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Sample records for deep sequencing approach

  1. 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

  2. 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.

  3. 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

  4. 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.

  5. 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...

  6. Quantitative phenotyping via deep barcode sequencing.

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    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.

  7. 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.

  8. 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...

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

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

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    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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. 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/.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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...

  19. 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

  20. 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...

  1. 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.

  2. 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.

  3. 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.

  4. 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 ...

  5. 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.

  6. 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...

  7. 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

  8. 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...

  9. 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.

  10. 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...

  11. Protein sequences bound to mineral surfaces persist into deep time

    DEFF Research Database (Denmark)

    Demarchi, Beatrice; Hall, Shaun; Roncal-Herrero, Teresa

    2016-01-01

    of Laetoli (3.8 Ma) and Olduvai Gorge (1.3 Ma) in Tanzania. By tracking protein diagenesis back in time we find consistent patterns of preservation, demonstrating authenticity of the surviving sequences. Molecular dynamics simulations of struthiocalcin-1 and -2, the dominant proteins within the eggshell......, reveal that distinct domains bind to the mineral surface. It is the domain with the strongest calculated binding energy to the calcite surface that is selectively preserved. Thermal age calculations demonstrate that the Laetoli and Olduvai peptides are 50 times older than any previously authenticated...

  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. 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.

  14. 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%.

  15. 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

  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 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

  18. 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.

  19. 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.

  20. 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.)

  1. 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.

  2. 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/.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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/.

  9. 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...

  10. 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.

  11. 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)

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. Proteomics-grade de novo sequencing approach

    DEFF Research Database (Denmark)

    Savitski, Mikhail M; Nielsen, Michael L; Kjeldsen, Frank

    2005-01-01

    The conventional approach in modern proteomics to identify proteins from limited information provided by molecular and fragment masses of their enzymatic degradation products carries an inherent risk of both false positive and false negative identifications. For reliable identification of even kn...

  5. 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

  6. 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.

  7. 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

  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. 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%).

  10. 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.

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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…

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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/.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  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. 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

  19. 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.

  20. 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.

  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. 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.…

  3. 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...

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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...

  9. 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

  10. DEEP DESULFURIZATION OF DIESEL FUELS BY A NOVEL INTEGRATED APPROACH

    Energy Technology Data Exchange (ETDEWEB)

    Xiaoliang Ma; Michael Sprague; Lu Sun; Chunshan Song

    2002-10-01

    In order to reduce the sulfur level in liquid hydrocarbon fuels for environmental protection and fuel cell applications, deep desulfurization of a model diesel fuel and a real diesel fuel was conducted by our SARS (selective adsorption for removing sulfur) process using the adsorbent A-2. Effect of temperature on the desulfurization process was examined. Adsorption desulfurization at ambient temperature, 24 h{sup -1} of LHSV over A-2 is efficient to remove dibenzothiophene (DBT) in the model diesel fuel, but difficult to remove 4-methyldibenzothiophene (4-MDBT) and 4,6-dimethyl-dibenzothiophene (4,6-DMDBT). Adsorption desulfurization at 150 C over A-2 can efficiently remove DBT, 4-MDBT and 4,6-DMDBT in the model diesel fuel. The sulfur content in the model diesel fuel can be reduced to less than 1 ppmw at 150 C without using hydrogen gas. The adsorption capacity corresponding to the break-through point is 6.9 milligram of sulfur per gram of A-2 (mg-S/g-A-2), and the saturate capacity is 13.7 mg-S/g-A-2. Adsorption desulfurization of a commercial diesel fuel with a total sulfur level of 47 ppmw was also performed at ambient temperature and 24 h{sup -1} of LHSV over the adsorbent A-2. The results show that only part of the sulfur compounds existing in the low sulfur diesel can be removed by adsorption over A-2 at such operating conditions, because (1) the all sulfur compounds in the low sulfur diesel are the refractory sulfur compounds that have one or two alkyl groups at the 4- and/or 6-positions of DBT, which inhibit the approach of the sulfur atom to the adsorption site; (2) some compounds coexisting in the commercial low sulfur diesel probably inhibit the interaction between the sulfur compounds and the adsorbent. Further work in determining the optimum operating conditions and screening better adsorbent is desired.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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).

  4. 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.

  5. 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.

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

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    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

  7. 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.

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. The Ebola virus VP35 protein binds viral immunostimulatory and host RNAs identified through deep sequencing.

    Directory of Open Access Journals (Sweden)

    Kari A Dilley

    Full Text Available Ebola virus and Marburg virus are members of the Filovirdae family and causative agents of hemorrhagic fever with high fatality rates in humans. Filovirus virulence is partially attributed to the VP35 protein, a well-characterized inhibitor of the RIG-I-like receptor pathway that triggers the antiviral interferon (IFN response. Prior work demonstrates the ability of VP35 to block potent RIG-I activators, such as Sendai virus (SeV, and this IFN-antagonist activity is directly correlated with its ability to bind RNA. Several structural studies demonstrate that VP35 binds short synthetic dsRNAs; yet, there are no data that identify viral immunostimulatory RNAs (isRNA or host RNAs bound to VP35 in cells. Utilizing a SeV infection model, we demonstrate that both viral isRNA and host RNAs are bound to Ebola and Marburg VP35s in cells. By deep sequencing the purified VP35-bound RNA, we identified the SeV copy-back defective interfering (DI RNA, previously identified as a robust RIG-I activator, as the isRNA bound by multiple filovirus VP35 proteins, including the VP35 protein from the West African outbreak strain (Makona EBOV. Moreover, RNAs isolated from a VP35 RNA-binding mutant were not immunostimulatory and did not include the SeV DI RNA. Strikingly, an analysis of host RNAs bound by wild-type, but not mutant, VP35 revealed that select host RNAs are preferentially bound by VP35 in cell culture. Taken together, these data support a model in which VP35 sequesters isRNA in virus-infected cells to avert RIG-I like receptor (RLR activation.

  17. 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.

  18. 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.

  19. The Ebola virus VP35 protein binds viral immunostimulatory and host RNAs identified through deep sequencing.

    Science.gov (United States)

    Dilley, Kari A; Voorhies, Alexander A; Luthra, Priya; Puri, Vinita; Stockwell, Timothy B; Lorenzi, Hernan; Basler, Christopher F; Shabman, Reed S

    2017-01-01

    Ebola virus and Marburg virus are members of the Filovirdae family and causative agents of hemorrhagic fever with high fatality rates in humans. Filovirus virulence is partially attributed to the VP35 protein, a well-characterized inhibitor of the RIG-I-like receptor pathway that triggers the antiviral interferon (IFN) response. Prior work demonstrates the ability of VP35 to block potent RIG-I activators, such as Sendai virus (SeV), and this IFN-antagonist activity is directly correlated with its ability to bind RNA. Several structural studies demonstrate that VP35 binds short synthetic dsRNAs; yet, there are no data that identify viral immunostimulatory RNAs (isRNA) or host RNAs bound to VP35 in cells. Utilizing a SeV infection model, we demonstrate that both viral isRNA and host RNAs are bound to Ebola and Marburg VP35s in cells. By deep sequencing the purified VP35-bound RNA, we identified the SeV copy-back defective interfering (DI) RNA, previously identified as a robust RIG-I activator, as the isRNA bound by multiple filovirus VP35 proteins, including the VP35 protein from the West African outbreak strain (Makona EBOV). Moreover, RNAs isolated from a VP35 RNA-binding mutant were not immunostimulatory and did not include the SeV DI RNA. Strikingly, an analysis of host RNAs bound by wild-type, but not mutant, VP35 revealed that select host RNAs are preferentially bound by VP35 in cell culture. Taken together, these data support a model in which VP35 sequesters isRNA in virus-infected cells to avert RIG-I like receptor (RLR) activation.

  20. 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.

  1. 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.

  2. Deep sequencing of the mitochondrial genome reveals common heteroplasmic sites in NADH dehydrogenase genes.

    Science.gov (United States)

    Liu, Chunyu; Fetterman, Jessica L; Liu, Poching; Luo, Yan; Larson, Martin G; Vasan, Ramachandran S; Zhu, Jun; Levy, Daniel

    2018-03-01

    Increasing evidence implicates mitochondrial dysfunction in aging and age-related conditions. But little is known about the molecular basis for this connection. A possible cause may be mutations in the mitochondrial DNA (mtDNA), which are often heteroplasmic-the joint presence of different alleles at a single locus in the same individual. However, the involvement of mtDNA heteroplasmy in aging and age-related conditions has not been investigated thoroughly. We deep-sequenced the complete mtDNA genomes of 356 Framingham Heart Study participants (52% women, mean age 43, mean coverage 4570-fold), identified 2880 unique mutations and comprehensively annotated them by MITOMAP and PolyPhen-2. We discovered 11 heteroplasmic "hot" spots [NADH dehydrogenase (ND) subunit 1, 4, 5 and 6 genes, n = 7; cytochrome c oxidase I (COI), n = 2; 16S rRNA, n = 1; D-loop, n = 1] for which the alternative-to-reference allele ratios significantly increased with advancing age (Bonferroni correction p < 0.001). Four of these heteroplasmic mutations in ND and COI genes were predicted to be deleterious nonsynonymous mutations which may have direct impact on ATP production. We confirmed previous findings that healthy individuals carry many low-frequency heteroplasmy mutations with potentially deleterious effects. We hypothesize that the effect of a single deleterious heteroplasmy may be minimal due to a low mutant-to-wildtype allele ratio, whereas the aggregate effects of many deleterious mutations may cause changes in mitochondrial function and contribute to age-related diseases. The identification of age-related mtDNA mutations is an important step to understand the genetic architecture of age-related diseases and may uncover novel therapeutic targets for such diseases.

  3. 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.

  4. 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...

  5. 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...

  6. 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.

  7. Genome-wide analysis of SRSF10-regulated alternative splicing by deep sequencing of chicken transcriptome

    Directory of Open Access Journals (Sweden)

    Xuexia Zhou

    2014-12-01

    Full Text Available Splicing factor SRSF10 is known to function as a sequence-specific splicing activator that is capable of regulating alternative splicing both in vitro and in vivo. We recently used an RNA-seq approach coupled with bioinformatics analysis to identify the extensive splicing network regulated by SRSF10 in chicken cells. We found that SRSF10 promoted both exon inclusion and exclusion. Functionally, many of the SRSF10-verified alternative exons are linked to pathways of response to external stimulus. Here we describe in detail the experimental design, bioinformatics analysis and GO/pathway enrichment analysis of SRSF10-regulated genes to correspond with our data in the Gene Expression Omnibus with accession number GSE53354. Our data thus provide a resource for studying regulation of alternative splicing in vivo that underlines biological functions of splicing regulatory proteins in cells.

  8. 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.

  9. 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.

  10. 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.

  11. A Deep Learning Approach to LIBS Spectroscopy for Planetary Applications

    Science.gov (United States)

    Mullen, T. H.; Parente, M.; Gemp, I.; Dyar, M. D.

    2017-12-01

    The ChemCam instrument on the Curiousity rover has collected >440,000 laser-induced breakdown spectra (LIBS) from 1500 different geological targets since 2012. The team is using a pipeline of preprocessing and partial least squares techniques to predict compositions of surface materials [1]. Unfortunately, such multivariate techniques are plagued by hard-to-meet assumptions involving constant hyperparameter tuning to specific elements and the amount of training data available; if the whole distribution of data is not seen, the method will overfit to the training data and generalizability will suffer. The rover only has 10 calibration targets on-board that represent a small subset of the geochemical samples the rover is expected to investigate. Deep neural networks have been used to bypass these issues in other fields. Semi-supervised techniques allow researchers to utilized small labeled datasets and vast amounts of unlabeled data. One example is the variational autoencoder model, a semi-supervised generative model in the form of a deep neural network. The autoencoder assumes that LIBS spectra are generated from a distribution conditioned on the elemental compositions in the sample and some nuisance. The system is broken into two models: one that predicts elemental composition from the spectra and one that generates spectra from compositions that may or may not be seen in the training set. The synthesized spectra show strong agreement with geochemical conventions to express specific compositions. The predictions of composition show improved generalizability to PLS. Deep neural networks have also been used to transfer knowledge from one dataset to another to solve unlabeled data problems. Given that vast amounts of laboratry LIBS spectra have been obtained in the past few years, it is now feasible train a deep net to predict elemental composition from lab spectra. Transfer learning (manifold alignment or calibration transfer) [2] is then used to fine-tune the model

  12. 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...

  13. Deep Learning Approach for Car Detection in UAV Imagery

    Directory of Open Access Journals (Sweden)

    Nassim Ammour

    2017-03-01

    Full Text Available This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres and the extremely high level of detail, which require suitable automatic analysis methods. Our proposed method begins by segmenting the input image into small homogeneous regions, which can be used as candidate locations for car detection. Next, a window is extracted around each region, and deep learning is used to mine highly descriptive features from these windows. We use a deep convolutional neural network (CNN system that is already pre-trained on huge auxiliary data as a feature extraction tool, combined with a linear support vector machine (SVM classifier to classify regions into “car” and “no-car” classes. The final step is devoted to a fine-tuning procedure which performs morphological dilation to smooth the detected regions and fill any holes. In addition, small isolated regions are analysed further using a few sliding rectangular windows to locate cars more accurately and remove false positives. To evaluate our method, experiments were conducted on a challenging set of real UAV images acquired over an urban area. The experimental results have proven that the proposed method outperforms the state-of-the-art methods, both in terms of accuracy and computational time.

  14. 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.

  15. Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling

    OpenAIRE

    Duong, Chi Nhan; Luu, Khoa; Quach, Kha Gia; Bui, Tien D.

    2016-01-01

    The "interpretation through synthesis" approach to analyze face images, particularly Active Appearance Models (AAMs) method, has become one of the most successful face modeling approaches over the last two decades. AAM models have ability to represent face images through synthesis using a controllable parameterized Principal Component Analysis (PCA) model. However, the accuracy and robustness of the synthesized faces of AAM are highly depended on the training sets and inherently on the genera...

  16. Encouraging Deep Approach to Learning in Civil and Geodetic Engineering

    Directory of Open Access Journals (Sweden)

    Gašper Mrak

    2016-10-01

    Full Text Available This paper presents activities and changes applied to the teaching process within selected courses offered by Faculty of civil and geodetic engineering, University of Ljubljana, Slovenia. Theoretical background, evaluated from the point of the technical education needs, is presented. It can be seen that special focus has to be made to the students' motivation for deep learning which guarantees optimal balance between acquisition of concepts and skills, information processing and integration of fragmented pieces of knowledge into complex structures. Three case studies used to test theoretical points of departure are presented. Results of the introduced novelties and changes have been evaluated through the assessment of knowledge, students' satisfaction and teaching staff evaluations. For conclusive results, monitoring over a longer period of time should be conducted.

  17. 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

  18. Imbalance aware lithography hotspot detection: a deep learning approach

    Science.gov (United States)

    Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

    2017-07-01

    With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

  19. 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.

  20. 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

  1. 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

  2. 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...

  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. 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.

  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. 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.

  7. Quicksilver: Fast predictive image registration - A deep learning approach.

    Science.gov (United States)

    Yang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, Marc

    2017-09-01

    This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. 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.

  9. Graph-based sequence annotation using a data integration approach.

    Science.gov (United States)

    Pesch, Robert; Lysenko, Artem; Hindle, Matthew; Hassani-Pak, Keywan; Thiele, Ralf; Rawlings, Christopher; Köhler, Jacob; Taubert, Jan

    2008-08-25

    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. The methods and algorithms presented in this publication are an integral part of the ONDEX system which is freely available from http://ondex.sf.net/.

  10. 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

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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...

  1. 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.

  2. 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. […

  3. Solving the Curriculum Sequencing Problem with DNA Computing Approach

    Science.gov (United States)

    Debbah, Amina; Ben Ali, Yamina Mohamed

    2014-01-01

    In the e-learning systems, a learning path is known as a sequence of learning materials linked to each others to help learners achieving their learning goals. As it is impossible to have the same learning path that suits different learners, the Curriculum Sequencing problem (CS) consists of the generation of a personalized learning path for each…

  4. Filling the gap between sequence and function: a bioinformatics approach

    NARCIS (Netherlands)

    Bargsten, J.W.

    2014-01-01

    The research presented in this thesis focuses on deriving function from sequence information, with the emphasis on plant sequence data. Unravelling the impact of genomic elements, in most cases genes, on the phenotype of an organism is a major challenge in biological research and modern plant

  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. 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.

  7. 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...

  8. 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…

  9. 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

  10. 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…

  11. 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.

  12. Ultra-deep sequencing reveals the subclonal structure and genomic evolution of oral squamous cell carcinoma

    DEFF Research Database (Denmark)

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

    Background: Oral squamous cell carcinoma (OSCC), a subgroup of head and neck squamous cell carcinoma (HNSCC), is primarily caused by alcohol consumption and tobacco use. Recent DNA sequencing studies suggests that HNSCC are very heterogeneous between patients; however the intra-patient subclonal...

  13. MicroRNA identity and abundance in porcine skeletal muscles determined by deep sequencing

    DEFF Research Database (Denmark)

    Nielsen, M; Hansen, J H; Hedegaard, J

    2010-01-01

    levels of 212 annotated miRNA genes, thereby providing a thorough account of the miRNA transcriptome in porcine muscle tissue. The expression levels displayed a very large range, as reflected by the number of sequence reads, which varied from single counts for rare miRNAs to several million reads...

  14. High diversity of picornaviruses in rats from different continents revealed by deep sequencing

    DEFF Research Database (Denmark)

    Arn Hansen, Thomas; Mollerup, Sarah; Nguyen, Nam-Phuong

    2016-01-01

    Outbreaks of zoonotic diseases in humans and livestock are not uncommon, and an important component in containment of such emerging viral diseases is rapid and reliable diagnostics. Such methods are often PCR-based and hence require the availability of sequence data from the pathogen. Rattus norv...

  15. High diversity of picornaviruses in rats from different continents revealed by deep sequencing

    DEFF Research Database (Denmark)

    Arn Hansen, Thomas; Mollerup, Sarah; Nguyen, Nam-Phuong

    2016-01-01

    Outbreaks of zoonotic diseases in humans and livestock are not uncommon, and an important component in containment of such emerging viral diseases is rapid and reliable diagnostics. Such methods are often PCR-based and hence require the availability of sequence data from the pathogen. Rattus...

  16. Deep-sequencing revealed Citrus bark cracking viroid (CBCVd) as a highly aggressive pathogen on hop

    Czech Academy of Sciences Publication Activity Database

    Jakše, J.; Radišek, S.; Pokorn, T.; Matoušek, Jaroslav; Javornik, B.

    2015-01-01

    Roč. 64, č. 4 (2015), s. 831-842 ISSN 0032-0862 R&D Projects: GA MŠk(CZ) LH14255 Institutional support: RVO:60077344 Keywords : Bioinformatic * Citrus bark cracking viroid * Hop * Next-generation sequencing Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 2.383, year: 2015

  17. Using small RNA (sRNA) deep sequencing to understand global virus distribution in plants

    Science.gov (United States)

    Small RNAs (sRNAs), a class of regulatory RNAs, have been used to serve as the specificity determinants of suppressing gene expression in plants and animals. Next generation sequencing (NGS) uncovered the sRNA landscape in most organisms including their associated microbes. In the current study, w...

  18. 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...

  19. 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.

  20. A Retrospective Examination of Feline Leukemia Subgroup Characterization: Viral Interference Assays to Deep Sequencing

    Directory of Open Access Journals (Sweden)

    Elliott S. Chiu

    2018-01-01

    Full Text Available Feline leukemia virus (FeLV was the first feline retrovirus discovered, and is associated with multiple fatal disease syndromes in cats, including lymphoma. The original research conducted on FeLV employed classical virological techniques. As methods have evolved to allow FeLV genetic characterization, investigators have continued to unravel the molecular pathology associated with this fascinating agent. In this review, we discuss how FeLV classification, transmission, and disease-inducing potential have been defined sequentially by viral interference assays, Sanger sequencing, PCR, and next-generation sequencing. In particular, we highlight the influences of endogenous FeLV and host genetics that represent FeLV research opportunities on the near horizon.

  1. A Retrospective Examination of Feline Leukemia Subgroup Characterization: Viral Interference Assays to Deep Sequencing.

    Science.gov (United States)

    Chiu, Elliott S; Hoover, Edward A; VandeWoude, Sue

    2018-01-10

    Feline leukemia virus (FeLV) was the first feline retrovirus discovered, and is associated with multiple fatal disease syndromes in cats, including lymphoma. The original research conducted on FeLV employed classical virological techniques. As methods have evolved to allow FeLV genetic characterization, investigators have continued to unravel the molecular pathology associated with this fascinating agent. In this review, we discuss how FeLV classification, transmission, and disease-inducing potential have been defined sequentially by viral interference assays, Sanger sequencing, PCR, and next-generation sequencing. In particular, we highlight the influences of endogenous FeLV and host genetics that represent FeLV research opportunities on the near horizon.

  2. High diversity of picornaviruses in rats from different continents revealed by deep sequencing.

    Science.gov (United States)

    Hansen, Thomas Arn; Mollerup, Sarah; Nguyen, Nam-Phuong; White, Nicole E; Coghlan, Megan; Alquezar-Planas, David E; Joshi, Tejal; Jensen, Randi Holm; Fridholm, Helena; Kjartansdóttir, Kristín Rós; Mourier, Tobias; Warnow, Tandy; Belsham, Graham J; Bunce, Michael; Willerslev, Eske; Nielsen, Lars Peter; Vinner, Lasse; Hansen, Anders Johannes

    2016-08-17

    Outbreaks of zoonotic diseases in humans and livestock are not uncommon, and an important component in containment of such emerging viral diseases is rapid and reliable diagnostics. Such methods are often PCR-based and hence require the availability of sequence data from the pathogen. Rattus norvegicus (R. norvegicus) is a known reservoir for important zoonotic pathogens. Transmission may be direct via contact with the animal, for example, through exposure to its faecal matter, or indirectly mediated by arthropod vectors. Here we investigated the viral content in rat faecal matter (n=29) collected from two continents by analyzing 2.2 billion next-generation sequencing reads derived from both DNA and RNA. Among other virus families, we found sequences from members of the Picornaviridae to be abundant in the microbiome of all the samples. Here we describe the diversity of the picornavirus-like contigs including near-full-length genomes closely related to the Boone cardiovirus and Theiler's encephalomyelitis virus. From this study, we conclude that picornaviruses within R. norvegicus are more diverse than previously recognized. The virome of R. norvegicus should be investigated further to assess the full potential for zoonotic virus transmission.

  3. Deep transcriptome sequencing provides new insights into the structural and functional organization of the wheat genome.

    Science.gov (United States)

    Pingault, Lise; Choulet, Frédéric; Alberti, Adriana; Glover, Natasha; Wincker, Patrick; Feuillet, Catherine; Paux, Etienne

    2015-02-10

    Because of its size, allohexaploid nature, and high repeat content, the bread wheat genome is a good model to study the impact of the genome structure on gene organization, function, and regulation. However, because of the lack of a reference genome sequence, such studies have long been hampered and our knowledge of the wheat gene space is still limited. The access to the reference sequence of the wheat chromosome 3B provided us with an opportunity to study the wheat transcriptome and its relationships to genome and gene structure at a level that has never been reached before. By combining this sequence with RNA-seq data, we construct a fine transcriptome map of the chromosome 3B. More than 8,800 transcription sites are identified, that are distributed throughout the entire chromosome. Expression level, expression breadth, alternative splicing as well as several structural features of genes, including transcript length, number of exons, and cumulative intron length are investigated. Our analysis reveals a non-monotonic relationship between gene expression and structure and leads to the hypothesis that gene structure is determined by its function, whereas gene expression is subject to energetic cost. Moreover, we observe a recombination-based partitioning at the gene structure and function level. Our analysis provides new insights into the relationships between gene and genome structure and function. It reveals mechanisms conserved with other plant species as well as superimposed evolutionary forces that shaped the wheat gene space, likely participating in wheat adaptation.

  4. MicroRNA repertoire for functional genome research in tilapia identified by deep sequencing.

    Science.gov (United States)

    Yan, Biao; Wang, Zhen-Hua; Zhu, Chang-Dong; Guo, Jin-Tao; Zhao, Jin-Liang

    2014-08-01

    The Nile tilapia (Oreochromis niloticus; Cichlidae) is an economically important species in aquaculture and occupies a prominent position in the aquaculture industry. MicroRNAs (miRNAs) are a class of noncoding RNAs that post-transcriptionally regulate gene expression involved in diverse biological and metabolic processes. To increase the repertoire of miRNAs characterized in tilapia, we used the Illumina/Solexa sequencing technology to sequence a small RNA library using pooled RNA sample isolated from the different developmental stages of tilapia. Bioinformatic analyses suggest that 197 conserved and 27 novel miRNAs are expressed in tilapia. Sequence alignments indicate that all tested miRNAs and miRNAs* are highly conserved across many species. In addition, we characterized the tissue expression patterns of five miRNAs using real-time quantitative PCR. We found that miR-1/206, miR-7/9, and miR-122 is abundantly expressed in muscle, brain, and liver, respectively, implying a potential role in the regulation of tissue differentiation or the maintenance of tissue identity. Overall, our results expand the number of tilapia miRNAs, and the discovery of miRNAs in tilapia genome contributes to a better understanding the role of miRNAs in regulating diverse biological processes.

  5. 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.

  6. Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library

    Directory of Open Access Journals (Sweden)

    Salem Mohamed

    2009-11-01

    Full Text Available Abstract Background To enhance capabilities for genomic analyses in rainbow trout, such as genomic selection, a large suite of polymorphic markers that are amenable to high-throughput genotyping protocols must be identified. Expressed Sequence Tags (ESTs have been used for single nucleotide polymorphism (SNP discovery in salmonids. In those strategies, the salmonid semi-tetraploid genomes often led to assemblies of paralogous sequences and therefore resulted in a high rate of false positive SNP identification. Sequencing genomic DNA using primers identified from ESTs proved to be an effective but time consuming methodology of SNP identification in rainbow trout, therefore not suitable for high throughput SNP discovery. In this study, we employed a high-throughput strategy that used pyrosequencing technology to generate data from a reduced representation library constructed with genomic DNA pooled from 96 unrelated rainbow trout that represent the National Center for Cool and Cold Water Aquaculture (NCCCWA broodstock population. Results The reduced representation library consisted of 440 bp fragments resulting from complete digestion with the restriction enzyme HaeIII; sequencing produced 2,000,000 reads providing an average 6 fold coverage of the estimated 150,000 unique genomic restriction fragments (300,000 fragment ends. Three independent data analyses identified 22,022 to 47,128 putative SNPs on 13,140 to 24,627 independent contigs. A set of 384 putative SNPs, randomly selected from the sets produced by the three analyses were genotyped on individual fish to determine the validation rate of putative SNPs among analyses, distinguish apparent SNPs that actually represent paralogous loci in the tetraploid genome, examine Mendelian segregation, and place the validated SNPs on the rainbow trout linkage map. Approximately 48% (183 of the putative SNPs were validated; 167 markers were successfully incorporated into the rainbow trout linkage map. In

  7. Single nucleotide polymorphism discovery in rainbow trout by deep sequencing of a reduced representation library.

    Science.gov (United States)

    Sánchez, Cecilia Castaño; Smith, Timothy P L; Wiedmann, Ralph T; Vallejo, Roger L; Salem, Mohamed; Yao, Jianbo; Rexroad, Caird E

    2009-11-25

    To enhance capabilities for genomic analyses in rainbow trout, such as genomic selection, a large suite of polymorphic markers that are amenable to high-throughput genotyping protocols must be identified. Expressed Sequence Tags (ESTs) have been used for single nucleotide polymorphism (SNP) discovery in salmonids. In those strategies, the salmonid semi-tetraploid genomes often led to assemblies of paralogous sequences and therefore resulted in a high rate of false positive SNP identification. Sequencing genomic DNA using primers identified from ESTs proved to be an effective but time consuming methodology of SNP identification in rainbow trout, therefore not suitable for high throughput SNP discovery. In this study, we employed a high-throughput strategy that used pyrosequencing technology to generate data from a reduced representation library constructed with genomic DNA pooled from 96 unrelated rainbow trout that represent the National Center for Cool and Cold Water Aquaculture (NCCCWA) broodstock population. The reduced representation library consisted of 440 bp fragments resulting from complete digestion with the restriction enzyme HaeIII; sequencing produced 2,000,000 reads providing an average 6 fold coverage of the estimated 150,000 unique genomic restriction fragments (300,000 fragment ends). Three independent data analyses identified 22,022 to 47,128 putative SNPs on 13,140 to 24,627 independent contigs. A set of 384 putative SNPs, randomly selected from the sets produced by the three analyses were genotyped on individual fish to determine the validation rate of putative SNPs among analyses, distinguish apparent SNPs that actually represent paralogous loci in the tetraploid genome, examine Mendelian segregation, and place the validated SNPs on the rainbow trout linkage map. Approximately 48% (183) of the putative SNPs were validated; 167 markers were successfully incorporated into the rainbow trout linkage map. In addition, 2% of the sequences from the

  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. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Metagenomes obtained by "deep sequencing" - what do they tell about the EBPR communities?

    DEFF Research Database (Denmark)

    Albertsen, Mads; Saunders, Aaron Marc; Nielsen, Kåre Lehmann

    2013-01-01

    Metagenomics enables studies of the genomic potential of complex microbial communities by sequencing bulk genomic DNA directly from the environment. Knowledge of the genetic potential of a community can be used to formulate and test ecological hypotheses about stability and performance...... demonstrate that metagenomics can be used as a powerful tool for system wide characterization of the EBPR community as well as for a deeper understanding of the function of specific community members. Furthermore, we discuss and illustrate some of the general pitfalls in metagenomics and stress the need...

  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. Network clustering coefficient approach to DNA sequence analysis

    Energy Technology Data Exchange (ETDEWEB)

    Gerhardt, Guenther J.L. [Universidade Federal do Rio Grande do Sul-Hospital de Clinicas de Porto Alegre, Rua Ramiro Barcelos 2350/sala 2040/90035-003 Porto Alegre (Brazil); Departamento de Fisica e Quimica da Universidade de Caxias do Sul, Rua Francisco Getulio Vargas 1130, 95001-970 Caxias do Sul (Brazil); Lemke, Ney [Programa Interdisciplinar em Computacao Aplicada, Unisinos, Av. Unisinos, 950, 93022-000 Sao Leopoldo, RS (Brazil); Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, Campus Universitario, 59072 970 Natal, RN (Brazil)]. E-mail: corso@dfte.ufrn.br

    2006-05-15

    In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine-cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content.

  7. Novel Approach to Analyzing MFE of Noncoding RNA Sequences.

    Science.gov (United States)

    George, Tina P; Thomas, Tessamma

    2016-01-01

    Genomic studies have become noncoding RNA (ncRNA) centric after the study of different genomes provided enormous information on ncRNA over the past decades. The function of ncRNA is decided by its secondary structure, and across organisms, the secondary structure is more conserved than the sequence itself. In this study, the optimal secondary structure or the minimum free energy (MFE) structure of ncRNA was found based on the thermodynamic nearest neighbor model. MFE of over 2600 ncRNA sequences was analyzed in view of its signal properties. Mathematical models linking MFE to the signal properties were found for each of the four classes of ncRNA analyzed. MFE values computed with the proposed models were in concordance with those obtained with the standard web servers. A total of 95% of the sequences analyzed had deviation of MFE values within ±15% relative to those obtained from standard web servers.

  8. Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer.

    Science.gov (United States)

    Kim, Jung H; Dhanasekaran, Saravana M; Prensner, John R; Cao, Xuhong; Robinson, Daniel; Kalyana-Sundaram, Shanker; Huang, Christina; Shankar, Sunita; Jing, Xiaojun; Iyer, Matthew; Hu, Ming; Sam, Lee; Grasso, Catherine; Maher, Christopher A; Palanisamy, Nallasivam; Mehra, Rohit; Kominsky, Hal D; Siddiqui, Javed; Yu, Jindan; Qin, Zhaohui S; Chinnaiyan, Arul M

    2011-07-01

    Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in select prostate tissues and cell lines using MethylPlex-next-generation sequencing (M-NGS). Hidden Markov model-based next-generation sequence analysis identified ∼68,000 methylated regions per sample. While global CpG island (CGI) methylation was not differential between benign adjacent and cancer samples, overall promoter CGI methylation significantly increased from ~12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues, respectively (P-value prostate tissues, 2481 differentially methylated regions (DMRs) are cancer-specific, including numerous novel DMRs. A novel cancer-specific DMR in the WFDC2 promoter showed frequent methylation in cancer (17/22 tissues, 6/6 cell lines), but not in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested an epigenetic mechanism for alternate transcription start site utilization, and these modifications segregated into distinct regions when present on the same promoter. Finally, we observed differences in repeat element methylation, particularly LINE-1, between ERG gene fusion-positive and -negative cancers, and we confirmed this observation using pyrosequencing on a tissue panel. This comprehensive methylome map will further our understanding of epigenetic regulation in prostate cancer progression.

  9. Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts.

    Science.gov (United States)

    Webster, Nicole S; Taylor, Michael W; Behnam, Faris; Lücker, Sebastian; Rattei, Thomas; Whalan, Stephen; Horn, Matthias; Wagner, Michael

    2010-08-01

    Marine sponges contain complex bacterial communities of considerable ecological and biotechnological importance, with many of these organisms postulated to be specific to sponge hosts. Testing this hypothesis in light of the recent discovery of the rare microbial biosphere, we investigated three Australian sponges by massively parallel 16S rRNA gene tag pyrosequencing. Here we show bacterial diversity that is unparalleled in an invertebrate host, with more than 250,000 sponge-derived sequence tags being assigned to 23 bacterial phyla and revealing up to 2996 operational taxonomic units (95% sequence similarity) per sponge species. Of the 33 previously described 'sponge-specific' clusters that were detected in this study, 48% were found exclusively in adults and larvae - implying vertical transmission of these groups. The remaining taxa, including 'Poribacteria', were also found at very low abundance among the 135,000 tags retrieved from surrounding seawater. Thus, members of the rare seawater biosphere may serve as seed organisms for widely occurring symbiont populations in sponges and their host association might have evolved much more recently than previously thought. © 2009 Society for Applied Microbiology and Blackwell Publishing Ltd.

  10. Deep Sequencing Insights in Therapeutic shRNA Processing and siRNA Target Cleavage Precision.

    Science.gov (United States)

    Denise, Hubert; Moschos, Sterghios A; Sidders, Benjamin; Burden, Frances; Perkins, Hannah; Carter, Nikki; Stroud, Tim; Kennedy, Michael; Fancy, Sally-Ann; Lapthorn, Cris; Lavender, Helen; Kinloch, Ross; Suhy, David; Corbau, Romu

    2014-02-04

    TT-034 (PF-05095808) is a recombinant adeno-associated virus serotype 8 (AAV8) agent expressing three short hairpin RNA (shRNA) pro-drugs that target the hepatitis C virus (HCV) RNA genome. The cytosolic enzyme Dicer cleaves each shRNA into multiple, potentially active small interfering RNA (siRNA) drugs. Using next-generation sequencing (NGS) to identify and characterize active shRNAs maturation products, we observed that each TT-034-encoded shRNA could be processed into as many as 95 separate siRNA strands. Few of these appeared active as determined by Sanger 5' RNA Ligase-Mediated Rapid Amplification of cDNA Ends (5-RACE) and through synthetic shRNA and siRNA analogue studies. Moreover, NGS scrutiny applied on 5-RACE products (RACE-seq) suggested that synthetic siRNAs could direct cleavage in not one, but up to five separate positions on targeted RNA, in a sequence-dependent manner. These data support an on-target mechanism of action for TT-034 without cytotoxicity and question the accepted precision of substrate processing by the key RNA interference (RNAi) enzymes Dicer and siRNA-induced silencing complex (siRISC).Molecular Therapy-Nucleic Acids (2014) 3, e145; doi:10.1038/mtna.2013.73; published online 4 February 2014.

  11. Evolutionary Relations of Hexanchiformes Deep-Sea Sharks Elucidated by Whole Mitochondrial Genome Sequences

    Science.gov (United States)

    Tanaka, Keiko; Tomita, Taketeru; Suzuki, Shingo; Hosomichi, Kazuyoshi; Sano, Kazumi; Doi, Hiroyuki; Kono, Azumi; Inoko, Hidetoshi; Kulski, Jerzy K.; Tanaka, Sho

    2013-01-01

    Hexanchiformes is regarded as a monophyletic taxon, but the morphological and genetic relationships between the five extant species within the order are still uncertain. In this study, we determined the whole mitochondrial DNA (mtDNA) sequences of seven sharks including representatives of the five Hexanchiformes, one squaliform, and one carcharhiniform and inferred the phylogenetic relationships among those species and 12 other Chondrichthyes (cartilaginous fishes) species for which the complete mitogenome is available. The monophyly of Hexanchiformes and its close relation with all other Squaliformes sharks were strongly supported by likelihood and Bayesian phylogenetic analysis of 13,749 aligned nucleotides of 13 protein coding genes and two rRNA genes that were derived from the whole mDNA sequences of the 19 species. The phylogeny suggested that Hexanchiformes is in the superorder Squalomorphi, Chlamydoselachus anguineus (frilled shark) is the sister species to all other Hexanchiformes, and the relations within Hexanchiformes are well resolved as Chlamydoselachus, (Notorynchus, (Heptranchias, (Hexanchus griseus, H. nakamurai))). Based on our phylogeny, we discussed evolutionary scenarios of the jaw suspension mechanism and gill slit numbers that are significant features in the sharks. PMID:24089661

  12. Statistical approaches to use a model organism for regulatory sequences annotation of newly sequenced species.

    Directory of Open Access Journals (Sweden)

    Pietro Liò

    Full Text Available A major goal of bioinformatics is the characterization of transcription factors and the transcriptional programs they regulate. Given the speed of genome sequencing, we would like to quickly annotate regulatory sequences in newly-sequenced genomes. In such cases, it would be helpful to predict sequence motifs by using experimental data from closely related model organism. Here we present a general algorithm that allow to identify transcription factor binding sites in one newly sequenced species by performing Bayesian regression on the annotated species. First we set the rationale of our method by applying it within the same species, then we extend it to use data available in closely related species. Finally, we generalise the method to handle the case when a certain number of experiments, from several species close to the species on which to make inference, are available. In order to show the performance of the method, we analyse three functionally related networks in the Ascomycota. Two gene network case studies are related to the G2/M phase of the Ascomycota cell cycle; the third is related to morphogenesis. We also compared the method with MatrixReduce and discuss other types of validation and tests. The first network is well known and provides a biological validation test of the method. The two cell cycle case studies, where the gene network size is conserved, demonstrate an effective utility in annotating new species sequences using all the available replicas from model species. The third case, where the gene network size varies among species, shows that the combination of information is less powerful but is still informative. Our methodology is quite general and could be extended to integrate other high-throughput data from model organisms.

  13. Deep sequencing of subseafloor eukaryotic rRNA reveals active Fungi across marine subsurface provinces.

    Directory of Open Access Journals (Sweden)

    William Orsi

    Full Text Available The deep marine subsurface is a vast habitat for microbial life where cells may live on geologic timescales. Because DNA in sediments may be preserved on long timescales, ribosomal RNA (rRNA is suggested to be a proxy for the active fraction of a microbial community in the subsurface. During an investigation of eukaryotic 18S rRNA by amplicon pyrosequencing, unique profiles of Fungi were found across a range of marine subsurface provinces including ridge flanks, continental margins, and abyssal plains. Subseafloor fungal populations exhibit statistically significant correlations with total organic carbon (TOC, nitrate, sulfide, and dissolved inorganic carbon (DIC. These correlations are supported by terminal restriction length polymorphism (TRFLP analyses of fungal rRNA. Geochemical correlations with fungal pyrosequencing and TRFLP data from this geographically broad sample set suggests environmental selection of active Fungi in the marine subsurface. Within the same dataset, ancient rRNA signatures were recovered from plants and diatoms in marine sediments ranging from 0.03 to 2.7 million years old, suggesting that rRNA from some eukaryotic taxa may be much more stable than previously considered in the marine subsurface.

  14. 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.

  15. 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.

  16. A Probabilistic Approach for Improved Sequence Mapping in Metatranscriptomic Studies

    Science.gov (United States)

    Mapping millions of short DNA sequences a reference genome is a necessary step in many experiments designed to investigate the expression of genes involved in disease resistance. This is a difficult task in which several challenges often arise resulting in a suboptimal mapping. This mapping process ...

  17. Fraisse sequences: category-theoretic approach to universal homogeneous structures

    Czech Academy of Sciences Publication Activity Database

    Kubiś, Wieslaw

    2014-01-01

    Roč. 165, č. 11 (2014), s. 1755-1811 ISSN 0168-0072 R&D Projects: GA ČR(CZ) GAP201/12/0290 Institutional support: RVO:67985840 Keywords : universal homogeneous object * Fraissé sequence * amalgamation Subject RIV: BA - General Mathematics Impact factor: 0.548, year: 2014 http://www.sciencedirect.com/science/article/pii/S0168007214000773

  18. An EM based approach for motion segmentation of video sequence

    NARCIS (Netherlands)

    Zhao, Wei; Roos, Nico; Pan, Zhigeng; Skala, Vaclav

    2016-01-01

    Motions are important features for robot vision as we live in a dynamic world. Detecting moving objects is crucial for mobile robots and computer vision systems. This paper investigates an architecture for the segmentation of moving objects from image sequences. Objects are represented as groups of

  19. Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

    Directory of Open Access Journals (Sweden)

    Seth A. Herd

    2013-01-01

    Full Text Available We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC and basal ganglia (BG cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”. The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

  20. Next-generation sequencing approaches to understanding the oral microbiome

    NARCIS (Netherlands)

    Zaura, E.

    2012-01-01

    Until recently, the focus in dental research has been on studying a small fraction of the oral microbiome—so-called opportunistic pathogens. With the advent of next-generation sequencing (NGS) technologies, researchers now have the tools that allow for profiling of the microbiomes and metagenomes at

  1. 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

  2. Deep Sequencing Reveals Uncharted Isoform Heterogeneity of the Protein-Coding Transcriptome in Cerebral Ischemia.

    Science.gov (United States)

    Bhattarai, Sunil; Aly, Ahmed; Garcia, Kristy; Ruiz, Diandra; Pontarelli, Fabrizio; Dharap, Ashutosh

    2018-06-03

    Gene expression in cerebral ischemia has been a subject of intense investigations for several years. Studies utilizing probe-based high-throughput methodologies such as microarrays have contributed significantly to our existing knowledge but lacked the capacity to dissect the transcriptome in detail. Genome-wide RNA-sequencing (RNA-seq) enables comprehensive examinations of transcriptomes for attributes such as strandedness, alternative splicing, alternative transcription start/stop sites, and sequence composition, thus providing a very detailed account of gene expression. Leveraging this capability, we conducted an in-depth, genome-wide evaluation of the protein-coding transcriptome of the adult mouse cortex after transient focal ischemia at 6, 12, or 24 h of reperfusion using RNA-seq. We identified a total of 1007 transcripts at 6 h, 1878 transcripts at 12 h, and 1618 transcripts at 24 h of reperfusion that were significantly altered as compared to sham controls. With isoform-level resolution, we identified 23 splice variants arising from 23 genes that were novel mRNA isoforms. For a subset of genes, we detected reperfusion time-point-dependent splice isoform switching, indicating an expression and/or functional switch for these genes. Finally, for 286 genes across all three reperfusion time-points, we discovered multiple, distinct, simultaneously expressed and differentially altered isoforms per gene that were generated via alternative transcription start/stop sites. Of these, 165 isoforms derived from 109 genes were novel mRNAs. Together, our data unravel the protein-coding transcriptome of the cerebral cortex at an unprecedented depth to provide several new insights into the flexibility and complexity of stroke-related gene transcription and transcript organization.

  3. Transcriptional responses of Acropora hyacinthus embryo under the benzo(a)pyrene stress by deep sequencing.

    Science.gov (United States)

    Xiao, Rong; Zhou, Hailong; Chen, Chien-Min; Cheng, Huamin; Li, Hongwu; Xie, Jia; Zhao, Hongwei; Han, Qian; Diao, Xiaoping

    2018-04-24

    Coral embryos are a critical and sensitive period for the early growth and development of coral. Benzo(a)pyrene (BaP) is widely distributed in the ocean and has strong toxicity, but there is little information on the toxic effects to coral embryos exposed to this widespread environmental contaminant. Thus, in this study, we utilized the Illumina Hiseq™ 4000 platform to explore the gene response of Acropora hyacinthus embryos under the BaP stress. A total of 130,042 Unigenes were obtained and analyzed, and approximately 37.67% of those matched with sequences from four different species. In total, 2606 Unigenes were up-regulated, and 3872 Unigenes were down-regulated. After Gene Ontology (GO) annotation, the results show that the "cellular process" and "metabolic process" were leading in the category of biological processes, which the "binding" and "catalytic activity" were the most abundant subcategories in molecular function. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the most differentially expressed genes (DEGs) were enriched, as well as down-regulated in the pathways of oxidative phosphorylation, metabolism of xenobiotics, immune-related genes, apoptosis and human disease genes. At the same time, 388,197 of Single-nucleotide Polymorphisms (SNPs) and 6164 of Simple Sequence Repeats (SSRs) were obtained, which can be served as the richer and more valuable SSRs molecular markers in the future. The results of this study can help to better understand the toxicological mechanism of coral embryo exposed to BaP, and it is also essential for the protection and restoration of coral reef ecosystem in the future. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. 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.

  5. 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.

  6. 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.

  7. Small RNA and transcriptome deep sequencing proffers insight into floral gene regulation in Rosa cultivars

    Directory of Open Access Journals (Sweden)

    Kim Jungeun

    2012-11-01

    Full Text Available Abstract Background Roses (Rosa sp., which belong to the family Rosaceae, are the most economically important ornamental plants—making up 30% of the floriculture market. However, given high demand for roses, rose breeding programs are limited in molecular resources which can greatly enhance and speed breeding efforts. A better understanding of important genes that contribute to important floral development and desired phenotypes will lead to improved rose cultivars. For this study, we analyzed rose miRNAs and the rose flower transcriptome in order to generate a database to expound upon current knowledge regarding regulation of important floral characteristics. A rose genetic database will enable comprehensive analysis of gene expression and regulation via miRNA among different Rosa cultivars. Results We produced more than 0.5 million reads from expressed sequences, totalling more than 110 million bp. From these, we generated 35,657, 31,434, 34,725, and 39,722 flower unigenes from Rosa hybrid: ‘Vital’, ‘Maroussia’, and ‘Sympathy’ and Rosa rugosa Thunb. , respectively. The unigenes were assigned functional annotations, domains, metabolic pathways, Gene Ontology (GO terms, Plant Ontology (PO terms, and MIPS Functional Catalogue (FunCat terms. Rose flower transcripts were compared with genes from whole genome sequences of Rosaceae members (apple, strawberry, and peach and grape. We also produced approximately 40 million small RNA reads from flower tissue for Rosa, representing 267 unique miRNA tags. Among identified miRNAs, 25 of them were novel and 242 of them were conserved miRNAs. Statistical analyses of miRNA profiles revealed both shared and species-specific miRNAs, which presumably effect flower development and phenotypes. Conclusions In this study, we constructed a Rose miRNA and transcriptome database, and we analyzed the miRNAs and transcriptome generated from the flower tissues of four Rosa cultivars. The database provides a

  8. 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.

  9. 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.

  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. Statistical Approaches for Next-Generation Sequencing Data

    OpenAIRE

    Qiao, Dandi

    2012-01-01

    During the last two decades, genotyping technology has advanced rapidly, which enabled the tremendous success of genome-wide association studies (GWAS) in the search of disease susceptibility loci (DSLs). However, only a small fraction of the overall predicted heritability can be explained by the DSLs discovered. One possible explanation for this ”missing heritability” phenomenon is that many causal variants are rare. The recent development of high-throughput next-generation sequencing (NGS) ...

  12. Deep sequencing of the Camellia chekiangoleosa transcriptome revealed candidate genes for anthocyanin biosynthesis.

    Science.gov (United States)

    Wang, Zhong-Wei; Jiang, Cong; Wen, Qiang; Wang, Na; Tao, Yuan-Yuan; Xu, Li-An

    2014-03-15

    Camellia chekiangoleosa is an important species of genus Camellia. It provides high-quality edible oil and has great ornamental value. The flowers are big and red which bloom between February and March. Flower pigmentation is closely related to the accumulation of anthocyanin. Although anthocyanin biosynthesis has been studied extensively in herbaceous plants, little molecular information on the anthocyanin biosynthesis pathway of C. chekiangoleosa is yet known. In the present study, a cDNA library was constructed to obtain detailed and general data from the flowers of C. chekiangoleosa. To explore the transcriptome of C. chekiangoleosa and investigate genes involved in anthocyanin biosynthesis, a 454 GS FLX Titanium platform was used to generate an EST dataset. About 46,279 sequences were obtained, and 24,593 (53.1%) were annotated. Using Blast search against the AGRIS, 1740 unigenes were found homologous to 599 Arabidopsis transcription factor genes. Based on the transcriptome dataset, nine anthocyanin biosynthesis pathway genes (PAL, CHS1, CHS2, CHS3, CHI, F3H, DFR, ANS, and UFGT) were identified and cloned. The spatio-temporal expression patterns of these genes were also analyzed using quantitative real-time polymerase chain reaction. The study results not only enrich the gene resource but also provide valuable information for further studies concerning anthocyanin biosynthesis. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. Genomic DNA sequences from mastodon and woolly mammoth reveal deep speciation of forest and savanna elephants.

    Directory of Open Access Journals (Sweden)

    Nadin Rohland

    2010-12-01

    Full Text Available To elucidate the history of living and extinct elephantids, we generated 39,763 bp of aligned nuclear DNA sequence across 375 loci for African savanna elephant, African forest elephant, Asian elephant, the extinct American mastodon, and the woolly mammoth. Our data establish that the Asian elephant is the closest living relative of the extinct mammoth in the nuclear genome, extending previous findings from mitochondrial DNA analyses. We also find that savanna and forest elephants, which some have argued are the same species, are as or more divergent in the nuclear genome as mammoths and Asian elephants, which are considered to be distinct genera, thus resolving a long-standing debate about the appropriate taxonomic classification of the African elephants. Finally, we document a much larger effective population size in forest elephants compared with the other elephantid taxa, likely reflecting species differences in ancient geographic structure and range and differences in life history traits such as variance in male reproductive success.

  14. Deep Sequencing of Porphyromonas gingivalis and comparative transcriptome analysis of a LuxS mutant

    Directory of Open Access Journals (Sweden)

    Takanoi eHirano

    2012-06-01

    Full Text Available Porphyromonas gingivalis is a major etiological agent and chronic and aggressive forms of periodontal disease. The organism is an assacharolytic anaerobe and is a constituent of mixed species biofilms in a variety of microenvironments in the oral cavity. P. gingivalis expresses a range of virulence factors over which it exerts tight control. High-throughput sequencing technologies provide the opportunity to relate functional genomics to basic biology. In this study we report qualitative and quantitative RNA-Seq analysis of the transcriptome of P. gingivalis. We have also applied RNA-Seq to the transcriptome of a ΔluxS mutant of P. gingivalis deficient in AI-2-mediated bacterial communication. The transcriptome analysis confirmed the expression of all predicted ORFs for strain ATCC 33277, including 854 hypothetical proteins, and allowed the identification of hitherto unknown transcriptional units. Twelve noncoding RNAs were identified, including 11 small RNAs and one cobalamine riboswitch. Fifty seven genes were differentially regulated in the LuxS mutant. Addition of exogenous synthetic 4,5-dihydroxy-2,3-pentanedione (DPD, AI-2 precursor to the ΔluxS mutant culture complemented expression of a subset of genes, indicating that LuxS is involved in both AI-2 signaling and non-signaling dependent systems in P. gingivalis. This work provides an important dataset for future study of P. gingivalis pathophysiology and further defines the LuxS regulon in this oral pathogen.

  15. Revealing stable processing products from ribosome-associated small RNAs by deep-sequencing data analysis.

    Science.gov (United States)

    Zywicki, Marek; Bakowska-Zywicka, Kamilla; Polacek, Norbert

    2012-05-01

    The exploration of the non-protein-coding RNA (ncRNA) transcriptome is currently focused on profiling of microRNA expression and detection of novel ncRNA transcription units. However, recent studies suggest that RNA processing can be a multi-layer process leading to the generation of ncRNAs of diverse functions from a single primary transcript. Up to date no methodology has been presented to distinguish stable functional RNA species from rapidly degraded side products of nucleases. Thus the correct assessment of widespread RNA processing events is one of the major obstacles in transcriptome research. Here, we present a novel automated computational pipeline, named APART, providing a complete workflow for the reliable detection of RNA processing products from next-generation-sequencing data. The major features include efficient handling of non-unique reads, detection of novel stable ncRNA transcripts and processing products and annotation of known transcripts based on multiple sources of information. To disclose the potential of APART, we have analyzed a cDNA library derived from small ribosome-associated RNAs in Saccharomyces cerevisiae. By employing the APART pipeline, we were able to detect and confirm by independent experimental methods multiple novel stable RNA molecules differentially processed from well known ncRNAs, like rRNAs, tRNAs or snoRNAs, in a stress-dependent manner.

  16. Deep sequencing and ecological characterization of gut microbial communities of diverse bumble bee species.

    Directory of Open Access Journals (Sweden)

    Haw Chuan Lim

    Full Text Available Gut bacterial communities of bumble bees are correlated with defense against pathogens. Further understanding this host-microbe association is vitally important as bumble bees are currently experiencing global population declines, potentially due in part to emergent diseases. In this study, we used pyrosequencing and community fingerprinting (ARISA to characterize the gut microbial communities of nine bumble species from across the Bombus phylogeny. Overall, we delimited 74 bacterial taxa (operational taxonomic units or OTUs belonging to Betaproteobacteria, Gammaproteobacteria, Bacilli, Actinobacteria, Flavobacteria and Alphaproteobacteria. Each bacterial community was taxonomically simple, containing an average of 1.9 common (relative abundance per sample > 5% bacterial OTUs. The most abundant and prevalent (occurring in 92% of the samples bacterial OTU, based on 16S rRNA sequences, closely matched that of the previously described Betaproteobacteria species Snodgrassella alvi. Bacteria that were first described in bee-related external environments dominated a number of gut bacterial communities, suggesting that they are not strictly dependent on the internal gut environment. The ARISA data showed a correlation between bacterial community structures and the geographic locations where the bees were sampled, suggesting that at least a subset of the bacterial species may be transmitted environmentally. Using light and fluorescent microscopy, we demonstrated that the gut bacteria form a biofilm on the internal epithelial surface of the ileum, corroborating results obtained from Apis mellifera.

  17. 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.

  18. 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

  19. 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.

  20. Identification and Characterization of Liver MicroRNAs of the Chinese Tree Shrew via Deep Sequencing.

    Science.gov (United States)

    Feng, Yue; Feng, Yue-Mei; Feng, Yang; Lu, Caixia; Liu, Li; Sun, Xiaomei; Dai, Jiejie; Xia, Xueshan

    2015-10-01

    Chinese tree shrew (Tupaia belangeri chinensis) is a small animal that possess many features, which are valuable in biomedical research, as experimental models. Currently, there are numerous attempts to utilize tree shrews as models for hepatitis C virus (HCV) infection. This study aimed to construct a liver microRNA (miRNA) data of the tree shrew. Three second filial generation tree shrews were used in this study. Total RNA was extracted from each liver of the tree shrew and equal quality mixed, then reverse-transcribed to complementary DNA (cDNA). The cDNAs were amplified by polymerase chain reaction and subjected to high-throughput sequencing. A total of 2060 conserved miRNAs were identified through alignment with the mature miRNAs in miRBase 20.0 database. The gene ontology and Kyoto encyclopedia of genes and genomes analyses of the target genes of the miRNAs revealed several candidate miRNAs, genes and pathways that may involve in the process of HCV infection. The abundance of miR-122 and Let-7 families and their other characteristics provided us more evidences for the utilization of this animal, as a potential model for HCV infection and other related biomedical research. Moreover, 80 novel microRNAs were predicted using the software Mireap. The top 3 abundant miRNAs were validated in other tree samples, based on stem-loop quantitative reverse transcription-polymerase chain reaction. According to the liver microRNA data of Chinese tree shrew, characteristics of the miR-122 and Let-7 families further highlight the suitability of tree shrew as the animal model in HCV research.

  1. Deep sequencing whole transcriptome exploration of the σE regulon in Neisseria meningitidis.

    Directory of Open Access Journals (Sweden)

    Robert Antonius Gerhardus Huis in 't Veld

    Full Text Available Bacteria live in an ever-changing environment and must alter protein expression promptly to adapt to these changes and survive. Specific response genes that are regulated by a subset of alternative σ(70-like transcription factors have evolved in order to respond to this changing environment. Recently, we have described the existence of a σ(E regulon including the anti-σ-factor MseR in the obligate human bacterial pathogen Neisseria meningitidis. To unravel the complete σ(E regulon in N. meningitidis, we sequenced total RNA transcriptional content of wild type meningococci and compared it with that of mseR mutant cells (ΔmseR in which σ(E is highly expressed. Eleven coding genes and one non-coding gene were found to be differentially expressed between H44/76 wildtype and H44/76ΔmseR cells. Five of the 6 genes of the σ(E operon, msrA/msrB, and the gene encoding a pepSY-associated TM helix family protein showed enhanced transcription, whilst aniA encoding a nitrite reductase and nspA encoding the vaccine candidate Neisserial surface protein A showed decreased transcription. Analysis of differential expression in IGRs showed enhanced transcription of a non-coding RNA molecule, identifying a σ(E dependent small non-coding RNA. Together this constitutes the first complete exploration of an alternative σ-factor regulon in N. meningitidis. The results direct to a relatively small regulon indicative for a strictly defined response consistent with a relatively stable niche, the human throat, where N. meningitidis resides.

  2. Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

    Science.gov (United States)

    Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan

    2015-12-01

    Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. Pre-main sequence sun: a dynamic approach

    International Nuclear Information System (INIS)

    Newman, M.J.; Winkler, K.H.A.

    1979-01-01

    The classical pre-main sequence evolutionary behavior found by Hayashi and his coworkers for the Sun depends crucially on the choice of initial conditions. The Hayashi picture results from beginning the calculation with an already centrally condensed, highly Jeans unstable object not terribly far removed from the stellar state initially. The present calculation follows the work of Larson in investigating the hydrodynamic collapse and self-gravitational accretion of an initially uniform, just Jeans unstable interstellar gas-dust cloud. The resulting picture for the early history of the Sun is quite different from that found by Hayashi. A rather small (R approx. = 2 R/sub sun/), low-luminosity (L greater than or equal to L/sub sun/) protostellar core develops. A fully convective stellar core, characteristic of Hayashi's work, is not found during the accretion process, and can only develop, if at all, in the subsequent pre-main sequence Kelvin-Helmholtz contraction of the core. 3 figures, 1 table

  11. 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.

  12. Analyses of Tissue Culture Adaptation of Human Herpesvirus-6A by Whole Genome Deep Sequencing Redefines the Reference Sequence and Identifies Virus Entry Complex Changes.

    Science.gov (United States)

    Tweedy, Joshua G; Escriva, Eric; Topf, Maya; Gompels, Ursula A

    2017-12-31

    Tissue-culture adaptation of viruses can modulate infection. Laboratory passage and bacterial artificial chromosome (BAC)mid cloning of human cytomegalovirus, HCMV, resulted in genomic deletions and rearrangements altering genes encoding the virus entry complex, which affected cellular tropism, virulence, and vaccine development. Here, we analyse these effects on the reference genome for related betaherpesviruses, Roseolovirus, human herpesvirus 6A (HHV-6A) strain U1102. This virus is also naturally "cloned" by germline subtelomeric chromosomal-integration in approximately 1% of human populations, and accurate references are key to understanding pathological relationships between exogenous and endogenous virus. Using whole genome next-generation deep-sequencing Illumina-based methods, we compared the original isolate to tissue-culture passaged and the BACmid-cloned virus. This re-defined the reference genome showing 32 corrections and 5 polymorphisms. Furthermore, minor variant analyses of passaged and BACmid virus identified emerging populations of a further 32 single nucleotide polymorphisms (SNPs) in 10 loci, half non-synonymous indicating cell-culture selection. Analyses of the BAC-virus genome showed deletion of the BAC cassette via loxP recombination removing green fluorescent protein (GFP)-based selection. As shown for HCMV culture effects, select HHV-6A SNPs mapped to genes encoding mediators of virus cellular entry, including virus envelope glycoprotein genes gB and the gH/gL complex. Comparative models suggest stabilisation of the post-fusion conformation. These SNPs are essential to consider in vaccine-design, antimicrobial-resistance, and pathogenesis.

  13. 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.

  14. Deep RNA sequencing reveals dynamic regulation of myocardial noncoding RNAs in failing human heart and remodeling with mechanical circulatory support.

    Science.gov (United States)

    Yang, Kai-Chien; Yamada, Kathryn A; Patel, Akshar Y; Topkara, Veli K; George, Isaac; Cheema, Faisal H; Ewald, Gregory A; Mann, Douglas L; Nerbonne, Jeanne M

    2014-03-04

    Microarrays have been used extensively to profile transcriptome remodeling in failing human heart, although the genomic coverage provided is limited and fails to provide a detailed picture of the myocardial transcriptome landscape. Here, we describe sequencing-based transcriptome profiling, providing comprehensive analysis of myocardial mRNA, microRNA (miRNA), and long noncoding RNA (lncRNA) expression in failing human heart before and after mechanical support with a left ventricular (LV) assist device (LVAD). Deep sequencing of RNA isolated from paired nonischemic (NICM; n=8) and ischemic (ICM; n=8) human failing LV samples collected before and after LVAD and from nonfailing human LV (n=8) was conducted. These analyses revealed high abundance of mRNA (37%) and lncRNA (71%) of mitochondrial origin. miRNASeq revealed 160 and 147 differentially expressed miRNAs in ICM and NICM, respectively, compared with nonfailing LV. Among these, only 2 (ICM) and 5 (NICM) miRNAs are normalized with LVAD. RNASeq detected 18 480, including 113 novel, lncRNAs in human LV. Among the 679 (ICM) and 570 (NICM) lncRNAs differentially expressed with heart failure, ≈10% are improved or normalized with LVAD. In addition, the expression signature of lncRNAs, but not miRNAs or mRNAs, distinguishes ICM from NICM. Further analysis suggests that cis-gene regulation represents a major mechanism of action of human cardiac lncRNAs. The myocardial transcriptome is dynamically regulated in advanced heart failure and after LVAD support. The expression profiles of lncRNAs, but not mRNAs or miRNAs, can discriminate failing hearts of different pathologies and are markedly altered in response to LVAD support. These results suggest an important role for lncRNAs in the pathogenesis of heart failure and in reverse remodeling observed with mechanical support.

  15. Whole genome sequencing: an efficient approach to ensuring food safety

    Science.gov (United States)

    Lakicevic, B.; Nastasijevic, I.; Dimitrijevic, M.

    2017-09-01

    Whole genome sequencing is an effective, powerful tool that can be applied to a wide range of public health and food safety applications. A major difference between WGS and the traditional typing techniques is that WGS allows all genes to be included in the analysis, instead of a well-defined subset of genes or variable intergenic regions. Also, the use of WGS can facilitate the understanding of contamination/colonization routes of foodborne pathogens within the food production environment, and can also afford efficient tracking of pathogens’ entry routes and distribution from farm-to-consumer. Tracking foodborne pathogens in the food processing-distribution-retail-consumer continuum is of the utmost importance for facilitation of outbreak investigations and rapid action in controlling/preventing foodborne outbreaks. Therefore, WGS likely will replace most of the numerous workflows used in public health laboratories to characterize foodborne pathogens into one consolidated, efficient workflow.

  16. [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.

  17. 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.

  18. 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...

  19. 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.

  20. Deconstructing the genetic basis of spent sulphite liquor tolerance using deep sequencing of genome-shuffled yeast.

    Science.gov (United States)

    Pinel, Dominic; Colatriano, David; Jiang, Heng; Lee, Hung; Martin, Vincent Jj

    2015-01-01

    Identifying the genetic basis of complex microbial phenotypes is currently a major barrier to our understanding of multigenic traits and our ability to rationally design biocatalysts with highly specific attributes for the biotechnology industry. Here, we demonstrate that strain evolution by meiotic recombination-based genome shuffling coupled with deep sequencing can be used to deconstruct complex phenotypes and explore the nature of multigenic traits, while providing concrete targets for strain development. We determined genomic variations found within Saccharomyces cerevisiae previously evolved in our laboratory by genome shuffling for tolerance to spent sulphite liquor. The representation of these variations was backtracked through parental mutant pools and cross-referenced with RNA-seq gene expression analysis to elucidate the importance of single mutations and key biological processes that play a role in our trait of interest. Our findings pinpoint novel genes and biological determinants of lignocellulosic hydrolysate inhibitor tolerance in yeast. These include the following: protein homeostasis constituents, including Ubp7p and Art5p, related to ubiquitin-mediated proteolysis; stress response transcriptional repressor, Nrg1p; and NADPH-dependent glutamate dehydrogenase, Gdh1p. Reverse engineering a prominent mutation in ubiquitin-specific protease gene UBP7 in a laboratory S. cerevisiae strain effectively increased spent sulphite liquor tolerance. This study advances understanding of yeast tolerance mechanisms to inhibitory substrates and biocatalyst design for a biomass-to-biofuel/biochemical industry, while providing insights into the process of mutation accumulation that occurs during genome shuffling.

  1. 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...

  2. 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.

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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)

  10. 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.

  11. 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.

  12. 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.

  13. Deep sequencing shows that oocytes are not prone to accumulate mtDNA heteroplasmic mutations during ovarian ageing.

    Science.gov (United States)

    Boucret, L; Bris, C; Seegers, V; Goudenège, D; Desquiret-Dumas, V; Domin-Bernhard, M; Ferré-L'Hotellier, V; Bouet, P E; Descamps, P; Reynier, P; Procaccio, V; May-Panloup, P

    2017-10-01

    Does ovarian ageing increase the number of heteroplasmic mitochondrial DNA (mtDNA) point mutations in oocytes? Our results suggest that oocytes are not subject to the accumulation of mtDNA point mutations during ovarian ageing. Ageing is associated with the alteration of mtDNA integrity in various tissues. Primary oocytes, present in the ovary since embryonic life, may accumulate mtDNA mutations during the process of ovarian ageing. This was an observational study of 53 immature oocyte-cumulus complexes retrieved from 35 women undergoing IVF at the University Hospital of Angers, France, from March 2013 to March 2014. The women were classified in two groups, one including 19 women showing signs of ovarian ageing objectified by a diminished ovarian reserve (DOR), and the other, including 16 women with a normal ovarian reserve (NOR), which served as a control group. mtDNA was extracted from isolated oocytes, and from their corresponding cumulus cells (CCs) considered as a somatic cell compartment. The average mtDNA content of each sample was assessed by using a quantitative real-time PCR technique. Deep sequencing was performed using the Ion Torrent Proton for Next-Generation Sequencing. Signal processing and base calling were done by the embedded pre-processing pipeline and the variants were analyzed using an in-house workflow. The distribution of the different variants between DOR and NOR patients, on one hand, and oocyte and CCs, on the other, was analyzed with the generalized mixed linear model to take into account the cluster of cells belonging to a given mother. There were no significant differences between the numbers of mtDNA variants between the DOR and the NOR patients, either in the oocytes (P = 0.867) or in the surrounding CCs (P = 0.154). There were also no differences in terms of variants with potential functional consequences. De-novo mtDNA variants were found in 28% of the oocytes and in 66% of the CCs with the mean number of variants being

  14. High-throughput deep sequencing reveals that microRNAs play important roles in salt tolerance of euhalophyte Salicornia europaea.

    Science.gov (United States)

    Feng, Juanjuan; Wang, Jinhui; Fan, Pengxiang; Jia, Weitao; Nie, Lingling; Jiang, Ping; Chen, Xianyang; Lv, Sulian; Wan, Lichuan; Chang, Sandra; Li, Shizhong; Li, Yinxin

    2015-02-26

    microRNAs (miRNAs) are implicated in plant development processes and play pivotal roles in plant adaptation to environmental stresses. Salicornia europaea, a salt mash euhalophyte, is a suitable model plant to study salt adaptation mechanisms. S. europaea is also a vegetable, forage, and oilseed that can be used for saline land reclamation and biofuel precursor production on marginal lands. Despite its importance, no miRNA has been identified from S. europaea thus far. Deep sequencing was performed to investigate small RNA transcriptome of S. europaea. Two hundred and ten conserved miRNAs comprising 51 families and 31 novel miRNAs (including seven miRNA star sequences) belonging to 30 families were identified. About half (13 out of 31) of the novel miRNAs were only detected in salt-treated samples. The expression of 43 conserved and 13 novel miRNAs significantly changed in response to salinity. In addition, 53 conserved and 13 novel miRNAs were differentially expressed between the shoots and roots. Furthermore, 306 and 195 S. europaea unigenes were predicted to be targets of 41 conserved and 29 novel miRNA families, respectively. These targets encoded a wide range of proteins, and genes involved in transcription regulation constituted the largest category. Four of these genes encoding laccase, F-box family protein, SAC3/GANP family protein, and NADPH cytochrome P-450 reductase were validated using 5'-RACE. Our results indicate that specific miRNAs are tightly regulated by salinity in the shoots and/or roots of S. europaea, which may play important roles in salt tolerance of this euhalophyte. The S. europaea salt-responsive miRNAs and miRNAs that target transcription factors, nucleotide binding site-leucine-rich repeat proteins and enzymes involved in lignin biosynthesis as well as carbon and nitrogen metabolism may be applied in genetic engineering of crops with high stress tolerance, and genetic modification of biofuel crops with high biomass and regulatable

  15. 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'…

  16. RNA deep sequencing reveals novel candidate genes and polymorphisms in boar testis and liver tissues with divergent androstenone levels.

    Directory of Open Access Journals (Sweden)

    Asep Gunawan

    Full Text Available Boar taint is an unpleasant smell and taste of pork meat derived from some entire male pigs. The main causes of boar taint are the two compounds androstenone (5α-androst-16-en-3-one and skatole (3-methylindole. It is crucial to understand the genetic mechanism of boar taint to select pigs for lower androstenone levels and thus reduce boar taint. The aim of the present study was to investigate transcriptome differences in boar testis and liver tissues with divergent androstenone levels using RNA deep sequencing (RNA-Seq. The total number of reads produced for each testis and liver sample ranged from 13,221,550 to 33,206,723 and 12,755,487 to 46,050,468, respectively. In testis samples 46 genes were differentially regulated whereas 25 genes showed differential expression in the liver. The fold change values ranged from -4.68 to 2.90 in testis samples and -2.86 to 3.89 in liver samples. Differentially regulated genes in high androstenone testis and liver samples were enriched in metabolic processes such as lipid metabolism, small molecule biochemistry and molecular transport. This study provides evidence for transcriptome profile and gene polymorphisms of boars with divergent androstenone level using RNA-Seq technology. Digital gene expression analysis identified candidate genes in flavin monooxygenease family, cytochrome P450 family and hydroxysteroid dehydrogenase family. Moreover, polymorphism and association analysis revealed mutation in IRG6, MX1, IFIT2, CYP7A1, FMO5 and KRT18 genes could be potential candidate markers for androstenone levels in boars. Further studies are required for proving the role of candidate genes to be used in genomic selection against boar taint in pig breeding programs.

  17. 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.

  18. 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.

  19. Synchronization of conference presentation sequence using delay equalization approach

    Science.gov (United States)

    Godavari, Rakesh K.; Celenk, Mehmet

    2001-11-01

    In this paper, a delay equalization approach is proposed for cohesive conference presentation with minimal screen- freezing effect. The underlying screen-freezing effect is due to varying delays in the communication channels involved in broadcasting. In turn, this poses a threat to the goal of uniform delay distribution among data packets for synchronized presentation broadcast. We wish to achieve uniformity among packet arrival times to the recipients. This objective is achieved by transforming a given input delay distribution (D) to the desired output density through a delay equalization process. Considering a general case of between packet and frame delay distributions for a given input, the desired output is obtained through a delay equalization process. In case of a specific normal delay distribution, a memory-less system g(D) is determined as an approximation to the delay equalizer such that the equalizer output O(t)equalsg[D(t)] is uniformly distributed in the allowable delay limits of (a,b) assuring the specified quality of service (QoS) parameters. The corresponding delay is added at each recipient workstation, which acts as the wait period required before it begins its designated presentation.For a zero mean normal delay density case, the equalizer transfer function can be given in a closed from solution as O(t)equalsg[D(t)[equals(b-a)]0.5+eft(-D/$RO OTR(0)))[+a, where erf(x) is the standard error function. The histogram approximation is adopted as an asymptotically delay equalization means for general cases. This technique provides a means for modifying the dynamic range of data acquired by altering it into a desired distribution. In experimentation, equalizer characteristic functions are derived for a set of selected input delays to obtain the desired output. The delay equalizer system developed here is suited for deployment in a distributed hierarchical conferencing environment. To accommodate a broadcast continuity, the multimedia presentation is

  20. 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:…

  1. Appearances can be deceptive: revealing a hidden viral infection with deep sequencing in a plant quarantine context.

    Science.gov (United States)

    Candresse, Thierry; Filloux, Denis; Muhire, Brejnev; Julian, Charlotte; Galzi, Serge; Fort, Guillaume; Bernardo, Pauline; Daugrois, Jean-Heindrich; Fernandez, Emmanuel; Martin, Darren P; Varsani, Arvind; Roumagnac, Philippe

    2014-01-01

    Comprehensive inventories of plant viral diversity are essential for effective quarantine and sanitation efforts. The safety of regulated plant material exchanges presently relies heavily on techniques such as PCR or nucleic acid hybridisation, which are only suited to the detection and characterisation of specific, well characterised pathogens. Here, we demonstrate the utility of sequence-independent next generation sequencing (NGS) of both virus-derived small interfering RNAs (siRNAs) and virion-associated nucleic acids (VANA) for the detailed identification and characterisation of viruses infecting two quarantined sugarcane plants. Both plants originated from Egypt and were known to be infected with Sugarcane streak Egypt Virus (SSEV; Genus Mastrevirus, Family Geminiviridae), but were revealed by the NGS approaches to also be infected by a second highly divergent mastrevirus, here named Sugarcane white streak Virus (SWSV). This novel virus had escaped detection by all routine quarantine detection assays and was found to also be present in sugarcane plants originating from Sudan. Complete SWSV genomes were cloned and sequenced from six plants and all were found to share >91% genome-wide identity. With the exception of two SWSV variants, which potentially express unusually large RepA proteins, the SWSV isolates display genome characteristics very typical to those of all other previously described mastreviruses. An analysis of virus-derived siRNAs for SWSV and SSEV showed them to be strongly influenced by secondary structures within both genomic single stranded DNA and mRNA transcripts. In addition, the distribution of siRNA size frequencies indicates that these mastreviruses are likely subject to both transcriptional and post-transcriptional gene silencing. Our study stresses the potential advantages of NGS-based virus metagenomic screening in a plant quarantine setting and indicates that such techniques could dramatically reduce the numbers of non

  2. Appearances can be deceptive: revealing a hidden viral infection with deep sequencing in a plant quarantine context.

    Directory of Open Access Journals (Sweden)

    Thierry Candresse

    Full Text Available Comprehensive inventories of plant viral diversity are essential for effective quarantine and sanitation efforts. The safety of regulated plant material exchanges presently relies heavily on techniques such as PCR or nucleic acid hybridisation, which are only suited to the detection and characterisation of specific, well characterised pathogens. Here, we demonstrate the utility of sequence-independent next generation sequencing (NGS of both virus-derived small interfering RNAs (siRNAs and virion-associated nucleic acids (VANA for the detailed identification and characterisation of viruses infecting two quarantined sugarcane plants. Both plants originated from Egypt and were known to be infected with Sugarcane streak Egypt Virus (SSEV; Genus Mastrevirus, Family Geminiviridae, but were revealed by the NGS approaches to also be infected by a second highly divergent mastrevirus, here named Sugarcane white streak Virus (SWSV. This novel virus had escaped detection by all routine quarantine detection assays and was found to also be present in sugarcane plants originating from Sudan. Complete SWSV genomes were cloned and sequenced from six plants and all were found to share >91% genome-wide identity. With the exception of two SWSV variants, which potentially express unusually large RepA proteins, the SWSV isolates display genome characteristics very typical to those of all other previously described mastreviruses. An analysis of virus-derived siRNAs for SWSV and SSEV showed them to be strongly influenced by secondary structures within both genomic single stranded DNA and mRNA transcripts. In addition, the distribution of siRNA size frequencies indicates that these mastreviruses are likely subject to both transcriptional and post-transcriptional gene silencing. Our study stresses the potential advantages of NGS-based virus metagenomic screening in a plant quarantine setting and indicates that such techniques could dramatically reduce the numbers of non

  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.

    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

  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. 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.)

  6. 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.

  7. 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...

  8. Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms.

    Science.gov (United States)

    Xu, Min; Chai, Xiaoqi; Muthakana, Hariank; Liang, Xiaodan; Yang, Ge; Zeev-Ben-Mordehai, Tzviya; Xing, Eric P

    2017-07-15

    Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data. To complement existing approaches, in this article we propose a new approach for subdividing subtomograms into smaller but relatively homogeneous subsets. The structures in these subsets can then be separately recovered using existing computation intensive methods. Our approach is based on supervised structural feature extraction using deep learning, in combination with unsupervised clustering and reference-free classification. Our experiments show that, compared with existing unsupervised rotation invariant feature and pose-normalization based approaches, our new approach achieves significant improvements in both discrimination ability and scalability. More importantly, our new approach is able to discover new structural classes and recover structures that do not exist in training data. Source code freely available at http://www.cs.cmu.edu/∼mxu1/software . mxu1@cs.cmu.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  9. 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.

  10. 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...

  11. Deep Sequencing of Three Loci Implicated in Large-Scale Genome-Wide Association Study Smoking Meta-Analyses.

    Science.gov (United States)

    Clark, Shaunna L; McClay, Joseph L; Adkins, Daniel E; Aberg, Karolina A; Kumar, Gaurav; Nerella, Sri; Xie, Linying; Collins, Ann L; Crowley, James J; Quakenbush, Corey R; Hillard, Christopher E; Gao, Guimin; Shabalin, Andrey A; Peterson, Roseann E; Copeland, William E; Silberg, Judy L; Maes, Hermine; Sullivan, Patrick F; Costello, Elizabeth J; van den Oord, Edwin J

    2016-05-01

    Genome-wide association study meta-analyses have robustly implicated three loci that affect susceptibility for smoking: CHRNA5\\CHRNA3\\CHRNB4, CHRNB3\\CHRNA6 and EGLN2\\CYP2A6. Functional follow-up studies of these loci are needed to provide insight into biological mechanisms. However, these efforts have been hampered by a lack of knowledge about the specific causal variant(s) involved. In this study, we prioritized variants in terms of the likelihood they account for the reported associations. We employed targeted capture of the CHRNA5\\CHRNA3\\CHRNB4, CHRNB3\\CHRNA6, and EGLN2\\CYP2A6 loci and flanking regions followed by next-generation deep sequencing (mean coverage 78×) to capture genomic variation in 363 individuals. We performed single locus tests to determine if any single variant accounts for the association, and examined if sets of (rare) variants that overlapped with biologically meaningful annotations account for the associations. In total, we investigated 963 variants, of which 71.1% were rare (minor allele frequency < 0.01), 6.02% were insertion/deletions, and 51.7% were catalogued in dbSNP141. The single variant results showed that no variant fully accounts for the association in any region. In the variant set results, CHRNB4 accounts for most of the signal with significant sets consisting of directly damaging variants. CHRNA6 explains most of the signal in the CHRNB3\\CHRNA6 locus with significant sets indicating a regulatory role for CHRNA6. Significant sets in CYP2A6 involved directly damaging variants while the significant variant sets suggested a regulatory role for EGLN2. We found that multiple variants implicating multiple processes explain the signal. Some variants can be prioritized for functional follow-up. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  12. Deep Sequencing Analysis of miRNA Expression in Breast Muscle of Fast-Growing and Slow-Growing Broilers

    Directory of Open Access Journals (Sweden)

    Hongjia Ouyang

    2015-07-01

    Full Text Available Growth performance is an important economic trait in chicken. MicroRNAs (miRNAs have been shown to play important roles in various biological processes, but their functions in chicken growth are not yet clear. To investigate the function of miRNAs in chicken growth, breast muscle tissues of the two-tail samples (highest and lowest body weight from Recessive White Rock (WRR and Xinghua Chickens (XH were performed on high throughput small RNA deep sequencing. In this study, a total of 921 miRNAs were identified, including 733 known mature miRNAs and 188 novel miRNAs. There were 200, 279, 257 and 297 differentially expressed miRNAs in the comparisons of WRRh vs. WRRl, WRRh vs. XHh, WRRl vs. XHl, and XHh vs. XHl group, respectively. A total of 22 highly differentially expressed miRNAs (fold change > 2 or < 0.5; p-value < 0.05; q-value < 0.01, which also have abundant expression (read counts > 1000 were found in our comparisons. As far as two analyses (WRRh vs. WRRl, and XHh vs. XHl are concerned, we found 80 common differentially expressed miRNAs, while 110 miRNAs were found in WRRh vs. XHh and WRRl vs. XHl. Furthermore, 26 common miRNAs were identified among all four comparisons. Four differentially expressed miRNAs (miR-223, miR-16, miR-205a and miR-222b-5p were validated by quantitative real-time RT-PCR (qRT-PCR. Regulatory networks of interactions among miRNAs and their targets were constructed using integrative miRNA target-prediction and network-analysis. Growth hormone receptor (GHR was confirmed as a target of miR-146b-3p by dual-luciferase assay and qPCR, indicating that miR-34c, miR-223, miR-146b-3p, miR-21 and miR-205a are key growth-related target genes in the network. These miRNAs are proposed as candidate miRNAs for future studies concerning miRNA-target function on regulation of chicken growth.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder

    Directory of Open Access Journals (Sweden)

    Xiaoling Tao

    2016-03-01

    Full Text Available Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN, and support vector machine (SVM by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.

  18. A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

    Science.gov (United States)

    Xiao, Ran; Xu, Yuan; Pelter, Michele M; Mortara, David W; Hu, Xiao

    2018-01-01

    Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.

  19. 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.

  20. 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.

  1. deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data.

    Science.gov (United States)

    Zheng, Ling-Ling; Li, Jun-Hao; Wu, Jie; Sun, Wen-Ju; Liu, Shun; Wang, Ze-Lin; Zhou, Hui; Yang, Jian-Hua; Qu, Liang-Hu

    2016-01-04

    Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

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

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    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.

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

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    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

  12. 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...

  13. 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.

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

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    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. Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach.

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    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.

  16. 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....

  17. 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

  18. 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.

  19. Minimal Residual Disease Detection and Evolved IGH Clones Analysis in Acute B Lymphoblastic Leukemia Using IGH Deep Sequencing.

    Science.gov (United States)

    Wu, Jinghua; Jia, Shan; Wang, Changxi; Zhang, Wei; Liu, Sixi; Zeng, Xiaojing; Mai, Huirong; Yuan, Xiuli; Du, Yuanping; Wang, Xiaodong; Hong, Xueyu; Li, Xuemei; Wen, Feiqiu; Xu, Xun; Pan, Jianhua; Li, Changgang; Liu, Xiao

    2016-01-01

    Acute B lymphoblastic leukemia (B-ALL) is one of the most common types of childhood cancer worldwide and chemotherapy is the main treatment approach. Despite good response rates to chemotherapy regiments, many patients eventually relapse and minimal residual disease (MRD) is the leading risk factor for relapse. The evolution of leukemic clones during disease development and treatment may have clinical significance. In this study, we performed immunoglobulin heavy chain ( IGH ) repertoire high throughput sequencing (HTS) on the diagnostic and post-treatment samples of 51 pediatric B-ALL patients. We identified leukemic IGH clones in 92.2% of the diagnostic samples and nearly half of the patients were polyclonal. About one-third of the leukemic clones have correct open reading frame in the complementarity determining region 3 (CDR3) of IGH , which demonstrates that the leukemic B cells were in the early developmental stage. We also demonstrated the higher sensitivity of HTS in MRD detection and investigated the clinical value of using peripheral blood in MRD detection and monitoring the clonal IGH evolution. In addition, we found leukemic clones were extensively undergoing continuous clonal IGH evolution by variable gene replacement. Dynamic frequency change and newly emerged evolved IGH clones were identified upon the pressure of chemotherapy. In summary, we confirmed the high sensitivity and universal applicability of HTS in MRD detection. We also reported the ubiquitous evolved IGH clones in B-ALL samples and their response to chemotherapy during treatment.

  20. Deep RNA sequencing reveals hidden features and dynamics of early gene transcription in Paramecium bursaria chlorella virus 1.

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    Guillaume Blanc

    Full Text Available Paramecium bursaria chlorella virus 1 (PBCV-1 is the prototype of the genus Chlorovirus (family Phycodnaviridae that infects the unicellular, eukaryotic green alga Chlorella variabilis NC64A. The 331-kb PBCV-1 genome contains 416 major open reading frames. A mRNA-seq approach was used to analyze PBCV-1 transcriptomes at 6 progressive times during the first hour of infection. The alignment of 17 million reads to the PBCV-1 genome allowed the construction of single-base transcriptome maps. Significant transcription was detected for a subset of 50 viral genes as soon as 7 min after infection. By 20 min post infection (p.i., transcripts were detected for most PBCV-1 genes and transcript levels continued to increase globally up to 60 min p.i., at which time 41% or the poly (A+-containing RNAs in the infected cells mapped to the PBCV-1 genome. For some viral genes, the number of transcripts in the latter time points (20 to 60 min p.i. was much higher than that of the most highly expressed host genes. RNA-seq data revealed putative polyadenylation signal sequences in PBCV-1 genes that were identical to the polyadenylation signal AAUAAA of green algae. Several transcripts have an RNA fragment excised. However, the frequency of excision and the resulting putative shortened protein products suggest that most of these excision events have no functional role but are probably the result of the activity of misled splicesomes.

  1. 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/.

  2. 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…

  3. Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

    Science.gov (United States)

    Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

    2017-04-01

    In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

  4. 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.

  5. Environmental drivers of epibenthic megafauna on a deep temperate continental shelf: A multiscale approach

    Science.gov (United States)

    Lacharité, Myriam; Metaxas, Anna

    2018-03-01

    Evaluating the role of abiotic factors in influencing the distribution of deep-water (>75-100 m depth) epibenthic megafaunal communities at mid-to-high latitudes is needed to estimate effects of environmental change, and support marine spatial planning since these factors can be effectively mapped. Given the disparity in scales at which these factors operate, incorporating multiple spatial and temporal scales is necessary. In this study, we determined the relative importance of 3 groups of environmental drivers at different scales (sediment, geomorphology, and oceanography) on epibenthic megafauna on a deep temperate continental shelf in the eastern Gulf of Maine (northwest Atlantic). Twenty benthic photographic transects (range: 611-1021 m; total length surveyed: 18,902 m; 996 images; average of 50 ± 16 images per transect) were performed in July and August 2009 to assess the abundance, composition and diversity of these communities. Surficial geology was assessed using seafloor imagery processed with a novel approach based on computer vision. A bathymetric terrain model (horizontal resolution: 100 m) was used to derive bathymetric variability in the vicinity of transects (1.5, 5 km). Oceanography at the seafloor (temperature, salinity, current speed, current direction) over 10 years (1999-2008) was determined using empirical (World Ocean Database 2013) and modelled data (Finite-Volume Community Ocean Model; 45 vertical layers; horizontal resolution: 1.7-9.5 km). The relative influence of environmental drivers differed between community traits. Abundance was enhanced primarily by swift current speeds, while higher diversity was observed in coarser and more heterogeneous substrates. In both cases, the role of geomorphological features was secondary to these drivers. Environmental variables were poor predictors of change in community composition at the scale of the eastern Gulf of Maine. This study demonstrated the need for explicitly incorporating scales into

  6. 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.

  7. 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

  8. 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.

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

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    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.

  10. 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.

  11. 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.

  12. 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…

  13. Genome-wide identification of soybean microRNA responsive to soybean cyst nematodes infection by deep sequencing.

    Science.gov (United States)

    Tian, Bin; Wang, Shichen; Todd, Timothy C; Johnson, Charles D; Tang, Guiliang; Trick, Harold N

    2017-08-02

    The soybean cyst nematode (SCN), Heterodera glycines, is one of the most devastating diseases limiting soybean production worldwide. It is known that small RNAs, including microRNAs (miRNAs) and small interfering RNAs (siRNAs), play important roles in regulating plant growth and development, defense against pathogens, and responses to environmental changes. In order to understand the role of soybean miRNAs during SCN infection, we analyzed 24 small RNA libraries including three biological replicates from two soybean cultivars (SCN susceptible KS4607, and SCN HG Type 7 resistant KS4313N) that were grown under SCN-infested and -noninfested soil at two different time points (SCN feeding establishment and egg production). In total, 537 known and 70 putative novel miRNAs in soybean were identified from a total of 0.3 billion reads (average about 13.5 million reads for each sample) with the programs of Bowtie and miRDeep2 mapper. Differential expression analyses were carried out using edgeR to identify miRNAs involved in the soybean-SCN interaction. Comparative analysis of miRNA profiling indicated a total of 60 miRNAs belonging to 25 families that might be specifically related to cultivar responses to SCN. Quantitative RT-PCR validated similar miRNA interaction patterns as sequencing results. These findings suggest that miRNAs are likely to play key roles in soybean response to SCN. The present work could provide a framework for miRNA functional identification and the development of novel approaches for improving soybean SCN resistance in future studies.

  14. 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...

  15. Genome sequence of Halorhabdus tiamatea, the first archaeon isolated from a deep-sea anoxic brine lake.

    KAUST Repository

    Antunes, Andre

    2011-09-01

    We present the draft genome of Halorhabdus tiamatea, the first member of the Archaea ever isolated from a deep-sea anoxic brine. Genome comparison with Halorhabdus utahensis revealed some striking differences, including a marked increase in genes associated with transmembrane transport and putative genes for a trehalose synthase and a lactate dehydrogenase.

  16. Genome sequence of Haloplasma contractile, an unusual contractile bacterium from a deep-sea anoxic brine lake.

    KAUST Repository

    Antunes, Andre; Alam, Intikhab; El Dorry, Hamza; Siam, Rania; Robertson, Anthony J.; Bajic, Vladimir B.; Stingl, Ulrich

    2011-01-01

    We present the draft genome of Haloplasma contractile, isolated from a deep-sea brine and representing a new order between Firmicutes and Mollicutes. Its complex morphology with contractile protrusions might be strongly influenced by the presence of seven MreB/Mbl homologs, which appears to be the highest copy number ever reported.

  17. Genome Sequence of Aeribacillus pallidus Strain GS3372, an Endospore-Forming Bacterium Isolated in a Deep Geothermal Reservoir

    OpenAIRE

    Sevasti Filippidou; Marion Jaussi; Thomas Junier; Tina Wunderlin; Nicole Jeanneret; Simona Regenspurg; Po-E Li; Chien-Chi Lo; Shannon Johnson; Kim McMurry; Cheryl D. Gleasner; Momchilo Vuyisich; Patrick S. Chain; Pilar Junier

    2015-01-01

    The genome of strain GS3372 is the first publicly available strain of Aeribacillus pallidus. This endospore-forming thermophilic strain was isolated from a deep geothermal reservoir. The availability of this genome can contribute to the clarification of the taxonomy of the closely related Anoxybacillus, Geobacillus, and Aeribacillus genera.

  18. Genome Sequence of Aeribacillus pallidus Strain GS3372, an Endospore-Forming Bacterium Isolated in a Deep Geothermal Reservoir.

    Science.gov (United States)

    Filippidou, Sevasti; Jaussi, Marion; Junier, Thomas; Wunderlin, Tina; Jeanneret, Nicole; Regenspurg, Simona; Li, Po-E; Lo, Chien-Chi; Johnson, Shannon; McMurry, Kim; Gleasner, Cheryl D; Vuyisich, Momchilo; Chain, Patrick S; Junier, Pilar

    2015-08-27

    The genome of strain GS3372 is the first publicly available strain of Aeribacillus pallidus. This endospore-forming thermophilic strain was isolated from a deep geothermal reservoir. The availability of this genome can contribute to the clarification of the taxonomy of the closely related Anoxybacillus, Geobacillus, and Aeribacillus genera. Copyright © 2015 Filippidou et al.

  19. Genome sequence of Haloplasma contractile, an unusual contractile bacterium from a deep-sea anoxic brine lake.

    KAUST Repository

    Antunes, Andre

    2011-09-01

    We present the draft genome of Haloplasma contractile, isolated from a deep-sea brine and representing a new order between Firmicutes and Mollicutes. Its complex morphology with contractile protrusions might be strongly influenced by the presence of seven MreB/Mbl homologs, which appears to be the highest copy number ever reported.

  20. Genome sequence of Halorhabdus tiamatea, the first archaeon isolated from a deep-sea anoxic brine lake.

    KAUST Repository

    Antunes, Andre; Alam, Intikhab; Bajic, Vladimir B.; Stingl, Ulrich

    2011-01-01

    We present the draft genome of Halorhabdus tiamatea, the first member of the Archaea ever isolated from a deep-sea anoxic brine. Genome comparison with Halorhabdus utahensis revealed some striking differences, including a marked increase in genes associated with transmembrane transport and putative genes for a trehalose synthase and a lactate dehydrogenase.

  1. 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

  2. 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...

  3. 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

  4. 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

  5. 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.

  6. 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...

  7. 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

  8. 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.

  9. Multiple viral infections in Agaricus bisporus - Characterisation of 18 unique RNA viruses and 8 ORFans identified by deep sequencing

    OpenAIRE

    Deakin, Gregory; Dobbs, Edward; Bennett, Julie M.; Jones, Ian M.; Grogan, Helen M.; Burton, Kerry S.

    2017-01-01

    Thirty unique non-host RNAs were sequenced in the cultivated fungus, Agaricus bisporus, comprising 18 viruses each encoding an RdRp domain with an additional 8 ORFans (non-host RNAs with no similarity to known sequences). Two viruses were multipartite with component RNAs showing correlative abundances and common 3′ motifs. The viruses, all positive sense single-stranded, were classified into diverse orders/families. Multiple infections of Agaricus may represent a diverse, dynamic and interact...

  10. Estimated effect of an integrated approach to suspected deep venous thrombosis using limited-compression ultrasound.

    Science.gov (United States)

    Poley, Rachel A; Newbigging, Joseph L; Sivilotti, Marco L A

    2014-09-01

    Deep vein thrombosis (DVT) is both common and serious, yet the desire to never miss the diagnosis, coupled with the low specificity of D-dimer testing, results in high imaging rates, return visits, and empirical anticoagulation. The objective of this study was to evaluate a new approach incorporating bedside limited-compression ultrasound (LC US) by emergency physicians (EPs) into the workup strategy for DVT. This was a cross-sectional observational study of emergency department (ED) patients with suspected DVT. Patients on anticoagulants; those with chronic DVT, leg cast, or amputation; or when the results of comprehensive imaging were already known were excluded. All patients were treated in the usual fashion based on the protocol in use at the center, including comprehensive imaging based on the modified Wells score and serum D-dimer testing. Seventeen physicians were trained and performed LC US in all subjects. The authors identified a priori an alternate workup strategy in which DVT would be ruled out in "DVT unlikely" (Wells score return visits for imaging and 10 (4.4%) cases of unnecessary anticoagulation. In 19% of cases, the treating and scanning physician disagreed whether the patient was DVT likely or DVT unlikely based on Wells score (κ = 0.62; 95% CI = 0.48 to 0.77). Limited-compression US holds promise as one component of the diagnostic approach to DVT, but should not be used as a stand-alone test due to imperfect sensitivity. Tradeoffs in diagnostic efficiency for the sake of perfect sensitivity remain a difficult issue collectively in emergency medicine (EM), but need to be scrutinized carefully in light of the costs of overinvestigation, delays in diagnosis, and risks of empirical anticoagulation. © 2014 by the Society for Academic Emergency Medicine.

  11. Deep Atomic Binding (DAB) Hypothesis: A New Approach of Fission Product Chemistry

    International Nuclear Information System (INIS)

    Ajlouni, Abdul-Wali M.S.

    2006-01-01

    Former studies assumed that, after fission process occurs, the highly ionized new born atoms (20-22 positive charge), ionize the media in which they pass through before becoming stable atoms in a manner similar to 4-MeV ?-particles. Via ordinary chemical reactions with the surroundings, each stable atom has a probability to form chemical compound. Since there are about 35 different elemental atoms created through fission processes, a large number of chemical species were suggested to be formed. But, these suggested chemical species were not found in the environment after actual releases of FP during accidents like TMI (USA, 1979), and Chernobyl (former USSR, 1986), also the models based on these suggested reactions and species could not interpret the behavior of these actual species. It is assumed here that the ionization states of the new born atoms and the long term high temperature were not dealt with in an appropriate way and they were the reasons of former models failure. Our new approach of Deep Atomic Binding (DAB) based on the following: 1-The new born atoms which are highly ionized, 10-12 electrons associated with each nucleus, having a large probability to create bonds between them to form molecules. These bonds are at the L, or M shells, and we call it DAB. 2-The molecules stay in the reactor at high temperatures for long periods, so they undergo many stages of composition and decomposition to form giant molecules. By applying DAB approach, field data from Chernobyl, TMI and nuclear detonations could be interpreted with a wide coincidence resulted. (author)

  12. [Enterovirus sequencing as a new approach to the laboratory diagnosis for clinical and epidemiological purposes].

    Science.gov (United States)

    Rainetová, P; Jiřincová, H; Musílek, M; Nováková, L; Vodičková, I; Štruncová, V; Švecová, M; Pazdiora, P; Piskunová, N; Trubač, P; Zajíc, T; Havlíčková, M

    2015-06-01

    Introducing enterovirus sequencing as an advanced approach to classify the viruses isolated according to the novel nomenclature and to characterize isolates in detail. Seventy-five specimens collected from 64 patients in two hospitals, Liberec Regional Hospital, and Plzeň University Hospital, were analyzed. The study patients' age ranged from four to 54 years, with a median of 15 years in males and 16 years in females. In most patients, the reasons for admission were intense headache, fever, vomiting, tiredness, meningeal symptoms, intestinal symptoms (in two patients), and skin symptoms (in one patient). The specimens collected were rectal and throat swabs, cerebrospinal fluid (CSF) and stool specimens. Molecular detection and typing were performed using the RT-PCR method. A segment of the 5´non-coding RNA was selected for typing. Specimens were amplified using single-step PCR with external primers and with the same primers extended to include M13 sequences (Generi-Biotech). The LASERGENE software (DIASTAR) was used in sequence editing, alignment, and quality check. The sequences obtained were checked against the central GenBank sequence database using the BLAST algorithm. The identification of the study isolates resulted in 61 ECHO viruses 30, three coxsackie viruses B1, one coxsackie virus B3, one coxsackie virus A9, one enterovirus 86, one enterovirus 71, Two ECHO viruses 13/coxsackie virus B5, one ECHO virus 7/30/coxsackie virus B4, one coxsackie virus B4/enterovirus B, one enterovirus 87/ECHO virus 30/enterovirus B, and one ECHO virus 3. All viruses isolated, except enterovirus 71 classified into group A, were of group B. The enteroviruses were identified unambigously, although the sequencing only targeted a short, conserved segment that showed considerable variability. The sequencing was an effective alternative to enterovirus identification by the neutralisation test and allowed for detailed characterization of the isolates. The predominance of ECHO 30 as

  13. BG7: A New Approach for Bacterial Genome Annotation Designed for Next Generation Sequencing Data

    Science.gov (United States)

    Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Pareja, Eduardo; Tobes, Raquel

    2012-01-01

    BG7 is a new system for de novo bacterial, archaeal and viral genome annotation based on a new approach specifically designed for annotating genomes sequenced with next generation sequencing technologies. The system is versatile and able to annotate genes even in the step of preliminary assembly of the genome. It is especially efficient detecting unexpected genes horizontally acquired from bacterial or archaeal distant genomes, phages, plasmids, and mobile elements. From the initial phases of the gene annotation process, BG7 exploits the massive availability of annotated protein sequences in databases. BG7 predicts ORFs and infers their function based on protein similarity with a wide set of reference proteins, integrating ORF prediction and functional annotation phases in just one step. BG7 is especially tolerant to sequencing errors in start and stop codons, to frameshifts, and to assembly or scaffolding errors. The system is also tolerant to the high level of gene fragmentation which is frequently found in not fully assembled genomes. BG7 current version – which is developed in Java, takes advantage of Amazon Web Services (AWS) cloud computing features, but it can also be run locally in any operating system. BG7 is a fast, automated and scalable system that can cope with the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies. Its capabilities and efficiency were demonstrated in the 2011 EHEC Germany outbreak in which BG7 was used to get the first annotations right the next day after the first entero-hemorrhagic E. coli genome sequences were made publicly available. The suitability of BG7 for genome annotation has been proved for Illumina, 454, Ion Torrent, and PacBio sequencing technologies. Besides, thanks to its plasticity, our system could be very easily adapted to work with new technologies in the future. PMID:23185310

  14. BG7: a new approach for bacterial genome annotation designed for next generation sequencing data.

    Directory of Open Access Journals (Sweden)

    Pablo Pareja-Tobes

    Full Text Available BG7 is a new system for de novo bacterial, archaeal and viral genome annotation based on a new approach specifically designed for annotating genomes sequenced with next generation sequencing technologies. The system is versatile and able to annotate genes even in the step of preliminary assembly of the genome. It is especially efficient detecting unexpected genes horizontally acquired from bacterial or archaeal distant genomes, phages, plasmids, and mobile elements. From the initial phases of the gene annotation process, BG7 exploits the massive availability of annotated protein sequences in databases. BG7 predicts ORFs and infers their function based on protein similarity with a wide set of reference proteins, integrating ORF prediction and functional annotation phases in just one step. BG7 is especially tolerant to sequencing errors in start and stop codons, to frameshifts, and to assembly or scaffolding errors. The system is also tolerant to the high level of gene fragmentation which is frequently found in not fully assembled genomes. BG7 current version - which is developed in Java, takes advantage of Amazon Web Services (AWS cloud computing features, but it can also be run locally in any operating system. BG7 is a fast, automated and scalable system that can cope with the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies. Its capabilities and efficiency were demonstrated in the 2011 EHEC Germany outbreak in which BG7 was used to get the first annotations right the next day after the first entero-hemorrhagic E. coli genome sequences were made publicly available. The suitability of BG7 for genome annotation has been proved for Illumina, 454, Ion Torrent, and PacBio sequencing technologies. Besides, thanks to its plasticity, our system could be very easily adapted to work with new technologies in the future.

  15. Trace Fossils as Indicators of Depositional Sequence Boundaries in Lower Carboniferous Deep-Sea Fan Environment Moravice Formation, Czech Republic

    Czech Academy of Sciences Publication Activity Database

    Lehotský, T.; Bábek, O.; Mikuláš, Radek; Zapletal, J.

    2002-01-01

    Roč. 14, - (2002), s. 59-60 ISSN 1210-9606. [Áelazno 2002. Meeting of the Czech Tectonic Studies Group /7./. Áelazno, 09.05.2002-12.05.2002] R&D Projects: GA ČR GA205/00/0118 Keywords : trace fossils * Carboniferous * Deep- Sea Environment Subject RIV: DB - Geology ; Mineralogy http://geolines.gli.cas.cz/fileadmin/volumes/volume14/G14-059.pdf

  16. 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.

  17. 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.

  18. Deep Sequencing of T-cell Receptor DNA as a Biomarker of Clonally Expanded TILs in Breast Cancer after Immunotherapy.

    Science.gov (United States)

    Page, David B; Yuan, Jianda; Redmond, David; Wen, Y Hanna; Durack, Jeremy C; Emerson, Ryan; Solomon, Stephen; Dong, Zhiwan; Wong, Phillip; Comstock, Christopher; Diab, Adi; Sung, Janice; Maybody, Majid; Morris, Elizabeth; Brogi, Edi; Morrow, Monica; Sacchini, Virgilio; Elemento, Olivier; Robins, Harlan; Patil, Sujata; Allison, James P; Wolchok, Jedd D; Hudis, Clifford; Norton, Larry; McArthur, Heather L

    2016-10-01

    In early-stage breast cancer, the degree of tumor-infiltrating lymphocytes (TIL) predicts response to chemotherapy and overall survival. Combination immunotherapy with immune checkpoint antibody plus tumor cryoablation can induce lymphocytic infiltrates and improve survival in mice. We used T-cell receptor (TCR) DNA sequencing to evaluate both the effect of cryoimmunotherapy in humans and the feasibility of TCR sequencing in early-stage breast cancer. In a pilot clinical trial, 18 women with early-stage breast cancer were treated preoperatively with cryoablation, single-dose anti-CTLA-4 (ipilimumab), or cryoablation + ipilimumab. TCRs within serially collected peripheral blood and tumor tissue were sequenced. In baseline tumor tissues, T-cell density as measured by TCR sequencing correlated with TIL scores obtained by hematoxylin and eosin (H&E) staining. However, tumors with little or no lymphocytes by H&E contained up to 3.6 × 10 6 TCR DNA sequences, highlighting the sensitivity of the ImmunoSEQ platform. In this dataset, ipilimumab increased intratumoral T-cell density over time, whereas cryoablation ± ipilimumab diversified and remodeled the intratumoral T-cell clonal repertoire. Compared with monotherapy, cryoablation plus ipilimumab was associated with numerically greater numbers of peripheral blood and intratumoral T-cell clones expanding robustly following therapy. In conclusion, TCR sequencing correlates with H&E lymphocyte scoring and provides additional information on clonal diversity. These findings support further study of the use of TCR sequencing as a biomarker for T-cell responses to therapy and for the study of cryoimmunotherapy in early-stage breast cancer. Cancer Immunol Res; 4(10); 835-44. ©2016 AACR. ©2016 American Association for Cancer Research.

  19. 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.

  20. Discovery and profiling of novel and conserved microRNAs during flower development in Carya cathayensis via deep sequencing.

    Science.gov (United States)

    Wang, Zheng Jia; Huang, Jian Qin; Huang, You Jun; Li, Zheng; Zheng, Bing Song

    2012-08-01

    Hickory (Carya cathayensis Sarg.) is an economically important woody plant in China, but its long juvenile phase delays yield. MicroRNAs (miRNAs) are critical regulators of genes and important for normal plant development and physiology, including flower development. We used Solexa technology to sequence two small RNA libraries from two floral differentiation stages in hickory to identify miRNAs related to flower development. We identified 39 conserved miRNA sequences from 114 loci belonging to 23 families as well as two novel and ten potential novel miRNAs belonging to nine families. Moreover, 35 conserved miRNA*s and two novel miRNA*s were detected. Twenty miRNA sequences from 49 loci belonging to 11 families were differentially expressed; all were up-regulated at the later stage of flower development in hickory. Quantitative real-time PCR of 12 conserved miRNA sequences, five novel miRNA families, and two novel miRNA*s validated that all were expressed during hickory flower development, and the expression patterns were similar to those detected with Solexa sequencing. Finally, a total of 146 targets of the novel and conserved miRNAs were predicted. This study identified a diverse set of miRNAs that were closely related to hickory flower development and that could help in plant floral induction.

  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. An Innovative and Modular Approach for Deep Learning for Constellations of Smallsats

    Data.gov (United States)

    National Aeronautics and Space Administration — The primary research and development goal of this research is to enable the usage of advanced machine learning algorithms, particularly deep learning, on embedded...

  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. 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 Jakob

    Background: Oral squamous cell carcinoma (OSCC), a subgroup of head and neck squamous cell carcinoma (HNSCC), is primarily caused by alcohol consumption and tobacco use. Recent DNA sequencing studies suggests that HNSCC are very heterogeneous between patients; however the intra-patient subclonal...

  5. 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

  6. 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.

  7. 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.

  8. 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.)

  9. 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

  10. Genome-wide discovery and differential regulation of conserved and novel microRNAs in chickpea via deep sequencing.

    Science.gov (United States)

    Jain, Mukesh; Chevala, V V S Narayana; Garg, Rohini

    2014-11-01

    MicroRNAs (miRNAs) are essential components of complex gene regulatory networks that orchestrate plant development. Although several genomic resources have been developed for the legume crop chickpea, miRNAs have not been discovered until now. For genome-wide discovery of miRNAs in chickpea (Cicer arietinum), we sequenced the small RNA content from seven major tissues/organs employing Illumina technology. About 154 million reads were generated, which represented more than 20 million distinct small RNA sequences. We identified a total of 440 conserved miRNAs in chickpea based on sequence similarity with known miRNAs in other plants. In addition, 178 novel miRNAs were identified using a miRDeep pipeline with plant-specific scoring. Some of the conserved and novel miRNAs with significant sequence similarity were grouped into families. The chickpea miRNAs targeted a wide range of mRNAs involved in diverse cellular processes, including transcriptional regulation (transcription factors), protein modification and turnover, signal transduction, and metabolism. Our analysis revealed several miRNAs with differential spatial expression. Many of the chickpea miRNAs were expressed in a tissue-specific manner. The conserved and differential expression of members of the same miRNA family in different tissues was also observed. Some of the same family members were predicted to target different chickpea mRNAs, which suggested the specificity and complexity of miRNA-mediated developmental regulation. This study, for the first time, reveals a comprehensive set of conserved and novel miRNAs along with their expression patterns and putative targets in chickpea, and provides a framework for understanding regulation of developmental processes in legumes. © The Author 2014. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  11. Genome re-sequencing of semi-wild soybean reveals a complex Soja population structure and deep introgression.

    Directory of Open Access Journals (Sweden)

    Jie Qiu

    Full Text Available Semi-wild soybean is a unique type of soybean that retains both wild and domesticated characteristics, which provides an important intermediate type for understanding the evolution of the subgenus Soja population in the Glycine genus. In this study, a semi-wild soybean line (Maliaodou and a wild line (Lanxi 1 collected from the lower Yangtze regions were deeply sequenced while nine other semi-wild lines were sequenced to a 3-fold genome coverage. Sequence analysis revealed that (1 no independent phylogenetic branch covering all 10 semi-wild lines was observed in the Soja phylogenetic tree; (2 besides two distinct subpopulations of wild and cultivated soybean in the Soja population structure, all semi-wild lines were mixed with some wild lines into a subpopulation rather than an independent one or an intermediate transition type of soybean domestication; (3 high heterozygous rates (0.19-0.49 were observed in several semi-wild lines; and (4 over 100 putative selective regions were identified by selective sweep analysis, including those related to the development of seed size. Our results suggested a hybridization origin for the semi-wild soybean, which makes a complex Soja population structure.

  12. Detection of low frequency FGFR3 mutations in the urine of bladder cancer patients using next-generation deep sequencing

    Directory of Open Access Journals (Sweden)

    Millholl

    2012-06-01

    Full Text Available John M Millholland, Shuqiang Li, Cecilia A Fernandez, Anthony P ShuberPredictive Biosciences Inc, Lexington, MA, USAAbstract: Biological fluid-based noninvasive biomarker assays for monitoring and diagnosing disease are clinically powerful. A major technical hurdle for developing these assays is the requirement of high analytical sensitivity so that biomarkers present at very low levels can be consistently detected. In the case of biological fluid-based cancer diagnostic assays, sensitivities similar to those of tissue-based assays are difficult to achieve with DNA markers due to the high abundance of normal DNA background present in the sample. Here we describe a new urine-based assay that uses ultradeep sequencing technology to detect single mutant molecules of fibroblast growth factor receptor 3 (FGFR3 DNA that are indicative of bladder cancer. Detection of FGFR3 mutations in urine would provide clinicians with a noninvasive means of diagnosing early-stage bladder cancer. The single-molecule assay detects FGFR3 mutant DNA when present at as low as 0.02% of total urine DNA and results in 91% concordance with the frequency that FGFR3 mutations are detected in bladder cancer tumors, significantly improving diagnostic performance. To our knowledge, this is the first practical application of next-generation sequencing technology for noninvasive cancer diagnostics.Keywords: FGFR3, mutation, urine, single molecule, sequencing, bladder cancer

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Hybrid sequencing approach applied to human fecal metagenomic clone libraries revealed clones with potential biotechnological applications.

    Science.gov (United States)

    Džunková, Mária; D'Auria, Giuseppe; Pérez-Villarroya, David; Moya, Andrés

    2012-01-01

    Natural environments represent an incredible source of microbial genetic diversity. Discovery of novel biomolecules involves biotechnological methods that often require the design and implementation of biochemical assays to screen clone libraries. However, when an assay is applied to thousands of clones, one may eventually end up with very few positive clones which, in most of the cases, have to be "domesticated" for downstream characterization and application, and this makes screening both laborious and expensive. The negative clones, which are not considered by the selected assay, may also have biotechnological potential; however, unfortunately they would remain unexplored. Knowledge of the clone sequences provides important clues about potential biotechnological application of the clones in the library; however, the sequencing of clones one-by-one would be very time-consuming and expensive. In this study, we characterized the first metagenomic clone library from the feces of a healthy human volunteer, using a method based on 454 pyrosequencing coupled with a clone-by-clone Sanger end-sequencing. Instead of whole individual clone sequencing, we sequenced 358 clones in a pool. The medium-large insert (7-15 kb) cloning strategy allowed us to assemble these clones correctly, and to assign the clone ends to maintain the link between the position of a living clone in the library and the annotated contig from the 454 assembly. Finally, we found several open reading frames (ORFs) with previously described potential medical application. The proposed approach allows planning ad-hoc biochemical assays for the clones of interest, and the appropriate sub-cloning strategy for gene expression in suitable vectors/hosts.

  18. Hybrid sequencing approach applied to human fecal metagenomic clone libraries revealed clones with potential biotechnological applications.

    Directory of Open Access Journals (Sweden)

    Mária Džunková

    Full Text Available Natural environments represent an incredible source of microbial genetic diversity. Discovery of novel biomolecules involves biotechnological methods that often require the design and implementation of biochemical assays to screen clone libraries. However, when an assay is applied to thousands of clones, one may eventually end up with very few positive clones which, in most of the cases, have to be "domesticated" for downstream characterization and application, and this makes screening both laborious and expensive. The negative clones, which are not considered by the selected assay, may also have biotechnological potential; however, unfortunately they would remain unexplored. Knowledge of the clone sequences provides important clues about potential biotechnological application of the clones in the library; however, the sequencing of clones one-by-one would be very time-consuming and expensive. In this study, we characterized the first metagenomic clone library from the feces of a healthy human volunteer, using a method based on 454 pyrosequencing coupled with a clone-by-clone Sanger end-sequencing. Instead of whole individual clone sequencing, we sequenced 358 clones in a pool. The medium-large insert (7-15 kb cloning strategy allowed us to assemble these clones correctly, and to assign the clone ends to maintain the link between the position of a living clone in the library and the annotated contig from the 454 assembly. Finally, we found several open reading frames (ORFs with previously described potential medical application. The proposed approach allows planning ad-hoc biochemical assays for the clones of interest, and the appropriate sub-cloning strategy for gene expression in suitable vectors/hosts.

  19. 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....

  20. Next-Generation Sequencing Workflow for NSCLC Critical Samples Using a Targeted Sequencing Approach by Ion Torrent PGM™ Platform.

    Science.gov (United States)

    Vanni, Irene; Coco, Simona; Truini, Anna; Rusmini, Marta; Dal Bello, Maria Giovanna; Alama, Angela; Banelli, Barbara; Mora, Marco; Rijavec, Erika; Barletta, Giulia; Genova, Carlo; Biello, Federica; Maggioni, Claudia; Grossi, Francesco

    2015-12-03

    Next-generation sequencing (NGS) is a cost-effective technology capable of screening several genes simultaneously; however, its application in a clinical context requires an established workflow to acquire reliable sequencing results. Here, we report an optimized NGS workflow analyzing 22 lung cancer-related genes to sequence critical samples such as DNA from formalin-fixed paraffin-embedded (FFPE) blocks and circulating free DNA (cfDNA). Snap frozen and matched FFPE gDNA from 12 non-small cell lung cancer (NSCLC) patients, whose gDNA fragmentation status was previously evaluated using a multiplex PCR-based quality control, were successfully sequenced with Ion Torrent PGM™. The robust bioinformatic pipeline allowed us to correctly call both Single Nucleotide Variants (SNVs) and indels with a detection limit of 5%, achieving 100% specificity and 96% sensitivity. This workflow was also validated in 13 FFPE NSCLC biopsies. Furthermore, a specific protocol for low input gDNA capable of producing good sequencing data with high coverage, high uniformity, and a low error rate was also optimized. In conclusion, we demonstrate the feasibility of obtaining gDNA from FFPE samples suitable for NGS by performing appropriate quality controls. The optimized workflow, capable of screening low input gDNA, highlights NGS as a potential tool in the detection, disease monitoring, and treatment of NSCLC.

  1. 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

  2. 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

  3. Virus pathotype and deep sequencing of the HA gene of a low pathogenicity H7N1 avian influenza virus causing mortality in Turkeys.

    Directory of Open Access Journals (Sweden)

    Munir Iqbal

    Full Text Available Low pathogenicity avian influenza (LPAI viruses of the H7 subtype generally cause mild disease in poultry. However the evolution of a LPAI virus into highly pathogenic avian influenza (HPAI virus results in the generation of a virus that can cause severe disease and death. The classification of these two pathotypes is based, in part, on disease signs and death in chickens, as assessed in an intravenous pathogenicity test, but the effect of LPAI viruses in turkeys is less well understood. During an investigation of LPAI virus infection of turkeys, groups of three-week-old birds inoculated with A/chicken/Italy/1279/99 (H7N1 showed severe disease signs and died or were euthanised within seven days of infection. Virus was detected in many internal tissues and organs from culled birds. To examine the possible evolution of the infecting virus to a highly pathogenic form in these turkeys, sequence analysis of the haemagglutinin (HA gene cleavage site was carried out by analysing multiple cDNA amplicons made from swabs and tissue sample extracts employing Sanger and Next Generation Sequencing. In addition, a RT-PCR assay to detect HPAI virus was developed. There was no evidence of the presence of HPAI virus in either the virus used as inoculum or from swabs taken from infected birds. However, a small proportion (<0.5% of virus carried in individual tracheal or liver samples did contain a molecular signature typical of a HPAI virus at the HA cleavage site. All the signature sequences were identical and were similar to HPAI viruses collected during the Italian epizootic in 1999/2000. We assume that the detection of HPAI virus in tissue samples following infection with A/chicken/Italy/1279/99 reflected amplification of a virus present at very low levels within the mixed inoculum but, strikingly, we observed no new HPAI virus signatures in the amplified DNA analysed by deep-sequencing.

  4. 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.

  5. Detection of Inter-lineage Natural Recombination in Avian Paramyxovirus Serotype 1 using Simplified Deep Sequencing Platform

    Directory of Open Access Journals (Sweden)

    Dilan Amila Satharasinghe

    2016-11-01

    Full Text Available Newcastle disease virus (NDV is a prototype member of avian paramyxovirus serotype 1 (APMV-1, which causes severe and contagious disease in the commercial poultry and wild birds. Despite extensive vaccination programs and other control measures, the disease remains endemic around the globe especially in Asia, Africa, and the Middle East. Being a single serotype, genotype II based vaccines remained most acceptable means of immunization. However, the evidence is emerging on failures of vaccines mainly due to evolving nature of the virus and higher genetic gaps between vaccine and field strains of APMV-1. Most of the epidemiological and genetic characterizations of APMVs are based on conventional methods, which are prone to mask the diverse population of viruses in complex samples. In this study, we report the application of a simple, robust, and less resource-demanding methodology for the whole genome sequencing of NDV, using next-generation sequencing on the Illumina MiSeq platform. Using this platform, we sequenced full genomes of five virulent Malaysian NDV strains collected during 2004-2013. All isolates clustered within highly prevalent lineage 5 (specifically in lineage 5a; however, a significantly greater genetic divergence was observed in isolates collected from 2004 to 2011. Interestingly, genetic characterization of one isolate collected in 2013 (IBS025/13 shown natural recombination between lineage 2 and lineage 5. In the event of recombination, the isolate (IBS025/13 carried nucleocapsid protein consist of 55-1801 nucleotides (nts and near-complete phosphoprotein (1804-3254 nts genes of lineage 2 whereas surface glycoproteins (fusion, hemagglutinin-neuraminidase and large polymerase of lineage 5. Additionally, the recombinant virus has a genome size of 15,186 nts which is characteristics for the old genotypes I to IV isolated from 1930 to 1960. Taken together, we report the occurrence of a natural recombination in circulating strains

  6. 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.

  7. Small RNA Deep Sequencing and the Effects of microRNA408 on Root Gravitropic Bending in Arabidopsis

    Science.gov (United States)

    Li, Huasheng; Lu, Jinying; Sun, Qiao; Chen, Yu; He, Dacheng; Liu, Min

    2015-11-01

    MicroRNA (miRNA) is a non-coding small RNA composed of 20 to 24 nucleotides that influences plant root development. This study analyzed the miRNA expression in Arabidopsis root tip cells using Illumina sequencing and real-time PCR before (sample 0) and 15 min after (sample 15) a 3-D clinostat rotational treatment was administered. After stimulation was performed, the expression levels of seven miRNA genes, including Arabidopsis miR160, miR161, miR394, miR402, miR403, miR408, and miR823, were significantly upregulated. Illumina sequencing results also revealed two novel miRNAsthat have not been previously reported, The target genes of these miRNAs included pentatricopeptide repeat-containing protein and diadenosine tetraphosphate hydrolase. An overexpression vector of Arabidopsis miR408 was constructed and transferred to Arabidopsis plant. The roots of plants over expressing miR408 exhibited a slower reorientation upon gravistimulation in comparison with those of wild-type. This result indicate that miR408 could play a role in root gravitropic response.

  8. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

    Science.gov (United States)

    Quang, Daniel; Xie, Xiaohui

    2016-06-20

    Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  9. 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)

  10. 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.

  11. Designing A General Deep Web Access Approach Based On A Newly Introduced Factor; Harvestability Factor (HF)

    NARCIS (Netherlands)

    Khelghati, Mohammadreza; van Keulen, Maurice; Hiemstra, Djoerd

    2014-01-01

    The growing need of accessing more and more information draws attentions to huge amount of data hidden behind web forms defined as deep web. To make this data accessible, harvesters have a crucial role. Targeting different domains and websites enhances the need to have a general-purpose harvester

  12. 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.

  13. An approach for in situ studies of deep-sea amphipods and their microbial gut flora

    Science.gov (United States)

    Jannasch, H. W.; Cuhel, R. L.; Wirsen, C. O.; Taylor, C. D.

    1980-10-01

    A technique has been developed and field-tested for the trapping, feeding, and timed incubation of amphipods on the deep-sea floor. Data obtained from experiments using radiolabeled foodstuffs indicate that shifts within the labeled fractions of the major biological polymers make it possible to distinguish between the metabolism of the amphipods and that of their intestinal microflora.

  14. 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.

  15. 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.

  16. 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

  17. A powerful and flexible approach to the analysis of RNA sequence count data.

    Science.gov (United States)

    Zhou, Yi-Hui; Xia, Kai; Wright, Fred A

    2011-10-01

    A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean-variance relationships provides a flexible testing regimen that 'borrows' information across genes, while easily incorporating design effects and additional covariates. We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data and (ii) an extension of an expression mean-variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility. An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq yzhou@bios.unc.edu; fwright@bios.unc.edu Supplementary data are available at Bioinformatics online.

  18. Detection of Inter-Lineage Natural Recombination in Avian Paramyxovirus Serotype 1 Using Simplified Deep Sequencing Platform.

    Science.gov (United States)

    Satharasinghe, Dilan A; Murulitharan, Kavitha; Tan, Sheau W; Yeap, Swee K; Munir, Muhammad; Ideris, Aini; Omar, Abdul R

    2016-01-01

    Newcastle disease virus (NDV) is a prototype member of avian paramyxovirus serotype 1 (APMV-1), which causes severe and contagious disease in the commercial poultry and wild birds. Despite extensive vaccination programs and other control measures, the disease remains endemic around the globe especially in Asia, Africa, and the Middle East. Being a single serotype, genotype II based vaccines remained most acceptable means of immunization. However, the evidence is emerging on failures of vaccines mainly due to evolving nature of the virus and higher genetic gaps between vaccine and field strains of APMV-1. Most of the epidemiological and genetic characterizations of APMVs are based on conventional methods, which are prone to mask the diverse population of viruses in complex samples. In this study, we report the application of a simple, robust, and less resource-demanding methodology for the whole genome sequencing of NDV, using next-generation sequencing (NGS) on the Illumina MiSeq platform. Using this platform, we sequenced full genomes of five virulent Malaysian NDV strains collected during 2004-2013. All isolates clustered within highly prevalent lineage 5 (specifically in lineage 5a); however, a significantly greater genetic divergence was observed in isolates collected from 2004 to 2011. Interestingly, genetic characterization of one isolate collected in 2013 (IBS025/13) shown natural recombination between lineage 2 and lineage 5. In the event of recombination, the isolate (IBS025/13) carried nucleocapsid protein consist of 55-1801 nucleotides (nts) and near-complete phosphoprotein (1804-3254 nts) genes of lineage 2 whereas surface glycoproteins (fusion, hemagglutinin-neuraminidase) and large polymerase of lineage 5. Additionally, the recombinant virus has a genome size of 15,186 nts which is characteristics for the old genotypes I-IV isolated from 1930 to 1960. Taken together, we report the occurrence of a natural recombination in circulating strains of NDV in

  19. "Polymeromics": Mass spectrometry based strategies in polymer science toward complete sequencing approaches: a review.

    Science.gov (United States)

    Altuntaş, Esra; Schubert, Ulrich S

    2014-01-15

    Mass spectrometry (MS) is the most versatile and comprehensive method in "OMICS" sciences (i.e. in proteomics, genomics, metabolomics and lipidomics). The applications of MS and tandem MS (MS/MS or MS(n)) provide sequence information of the full complement of biological samples in order to understand the importance of the sequences on their precise and specific functions. Nowadays, the control of polymer sequences and their accurate characterization is one of the significant challenges of current polymer science. Therefore, a similar approach can be very beneficial for characterizing and understanding the complex structures of synthetic macromolecules. MS-based strategies allow a relatively precise examination of polymeric structures (e.g. their molar mass distributions, monomer units, side chain substituents, end-group functionalities, and copolymer compositions). Moreover, tandem MS offer accurate structural information from intricate macromolecular structures; however, it produces vast amount of data to interpret. In "OMICS" sciences, the software application to interpret the obtained data has developed satisfyingly (e.g. in proteomics), because it is not possible to handle the amount of data acquired via (tandem) MS studies on the biological samples manually. It can be expected that special software tools will improve the interpretation of (tandem) MS output from the investigations of synthetic polymers as well. Eventually, the MS/MS field will also open up for polymer scientists who are not MS-specialists. In this review, we dissect the overall framework of the MS and MS/MS analysis of synthetic polymers into its key components. We discuss the fundamentals of polymer analyses as well as recent advances in the areas of tandem mass spectrometry, software developments, and the overall future perspectives on the way to polymer sequencing, one of the last Holy Grail in polymer science. Copyright © 2013 Elsevier B.V. All rights reserved.

  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. Targeted deep sequencing of mucinous ovarian tumors reveals multiple overlapping RAS-pathway activating mutations in borderline and cancerous neoplasms

    International Nuclear Information System (INIS)

    Mackenzie, Robertson; Kommoss, Stefan; Winterhoff, Boris J.; Kipp, Benjamin R.; Garcia, Joaquin J.; Voss, Jesse; Halling, Kevin; Karnezis, Anthony; Senz, Janine; Yang, Winnie; Prigge, Elena-Sophie; Reuschenbach, Miriam; Doeberitz, Magnus Von Knebel; Gilks, Blake C.; Huntsman, David G.; Bakkum-Gamez, Jamie; McAlpine, Jessica N.; Anglesio, Michael S.

    2015-01-01

    Mucinous ovarian tumors represent a distinct histotype of epithelial ovarian cancer. The rarest (2-4 % of ovarian carcinomas) of the five major histotypes, their genomic landscape remains poorly described. We undertook hotspot sequencing of 50 genes commonly mutated in human cancer across 69 mucinous ovarian tumors. Our goals were to establish the overall frequency of cancer-hotspot mutations across a large cohort, especially those tumors previously thought to be “RAS-pathway alteration negative”, using highly-sensitive next-generation sequencing as well as further explore a small number of cases with apparent heterogeneity in RAS-pathway activating alterations. Using the Ion Torrent PGM platform, we performed next generation sequencing analysis using the v2 Cancer Hotspot Panel. Regions of disparate ERBB2-amplification status were sequenced independently for two mucinous carcinoma (MC) cases, previously established as showing ERBB2 amplification/overexpression heterogeneity, to assess the hypothesis of subclonal populations containing either KRAS mutation or ERBB2 amplification independently or simultaneously. We detected mutations in KRAS, TP53, CDKN2A, PIK3CA, PTEN, BRAF, FGFR2, STK11, CTNNB1, SRC, SMAD4, GNA11 and ERBB2. KRAS mutations remain the most frequently observed alteration among MC (64.9 %) and mucinous borderline tumors (MBOT) (92.3 %). TP53 mutation occurred more frequently in carcinomas than borderline tumors (56.8 % and 11.5 %, respectively), and combined IHC and mutation data suggest alterations occur in approximately 68 % of MC and as many as 20 % of MBOT. Proven and potential RAS-pathway activating changes were observed in all but one MC. Concurrent ERBB2 amplification and KRAS mutation were observed in a substantial number of cases (7/63 total), as was co-occurrence of KRAS and BRAF mutations (one case). Microdissection of ERBB2-amplified regions of tumors harboring KRAS mutation suggests these alterations are occurring in the same cell

  3. Deep sequencing reveals transcriptome re-programming of Taxus × media cells to the elicitation with methyl jasmonate.

    Science.gov (United States)

    Sun, Guiling; Yang, Yanfang; Xie, Fuliang; Wen, Jian-Fan; Wu, Jianqiang; Wilson, Iain W; Tang, Qi; Liu, Hongwei; Qiu, Deyou

    2013-01-01

    Plant cell culture represents an alternative source for producing high-value secondary metabolites including paclitaxel (Taxol®), which is mainly produced in Taxus and has been widely used in cancer chemotherapy. The phytohormone methyl jasmonate (MeJA) can significantly increase the production of paclitaxel, which is induced in plants as a secondary metabolite possibly in defense against herbivores and pathogens. In cell culture, MeJA also elicits the accumulation of paclitaxel; however, the mechanism is still largely unknown. To obtain insight into the global regulation mechanism of MeJA in the steady state of paclitaxel production (7 days after MeJA addition), especially on paclitaxel biosynthesis, we sequenced the transcriptomes of MeJA-treated and untreated Taxus × media cells and obtained ∼ 32.5 M high quality reads, from which 40,348 unique sequences were obtained by de novo assembly. Expression level analysis indicated that a large number of genes were associated with transcriptional regulation, DNA and histone modification, and MeJA signaling network. All the 29 known genes involved in the biosynthesis of terpenoid backbone and paclitaxel were found with 18 genes showing increased transcript abundance following elicitation of MeJA. The significantly up-regulated changes of 9 genes in paclitaxel biosynthesis were validated by qRT-PCR assays. According to the expression changes and the previously proposed enzyme functions, multiple candidates for the unknown steps in paclitaxel biosynthesis were identified. We also found some genes putatively involved in the transport and degradation of paclitaxel. Potential target prediction of miRNAs indicated that miRNAs may play an important role in the gene expression regulation following the elicitation of MeJA. Our results shed new light on the global regulation mechanism by which MeJA regulates the physiology of Taxus cells and is helpful to understand how MeJA elicits other plant species besides Taxus.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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

  9. Deep sequencing analysis of small noncoding RNA and mRNA targets of the global post-transcriptional regulator, Hfq

    DEFF Research Database (Denmark)

    Sittka, A; Lucchini, S; Papenfort, K

    2008-01-01

    would be rescued by overexpression of HilD and FlhDC, and we proved this to be correct. The combination of epitope-tagging and HTPS of immunoprecipitated RNA detected the expression of many intergenic chromosomal regions of Salmonella. Our approach overcomes the limited availability of high...

  10. Discovery of MicroRNAs associated with myogenesis by deep sequencing of serial developmental skeletal muscles in pigs.

    Directory of Open Access Journals (Sweden)

    Xinhua Hou

    Full Text Available MicroRNAs (miRNAs are short, single-stranded non-coding RNAs that repress their target genes by binding their 3' UTRs. These RNAs play critical roles in myogenesis. To gain knowledge about miRNAs involved in the regulation of myogenesis, porcine longissimus muscles were collected from 18 developmental stages (33-, 40-, 45-, 50-, 55-, 60-, 65-, 70-, 75-, 80-, 85-, 90-, 95-, 100- and 105-day post-gestation fetuses, 0 and 10-day postnatal piglets and adult pigs to identify miRNAs using Solexa sequencing technology. We detected 197 known miRNAs and 78 novel miRNAs according to comparison with known miRNAs in the miRBase (release 17.0 database. Moreover, variations in sequence length and single nucleotide polymorphisms were also observed in 110 known miRNAs. Expression analysis of the 11 most abundant miRNAs were conducted using quantitative PCR (qPCR in eleven tissues (longissimus muscles, leg muscles, heart, liver, spleen, lung, kidney, stomach, small intestine and colon, and the results revealed that ssc-miR-378, ssc-miR-1 and ssc-miR-206 were abundantly expressed in skeletal muscles. During skeletal muscle development, the expression level of ssc-miR-378 was low at 33 days post-coitus (dpc, increased at 65 and 90 dpc, peaked at postnatal day 0, and finally declined and maintained a comparatively stable level. This expression profile suggested that ssc-miR-378 was a new candidate miRNA for myogenesis and participated in skeletal muscle development in pigs. Target prediction and KEGG pathway analysis suggested that bone morphogenetic protein 2 (BMP2 and mitogen-activated protein kinase 1 (MAPK1, both of which were relevant to proliferation and differentiation, might be the potential targets of miR-378. Luciferase activities of report vectors containing the 3'UTR of porcine BMP2 or MAPK1 were downregulated by miR-378, which suggested that miR-378 probably regulated myogenesis though the regulation of these two genes.

  11. 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.)

  12. Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing.

    Directory of Open Access Journals (Sweden)

    Zhifu Sun

    Full Text Available We used deep sequencing technology to profile the transcriptome, gene copy number, and CpG island methylation status simultaneously in eight commonly used breast cell lines to develop a model for how these genomic features are integrated in estrogen receptor positive (ER+ and negative breast cancer. Total mRNA sequence, gene copy number, and genomic CpG island methylation were carried out using the Illumina Genome Analyzer. Sequences were mapped to the human genome to obtain digitized gene expression data, DNA copy number in reference to the non-tumor cell line (MCF10A, and methylation status of 21,570 CpG islands to identify differentially expressed genes that were correlated with methylation or copy number changes. These were evaluated in a dataset from 129 primary breast tumors. Gene expression in cell lines was dominated by ER-associated genes. ER+ and ER- cell lines formed two distinct, stable clusters, and 1,873 genes were differentially expressed in the two groups. Part of chromosome 8 was deleted in all ER- cells and part of chromosome 17 amplified in all ER+ cells. These loci encoded 30 genes that were overexpressed in ER+ cells; 9 of these genes were overexpressed in ER+ tumors. We identified 149 differentially expressed genes that exhibited differential methylation of one or more CpG islands within 5 kb of the 5' end of the gene and for which mRNA abundance was inversely correlated with CpG island methylation status. In primary tumors we identified 84 genes that appear to be robust components of the methylation signature that we identified in ER+ cell lines. Our analyses reveal a global pattern of differential CpG island methylation that contributes to the transcriptome landscape of ER+ and ER- breast cancer cells and tumors. The role of gene amplification/deletion appears to more modest, although several potentially significant genes appear to be regulated by copy number aberrations.

  13. 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.)

  14. Complex approach mechanical properties and formability assessment of selected deep-drawing steels

    Directory of Open Access Journals (Sweden)

    J. Štaba

    2009-07-01

    Full Text Available The paper analyses the properties of deep-drawing sheets of three grades (Re = 320 to 475 MPa, surface-treated with hot-dip galvanizing, made of microalloyed steels. Deformation properties are assessed using tensile tests, technological Erichsen or cupping tests. These characteristics, as well as the behaviour of the surface layer, are also investigated under dynamic conditions (modified Erichsen test using a drop tester, or using flat bending fatigue tests. Using microscopic analysis the deformation properties of the surface layer are evaluated. The results show the compactness of the surface layer, high deformation characteristics, as well as fatigue properties of the investigated deep-drawing materials, suitable for application in the automotive industry.

  15. Visual astronomy under dark skies a new approach to observing deep space

    CERN Document Server

    Cooke, Antony

    2005-01-01

    Modern astronomical telescopes, along with other advances in technology, have brought the deep sky - star clusters, nebulae and the galaxies - within reach of amateur astronomers. And it isn't even necessary to image many of these deep-sky objects in order to see them; they are within reach of visual observers using modern techniques and enhancement technology. The first requirement is truly dark skies; if you are observing from a light-polluted environment you need Tony Cooke's book, Visual Astronomy in the Suburbs. Given a site with clear, dark night skies everything else follows… this book will provide the reader with everything he needs to know about what to observe, and using some of today's state-of-the-art technique and commercial equipment, how to get superb views of faint and distant astronomical objects.

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

    OpenAIRE

    Hong Wang; Qingsong Xu; Lifeng Zhou

    2014-01-01

    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...

  17. Complex approach mechanical properties and formability assessment of selected deep-drawing steels

    OpenAIRE

    J. Štaba; M. Buršák

    2009-01-01

    The paper analyses the properties of deep-drawing sheets of three grades (Re = 320 to 475 MPa), surface-treated with hot-dip galvanizing, made of microalloyed steels. Deformation properties are assessed using tensile tests, technological Erichsen or cupping tests. These characteristics, as well as the behaviour of the surface layer, are also investigated under dynamic conditions (modified Erichsen test using a drop tester), or using flat bending fatigue tests. Using microscopic analysis the d...

  18. Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data

    OpenAIRE

    Li, Yuncheng; Cao, LiangLiang; Zhu, Jiang; Luo, Jiebo

    2016-01-01

    Composing fashion outfits involves deep understanding of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., Jewelry, Bag, Pants, Dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper, we propose a machine learning system to compose fashion outfits automatically. The core of the proposed automatic composition system is to score fashion outfit candidates...

  19. An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

    Science.gov (United States)

    Shamwell, E Jared; Nothwang, William D; Perlis, Donald

    2018-05-04

    Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

  20. A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

    Science.gov (United States)

    Dhungel, Neeraj; Carneiro, Gustavo; Bradley, Andrew P

    2017-04-01

    We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Deep sequencing-based transcriptome analysis of chicken spleen in response to avian pathogenic Escherichia coli (APEC infection.

    Directory of Open Access Journals (Sweden)

    Qinghua Nie

    Full Text Available Avian pathogenic Escherichia coli (APEC leads to economic losses in poultry production and is also a threat to human health. The goal of this study was to characterize the chicken spleen transcriptome and to identify candidate genes for response and resistance to APEC infection using Solexa sequencing. We obtained 14422935, 14104324, and 14954692 Solexa read pairs for non-challenged (NC, challenged-mild pathology (MD, and challenged-severe pathology (SV, respectively. A total of 148197 contigs and 98461 unigenes were assembled, of which 134949 contigs and 91890 unigenes match the chicken genome. In total, 12272 annotated unigenes take part in biological processes (11664, cellular components (11927, and molecular functions (11963. Summing three specific contrasts, 13650 significantly differentially expressed unigenes were found in NC Vs. MD (6844, NC Vs. SV (7764, and MD Vs. SV (2320. Some unigenes (e.g. CD148, CD45 and LCK were involved in crucial pathways, such as the T cell receptor (TCR signaling pathway and microbial metabolism in diverse environments. This study facilitates understanding of the genetic architecture of the chicken spleen transcriptome, and has identified candidate genes for host response to APEC infection.

  2. Deep sequencing of the Mexican avocado transcriptome, an ancient angiosperm with a high content of fatty acids.

    Science.gov (United States)

    Ibarra-Laclette, Enrique; Méndez-Bravo, Alfonso; Pérez-Torres, Claudia Anahí; Albert, Victor A; Mockaitis, Keithanne; Kilaru, Aruna; López-Gómez, Rodolfo; Cervantes-Luevano, Jacob Israel; Herrera-Estrella, Luis

    2015-08-13

    Avocado (Persea americana) is an economically important tropical fruit considered to be a good source of fatty acids. Despite its importance, the molecular and cellular characterization of biochemical and developmental processes in avocado is limited due to the lack of transcriptome and genomic information. The transcriptomes of seeds, roots, stems, leaves, aerial buds and flowers were determined using different sequencing platforms. Additionally, the transcriptomes of three different stages of fruit ripening (pre-climacteric, climacteric and post-climacteric) were also analyzed. The analysis of the RNAseqatlas presented here reveals strong differences in gene expression patterns between different organs, especially between root and flower, but also reveals similarities among the gene expression patterns in other organs, such as stem, leaves and aerial buds (vegetative organs) or seed and fruit (storage organs). Important regulators, functional categories, and differentially expressed genes involved in avocado fruit ripening were identified. Additionally, to demonstrate the utility of the avocado gene expression atlas, we investigated the expression patterns of genes implicated in fatty acid metabolism and fruit ripening. A description of transcriptomic changes occurring during fruit ripening was obtained in Mexican avocado, contributing to a dynamic view of the expression patterns of genes involved in fatty acid biosynthesis and the fruit ripening process.

  3. 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.

  4. RNA deep sequencing reveals differential microRNA expression during development of sea urchin and sea star.

    Directory of Open Access Journals (Sweden)

    Sabah Kadri

    Full Text Available microRNAs (miRNAs are small (20-23 nt, non-coding single stranded RNA molecules that act as post-transcriptional regulators of mRNA gene expression. They have been implicated in regulation of developmental processes in diverse organisms. The echinoderms, Strongylocentrotus purpuratus (sea urchin and Patiria miniata (sea star are excellent model organisms for studying development with well-characterized transcriptional networks. However, to date, nothing is known about the role of miRNAs during development in these organisms, except that the genes that are involved in the miRNA biogenesis pathway are expressed during their developmental stages. In this paper, we used Illumina Genome Analyzer (Illumina, Inc. to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of sea urchin and sea star (total of 22,670,000 reads. Analysis of these data revealed the miRNA populations in these two species. We found that 47 and 38 known miRNAs are expressed in sea urchin and sea star, respectively, during early development (32 in common. We also found 13 potentially novel miRNAs in the sea urchin embryonic library. miRNA expression is generally conserved between the two species during development, but 7 miRNAs are highly expressed in only one species. We expect that our two datasets will be a valuable resource for everyone working in the field of developmental biology and the regulatory networks that affect it. The computational pipeline to analyze Illumina reads is available at http://www.benoslab.pitt.edu/services.html.

  5. RNA Deep Sequencing Reveals Differential MicroRNA Expression during Development of Sea Urchin and Sea Star

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    Kadri, Sabah; Hinman, Veronica F.; Benos, Panayiotis V.

    2011-01-01

    microRNAs (miRNAs) are small (20–23 nt), non-coding single stranded RNA molecules that act as post-transcriptional regulators of mRNA gene expression. They have been implicated in regulation of developmental processes in diverse organisms. The echinoderms, Strongylocentrotus purpuratus (sea urchin) and Patiria miniata (sea star) are excellent model organisms for studying development with well-characterized transcriptional networks. However, to date, nothing is known about the role of miRNAs during development in these organisms, except that the genes that are involved in the miRNA biogenesis pathway are expressed during their developmental stages. In this paper, we used Illumina Genome Analyzer (Illumina, Inc.) to sequence small RNA libraries in mixed stage population of embryos from one to three days after fertilization of sea urchin and sea star (total of 22,670,000 reads). Analysis of these data revealed the miRNA populations in these two species. We found that 47 and 38 known miRNAs are expressed in sea urchin and sea star, respectively, during early development (32 in common). We also found 13 potentially novel miRNAs in the sea urchin embryonic library. miRNA expression is generally conserved between the two species during development, but 7 miRNAs are highly expressed in only one species. We expect that our two datasets will be a valuable resource for everyone working in the field of developmental biology and the regulatory networks that affect it. The computational pipeline to analyze Illumina reads is available at http://www.benoslab.pitt.edu/services.html. PMID:22216218

  6. Deep sequencing of natural and experimental populations of Drosophila melanogaster reveals biases in the spectrum of new mutations.

    Science.gov (United States)

    Assaf, Zoe June; Tilk, Susanne; Park, Jane; Siegal, Mark L; Petrov, Dmitri A

    2017-12-01

    Mutations provide the raw material of evolution, and thus our ability to study evolution depends fundamentally on having precise measurements of mutational rates and patterns. We generate a data set for this purpose using (1) de novo mutations from mutation accumulation experiments and (2) extremely rare polymorphisms from natural populations. The first, mutation accumulation (MA) lines are the product of maintaining flies in tiny populations for many generations, therefore rendering natural selection ineffective and allowing new mutations to accrue in the genome. The second, rare genetic variation from natural populations allows the study of mutation because extremely rare polymorphisms are relatively unaffected by the filter of natural selection. We use both methods in Drosophila melanogaster , first generating our own novel data set of sequenced MA lines and performing a meta-analysis of all published MA mutations (∼2000 events) and then identifying a high quality set of ∼70,000 extremely rare (≤0.1%) polymorphisms that are fully validated with resequencing. We use these data sets to precisely measure mutational rates and patterns. Highlights of our results include: a high rate of multinucleotide mutation events at both short (∼5 bp) and long (∼1 kb) genomic distances, showing that mutation drives GC content lower in already GC-poor regions, and using our precise context-dependent mutation rates to predict long-term evolutionary patterns at synonymous sites. We also show that de novo mutations from independent MA experiments display similar patterns of single nucleotide mutation and well match the patterns of mutation found in natural populations. © 2017 Assaf et al.; Published by Cold Spring Harbor Laboratory Press.

  7. Identification of novel and conserved microRNAs related to drought stress in potato by deep sequencing.

    Science.gov (United States)

    Zhang, Ning; Yang, Jiangwei; Wang, Zemin; Wen, Yikai; Wang, Jie; He, Wenhui; Liu, Bailin; Si, Huaijun; Wang, Di

    2014-01-01

    MicroRNAs (miRNAs) are a group of small, non-coding RNAs that play important roles in plant growth, development and stress response. There have been an increasing number of investigations aimed at discovering miRNAs and analyzing their functions in model plants (such as Arabidopsis thaliana and rice). In this research, we constructed small RNA libraries from both polyethylene glycol (PEG 6,000) treated and control potato samples, and a large number of known and novel miRNAs were identified. Differential expression analysis showed that 100 of the known miRNAs were down-regulated and 99 were up-regulated as a result of PEG stress, while 119 of the novel miRNAs were up-regulated and 151 were down-regulated. Based on target prediction, annotation and expression analysis of the miRNAs and their putative target genes, 4 miRNAs were identified as regulating drought-related genes (miR811, miR814, miR835, miR4398). Their target genes were MYB transcription factor (CV431094), hydroxyproline-rich glycoprotein (TC225721), quaporin (TC223412) and WRKY transcription factor (TC199112), respectively. Relative expression trends of those miRNAs were the same as that predicted by Solexa sequencing and they showed a negative correlation with the expression of the target genes. The results provide molecular evidence for the possible involvement of miRNAs in the process of drought response and/or tolerance in the potato plant.

  8. Transcriptome profiling and digital gene expression by deep sequencing in early somatic embryogenesis of endangered medicinal Eleutherococcus senticosus Maxim.

    Science.gov (United States)

    Tao, Lei; Zhao, Yue; Wu, Ying; Wang, Qiuyu; Yuan, Hongmei; Zhao, Lijuan; Guo, Wendong; You, Xiangling

    2016-03-01

    Somatic embryogenesis (SE) has been studied as a model system to understand molecular events in physiology, biochemistry, and cytology during plant embryo development. In particular, it is exceedingly difficult to access the morphological and early regulatory events in zygotic embryos. To understand the molecular mechanisms regulating early SE in Eleutherococcus senticosus Maxim., we used high-throughput RNA-Seq technology to investigate its transcriptome. We obtained 58,327,688 reads, which were assembled into 75,803 unique unigenes. To better understand their functions, the unigenes were annotated using the Clusters of Orthologous Groups, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes databases. Digital gene expression libraries revealed differences in gene expression profiles at different developmental stages (embryogenic callus, yellow embryogenic callus, global embryo). We obtained a sequencing depth of >5.6 million tags per sample and identified many differentially expressed genes at various stages of SE. The initiation of SE affected gene expression in many KEGG pathways, but predominantly that in metabolic pathways, biosynthesis of secondary metabolites, and plant hormone signal transduction. This information on the changes in the multiple pathways related to SE induction in E. senticosus Maxim. embryogenic tissue will contribute to a more comprehensive understanding of the mechanisms involved in early SE. Additionally, the differentially expressed genes may act as molecular markers and could play very important roles in the early stage of SE. The results are a comprehensive molecular biology resource for investigating SE of E. senticosus Maxim. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Deep sequencing of Brachypodium small RNAs at the global genome level identifies microRNAs involved in cold stress response

    Directory of Open Access Journals (Sweden)

    Chong Kang

    2009-09-01

    Full Text Available Abstract Background MicroRNAs (miRNAs are endogenous small RNAs having large-scale regulatory effects on plant development and stress responses. Extensive studies of miRNAs have only been performed in a few model plants. Although miRNAs are proved to be involved in plant cold stress responses, little is known for winter-habit monocots. Brachypodium distachyon, with close evolutionary relationship to cool-season cereals, has recently emerged as a novel model plant. There are few reports of Brachypodium miRNAs. Results High-throughput sequencing and whole-genome-wide data mining led to the identification of 27 conserved miRNAs, as well as 129 predicted miRNAs in Brachypodium. For multiple-member conserved miRNA families, their sizes in Brachypodium were much smaller than those in rice and Populus. The genome organization of miR395 family in Brachypodium was quite different from that in rice. The expression of 3 conserved miRNAs and 25 predicted miRNAs showed significant changes in response to cold stress. Among these miRNAs, some were cold-induced and some were cold-suppressed, but all the conserved miRNAs were up-regulated under cold stress condition. Conclusion Our results suggest that Brachypodium miRNAs are composed of a set of conserved miRNAs and a large proportion of non-conserved miRNAs with low expression levels. Both kinds of miRNAs were involved in cold stress response, but all the conserved miRNAs were up-regulated, implying an important role for cold-induced miRNAs. The different size and genome organization of miRNA families in Brachypodium and rice suggest that the frequency of duplication events or the selection pressure on duplicated miRNAs are different between these two closely related plant species.

  10. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

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    Ertosun, Mehmet Günhan; Rubin, Daniel L

    2015-01-01

    Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository.