biodiversity estimates. Our results indicate that the two commercial kits should be preferred for the molecular analysis of biodiversity of deep-sea nematodes since they consistently provide amplifiable DNA suitable for sequencing. We report that the morphological identification of deep-sea nematodes matches the results obtained by metabarcoding analysis only at the order-family level and that a large portion of Operational Clustered Taxonomic Units (OCTUs) was not assigned. We also show that independently from the cut-off criteria and bioinformatic pipelines used, the number of OCTUs largely exceeds the number of individuals and that 18S rRNA gene of different morpho-species of nematodes displayed intra-genomic polymorphisms. Our results indicate that metabarcoding is an important tool to explore the diversity of deep-sea nematodes, but still fails in identifying most of the species due to limited number of sequences deposited in the public databases, and in providing quantitative data on the species encountered. These aspects

  10. High-throughput verification of transcriptional starting sites by Deep-RACE

    DEFF Research Database (Denmark)

    Olivarius, Signe; Plessy, Charles; Carninci, Piero

    2009-01-01

    We present a high-throughput method for investigating the transcriptional starting sites of genes of interest, which we named Deep-RACE (Deep–rapid amplification of cDNA ends). Taking advantage of the latest sequencing technology, it allows the parallel analysis of multiple genes and is free...

  11. Nicotiana small RNA sequences support a host genome origin of cucumber mosaic virus satellite RNA.

    Directory of Open Access Journals (Sweden)

    Kiran Zahid

    2015-01-01

    Full Text Available Satellite RNAs (satRNAs are small noncoding subviral RNA pathogens in plants that depend on helper viruses for replication and spread. Despite many decades of research, the origin of satRNAs remains unknown. In this study we show that a β-glucuronidase (GUS transgene fused with a Cucumber mosaic virus (CMV Y satellite RNA (Y-Sat sequence (35S-GUS:Sat was transcriptionally repressed in N. tabacum in comparison to a 35S-GUS transgene that did not contain the Y-Sat sequence. This repression was not due to DNA methylation at the 35S promoter, but was associated with specific DNA methylation at the Y-Sat sequence. Both northern blot hybridization and small RNA deep sequencing detected 24-nt siRNAs in wild-type Nicotiana plants with sequence homology to Y-Sat, suggesting that the N. tabacum genome contains Y-Sat-like sequences that give rise to 24-nt sRNAs capable of guiding RNA-directed DNA methylation (RdDM to the Y-Sat sequence in the 35S-GUS:Sat transgene. Consistent with this, Southern blot hybridization detected multiple DNA bands in Nicotiana plants that had sequence homology to Y-Sat, suggesting that Y-Sat-like sequences exist in the Nicotiana genome as repetitive DNA, a DNA feature associated with 24-nt sRNAs. Our results point to a host genome origin for CMV satRNAs, and suggest novel approach of using small RNA sequences for finding the origin of other satRNAs.

  12. Graph-based sequence annotation using a data integration approach

    Directory of Open Access Journals (Sweden)

    Pesch Robert

    2008-06-01

    Full Text Available The automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database Ara- Cyc which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation.

  13. Boosting compound-protein interaction prediction by deep learning.

    Science.gov (United States)

    Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

    2016-11-01

    The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Phylo: a citizen science approach for improving multiple sequence alignment.

    Directory of Open Access Journals (Sweden)

    Alexander Kawrykow

    Full Text Available BACKGROUND: Comparative genomics, or the study of the relationships of genome structure and function across different species, offers a powerful tool for studying evolution, annotating genomes, and understanding the causes of various genetic disorders. However, aligning multiple sequences of DNA, an essential intermediate step for most types of analyses, is a difficult computational task. In parallel, citizen science, an approach that takes advantage of the fact that the human brain is exquisitely tuned to solving specific types of problems, is becoming increasingly popular. There, instances of hard computational problems are dispatched to a crowd of non-expert human game players and solutions are sent back to a central server. METHODOLOGY/PRINCIPAL FINDINGS: We introduce Phylo, a human-based computing framework applying "crowd sourcing" techniques to solve the Multiple Sequence Alignment (MSA problem. The key idea of Phylo is to convert the MSA problem into a casual game that can be played by ordinary web users with a minimal prior knowledge of the biological context. We applied this strategy to improve the alignment of the promoters of disease-related genes from up to 44 vertebrate species. Since the launch in November 2010, we received more than 350,000 solutions submitted from more than 12,000 registered users. Our results show that solutions submitted contributed to improving the accuracy of up to 70% of the alignment blocks considered. CONCLUSIONS/SIGNIFICANCE: We demonstrate that, combined with classical algorithms, crowd computing techniques can be successfully used to help improving the accuracy of MSA. More importantly, we show that an NP-hard computational problem can be embedded in casual game that can be easily played by people without significant scientific training. This suggests that citizen science approaches can be used to exploit the billions of "human-brain peta-flops" of computation that are spent every day playing games

  15. Deep sequencing-based analysis of the Cymbidium ensifolium floral transcriptome.

    Directory of Open Access Journals (Sweden)

    Xiaobai Li

    Full Text Available Cymbidium ensifolium is a Chinese Cymbidium with an elegant shape, beautiful appearance, and a fragrant aroma. C. ensifolium has a long history of cultivation in China and it has excellent commercial value as a potted plant and cut flower. The development of C. ensifolium genomic resources has been delayed because of its large genome size. Taking advantage of technical and cost improvement of RNA-Seq, we extracted total mRNA from flower buds and mature flowers and obtained a total of 9.52 Gb of filtered nucleotides comprising 98,819,349 filtered reads. The filtered reads were assembled into 101,423 isotigs, representing 51,696 genes. Of the 101,423 isotigs, 41,873 were putative homologs of annotated sequences in the public databases, of which 158 were associated with floral development and 119 were associated with flowering. The isotigs were categorized according to their putative functions. In total, 10,212 of the isotigs were assigned into 25 eukaryotic orthologous groups (KOGs, 41,690 into 58 gene ontology (GO terms, and 9,830 into 126 Arabidopsis Kyoto Encyclopedia of Genes and Genomes (KEGG pathways, and 9,539 isotigs into 123 rice pathways. Comparison of the isotigs with those of the two related orchid species P. equestris and C. sinense showed that 17,906 isotigs are unique to C. ensifolium. In addition, a total of 7,936 SSRs and 16,676 putative SNPs were identified. To our knowledge, this transcriptome database is the first major genomic resource for C. ensifolium and the most comprehensive transcriptomic resource for genus Cymbidium. These sequences provide valuable information for understanding the molecular mechanisms of floral development and flowering. Sequences predicted to be unique to C. ensifolium would provide more insights into C. ensifolium gene diversity. The numerous SNPs and SSRs identified in the present study will contribute to marker development for C. ensifolium.

  16. Deep and surface learning in problem-based learning: a review of the literature.

    Science.gov (United States)

    Dolmans, Diana H J M; Loyens, Sofie M M; Marcq, Hélène; Gijbels, David

    2016-12-01

    In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review investigates: (1) the effects of PBL on students' deep and surface approaches to learning, (2) whether and why these effects do differ across (a) the context of the learning environment (single vs. curriculum wide implementation), and (b) study quality. Studies were searched dealing with PBL and students' approaches to learning. Twenty-one studies were included. The results indicate that PBL does enhance deep learning with a small positive average effect size of .11 and a positive effect in eleven of the 21 studies. Four studies show a decrease in deep learning and six studies show no effect. PBL does not seem to have an effect on surface learning as indicated by a very small average effect size (.08) and eleven studies showing no increase in the surface approach. Six studies demonstrate a decrease and four an increase in surface learning. It is concluded that PBL does seem to enhance deep learning and has little effect on surface learning, although more longitudinal research using high quality measurement instruments is needed to support this conclusion with stronger evidence. Differences cannot be explained by the study quality but a curriculum wide implementation of PBL has a more positive impact on the deep approach (effect size .18) compared to an implementation within a single course (effect size of -.05). PBL is assumed to enhance active learning and students' intrinsic motivation, which enhances deep learning. A high perceived workload and assessment that is perceived as not rewarding deep learning are assumed to enhance surface learning.

  17. Use of whole genome deep sequencing to define emerging minority variants in virus envelope genes in herpesvirus treated with novel antimicrobial K21.

    Science.gov (United States)

    Tweedy, Joshua G; Prusty, Bhupesh K; Gompels, Ursula A

    2017-10-01

    New antivirals are required to prevent rising antimicrobial resistance from replication inhibitors. The aim of this study was to analyse the range of emerging mutations in herpesvirus by whole genome deep sequencing. We tested human herpesvirus 6 treatment with novel antiviral K21, where evidence indicated distinct effects on virus envelope proteins. We treated BACmid cloned virus in order to analyse mechanisms and candidate targets for resistance. Illumina based next generation sequencing technology enabled analyses of mutations in 85 genes to depths of 10,000 per base detecting low prevalent minority variants (<1%). After four passages in tissue culture the untreated virus accumulated mutations in infected cells giving an emerging mixed population (45-73%) of non-synonymous SNPs in six genes including two envelope glycoproteins. Strikingly, treatment with K21 did not accumulate the passage mutations; instead a high frequency mutation was selected in envelope protein gQ2, part of the gH/gL complex essential for herpesvirus infection. This introduced a stop codon encoding a truncation mutation previously observed in increased virion production. There was reduced detection of the glycoprotein complex in infected cells. This supports a novel pathway for K21 targeting virion envelopes distinct from replication inhibition. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  18. Draft Sequencing of the Heterozygous Diploid Genome of Satsuma (Citrus unshiu Marc. Using a Hybrid Assembly Approach

    Directory of Open Access Journals (Sweden)

    Tokurou Shimizu

    2017-12-01

    Full Text Available Satsuma (Citrus unshiu Marc. is one of the most abundantly produced mandarin varieties of citrus, known for its seedless fruit production and as a breeding parent of citrus. De novo assembly of the heterozygous diploid genome of Satsuma (“Miyagawa Wase” was conducted by a hybrid assembly approach using short-read sequences, three mate-pair libraries, and a long-read sequence of PacBio by the PLATANUS assembler. The assembled sequence, with a total size of 359.7 Mb at the N50 length of 386,404 bp, consisted of 20,876 scaffolds. Pseudomolecules of Satsuma constructed by aligning the scaffolds to three genetic maps showed genome-wide synteny to the genomes of Clementine, pummelo, and sweet orange. Gene prediction by modeling with MAKER-P proposed 29,024 genes and 37,970 mRNA; additionally, gene prediction analysis found candidates for novel genes in several biosynthesis pathways for gibberellin and violaxanthin catabolism. BUSCO scores for the assembled scaffold and predicted transcripts, and another analysis by BAC end sequence mapping indicated the assembled genome consistency was close to those of the haploid Clementine, pummel, and sweet orange genomes. The number of repeat elements and long terminal repeat retrotransposon were comparable to those of the seven citrus genomes; this suggested no significant failure in the assembly at the repeat region. A resequencing application using the assembled sequence confirmed that both kunenbo-A and Satsuma are offsprings of Kishu, and Satsuma is a back-crossed offspring of Kishu. These results illustrated the performance of the hybrid assembly approach and its ability to construct an accurate heterozygous diploid genome.

  19. Applications of Deep Learning in Biomedicine.

    Science.gov (United States)

    Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

    2016-05-02

    Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

  20. Deep learning for single-molecule science

    Science.gov (United States)

    Albrecht, Tim; Slabaugh, Gregory; Alonso, Eduardo; Al-Arif, SM Masudur R.

    2017-10-01

    Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in machine learning (ML), so-called deep learning (DL) offer interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional ML strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the ‘internal workings’ of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a convolutional neural network (CNN), may be used for base calling in DNA sequencing applications. We compare it with a SVM as a more conventional ML method, and discuss some of the strengths and weaknesses of the approach. In particular, a ‘deep’ neural network has many features of a ‘black box’, which has important implications on how we look at and interpret data.