Evolutionary rate variation and RNA secondary structure prediction

DEFF Research Database (Denmark)

Knudsen, B.; Andersen, E.S.; Damgaard, C.

2004-01-01

Predicting RNA secondary structure using evolutionary history can be carried out by using an alignment of related RNA sequences with conserved structure. Accurately determining evolutionary substitution rates for base pairs and single stranded nucleotides is a concern for methods based on this type...... by applying rates derived from tRNA and rRNA to the prediction of the much more rapidly evolving 5'-region of HIV-1. We find that the HIV-1 prediction is in agreement with experimental data, even though the relative evolutionary rate between A and G is significantly increased, both in stem and loop regions...

Accurate microRNA target prediction correlates with protein repression levels

Directory of Open Access Journals (Sweden)

Simossis Victor A

2009-09-01

Full Text Available Abstract Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

Occupational-Specific Strength Predicts Astronaut-Related Task Performance in a Weighted Suit.

Science.gov (United States)

Taylor, Andrew; Kotarsky, Christopher J; Bond, Colin W; Hackney, Kyle J

2018-01-01

Future space missions beyond low Earth orbit will require deconditioned astronauts to perform occupationally relevant tasks within a planetary spacesuit. The prediction of time-to-completion (TTC) of astronaut tasks will be critical for crew safety, autonomous operations, and mission success. This exploratory study determined if the addition of task-specific strength testing to current standard lower body testing would enhance the prediction of TTC in a 1-G test battery. Eight healthy participants completed NASA lower body strength tests, occupationally specific strength tests, and performed six task simulations (hand drilling, construction wrenching, incline walking, collecting weighted samples, and dragging an unresponsive crewmember to safety) in a 48-kg weighted suit. The TTC for each task was recorded and summed to obtain a total TTC for the test battery. Linear regression was used to predict total TTC with two models: 1) NASA lower body strength tests; and 2) NASA lower body strength tests + occupationally specific strength tests. Total TTC of the test battery ranged from 20.2-44.5 min. The lower body strength test alone accounted for 61% of the variability in total TTC. The addition of hand drilling and wrenching strength tests accounted for 99% of the variability in total TTC. Adding occupationally specific strength tests (hand drilling and wrenching) to standard lower body strength tests successfully predicted total TTC in a performance test battery within a weighted suit. Future research should couple these strength tests with higher fidelity task simulations to determine the utility and efficacy of task performance prediction.Taylor A, Kotarsky CJ, Bond CW, Hackney KJ. Occupational-specific strength predicts astronaut-related task performance in a weighted suit. Aerosp Med Hum Perform. 2018; 89(1):58-62.

Topology and prediction of RNA pseudoknots

DEFF Research Database (Denmark)

Reidys, Christian; Huang, Fenix; Andersen, Jørgen Ellegaard

2011-01-01

Motivation: Several dynamic programming algorithms for predicting RNA structures with pseudoknots have been proposed that differ dramatically from one another in the classes of structures considered. Results: Here, we use the natural topological classification of RNA structures in terms...

piRNA analysis framework from small RNA-Seq data by a novel cluster prediction tool - PILFER.

Science.gov (United States)

Ray, Rishav; Pandey, Priyanka

2017-12-19

With the increasing number of studies focusing on PIWI-interacting RNA (piRNAs), it is now pertinent to develop efficient tools dedicated towards piRNA analysis. We have developed a novel cluster prediction tool called PILFER (PIrna cLuster FindER), which can accurately predict piRNA clusters from small RNA sequencing data. PILFER is an open source, easy to use tool, and can be executed even on a personal computer with minimum resources. It uses a sliding-window mechanism by integrating the expression of the reads along with the spatial information to predict the piRNA clusters. We have additionally defined a piRNA analysis pipeline incorporating PILFER to detect and annotate piRNAs and their clusters from raw small RNA sequencing data and implemented it on publicly available data from healthy germline and somatic tissues. We compared PILFER with other existing piRNA cluster prediction tools and found it to be statistically more accurate and superior in many aspects such as the robustness of PILFER clusters is higher and memory efficiency is more. Overall, PILFER provides a fast and accurate solution to piRNA cluster prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

TargetRNA: a tool for predicting targets of small RNA action in bacteria

OpenAIRE

Tjaden, Brian

2008-01-01

Many small RNA (sRNA) genes in bacteria act as posttranscriptional regulators of target messenger RNAs. Here, we present TargetRNA, a web tool for predicting mRNA targets of sRNA action in bacteria. TargetRNA takes as input a genomic sequence that may correspond to an sRNA gene. TargetRNA then uses a dynamic programming algorithm to search each annotated message in a specified genome for mRNAs that evince basepair-binding potential to the input sRNA sequence. Based on the calculated basepair-...

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction

Science.gov (United States)

Puton, Tomasz; Kozlowski, Lukasz P.; Rother, Kristian M.; Bujnicki, Janusz M.

2013-01-01

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks. PMID:23435231

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction.

Science.gov (United States)

Puton, Tomasz; Kozlowski, Lukasz P; Rother, Kristian M; Bujnicki, Janusz M

2013-04-01

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.

A comprehensive comparison of comparative RNA structure prediction approaches

DEFF Research Database (Denmark)

Gardner, P. P.; Giegerich, R.

2004-01-01

-finding and multiple-sequence-alignment algorithms. Results Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. Conclusions We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms......Background An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene...

Predicting protein-binding RNA nucleotides with consideration of binding partners.

Science.gov (United States)

Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

2015-06-01

In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in

LDAP: a web server for lncRNA-disease association prediction.

Science.gov (United States)

Lan, Wei; Li, Min; Zhao, Kaijie; Liu, Jin; Wu, Fang-Xiang; Pan, Yi; Wang, Jianxin

2017-02-01

Increasing evidences have demonstrated that long noncoding RNAs (lncRNAs) play important roles in many human diseases. Therefore, predicting novel lncRNA-disease associations would contribute to dissect the complex mechanisms of disease pathogenesis. Some computational methods have been developed to infer lncRNA-disease associations. However, most of these methods infer lncRNA-disease associations only based on single data resource. In this paper, we propose a new computational method to predict lncRNA-disease associations by integrating multiple biological data resources. Then, we implement this method as a web server for lncRNA-disease association prediction (LDAP). The input of the LDAP server is the lncRNA sequence. The LDAP predicts potential lncRNA-disease associations by using a bagging SVM classifier based on lncRNA similarity and disease similarity. The web server is available at http://bioinformatics.csu.edu.cn/ldap jxwang@mail.csu.edu.cn. Supplementary data are available at Bioinformatics online.

IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

Science.gov (United States)

Mann, Martin; Wright, Patrick R.

2017-01-01

Abstract The IntaRNA algorithm enables fast and accurate prediction of RNA–RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA–RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage. PMID:28472523

Efficient RNA extraction protocol for the wood mangrove species Laguncularia racemosa suited for next-generation RNA sequencing

International Nuclear Information System (INIS)

Wilwerth, M. W.; Rossetto, P.

2016-01-01

Mangrove flora and habitat have immeasurable importance in marine and coastal ecology as well as in the economy. Despite their importance, they are constantly threatened by oil spill accidents and environmental contamination; therefore, it is crucial to understand the changes in gene expression to better predict toxicity in these plants. Among the species of Atlantic coast mangrove (Americas and Africa), Laguncularia racemosa, or white mangrove, is a conspicuous species. The wide distribution of L. racemosa in areas where marine oil exploration is rapidly increasing make it a candidate mangrove species model to uncover the impact of oil spills at the molecular level with the use of massive transcriptome sequencing. However, for this purpose, the RNA extraction protocol should ensure low levels of contaminants and structure integrity. In this study, eight RNA extraction methods were tested and analysed using downstream applications. The InviTrap Spin Plant RNA Mini Kit performed best with regard to purity and integrity. Moreover, the obtained RNA was submitted to cDNA synthesis and RT-PCR, successfully generating amplification products of the expected size. These Results show the applicability of the RNA obtained here for downstream methodologies, such as the construction of cDNA libraries for the Illumina Hi-seq platform. (author)

RNA 3D modules in genome-wide predictions of RNA 2D structure

DEFF Research Database (Denmark)

Theis, Corinna; Zirbel, Craig L; Zu Siederdissen, Christian Höner

2015-01-01

. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D......Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational...... approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution...

A semi-supervised learning approach for RNA secondary structure prediction.

Science.gov (United States)

Yonemoto, Haruka; Asai, Kiyoshi; Hamada, Michiaki

2015-08-01

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

Science.gov (United States)

Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-01

For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

Science.gov (United States)

Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Evolving stochastic context-free grammars for RNA secondary structure prediction

DEFF Research Database (Denmark)

Anderson, James WJ; Tataru, Paula Cristina; Stains, Joe

2012-01-01

Background Stochastic Context-Free Grammars (SCFGs) were applied successfully to RNA secondary structure prediction in the early 90s, and used in combination with comparative methods in the late 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is very large, but a few...... to structure prediction as has been previously suggested. Results These search techniques were applied to predict RNA secondary structure on a maximal data set and revealed new and interesting grammars, though none are dramatically better than classic grammars. In general, results showed that many grammars...... with quite different structure could have very similar predictive ability. Many ambiguous grammars were found which were at least as effective as the best current unambiguous grammars. Conclusions Overall the method of evolving SCFGs for RNA secondary structure prediction proved effective in finding many...

A quick reality check for microRNA target prediction.

Science.gov (United States)

Kast, Juergen

2011-04-01

The regulation of protein abundance by microRNA (miRNA)-mediated repression of mRNA translation is a rapidly growing area of interest in biochemical research. In animal cells, the miRNA seed sequence does not perfectly match that of the mRNA it targets, resulting in a large number of possible miRNA targets and varied extents of repression. Several software tools are available for the prediction of miRNA targets, yet the overlap between them is limited. Jovanovic et al. have developed and applied a targeted, quantitative approach to validate predicted miRNA target proteins. Using a proteome database, they have set up and tested selected reaction monitoring assays for approximately 20% of more than 800 predicted let-7 targets, as well as control genes in Caenorhabditis elegans. Their results demonstrate that such assays can be developed quickly and with relative ease, and applied in a high-throughput setup to verify known and identify novel miRNA targets. They also show, however, that the choice of the biological system and material has a noticeable influence on the frequency, extent and direction of the observed changes. Nonetheless, selected reaction monitoring assays, such as those developed by Jovanovic et al., represent an attractive new tool in the study of miRNA function at the organism level.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

Science.gov (United States)

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

2018-01-01

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

RNA folding: structure prediction, folding kinetics and ion electrostatics.

Science.gov (United States)

Tan, Zhijie; Zhang, Wenbing; Shi, Yazhou; Wang, Fenghua

2015-01-01

Beyond the "traditional" functions such as gene storage, transport and protein synthesis, recent discoveries reveal that RNAs have important "new" biological functions including the RNA silence and gene regulation of riboswitch. Such functions of noncoding RNAs are strongly coupled to the RNA structures and proper structure change, which naturally leads to the RNA folding problem including structure prediction and folding kinetics. Due to the polyanionic nature of RNAs, RNA folding structure, stability and kinetics are strongly coupled to the ion condition of solution. The main focus of this chapter is to review the recent progress in the three major aspects in RNA folding problem: structure prediction, folding kinetics and ion electrostatics. This chapter will introduce both the recent experimental and theoretical progress, while emphasize the theoretical modelling on the three aspects in RNA folding.

CentroidFold: a web server for RNA secondary structure prediction

OpenAIRE

Sato, Kengo; Hamada, Michiaki; Asai, Kiyoshi; Mituyama, Toutai

2009-01-01

The CentroidFold web server (http://www.ncrna.org/centroidfold/) is a web application for RNA secondary structure prediction powered by one of the most accurate prediction engine. The server accepts two kinds of sequence data: a single RNA sequence and a multiple alignment of RNA sequences. It responses with a prediction result shown as a popular base-pair notation and a graph representation. PDF version of the graph representation is also available. For a multiple alignment sequence, the ser...

Wireless Wearable Multisensory Suite and Real-Time Prediction of Obstructive Sleep Apnea Episodes.

Science.gov (United States)

Le, Trung Q; Cheng, Changqing; Sangasoongsong, Akkarapol; Wongdhamma, Woranat; Bukkapatnam, Satish T S

2013-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder found in 24% of adult men and 9% of adult women. Although continuous positive airway pressure (CPAP) has emerged as a standard therapy for OSA, a majority of patients are not tolerant to this treatment, largely because of the uncomfortable nasal air delivery during their sleep. Recent advances in wireless communication and advanced ("bigdata") preditive analytics technologies offer radically new point-of-care treatment approaches for OSA episodes with unprecedented comfort and afforadability. We introduce a Dirichlet process-based mixture Gaussian process (DPMG) model to predict the onset of sleep apnea episodes based on analyzing complex cardiorespiratory signals gathered from a custom-designed wireless wearable multisensory suite. Extensive testing with signals from the multisensory suite as well as PhysioNet's OSA database suggests that the accuracy of offline OSA classification is 88%, and accuracy for predicting an OSA episode 1-min ahead is 83% and 3-min ahead is 77%. Such accurate prediction of an impending OSA episode can be used to adaptively adjust CPAP airflow (toward improving the patient's adherence) or the torso posture (e.g., minor chin adjustments to maintain steady levels of the airflow).

Comparison of RNA-seq and microarray-based models for clinical endpoint prediction.

Science.gov (United States)

Zhang, Wenqian; Yu, Ying; Hertwig, Falk; Thierry-Mieg, Jean; Zhang, Wenwei; Thierry-Mieg, Danielle; Wang, Jian; Furlanello, Cesare; Devanarayan, Viswanath; Cheng, Jie; Deng, Youping; Hero, Barbara; Hong, Huixiao; Jia, Meiwen; Li, Li; Lin, Simon M; Nikolsky, Yuri; Oberthuer, André; Qing, Tao; Su, Zhenqiang; Volland, Ruth; Wang, Charles; Wang, May D; Ai, Junmei; Albanese, Davide; Asgharzadeh, Shahab; Avigad, Smadar; Bao, Wenjun; Bessarabova, Marina; Brilliant, Murray H; Brors, Benedikt; Chierici, Marco; Chu, Tzu-Ming; Zhang, Jibin; Grundy, Richard G; He, Min Max; Hebbring, Scott; Kaufman, Howard L; Lababidi, Samir; Lancashire, Lee J; Li, Yan; Lu, Xin X; Luo, Heng; Ma, Xiwen; Ning, Baitang; Noguera, Rosa; Peifer, Martin; Phan, John H; Roels, Frederik; Rosswog, Carolina; Shao, Susan; Shen, Jie; Theissen, Jessica; Tonini, Gian Paolo; Vandesompele, Jo; Wu, Po-Yen; Xiao, Wenzhong; Xu, Joshua; Xu, Weihong; Xuan, Jiekun; Yang, Yong; Ye, Zhan; Dong, Zirui; Zhang, Ke K; Yin, Ye; Zhao, Chen; Zheng, Yuanting; Wolfinger, Russell D; Shi, Tieliu; Malkas, Linda H; Berthold, Frank; Wang, Jun; Tong, Weida; Shi, Leming; Peng, Zhiyu; Fischer, Matthias

2015-06-25

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model. We generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models. We demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.

Vfold: a web server for RNA structure and folding thermodynamics prediction.

Science.gov (United States)

Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie

2014-01-01

The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at "http://rna.physics.missouri.edu".

SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction.

Science.gov (United States)

Boniecki, Michal J; Lach, Grzegorz; Dawson, Wayne K; Tomala, Konrad; Lukasz, Pawel; Soltysinski, Tomasz; Rother, Kristian M; Bujnicki, Janusz M

2016-04-20

RNA molecules play fundamental roles in cellular processes. Their function and interactions with other biomolecules are dependent on the ability to form complex three-dimensional (3D) structures. However, experimental determination of RNA 3D structures is laborious and challenging, and therefore, the majority of known RNAs remain structurally uncharacterized. Here, we present SimRNA: a new method for computational RNA 3D structure prediction, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. SimRNA can fold RNA molecules using only sequence information, and, on established test sequences, it recapitulates secondary structure with high accuracy, including correct prediction of pseudoknots. For modeling of complex 3D structures, it can use additional restraints, derived from experimental or computational analyses, including information about secondary structure and/or long-range contacts. SimRNA also can be used to analyze conformational landscapes and identify potential alternative structures. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

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.

DIANA-microT web server: elucidating microRNA functions through target prediction.

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Maragkakis, M; Reczko, M; Simossis, V A; Alexiou, P; Papadopoulos, G L; Dalamagas, T; Giannopoulos, G; Goumas, G; Koukis, E; Kourtis, K; Vergoulis, T; Koziris, N; Sellis, T; Tsanakas, P; Hatzigeorgiou, A G

2009-07-01

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

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Donald Adjeroh

2018-03-01

Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

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Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free

GIMDA: Graphlet interaction-based MiRNA-disease association prediction.

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Chen, Xing; Guan, Na-Na; Li, Jian-Qiang; Yan, Gui-Ying

2018-03-01

MicroRNAs (miRNAs) have been confirmed to be closely related to various human complex diseases by many experimental studies. It is necessary and valuable to develop powerful and effective computational models to predict potential associations between miRNAs and diseases. In this work, we presented a prediction model of Graphlet Interaction for MiRNA-Disease Association prediction (GIMDA) by integrating the disease semantic similarity, miRNA functional similarity, Gaussian interaction profile kernel similarity and the experimentally confirmed miRNA-disease associations. The related score of a miRNA to a disease was calculated by measuring the graphlet interactions between two miRNAs or two diseases. The novelty of GIMDA lies in that we used graphlet interaction to analyse the complex relationships between two nodes in a graph. The AUCs of GIMDA in global and local leave-one-out cross-validation (LOOCV) turned out to be 0.9006 and 0.8455, respectively. The average result of five-fold cross-validation reached to 0.8927 ± 0.0012. In case study for colon neoplasms, kidney neoplasms and prostate neoplasms based on the database of HMDD V2.0, 45, 45, 41 of the top 50 potential miRNAs predicted by GIMDA were validated by dbDEMC and miR2Disease. Additionally, in the case study of new diseases without any known associated miRNAs and the case study of predicting potential miRNA-disease associations using HMDD V1.0, there were also high percentages of top 50 miRNAs verified by the experimental literatures. © 2017 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.

MCTBI: a web server for predicting metal ion effects in RNA structures.

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Sun, Li-Zhen; Zhang, Jing-Xiang; Chen, Shi-Jie

2017-08-01

Metal ions play critical roles in RNA structure and function. However, web servers and software packages for predicting ion effects in RNA structures are notably scarce. Furthermore, the existing web servers and software packages mainly neglect ion correlation and fluctuation effects, which are potentially important for RNAs. We here report a new web server, the MCTBI server (http://rna.physics.missouri.edu/MCTBI), for the prediction of ion effects for RNA structures. This server is based on the recently developed MCTBI, a model that can account for ion correlation and fluctuation effects for nucleic acid structures and can provide improved predictions for the effects of metal ions, especially for multivalent ions such as Mg 2+ effects, as shown by extensive theory-experiment test results. The MCTBI web server predicts metal ion binding fractions, the most probable bound ion distribution, the electrostatic free energy of the system, and the free energy components. The results provide mechanistic insights into the role of metal ions in RNA structure formation and folding stability, which is important for understanding RNA functions and the rational design of RNA structures. © 2017 Sun et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.

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Yang, Yuedong; Li, Xiaomei; Zhao, Huiying; Zhan, Jian; Wang, Jihua; Zhou, Yaoqi

2017-01-01

As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org. © 2016 Yang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Pre-mRNA Splicing in Plants: In Vivo Functions of RNA-Binding Proteins Implicated in the Splicing Process

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Katja Meyer

2015-07-01

Full Text Available Alternative pre-messenger RNA splicing in higher plants emerges as an important layer of regulation upon exposure to exogenous and endogenous cues. Accordingly, mutants defective in RNA-binding proteins predicted to function in the splicing process show severe phenotypic alterations. Among those are developmental defects, impaired responses to pathogen threat or abiotic stress factors, and misregulation of the circadian timing system. A suite of splicing factors has been identified in the model plant Arabidopsis thaliana. Here we summarize recent insights on how defects in these splicing factors impair plant performance.

Prediction of RNA-Binding Proteins by Voting Systems

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C. R. Peng

2011-01-01

Full Text Available It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy value of 79.72% and MCC (Matthew’s correlation coefficient value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier’s vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

TBI server: a web server for predicting ion effects in RNA folding.

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Zhu, Yuhong; He, Zhaojian; Chen, Shi-Jie

2015-01-01

Metal ions play a critical role in the stabilization of RNA structures. Therefore, accurate prediction of the ion effects in RNA folding can have a far-reaching impact on our understanding of RNA structure and function. Multivalent ions, especially Mg²⁺, are essential for RNA tertiary structure formation. These ions can possibly become strongly correlated in the close vicinity of RNA surface. Most of the currently available software packages, which have widespread success in predicting ion effects in biomolecular systems, however, do not explicitly account for the ion correlation effect. Therefore, it is important to develop a software package/web server for the prediction of ion electrostatics in RNA folding by including ion correlation effects. The TBI web server http://rna.physics.missouri.edu/tbi_index.html provides predictions for the total electrostatic free energy, the different free energy components, and the mean number and the most probable distributions of the bound ions. A novel feature of the TBI server is its ability to account for ion correlation and ion distribution fluctuation effects. By accounting for the ion correlation and fluctuation effects, the TBI server is a unique online tool for computing ion-mediated electrostatic properties for given RNA structures. The results can provide important data for in-depth analysis for ion effects in RNA folding including the ion-dependence of folding stability, ion uptake in the folding process, and the interplay between the different energetic components.

TBI server: a web server for predicting ion effects in RNA folding.

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Yuhong Zhu

Full Text Available Metal ions play a critical role in the stabilization of RNA structures. Therefore, accurate prediction of the ion effects in RNA folding can have a far-reaching impact on our understanding of RNA structure and function. Multivalent ions, especially Mg²⁺, are essential for RNA tertiary structure formation. These ions can possibly become strongly correlated in the close vicinity of RNA surface. Most of the currently available software packages, which have widespread success in predicting ion effects in biomolecular systems, however, do not explicitly account for the ion correlation effect. Therefore, it is important to develop a software package/web server for the prediction of ion electrostatics in RNA folding by including ion correlation effects.The TBI web server http://rna.physics.missouri.edu/tbi_index.html provides predictions for the total electrostatic free energy, the different free energy components, and the mean number and the most probable distributions of the bound ions. A novel feature of the TBI server is its ability to account for ion correlation and ion distribution fluctuation effects.By accounting for the ion correlation and fluctuation effects, the TBI server is a unique online tool for computing ion-mediated electrostatic properties for given RNA structures. The results can provide important data for in-depth analysis for ion effects in RNA folding including the ion-dependence of folding stability, ion uptake in the folding process, and the interplay between the different energetic components.

Ranking of microRNA target prediction scores by Pareto front analysis.

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Sahoo, Sudhakar; Albrecht, Andreas A

2010-12-01

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure, which encourages further research towards a higher-dimensional analysis of Pareto fronts. Copyright © 2010 Elsevier Ltd. All rights reserved.

Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops.

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Phan, Andy; Mailey, Katherine; Saeki, Jessica; Gu, Xiaobo; Schroeder, Susan J

2017-05-01

Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites. © 2017 Phan et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Computational prediction and experimental validation of Ciona intestinalis microRNA genes

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Pasquinelli Amy E

2007-11-01

Full Text Available Abstract Background This study reports the first collection of validated microRNA genes in the sea squirt, Ciona intestinalis. MicroRNAs are processed from hairpin precursors to ~22 nucleotide RNAs that base pair to target mRNAs and inhibit expression. As a member of the subphylum Urochordata (Tunicata whose larval form has a notochord, the sea squirt is situated at the emergence of vertebrates, and therefore may provide information about the evolution of molecular regulators of early development. Results In this study, computational methods were used to predict 14 microRNA gene families in Ciona intestinalis. The microRNA prediction algorithm utilizes configurable microRNA sequence conservation and stem-loop specificity parameters, grouping by miRNA family, and phylogenetic conservation to the related species, Ciona savignyi. The expression for 8, out of 9 attempted, of the putative microRNAs in the adult tissue of Ciona intestinalis was validated by Northern blot analyses. Additionally, a target prediction algorithm was implemented, which identified a high confidence list of 240 potential target genes. Over half of the predicted targets can be grouped into the gene ontology categories of metabolism, transport, regulation of transcription, and cell signaling. Conclusion The computational techniques implemented in this study can be applied to other organisms and serve to increase the understanding of the origins of non-coding RNAs, embryological and cellular developmental pathways, and the mechanisms for microRNA-controlled gene regulatory networks.

Improved nucleic acid descriptors for siRNA efficacy prediction.

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Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

2013-02-01

Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

Dinucleotide controlled null models for comparative RNA gene prediction.

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Gesell, Tanja; Washietl, Stefan

2008-05-27

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require randomization of multiple alignments can be considered. SISSIz

Dinucleotide controlled null models for comparative RNA gene prediction

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Gesell Tanja

2008-05-01

Full Text Available Abstract Background Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babak et al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available. Results We present a program called SISSIz that simulates multiple alignments of a given average dinucleotide content. Meeting additional requirements of an accurate null model, the randomized alignments are on average of the same sequence diversity and preserve local conservation and gap patterns. We make use of a phylogenetic substitution model that includes overlapping dependencies and site-specific rates. Using fast heuristics and a distance based approach, a tree is estimated under this model which is used to guide the simulations. The new algorithm is tested on vertebrate genomic alignments and the effect on RNA structure predictions is studied. In addition, we directly combined the new null model with the RNAalifold consensus folding algorithm giving a new variant of a thermodynamic structure based RNA gene finding program that is not biased by the dinucleotide content. Conclusion SISSIz implements an efficient algorithm to randomize multiple alignments preserving dinucleotide content. It can be used to get more accurate estimates of false positive rates of existing programs, to produce negative controls for the training of machine learning based programs, or as standalone RNA gene finding program. Other applications in comparative genomics that require

A Meta-Path-Based Prediction Method for Human miRNA-Target Association

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Jiawei Luo

2016-01-01

Full Text Available MicroRNAs (miRNAs are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction.

Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

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Legendre, Audrey; Angel, Eric; Tahi, Fariza

2018-01-15

RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

Combinatorial microRNA target predictions

DEFF Research Database (Denmark)

Krek, Azra; Grün, Dominic; Poy, Matthew N.

2005-01-01

MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript1, 2, 3. Different combinations of microRNAs are expressed...... in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published micro......RNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results...

RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

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Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses…

Analytical Tools for Space Suit Design

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Aitchison, Lindsay

2011-01-01

As indicated by the implementation of multiple small project teams within the agency, NASA is adopting a lean approach to hardware development that emphasizes quick product realization and rapid response to shifting program and agency goals. Over the past two decades, space suit design has been evolutionary in approach with emphasis on building prototypes then testing with the largest practical range of subjects possible. The results of these efforts show continuous improvement but make scaled design and performance predictions almost impossible with limited budgets and little time. Thus, in an effort to start changing the way NASA approaches space suit design and analysis, the Advanced Space Suit group has initiated the development of an integrated design and analysis tool. It is a multi-year-if not decadal-development effort that, when fully implemented, is envisioned to generate analysis of any given space suit architecture or, conversely, predictions of ideal space suit architectures given specific mission parameters. The master tool will exchange information to and from a set of five sub-tool groups in order to generate the desired output. The basic functions of each sub-tool group, the initial relationships between the sub-tools, and a comparison to state of the art software and tools are discussed.

Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

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Cobaugh Christian W

2004-08-01

Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

Combining Results from Distinct MicroRNA Target Prediction Tools Enhances the Performance of Analyses

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Arthur C. Oliveira

2017-05-01

Full Text Available Target prediction is generally the first step toward recognition of bona fide microRNA (miRNA-target interactions in living cells. Several target prediction tools are now available, which use distinct criteria and stringency to provide the best set of candidate targets for a single miRNA or a subset of miRNAs. However, there are many false-negative predictions, and consensus about the optimum strategy to select and use the output information provided by the target prediction tools is lacking. We compared the performance of four tools cited in literature—TargetScan (TS, miRanda-mirSVR (MR, Pita, and RNA22 (R22, and we determined the most effective approach for analyzing target prediction data (individual, union, or intersection. For this purpose, we calculated the sensitivity, specificity, precision, and correlation of these approaches using 10 miRNAs (miR-1-3p, miR-17-5p, miR-21-5p, miR-24-3p, miR-29a-3p, miR-34a-5p, miR-124-3p, miR-125b-5p, miR-145-5p, and miR-155-5p and 1,400 genes (700 validated and 700 non-validated as targets of these miRNAs. The four tools provided a subset of high-quality predictions and returned few false-positive predictions; however, they could not identify several known true targets. We demonstrate that union of TS/MR and TS/MR/R22 enhanced the quality of in silico prediction analysis of miRNA targets. We conclude that the union rather than the intersection of the aforementioned tools is the best strategy for maximizing performance while minimizing the loss of time and resources in subsequent in vivo and in vitro experiments for functional validation of miRNA-target interactions.

Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

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Zeenia Jagga

Full Text Available Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a

A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

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Al-Khatib, Ra’ed M.; Abdullah, Rosni; Rashid, Nur’Aini Abdul

2010-01-01

RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods. PMID:20458364

Correlation of microRNA levels during hypoxia with predicted target mRNAs through genome-wide microarray analysis

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Page Grier P

2009-03-01

Full Text Available Abstract Background Low levels of oxygen in tissues, seen in situations such as chronic lung disease, necrotic tumors, and high altitude exposures, initiate a signaling pathway that results in active transcription of genes possessing a hypoxia response element (HRE. The aim of this study was to investigate whether a change in miRNA expression following hypoxia could account for changes in the cellular transcriptome based on currently available miRNA target prediction tools. Methods To identify changes induced by hypoxia, we conducted mRNA- and miRNA-array-based experiments in HT29 cells, and performed comparative analysis of the resulting data sets based on multiple target prediction algorithms. To date, few studies have investigated an environmental perturbation for effects on genome-wide miRNA levels, or their consequent influence on mRNA output. Results Comparison of miRNAs with predicted mRNA targets indicated a lower level of concordance than expected. We did, however, find preliminary evidence of combinatorial regulation of mRNA expression by miRNA. Conclusion Target prediction programs and expression profiling techniques do not yet adequately represent the complexity of miRNA-mediated gene repression, and new methods may be required to better elucidate these pathways. Our data suggest the physiologic impact of miRNAs on cellular transcription results from a multifaceted network of miRNA and mRNA relationships, working together in an interconnected system and in context of hundreds of RNA species. The methods described here for comparative analysis of cellular miRNA and mRNA will be useful for understanding genome wide regulatory responsiveness and refining miRNA predictive algorithms.

Statistical properties of thermodynamically predicted RNA secondary structures in viral genomes

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Spanò, M.; Lillo, F.; Miccichè, S.; Mantegna, R. N.

2008-10-01

By performing a comprehensive study on 1832 segments of 1212 complete genomes of viruses, we show that in viral genomes the hairpin structures of thermodynamically predicted RNA secondary structures are more abundant than expected under a simple random null hypothesis. The detected hairpin structures of RNA secondary structures are present both in coding and in noncoding regions for the four groups of viruses categorized as dsDNA, dsRNA, ssDNA and ssRNA. For all groups, hairpin structures of RNA secondary structures are detected more frequently than expected for a random null hypothesis in noncoding rather than in coding regions. However, potential RNA secondary structures are also present in coding regions of dsDNA group. In fact, we detect evolutionary conserved RNA secondary structures in conserved coding and noncoding regions of a large set of complete genomes of dsDNA herpesviruses.

Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees.

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Williams, Philip H; Eyles, Rod; Weiller, Georg

2012-01-01

MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require "read count" to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA(∗) duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.

An integrated computational validation approach for potential novel miRNA prediction

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Pooja Viswam

2017-12-01

Full Text Available MicroRNAs (miRNAs are short, non-coding RNAs between 17bp-24bp length that regulate gene expression by targeting mRNA molecules. The regulatory functions of miRNAs are known to be majorly associated with disease phenotypes such as cancer, cell signaling, cell division, growth and other metabolisms. Novel miRNAs are defined as sequences which does not have any similarity with the existing known sequences and void of any experimental evidences. In recent decades, the advent of next-generation sequencing allows us to capture the small RNA molecules form the cells and developing methods to estimate their expression levels. Several computational algorithms are available to predict the novel miRNAs from the deep sequencing data. In this work, we integrated three novel miRNA prediction programs miRDeep, miRanalyzer and miRPRo to compare and validate their prediction efficiency. The dicer cleavage sites, alignment density, seed conservation, minimum free energy, AU-GC percentage, secondary loop scores, false discovery rates and confidence scores will be considered for comparison and evaluation. Efficiency to identify isomiRs and base pair mismatches in a strand specific manner will also be considered for the computational validation. Further, the criteria and parameters for the identification of the best possible novel miRNA with minimal false positive rates were deduced.

Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas

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Petrov, Anton I.; Zirbel, Craig L.; Leontis, Neocles B.

2013-01-01

The analysis of atomic-resolution RNA three-dimensional (3D) structures reveals that many internal and hairpin loops are modular, recurrent, and structured by conserved non-Watson–Crick base pairs. Structurally similar loops define RNA 3D motifs that are conserved in homologous RNA molecules, but can also occur at nonhomologous sites in diverse RNAs, and which often vary in sequence. To further our understanding of RNA motif structure and sequence variability and to provide a useful resource for structure modeling and prediction, we present a new method for automated classification of internal and hairpin loop RNA 3D motifs and a new online database called the RNA 3D Motif Atlas. To classify the motif instances, a representative set of internal and hairpin loops is automatically extracted from a nonredundant list of RNA-containing PDB files. Their structures are compared geometrically, all-against-all, using the FR3D program suite. The loops are clustered into motif groups, taking into account geometric similarity and structural annotations and making allowance for a variable number of bulged bases. The automated procedure that we have implemented identifies all hairpin and internal loop motifs previously described in the literature. All motif instances and motif groups are assigned unique and stable identifiers and are made available in the RNA 3D Motif Atlas (http://rna.bgsu.edu/motifs), which is automatically updated every four weeks. The RNA 3D Motif Atlas provides an interactive user interface for exploring motif diversity and tools for programmatic data access. PMID:23970545

MicroRNA prediction using a fixed-order Markov model based on the secondary structure pattern.

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Wei Shen

Full Text Available Predicting miRNAs is an arduous task, due to the diversity of the precursors and complexity of enzyme processes. Although several prediction approaches have reached impressive performances, few of them could achieve a full-function recognition of mature miRNA directly from the candidate hairpins across species. Therefore, researchers continue to seek a more powerful model close to biological recognition to miRNA structure. In this report, we describe a novel miRNA prediction algorithm, known as FOMmiR, using a fixed-order Markov model based on the secondary structural pattern. For a training dataset containing 809 human pre-miRNAs and 6441 human pseudo-miRNA hairpins, the model's parameters were defined and evaluated. The results showed that FOMmiR reached 91% accuracy on the human dataset through 5-fold cross-validation. Moreover, for the independent test datasets, the FOMmiR presented an outstanding prediction in human and other species including vertebrates, Drosophila, worms and viruses, even plants, in contrast to the well-known algorithms and models. Especially, the FOMmiR was not only able to distinguish the miRNA precursors from the hairpins, but also locate the position and strand of the mature miRNA. Therefore, this study provides a new generation of miRNA prediction algorithm, which successfully realizes a full-function recognition of the mature miRNAs directly from the hairpin sequences. And it presents a new understanding of the biological recognition based on the strongest signal's location detected by FOMmiR, which might be closely associated with the enzyme cleavage mechanism during the miRNA maturation.

3dRPC: a web server for 3D RNA-protein structure prediction.

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Huang, Yangyu; Li, Haotian; Xiao, Yi

2018-04-01

RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction. The address of 3dRPC web server is http://biophy.hust.edu.cn/3dRPC. yxiao@hust.edu.cn.

Predicting RNA hyper-editing with a novel tool when unambiguous alignment is impossible.

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McKerrow, Wilson H; Savva, Yiannis A; Rezaei, Ali; Reenan, Robert A; Lawrence, Charles E

2017-07-10

Repetitive elements are now known to have relevant cellular functions, including self-complementary sequences that form double stranded (ds) RNA. There are numerous pathways that determine the fate of endogenous dsRNA, and misregulation of endogenous dsRNA is a driver of autoimmune disease, particularly in the brain. Unfortunately, the alignment of high-throughput, short-read sequences to repeat elements poses a dilemma: Such sequences may align equally well to multiple genomic locations. In order to differentiate repeat elements, current alignment methods depend on sequence variation in the reference genome. Reads are discarded when no such variations are present. However, RNA hyper-editing, a possible fate for dsRNA, introduces enough variation to distinguish between repeats that are otherwise identical. To take advantage of this variation, we developed a new algorithm, RepProfile, that simultaneously aligns reads and predicts novel variations. RepProfile accurately aligns hyper-edited reads that other methods discard. In particular we predict hyper-editing of Drosophila melanogaster repeat elements in vivo at levels previously described only in vitro, and provide validation by Sanger sequencing sixty-two individual cloned sequences. We find that hyper-editing is concentrated in genes involved in cell-cell communication at the synapse, including some that are associated with neurodegeneration. We also find that hyper-editing tends to occur in short runs. Previous studies of RNA hyper-editing discarded ambiguously aligned reads, ignoring hyper-editing in long, perfect dsRNA - the perfect substrate for hyper-editing. We provide a method that simulation and Sanger validation show accurately predicts such RNA editing, yielding a superior picture of hyper-editing.

Integrating chemical footprinting data into RNA secondary structure prediction.

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Kourosh Zarringhalam

Full Text Available Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension, yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints, which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be 'correct', in as much as the shape data is 'correct'. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

Improved Model for Predicting the Free Energy Contribution of Dinucleotide Bulges to RNA Duplex Stability.

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Tomcho, Jeremy C; Tillman, Magdalena R; Znosko, Brent M

2015-09-01

Predicting the secondary structure of RNA is an intermediate in predicting RNA three-dimensional structure. Commonly, determining RNA secondary structure from sequence uses free energy minimization and nearest neighbor parameters. Current algorithms utilize a sequence-independent model to predict free energy contributions of dinucleotide bulges. To determine if a sequence-dependent model would be more accurate, short RNA duplexes containing dinucleotide bulges with different sequences and nearest neighbor combinations were optically melted to derive thermodynamic parameters. These data suggested energy contributions of dinucleotide bulges were sequence-dependent, and a sequence-dependent model was derived. This model assigns free energy penalties based on the identity of nucleotides in the bulge (3.06 kcal/mol for two purines, 2.93 kcal/mol for two pyrimidines, 2.71 kcal/mol for 5'-purine-pyrimidine-3', and 2.41 kcal/mol for 5'-pyrimidine-purine-3'). The predictive model also includes a 0.45 kcal/mol penalty for an A-U pair adjacent to the bulge and a -0.28 kcal/mol bonus for a G-U pair adjacent to the bulge. The new sequence-dependent model results in predicted values within, on average, 0.17 kcal/mol of experimental values, a significant improvement over the sequence-independent model. This model and new experimental values can be incorporated into algorithms that predict RNA stability and secondary structure from sequence.

Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization

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Junilda Spirollari

2009-01-01

Full Text Available Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http:// datalab.njit.edu/biology/RSpredict.

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.

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Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-12

Recently, as the research of microRNA (miRNA) continues, there are plenty of experimental evidences indicating that miRNA could be associated with various human complex diseases development and progression. Hence, it is necessary and urgent to pay more attentions to the relevant study of predicting diseases associated miRNAs, which may be helpful for effective prevention, diagnosis and treatment of human diseases. Especially, constructing computational methods to predict potential miRNA-disease associations is worthy of more studies because of the feasibility and effectivity. In this work, we developed a novel computational model of multiple kernels learning-based Kronecker regularized least squares for MiRNA-disease association prediction (MKRMDA), which could reveal potential miRNA-disease associations by automatically optimizing the combination of multiple kernels for disease and miRNA. MKRMDA obtained AUCs of 0.9040 and 0.8446 in global and local leave-one-out cross validation, respectively. Meanwhile, MKRMDA achieved average AUCs of 0.8894 ± 0.0015 in fivefold cross validation. Furthermore, we conducted three different kinds of case studies on some important human cancers for further performance evaluation. In the case studies of colonic cancer, esophageal cancer and lymphoma based on known miRNA-disease associations in HMDDv2.0 database, 76, 94 and 88% of the corresponding top 50 predicted miRNAs were confirmed by experimental reports, respectively. In another two kinds of case studies for new diseases without any known associated miRNAs and diseases only with known associations in HMDDv1.0 database, the verified ratios of two different cancers were 88 and 94%, respectively. All the results mentioned above adequately showed the reliable prediction ability of MKRMDA. We anticipated that MKRMDA could serve to facilitate further developments in the field and the follow-up investigations by biomedical researchers.

Genome wide predictions of miRNA regulation by transcription factors.

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Ruffalo, Matthew; Bar-Joseph, Ziv

2016-09-01

Reconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and microRNAs (miRNAs) that regulate genes in these networks, relatively little work has focused on inferring the regulation of miRNAs by TFs. Such regulation can play an important role in several biological processes including development and disease. The main challenge for predicting such interactions is the very small positive training set currently available. Another challenge is the fact that a large fraction of miRNAs are encoded within genes making it hard to determine the specific way in which they are regulated. To enable genome wide predictions of TF-miRNA interactions, we extended semi-supervised machine-learning approaches to integrate a large set of different types of data including sequence, expression, ChIP-seq and epigenetic data. As we show, the methods we develop achieve good performance on both a labeled test set, and when analyzing general co-expression networks. We next analyze mRNA and miRNA cancer expression data, demonstrating the advantage of using the predicted set of interactions for identifying more coherent and relevant modules, genes, and miRNAs. The complete set of predictions is available on the supporting website and can be used by any method that combines miRNAs, genes, and TFs. Code and full set of predictions are available from the supporting website: http://cs.cmu.edu/~mruffalo/tf-mirna/ zivbj@cs.cmu.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The Earth System Prediction Suite: Toward a Coordinated U.S. Modeling Capability

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Theurich, Gerhard; DeLuca, C.; Campbell, T.; Liu, F.; Saint, K.; Vertenstein, M.; Chen, J.; Oehmke, R.; Doyle, J.; Whitcomb, T.;

TargetSpy: a supervised machine learning approach for microRNA target prediction.

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Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila

TargetSpy: a supervised machine learning approach for microRNA target prediction

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Langenberger David

2010-05-01

Full Text Available Abstract Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I no seed match requirement, II seed match requirement, and III conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on

Analysis and prediction of translation rate based on sequence and functional features of the mRNA.

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Tao Huang

Full Text Available Protein concentrations depend not only on the mRNA level, but also on the translation rate and the degradation rate. Prediction of mRNA's translation rate would provide valuable information for in-depth understanding of the translation mechanism and dynamic proteome. In this study, we developed a new computational model to predict the translation rate, featured by (1 integrating various sequence-derived and functional features, (2 applying the maximum relevance & minimum redundancy method and incremental feature selection to select features to optimize the prediction model, and (3 being able to predict the translation rate of RNA into high or low translation rate category. The prediction accuracies under rich and starvation condition were 68.8% and 70.0%, respectively, evaluated by jackknife cross-validation. It was found that the following features were correlated with translation rate: codon usage frequency, some gene ontology enrichment scores, number of RNA binding proteins known to bind its mRNA product, coding sequence length, protein abundance and 5'UTR free energy. These findings might provide useful information for understanding the mechanisms of translation and dynamic proteome. Our translation rate prediction model might become a high throughput tool for annotating the translation rate of mRNAs in large-scale.

DNApi: A De Novo Adapter Prediction Algorithm for Small RNA Sequencing Data.

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Tsuji, Junko; Weng, Zhiping

2016-01-01

With the rapid accumulation of publicly available small RNA sequencing datasets, third-party meta-analysis across many datasets is becoming increasingly powerful. Although removing the 3´ adapter is an essential step for small RNA sequencing analysis, the adapter sequence information is not always available in the metadata. The information can be also erroneous even when it is available. In this study, we developed DNApi, a lightweight Python software package that predicts the 3´ adapter sequence de novo and provides the user with cleansed small RNA sequences ready for down stream analysis. Tested on 539 publicly available small RNA libraries accompanied with 3´ adapter sequences in their metadata, DNApi shows near-perfect accuracy (98.5%) with fast runtime (~2.85 seconds per library) and efficient memory usage (~43 MB on average). In addition to 3´ adapter prediction, it is also important to classify whether the input small RNA libraries were already processed, i.e. the 3´ adapters were removed. DNApi perfectly judged that given another batch of datasets, 192 publicly available processed libraries were "ready-to-map" small RNA sequence. DNApi is compatible with Python 2 and 3, and is available at https://github.com/jnktsj/DNApi. The 731 small RNA libraries used for DNApi evaluation were from human tissues and were carefully and manually collected. This study also provides readers with the curated datasets that can be integrated into their studies.

Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees

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Philip H. Williams

2012-01-01

Full Text Available MicroRNAs (miRNAs are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require “read count” to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA∗ duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.

miRNAFold: a web server for fast miRNA precursor prediction in genomes.

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Tav, Christophe; Tempel, Sébastien; Poligny, Laurent; Tahi, Fariza

2016-07-08

Computational methods are required for prediction of non-coding RNAs (ncRNAs), which are involved in many biological processes, especially at post-transcriptional level. Among these ncRNAs, miRNAs have been largely studied and biologists need efficient and fast tools for their identification. In particular, ab initio methods are usually required when predicting novel miRNAs. Here we present a web server dedicated for miRNA precursors identification at a large scale in genomes. It is based on an algorithm called miRNAFold that allows predicting miRNA hairpin structures quickly with high sensitivity. miRNAFold is implemented as a web server with an intuitive and user-friendly interface, as well as a standalone version. The web server is freely available at: http://EvryRNA.ibisc.univ-evry.fr/miRNAFold. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

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Lim, Chun Shen; Brown, Chris M.

2018-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community. PMID:29354101

RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

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Ellington, Roni; Wachira, James

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968

RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

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Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study.

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Liu, Qi; Xu, Qian; Zheng, Vincent W; Xue, Hong; Cao, Zhiwei; Yang, Qiang

2010-04-10

Gene silencing using exogenous small interfering RNAs (siRNAs) is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC) to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs) have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. The knowledge gained from our study provides useful insights on how to analyze various cross-platform RNAi data for uncovering

Multi-task learning for cross-platform siRNA efficacy prediction: an in-silico study

Directory of Open Access Journals (Sweden)

Xue Hong

2010-04-01

Full Text Available Abstract Background Gene silencing using exogenous small interfering RNAs (siRNAs is now a widespread molecular tool for gene functional study and new-drug target identification. The key mechanism in this technique is to design efficient siRNAs that incorporated into the RNA-induced silencing complexes (RISC to bind and interact with the mRNA targets to repress their translations to proteins. Although considerable progress has been made in the computational analysis of siRNA binding efficacy, few joint analysis of different RNAi experiments conducted under different experimental scenarios has been done in research so far, while the joint analysis is an important issue in cross-platform siRNA efficacy prediction. A collective analysis of RNAi mechanisms for different datasets and experimental conditions can often provide new clues on the design of potent siRNAs. Results An elegant multi-task learning paradigm for cross-platform siRNA efficacy prediction is proposed. Experimental studies were performed on a large dataset of siRNA sequences which encompass several RNAi experiments recently conducted by different research groups. By using our multi-task learning method, the synergy among different experiments is exploited and an efficient multi-task predictor for siRNA efficacy prediction is obtained. The 19 most popular biological features for siRNA according to their jointly importance in multi-task learning were ranked. Furthermore, the hypothesis is validated out that the siRNA binding efficacy on different messenger RNAs(mRNAs have different conditional distribution, thus the multi-task learning can be conducted by viewing tasks at an "mRNA"-level rather than at the "experiment"-level. Such distribution diversity derived from siRNAs bound to different mRNAs help indicate that the properties of target mRNA have important implications on the siRNA binding efficacy. Conclusions The knowledge gained from our study provides useful insights on how to

Prediction of RNA secondary structure using generalized centroid estimators.

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Hamada, Michiaki; Kiryu, Hisanori; Sato, Kengo; Mituyama, Toutai; Asai, Kiyoshi

2009-02-15

Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximized in the posterior decoding with respect to the accuracy measures for secondary structures. We propose novel estimators which improve the accuracy of secondary structure prediction of RNAs. The proposed estimators maximize an objective function which is the weighted sum of the expected number of the true positives and that of the true negatives of the base pairs. The proposed estimators are also improved versions of the ones used in previous works, namely CONTRAfold for secondary structure prediction from a single RNA sequence and McCaskill-MEA for common secondary structure prediction from multiple alignments of RNA sequences. We clarify the relations between the proposed estimators and the estimators presented in previous works, and theoretically show that the previous estimators include additional unnecessary terms in the evaluation measures with respect to the accuracy. Furthermore, computational experiments confirm the theoretical analysis by indicating improvement in the empirical accuracy. The proposed estimators represent extensions of the centroid estimators proposed in Ding et al. and Carvalho and Lawrence, and are applicable to a wide variety of problems in bioinformatics. Supporting information and the CentroidFold software are available online at: http://www.ncrna.org/software/centroidfold/.

TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.

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Bonnet, Eric; He, Ying; Billiau, Kenny; Van de Peer, Yves

2010-06-15

We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. Supplementary data are available at Bioinformatics online.

Prediction of RNA secondary structures: from theory to models and real molecules

International Nuclear Information System (INIS)

Schuster, Peter

2006-01-01

RNA secondary structures are derived from RNA sequences, which are strings built form the natural four letter nucleotide alphabet, {AUGC}. These coarse-grained structures, in turn, are tantamount to constrained strings over a three letter alphabet. Hence, the secondary structures are discrete objects and the number of sequences always exceeds the number of structures. The sequences built from two letter alphabets form perfect structures when the nucleotides can form a base pair, as is the case with {GC} or {AU}, but the relation between the sequences and structures differs strongly from the four letter alphabet. A comprehensive theory of RNA structure is presented, which is based on the concepts of sequence space and shape space, being a space of structures. It sets the stage for modelling processes in ensembles of RNA molecules like evolutionary optimization or kinetic folding as dynamical phenomena guided by mappings between the two spaces. The number of minimum free energy (mfe) structures is always smaller than the number of sequences, even for two letter alphabets. Folding of RNA molecules into mfe energy structures constitutes a non-invertible mapping from sequence space onto shape space. The preimage of a structure in sequence space is defined as its neutral network. Similarly the set of suboptimal structures is the preimage of a sequence in shape space. This set represents the conformation space of a given sequence. The evolutionary optimization of structures in populations is a process taking place in sequence space, whereas kinetic folding occurs in molecular ensembles that optimize free energy in conformation space. Efficient folding algorithms based on dynamic programming are available for the prediction of secondary structures for given sequences. The inverse problem, the computation of sequences for predefined structures, is an important tool for the design of RNA molecules with tailored properties. Simultaneous folding or cofolding of two or more RNA

Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.

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Zhu, Mingzhu; Dahmen, Jeremy L; Stacey, Gary; Cheng, Jianlin

2013-09-22

High-throughput RNA sequencing (RNA-Seq) is a revolutionary technique to study the transcriptome of a cell under various conditions at a systems level. Despite the wide application of RNA-Seq techniques to generate experimental data in the last few years, few computational methods are available to analyze this huge amount of transcription data. The computational methods for constructing gene regulatory networks from RNA-Seq expression data of hundreds or even thousands of genes are particularly lacking and urgently needed. We developed an automated bioinformatics method to predict gene regulatory networks from the quantitative expression values of differentially expressed genes based on RNA-Seq transcriptome data of a cell in different stages and conditions, integrating transcriptional, genomic and gene function data. We applied the method to the RNA-Seq transcriptome data generated for soybean root hair cells in three different development stages of nodulation after rhizobium infection. The method predicted a soybean nodulation-related gene regulatory network consisting of 10 regulatory modules common for all three stages, and 24, 49 and 70 modules separately for the first, second and third stage, each containing both a group of co-expressed genes and several transcription factors collaboratively controlling their expression under different conditions. 8 of 10 common regulatory modules were validated by at least two kinds of validations, such as independent DNA binding motif analysis, gene function enrichment test, and previous experimental data in the literature. We developed a computational method to reliably reconstruct gene regulatory networks from RNA-Seq transcriptome data. The method can generate valuable hypotheses for interpreting biological data and designing biological experiments such as ChIP-Seq, RNA interference, and yeast two hybrid experiments.

Systematic Prediction of the Impacts of Mutations in MicroRNA Seed Sequences

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Bhattacharya Anindya

2017-05-01

Full Text Available MicroRNAs are a class of small non-coding RNAs that are involved in many important biological processes and the dysfunction of microRNA has been associated with many diseases. The seed region of a microRNA is of crucial importance to its target recognition. Mutations in microRNA seed regions may disrupt the binding of microRNAs to their original target genes and make them bind to new target genes. Here we use a knowledge-based computational method to systematically predict the functional effects of all the possible single nucleotide mutations in human microRNA seed regions. The result provides a comprehensive reference for the functional assessment of the impacts of possible natural and artificial single nucleotide mutations in microRNA seed regions.

Computational tools for genome-wide miRNA prediction and study

KAUST Repository

Malas, T.B.

2012-11-02

MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages. Malas and Ravasi.

Computational tools for genome-wide miRNA prediction and study

KAUST Repository

Malas, T.B.; Ravasi, Timothy

2012-01-01

MicroRNAs (miRNAs) are single-stranded non-coding RNA susually of 22 nucleotidesin length that play an important post-transcriptional regulation role in many organisms. MicroRNAs bind a seed sequence to the 3-untranslated region (UTR) region of the target messenger RNA (mRNA), inducing degradation or inhibition of translation and resulting in a reduction in the protein level. This regulatory mechanism is central to many biological processes and perturbation could lead to diseases such as cancer. Given the biological importance, of miRNAs, there is a great need to identify and study their targets and functions. However, miRNAs are very difficult to clone in the lab and this has hindered the identification of novel miRNAs. Next-generation sequencing coupled with new computational tools has recently evolved to help researchers efficiently identify large numbers of novel miRNAs. In this review, we describe recent miRNA prediction tools and discuss their priorities, advantages and disadvantages. Malas and Ravasi.

Matrix factorization-based data fusion for the prediction of lncRNA-disease associations.

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Fu, Guangyuan; Wang, Jun; Domeniconi, Carlotta; Yu, Guoxian

2018-05-01

Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be. To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological

StructRNAfinder: an automated pipeline and web server for RNA families prediction.

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Arias-Carrasco, Raúl; Vásquez-Morán, Yessenia; Nakaya, Helder I; Maracaja-Coutinho, Vinicius

2018-02-17

The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills. We present an automated pipeline named StructRNAfinder that predicts and annotates RNA families in transcript or genome sequences. This single tool not only displays the sequence/structural consensus alignments for each RNA family, according to Rfam database but also provides a taxonomic overview for each assigned functional RNA. Moreover, we implemented a user-friendly web service that allows researchers to upload their own nucleotide sequences in order to perform the whole analysis. Finally, we provided a stand-alone version of StructRNAfinder to be used in large-scale projects. The tool was developed under GNU General Public License (GPLv3) and is freely available at http://structrnafinder.integrativebioinformatics.me . The main advantage of StructRNAfinder relies on the large-scale processing and integrating the data obtained by each tool and database employed along the workflow, of which several files are generated and displayed in user-friendly reports, useful for downstream analyses and data exploration.

Analysis of energy-based algorithms for RNA secondary structure prediction

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Hajiaghayi Monir

2012-02-01

Full Text Available Abstract Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA or pseudo-expected accuracy (pseudo-MEA methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-MEA-based methods, with respect to the latest datasets and energy parameters. Results We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms

Avian reovirus L2 genome segment sequences and predicted structure/function of the encoded RNA-dependent RNA polymerase protein

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Xu Wanhong

2008-12-01

Full Text Available Abstract Background The orthoreoviruses are infectious agents that possess a genome comprised of 10 double-stranded RNA segments encased in two concentric protein capsids. Like virtually all RNA viruses, an RNA-dependent RNA polymerase (RdRp enzyme is required for viral propagation. RdRp sequences have been determined for the prototype mammalian orthoreoviruses and for several other closely-related reoviruses, including aquareoviruses, but have not yet been reported for any avian orthoreoviruses. Results We determined the L2 genome segment nucleotide sequences, which encode the RdRp proteins, of two different avian reoviruses, strains ARV138 and ARV176 in order to define conserved and variable regions within reovirus RdRp proteins and to better delineate structure/function of this important enzyme. The ARV138 L2 genome segment was 3829 base pairs long, whereas the ARV176 L2 segment was 3830 nucleotides long. Both segments were predicted to encode λB RdRp proteins 1259 amino acids in length. Alignments of these newly-determined ARV genome segments, and their corresponding proteins, were performed with all currently available homologous mammalian reovirus (MRV and aquareovirus (AqRV genome segment and protein sequences. There was ~55% amino acid identity between ARV λB and MRV λ3 proteins, making the RdRp protein the most highly conserved of currently known orthoreovirus proteins, and there was ~28% identity between ARV λB and homologous MRV and AqRV RdRp proteins. Predictive structure/function mapping of identical and conserved residues within the known MRV λ3 atomic structure indicated most identical amino acids and conservative substitutions were located near and within predicted catalytic domains and lining RdRp channels, whereas non-identical amino acids were generally located on the molecule's surfaces. Conclusion The ARV λB and MRV λ3 proteins showed the highest ARV:MRV identity values (~55% amongst all currently known ARV and MRV

MicroRNA classifier and nomogram for metastasis prediction in colon cancer.

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Goossens-Beumer, Inès J; Derr, Remco S; Buermans, Henk P J; Goeman, Jelle J; Böhringer, Stefan; Morreau, Hans; Nitsche, Ulrich; Janssen, Klaus-Peter; van de Velde, Cornelis J H; Kuppen, Peter J K

2015-01-01

Colon cancer prognosis and treatment are currently based on a classification system still showing large heterogeneity in clinical outcome, especially in TNM stages II and III. Prognostic biomarkers for metastasis risk are warranted as development of distant recurrent disease mainly accounts for the high lethality rates of colon cancer. miRNAs have been proposed as potential biomarkers for cancer. Furthermore, a verified standard for normalization of the amount of input material in PCR-based relative quantification of miRNA expression is lacking. A selection of frozen tumor specimens from two independent patient cohorts with TNM stage II-III microsatellite stable primary adenocarcinomas was used for laser capture microdissection. Next-generation sequencing was performed on small RNAs isolated from colorectal tumors from the Dutch cohort (N = 50). Differential expression analysis, comparing in metastasized and nonmetastasized tumors, identified prognostic miRNAs. Validation was performed on colon tumors from the German cohort (N = 43) using quantitative PCR (qPCR). miR25-3p and miR339-5p were identified and validated as independent prognostic markers and used to construct a multivariate nomogram for metastasis risk prediction. The nomogram showed good probability prediction in validation. In addition, we recommend combination of miR16-5p and miR26a-5p as standard for normalization in qPCR of colon cancer tissue-derived miRNA expression. In this international study, we identified and validated a miRNA classifier in primary cancers, and propose a nomogram capable of predicting metastasis risk in microsatellite stable TNM stage II-III colon cancer. In conjunction with TNM staging, by means of a nomogram, this miRNA classifier may allow for personalized treatment decisions based on individual tumor characteristics. ©2014 American Association for Cancer Research.

RNAspa: a shortest path approach for comparative prediction of the secondary structure of ncRNA molecules

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Michaeli Shulamit

2007-10-01

Full Text Available Abstract Background In recent years, RNA molecules that are not translated into proteins (ncRNAs have drawn a great deal of attention, as they were shown to be involved in many cellular functions. One of the most important computational problems regarding ncRNA is to predict the secondary structure of a molecule from its sequence. In particular, we attempted to predict the secondary structure for a set of unaligned ncRNA molecules that are taken from the same family, and thus presumably have a similar structure. Results We developed the RNAspa program, which comparatively predicts the secondary structure for a set of ncRNA molecules in linear time in the number of molecules. We observed that in a list of several hundred suboptimal minimal free energy (MFE predictions, as provided by the RNAsubopt program of the Vienna package, it is likely that at least one suggested structure would be similar to the true, correct one. The suboptimal solutions of each molecule are represented as a layer of vertices in a graph. The shortest path in this graph is the basis for structural predictions for the molecule. We also show that RNA secondary structures can be compared very rapidly by a simple string Edit-Distance algorithm with a minimal loss of accuracy. We show that this approach allows us to more deeply explore the suboptimal structure space. Conclusion The algorithm was tested on three datasets which include several ncRNA families taken from the Rfam database. These datasets allowed for comparison of the algorithm with other methods. In these tests, RNAspa performed better than four other programs.

SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

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Xiaoying Li

2018-01-01

Full Text Available Aberrant expression of microRNAs (miRNAs can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP. The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

CircRNA-0004904, CircRNA-0001855, and PAPP-A: Potential Novel Biomarkers for the Prediction of Preeclampsia

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Min Jiang

2018-05-01

Full Text Available Background/Aims: Circular RNAs (circRNAs are transcribed prevalently in the genome; however, their potential roles in multiple cardiovascular diseases, particularly preeclampsia (PE, are not yet well understood. This study investigated the expression profiles of circRNAs and explored circRNA-mediated pregnancy-associated plasma protein A (PAPP-A expression as a potential biomarker for PE before 20 weeks of pregnancy. Methods: A nested case-control two-phase screening/validation study was performed in pregnant women before 20 weeks of gestation (before clinical diagnosis at Guangzhou Women and Children’s Medical Center from 2012 to 2015. In the screening phase, circRNA expression profiles of blood cells were assessed using a human circRNA microarray, which was designed to detect simultaneously 5396 circRNAs, in 5 patients with PE and 5 age- and gestational week-matched controls. In the validation phase, 18 circRNAs in blood cells predicted by bioinformatics tools were validated by quantitative reverse transcription PCR in a cohort of 60 patients (PE and age-, gestational week-, and sample storage time-matched controls. Then, we examined the involvement of circRNAs in PE-related pathways via interactions with miRNAs by multiple bioinformatics approaches. Bioinformatics analysis predicted that hsa_circ_0004904 and hsa_circ_0001855 miRNA sponges directly target PAPP-A. PAPP-A was verified in the serum of the same cohort of patients using an enzyme-linked immunosorbent assay. Finally, we combined PAPP-A with circRNAs to create a novel preclinical diagnostic model for PE with logistic regression and evaluated the efficiency of this model with receiver operating curve analysis. Results: Volcano plot analysis using various parameters showed that circRNAs were differentially expressed among both groups (P < 0.01, fold change > 2. In the screening phase, we found that 2178 circRNAs were differentially expressed between the control and PE groups, in

Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences

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Robert C. Edgar

2018-04-01

Full Text Available Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creating a challenge for prediction methods. I assessed the accuracy of several algorithms using cross-validation by identity, a new benchmark strategy which explicitly models the variation in distances between query sequences and the closest entry in a reference database. When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%, all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA. Accuracy was found to fall rapidly with identity; for example, better methods were found to have V4 genus prediction accuracy of ∼100% at 100% identity but ∼50% at 97% identity. The relationship between identity and taxonomy was quantified as the probability that a rank is the lowest shared by a pair of sequences with a given pair-wise identity. With the V4 region, 95% identity was found to be a twilight zone where taxonomy is highly ambiguous because the probabilities that the lowest shared rank between pairs of sequences is genus, family, order or class are approximately equal.

Over-expression of the miRNA cluster at chromosome 14q32 in the alcoholic brain correlates with suppression of predicted target mRNA required for oligodendrocyte proliferation.

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Manzardo, A M; Gunewardena, S; Butler, M G

2013-09-10

We examined miRNA expression from RNA isolated from the frontal cortex (Broadman area 9) of 9 alcoholics (6 males, 3 females, mean age 48 years) and 9 matched controls using both the Affymetrix GeneChip miRNA 2.0 and Human Exon 1.0 ST Arrays to further characterize genetic influences in alcoholism and the effects of alcohol consumption on predicted target mRNA expression. A total of 12 human miRNAs were significantly up-regulated in alcohol dependent subjects (fold change≥1.5, false discovery rate (FDR)≤0.3; p<0.05) compared with controls including a cluster of 4 miRNAs (e.g., miR-377, miR-379) from the maternally expressed 14q32 chromosome region. The status of the up-regulated miRNAs was supported using the high-throughput method of exon microarrays showing decreased predicted mRNA gene target expression as anticipated from the same RNA aliquot. Predicted mRNA targets were involved in cellular adhesion (e.g., THBS2), tissue differentiation (e.g., CHN2), neuronal migration (e.g., NDE1), myelination (e.g., UGT8, CNP) and oligodendrocyte proliferation (e.g., ENPP2, SEMA4D1). Our data support an association of alcoholism with up-regulation of a cluster of miRNAs located in the genomic imprinted domain on chromosome 14q32 with their predicted gene targets involved with oligodendrocyte growth, differentiation and signaling. Copyright © 2013 Elsevier B.V. All rights reserved.

GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

DEFF Research Database (Denmark)

Swenson, M Shel; Anderson, Joshua; Ash, Andrew

2012-01-01

achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage...

A Parametric Model of Shoulder Articulation for Virtual Assessment of Space Suit Fit

Science.gov (United States)

Kim, K. Han; Young, Karen S.; Bernal, Yaritza; Boppana, Abhishektha; Vu, Linh Q.; Benson, Elizabeth A.; Jarvis, Sarah; Rajulu, Sudhakar L.

2016-01-01

Suboptimal suit fit is a known risk factor for crewmember shoulder injury. Suit fit assessment is however prohibitively time consuming and cannot be generalized across wide variations of body shapes and poses. In this work, we have developed a new design tool based on the statistical analysis of body shape scans. This tool is aimed at predicting the skin deformation and shape variations for any body size and shoulder pose for a target population. This new process, when incorporated with CAD software, will enable virtual suit fit assessments, predictively quantifying the contact volume, and clearance between the suit and body surface at reduced time and cost.

Clinical values of AFP, GPC3 mRNA in peripheral blood for prediction of hepatocellular carcinoma recurrence following OLT: AFP, GPC3 mRNA for prediction of HCC.

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Wang, Yuliang; Shen, Zhongyang; Zhu, Zhijun; Han, Ruifa; Huai, Mingsheng

2011-03-01

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Annually, about 200,000 patients died of HCC in China. Liver transplantation (LT) holds great theoretical appeal in treating HCC. However, the high recurrence rate after transplantation is the most important limiting factor for long-term survival. To assess the value of alpha-fetoprotein (AFP) messenger RNA (mRNA), Glypican-3 (GPC3) mRNA-expressing cells in the peripheral blood (PB) for prediction of HCC recurrence following orthotopic liver transplantation (OLT). 29 patients with HCC who underwent OLT with a minimum clinical follow-up of 12 months were included in this retrospective study. We detected AFP mRNA, GPC3 mRNA-expressing cells in the PB by TaqMan real-time reverse transcriptase-polymerase chain reaction (RT-PCR), pre-, intra- and post-operatively. The early recurrence of patients was evaluated. 8 (28%), 15 (52%), and 9 (31%) patients had AFP mRNA detected pre-, intra-, and post-operatively, respectively. With 12 months of follow-up, HCC recurred in 7 (24%) patients. Univariate analysis revealed that positive pre- and post-operative AFP mRNA, TNM stage as well as vascular invasion were significant predictors for the HCC recurrence. Multivariate analysis revealed that being positive for AFP mRNA pre-operatively remained a significant risk factor for HCC recurrence after OLT. GPC3 mRNA was expressed in all PB samples. There was no significant difference in the expression levels of GPC3 mRNA between the HCC and control groups. There were no significant differences in GPC3 mRNA expression values between those patients with and without tumor recurrence. The pre-operative detection of circulating AFP mRNA-expressing cells could be a useful predictor for HCC recurrence following OLT. GPC3 mRNA-expressing cells in PB seem to have no diagnostic value.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

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Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

2013-02-01

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

RNA-PAIRS: RNA probabilistic assignment of imino resonance shifts

International Nuclear Information System (INIS)

Bahrami, Arash; Clos, Lawrence J.; Markley, John L.; Butcher, Samuel E.; Eghbalnia, Hamid R.

2012-01-01

The significant biological role of RNA has further highlighted the need for improving the accuracy, efficiency and the reach of methods for investigating RNA structure and function. Nuclear magnetic resonance (NMR) spectroscopy is vital to furthering the goals of RNA structural biology because of its distinctive capabilities. However, the dispersion pattern in the NMR spectra of RNA makes automated resonance assignment, a key step in NMR investigation of biomolecules, remarkably challenging. Herein we present RNA Probabilistic Assignment of Imino Resonance Shifts (RNA-PAIRS), a method for the automated assignment of RNA imino resonances with synchronized verification and correction of predicted secondary structure. RNA-PAIRS represents an advance in modeling the assignment paradigm because it seeds the probabilistic network for assignment with experimental NMR data, and predicted RNA secondary structure, simultaneously and from the start. Subsequently, RNA-PAIRS sets in motion a dynamic network that reverberates between predictions and experimental evidence in order to reconcile and rectify resonance assignments and secondary structure information. The procedure is halted when assignments and base-parings are deemed to be most consistent with observed crosspeaks. The current implementation of RNA-PAIRS uses an initial peak list derived from proton-nitrogen heteronuclear multiple quantum correlation ( 1 H– 15 N 2D HMQC) and proton–proton nuclear Overhauser enhancement spectroscopy ( 1 H– 1 H 2D NOESY) experiments. We have evaluated the performance of RNA-PAIRS by using it to analyze NMR datasets from 26 previously studied RNAs, including a 111-nucleotide complex. For moderately sized RNA molecules, and over a range of comparatively complex structural motifs, the average assignment accuracy exceeds 90%, while the average base pair prediction accuracy exceeded 93%. RNA-PAIRS yielded accurate assignments and base pairings consistent with imino resonances for a

RNA-PAIRS: RNA probabilistic assignment of imino resonance shifts

Energy Technology Data Exchange (ETDEWEB)

Bahrami, Arash; Clos, Lawrence J.; Markley, John L.; Butcher, Samuel E. [National Magnetic Resonance Facility at Madison (United States); Eghbalnia, Hamid R., E-mail: eghbalhd@uc.edu [University of Cincinnati, Department of Molecular and Cellular Physiology (United States)

2012-04-15

The significant biological role of RNA has further highlighted the need for improving the accuracy, efficiency and the reach of methods for investigating RNA structure and function. Nuclear magnetic resonance (NMR) spectroscopy is vital to furthering the goals of RNA structural biology because of its distinctive capabilities. However, the dispersion pattern in the NMR spectra of RNA makes automated resonance assignment, a key step in NMR investigation of biomolecules, remarkably challenging. Herein we present RNA Probabilistic Assignment of Imino Resonance Shifts (RNA-PAIRS), a method for the automated assignment of RNA imino resonances with synchronized verification and correction of predicted secondary structure. RNA-PAIRS represents an advance in modeling the assignment paradigm because it seeds the probabilistic network for assignment with experimental NMR data, and predicted RNA secondary structure, simultaneously and from the start. Subsequently, RNA-PAIRS sets in motion a dynamic network that reverberates between predictions and experimental evidence in order to reconcile and rectify resonance assignments and secondary structure information. The procedure is halted when assignments and base-parings are deemed to be most consistent with observed crosspeaks. The current implementation of RNA-PAIRS uses an initial peak list derived from proton-nitrogen heteronuclear multiple quantum correlation ({sup 1}H-{sup 15}N 2D HMQC) and proton-proton nuclear Overhauser enhancement spectroscopy ({sup 1}H-{sup 1}H 2D NOESY) experiments. We have evaluated the performance of RNA-PAIRS by using it to analyze NMR datasets from 26 previously studied RNAs, including a 111-nucleotide complex. For moderately sized RNA molecules, and over a range of comparatively complex structural motifs, the average assignment accuracy exceeds 90%, while the average base pair prediction accuracy exceeded 93%. RNA-PAIRS yielded accurate assignments and base pairings consistent with imino

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

Directory of Open Access Journals (Sweden)

Peek Andrew S

2007-06-01

Full Text Available Abstract Background RNA interference (RNAi is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection

Directory of Open Access Journals (Sweden)

Xin Ma

2015-01-01

Full Text Available The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR method, followed by incremental feature selection (IFS. We incorporated features of conjoint triad features and three novel features: binding propensity (BP, nonbinding propensity (NBP, and evolutionary information combined with physicochemical properties (EIPP. The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient. High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

SUIT

DEFF Research Database (Denmark)

Algreen-Ussing, Gregers; Wedebrunn, Ola

2003-01-01

Leaflet om project SUIT udgivet af European Commission. Tryksagen forklarer i korte ord resultatet af projektet SUIT. Kulturværdier i Miljøspørgsmål. Vurdering af projekter og indvirkning på miljø....

MirZ: an integrated microRNA expression atlas and target prediction resource.

Science.gov (United States)

Hausser, Jean; Berninger, Philipp; Rodak, Christoph; Jantscher, Yvonne; Wirth, Stefan; Zavolan, Mihaela

2009-07-01

MicroRNAs (miRNAs) are short RNAs that act as guides for the degradation and translational repression of protein-coding mRNAs. A large body of work showed that miRNAs are involved in the regulation of a broad range of biological functions, from development to cardiac and immune system function, to metabolism, to cancer. For most of the over 500 miRNAs that are encoded in the human genome the functions still remain to be uncovered. Identifying miRNAs whose expression changes between cell types or between normal and pathological conditions is an important step towards characterizing their function as is the prediction of mRNAs that could be targeted by these miRNAs. To provide the community the possibility of exploring interactively miRNA expression patterns and the candidate targets of miRNAs in an integrated environment, we developed the MirZ web server, which is accessible at www.mirz.unibas.ch. The server provides experimental and computational biologists with statistical analysis and data mining tools operating on up-to-date databases of sequencing-based miRNA expression profiles and of predicted miRNA target sites in species ranging from Caenorhabditis elegans to Homo sapiens.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

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Michael F Sloma

2017-11-01

Full Text Available Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

Science.gov (United States)

Sloma, Michael F; Mathews, David H

2017-11-01

Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

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/.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

Science.gov (United States)

Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

2018-05-12

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

Z-2 Suit Support Stand and MKIII Suit Center of Gravity Test

Science.gov (United States)

Nguyen, Tuan Q.

2014-01-01

NASA's next generation spacesuits are the Z-Series suits, made for a range of possible exploration missions in the near future. The prototype Z-1 suit has been developed and assembled to incorporate new technologies that has never been utilized before in the Apollo suits and the Extravehicular Mobility Unit (EMU). NASA engineers tested the Z-1 suit extensively in order to developed design requirements for the new Z-2 suit. At the end of 2014, NASA will be receiving the new Z-2 suit to perform more testing and to further develop the new technologies of the suit. In order to do so, a suit support stand will be designed and fabricated to support the Z-2 suit during maintenance, sizing, and structural leakage testing. The Z-2 Suit Support Stand (Z2SSS) will be utilized for these purposes in the early testing stages of the Z-2 suit.

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

Science.gov (United States)

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

RNAcontext: a new method for learning the sequence and structure binding preferences of RNA-binding proteins.

Directory of Open Access Journals (Sweden)

Hilal Kazan

2010-07-01

Full Text Available Metazoan genomes encode hundreds of RNA-binding proteins (RBPs. These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures.

Peripheral blood cell microRNA quantification during the first trimester predicts preeclampsia: Proof of concept.

Directory of Open Access Journals (Sweden)

Edward E Winger

Full Text Available We investigated the capacity of microRNAs isolated from peripheral blood buffy coat collected late during the first trimester to predict preeclampsia.The cohort study comprised 48 pregnant women with the following pregnancy outcomes: 8 preeclampsia and 40 with normal delivery outcomes. Quantitative rtPCR was performed on a panel of 30 microRNAs from buffy coat samples drawn at a mean of 12.7±0.5 weeks gestation. MicroRNA Risk Scores were calculated and AUC-ROC calculations derived.The AUC-ROC for preeclampsia risk was 0.91 (p<0.0001. When women with normal delivery and high-risk background (those with SLE/APS, chronic hypertension and/or Type 2 Diabetes were compared to women who developed preeclampsia but with a normal risk background (without these mentioned risk factors, preeclampsia was still predicted with an AUC-ROC of 0.92 (p<0.0001.MicroRNA quantification of peripheral immune cell microRNA provides sensitive and specific prediction of preeclampsia in the first trimester of pregnant women. With this study, we extend the range during which disorders of the placental bed may be predicted from early to the end of the first trimester. This study confirms that buffy coat may be used as a sample preparation.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Science.gov (United States)

2014-01-01

Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in

HuMiTar: A sequence-based method for prediction of human microRNA targets

Directory of Open Access Journals (Sweden)

Chen Ke

2008-12-01

Full Text Available Abstract Background MicroRNAs (miRs are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved. Results We present HuMiTar, a computational method for identifying common targets of miRs, which is based on a scoring function that considers base-pairing for both seed and non-seed positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two best performing traditional methods, PicTar and TargetScanS, and a representative ML method that considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of the predictions of the other three methods. At the same time, the proposed method is also capable of finding more true positive targets as a trade-off for an increased number of predictions. Genome-wide predictions show that the proposed method is characterized by 1.99 signal

Improved crystallization of the coxsackievirus B3 RNA-dependent RNA polymerase

Energy Technology Data Exchange (ETDEWEB)

Jabafi, Ilham; Selisko, Barbara; Coutard, Bruno; De Palma, Armando M.; Neyts, Johan; Egloff, Marie-Pierre; Grisel, Sacha; Dalle, Karen; Campanacci, Valerie; Spinelli, Silvia; Cambillau, Christian; Canard, Bruno; Gruez, Arnaud, E-mail: arnaud.gruez@maem.uhp-nancy.fr [Centre National de la Recherche Scientifique and Universités d’Aix-Marseille I et II, UMR 6098, Architecture et Fonction des Macromolécules Biologiques, Ecole Supérieure d’Ingénieurs de Luminy-Case 925, 163 Avenue de Luminy, 13288 Marseille CEDEX 9 (France)

2007-06-01

The first crystal of a coxsackievirus RNA-dependent RNA polymerase is reported. The Picornaviridae virus family contains a large number of human pathogens such as poliovirus, hepatitis A virus and rhinoviruses. Amongst the viruses belonging to the genus Enterovirus, several serotypes of coxsackievirus coexist for which neither vaccine nor therapy is available. Coxsackievirus B3 is involved in the development of acute myocarditis and dilated cardiomyopathy and is thought to be an important cause of sudden death in young adults. Here, the first crystal of a coxsackievirus RNA-dependent RNA polymerase is reported. Standard crystallization methods yielded crystals that were poorly suited to X-ray diffraction studies, with one axis being completely disordered. Crystallization was improved by testing crystallization solutions from commercial screens as additives. This approach yielded crystals that diffracted to 2.1 Å resolution and that were suitable for structure determination.

Improved crystallization of the coxsackievirus B3 RNA-dependent RNA polymerase

International Nuclear Information System (INIS)

Jabafi, Ilham; Selisko, Barbara; Coutard, Bruno; De Palma, Armando M.; Neyts, Johan; Egloff, Marie-Pierre; Grisel, Sacha; Dalle, Karen; Campanacci, Valerie; Spinelli, Silvia; Cambillau, Christian; Canard, Bruno; Gruez, Arnaud

2007-01-01

The first crystal of a coxsackievirus RNA-dependent RNA polymerase is reported. The Picornaviridae virus family contains a large number of human pathogens such as poliovirus, hepatitis A virus and rhinoviruses. Amongst the viruses belonging to the genus Enterovirus, several serotypes of coxsackievirus coexist for which neither vaccine nor therapy is available. Coxsackievirus B3 is involved in the development of acute myocarditis and dilated cardiomyopathy and is thought to be an important cause of sudden death in young adults. Here, the first crystal of a coxsackievirus RNA-dependent RNA polymerase is reported. Standard crystallization methods yielded crystals that were poorly suited to X-ray diffraction studies, with one axis being completely disordered. Crystallization was improved by testing crystallization solutions from commercial screens as additives. This approach yielded crystals that diffracted to 2.1 Å resolution and that were suitable for structure determination

NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

Directory of Open Access Journals (Sweden)

Elize A Shirdel

2011-02-01

Full Text Available MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP.mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05, suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001, to be more studied (p<0.0002, and to have higher degree in the KEGG cancer pathway (p<0.0001, compared to intra-pathway microRNAs.Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

A Semi-Supervised Learning Algorithm for Predicting Four Types MiRNA-Disease Associations by Mutual Information in a Heterogeneous Network.

Science.gov (United States)

Zhang, Xiaotian; Yin, Jian; Zhang, Xu

2018-03-02

Increasing evidence suggests that dysregulation of microRNAs (miRNAs) may lead to a variety of diseases. Therefore, identifying disease-related miRNAs is a crucial problem. Currently, many computational approaches have been proposed to predict binary miRNA-disease associations. In this study, in order to predict underlying miRNA-disease association types, a semi-supervised model called the network-based label propagation algorithm is proposed to infer multiple types of miRNA-disease associations (NLPMMDA) by mutual information derived from the heterogeneous network. The NLPMMDA method integrates disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity information of miRNAs and diseases to construct a heterogeneous network. NLPMMDA is a semi-supervised model which does not require verified negative samples. Leave-one-out cross validation (LOOCV) was implemented for four known types of miRNA-disease associations and demonstrated the reliable performance of our method. Moreover, case studies of lung cancer and breast cancer confirmed effective performance of NLPMMDA to predict novel miRNA-disease associations and their association types.

The ViennaRNA web services.

Science.gov (United States)

Gruber, Andreas R; Bernhart, Stephan H; Lorenz, Ronny

2015-01-01

The ViennaRNA package is a widely used collection of programs for thermodynamic RNA secondary structure prediction. Over the years, many additional tools have been developed building on the core programs of the package to also address issues related to noncoding RNA detection, RNA folding kinetics, or efficient sequence design considering RNA-RNA hybridizations. The ViennaRNA web services provide easy and user-friendly web access to these tools. This chapter describes how to use this online platform to perform tasks such as prediction of minimum free energy structures, prediction of RNA-RNA hybrids, or noncoding RNA detection. The ViennaRNA web services can be used free of charge and can be accessed via http://rna.tbi.univie.ac.at.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation.

Science.gov (United States)

Li, Ying; Shi, Xiaohu; Liang, Yanchun; Xie, Juan; Zhang, Yu; Ma, Qin

2017-01-21

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA

MicroRNA-target binding structures mimic microRNA duplex structures in humans.

Directory of Open Access Journals (Sweden)

Xi Chen

Full Text Available Traditionally, researchers match a microRNA guide strand to mRNA sequences using sequence comparisons to predict its potential target genes. However, many of the predictions can be false positives due to limitations in sequence comparison alone. In this work, we consider the association of two related RNA structures that share a common guide strand: the microRNA duplex and the microRNA-target binding structure. We have analyzed thousands of such structure pairs and found many of them share high structural similarity. Therefore, we conclude that when predicting microRNA target genes, considering just the microRNA guide strand matches to gene sequences may not be sufficient--the microRNA duplex structure formed by the guide strand and its companion passenger strand must also be considered. We have developed software to translate RNA binding structure into encoded representations, and we have also created novel automatic comparison methods utilizing such encoded representations to determine RNA structure similarity. Our software and methods can be utilized in the other RNA secondary structure comparisons as well.

Bioinformatical approaches to RNA structure prediction & Sequencing of an ancient human genome

DEFF Research Database (Denmark)

Lindgreen, Stinus

Stinus Lindgreen has been working in two different fields during his Ph.D. The first part has been focused on computational approaches to predict the structure of non-coding RNA molecules at the base pairing level. This has resulted in the analysis of various measures of the base pairing potentia...

A Circulating microRNA Signature Predicts Age-Based Development of Lymphoma.

Directory of Open Access Journals (Sweden)

Afshin Beheshti

Full Text Available Extensive epidemiological data have demonstrated an exponential rise in the incidence of non-Hodgkin lymphoma (NHL that is associated with increasing age. The molecular etiology of this remains largely unknown, which impacts the effectiveness of treatment for patients. We proposed that age-dependent circulating microRNA (miRNA signatures in the host influence diffuse large B cell lymphoma (DLBCL development. Our objective was to examine tumor development in an age-based DLBCL system using an inventive systems biology approach. We harnessed a novel murine model of spontaneous DLBCL initiation (Smurf2-deficient at two age groups: 3 and 15 months old. All Smurf2-deficient mice develop visible DLBCL tumor starting at 15 months of age. Total miRNA was isolated from serum, bone marrow and spleen and were collected for all age groups for Smurf2-deficient mice and age-matched wild-type C57BL/6 mice. Using systems biology techniques, we identified a list of 10 circulating miRNAs being regulated in both the spleen and bone marrow that were present in DLBCL forming mice starting at 3 months of age that were not present in the control mice. Furthermore, this miRNA signature was found to occur circulating in the blood and it strongly impacted JUN and MYC oncogenic signaling. In addition, quantification of the miRNA signature was performed via Droplet Digital PCR technology. It was discovered that a key miRNA signature circulates throughout a host prior to the formation of a tumor starting at 3 months old, which becomes further modulated by age and yielded calculation of a 'carcinogenic risk score'. This novel age-based circulating miRNA signature may potentially be leveraged as a DLBCL risk profile at a young age to predict future lymphoma development or disease progression as well as for potential innovative miRNA-based targeted therapeutic strategies in lymphoma.

Development and evaluation of SBLINE, a suite of models for the prediction of pollution concentrations from vehicles in urban areas

Energy Technology Data Exchange (ETDEWEB)

Namdeo, A.K.; Colls, J.J. [Environmental Science, University of Nottingham, Loughborough (United Kingdom)

1996-09-06

The assessment of air quality impacts from roadways is a major concern to urban planners, developers, health officials and engineers. This paper describes the development of a suite of models, called SBLINE, for prediction of pollution concentrations from vehicles in urban road networks. The first component of the suite is ROADFAC, an emission model for calculating emission rates for a road link with known vehicle fleet structure and operational details. ROADFAC can also calculate modal emission rates, caused by deceleration, idle, acceleration and cruise operational modes, by determining queue length and vehicle delay from traffic volume and signal phasing information. The other components of SBLINE are two dispersion models, called NOTLINE and CPB, for prediction of pollution concentrations contributed by different roadlinks in the network. These models use site geometry, meteorology, and traffic emissions calculated by ROADFAC to predict pollutant concentrations. The contribution from a given link is calculated by using NOTLINE if that link is situated in simple topography, or CPB is run if the link is inside a canyon or a cut-section. Finally, cumulative concentrations at any receptor location are calculated by adding the contributions from all roadlinks. SBLINE can be applied to any urban network of roads, with roadlinks located in either simple topography or street canyons. The program has been evaluated in one region of Leicester in the UK. The region represents a typical urban network of roads with some roads located in plain topography and some inside medium size canyons. Observed values of pollutant concentrations are compared with predictions made from detailed measurements of the vehicle population parameters, meteorology, and local street and building topography. Well-established statistical techniques have been used to show the potential of SBLINE for application to other road networks

Analysis of electric moments of RNA-binding proteins: implications for mechanism and prediction

Directory of Open Access Journals (Sweden)

Sarai Akinori

2011-02-01

Full Text Available Abstract Background Protein-RNA interactions play important role in many biological processes such as gene regulation, replication, protein synthesis and virus assembly. Although many structures of various types of protein-RNA complexes have been determined, the mechanism of protein-RNA recognition remains elusive. We have earlier shown that the simplest electrostatic properties viz. charge, dipole and quadrupole moments, calculated from backbone atomic coordinates of proteins are biased relative to other proteins, and these quantities can be used to identify DNA-binding proteins. Closely related, RNA-binding proteins are investigated in this study. In particular, discrimination between various types of RNA-binding proteins, evolutionary conservation of these bulk electrostatic features and effect of conformational changes by complex formation are investigated. Basic binding mechanism of a putative RNA-binding protein (HI1333 from Haemophilus influenza is suggested as a potential application of this study. Results We found that similar to DNA-binding proteins (DBPs, RNA-binding proteins (RBPs also show significantly higher values of electric moments. However, higher moments in RBPs are found to strongly depend on their functional class: proteins binding to ribosomal RNA (rRNA constitute the only class with all three of the properties (charge, dipole and quadrupole moments being higher than control proteins. Neural networks were trained using leave-one-out cross-validation to predict RBPs from control data as well as pair-wise classification capacity between proteins binding to various RNA types. RBPs and control proteins reached up to 78% accuracy measured by the area under the ROC curve. Proteins binding to rRNA are found to be best distinguished (AUC = 79%. Changes in dipole and quadrupole moments between unbound and bound structures were small and these properties are found to be robust under complex formation. Conclusions Bulk electric

A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction.

Science.gov (United States)

Qiu, Shibin; Lane, Terran

2009-01-01

The cell defense mechanism of RNA interference has applications in gene function analysis and promising potentials in human disease therapy. To effectively silence a target gene, it is desirable to select appropriate initiator siRNA molecules having satisfactory silencing capabilities. Computational prediction for silencing efficacy of siRNAs can assist this screening process before using them in biological experiments. String kernel functions, which operate directly on the string objects representing siRNAs and target mRNAs, have been applied to support vector regression for the prediction and improved accuracy over numerical kernels in multidimensional vector spaces constructed from descriptors of siRNA design rules. To fully utilize information provided by string and numerical data, we propose to unify the two in a kernel feature space by devising a multiple kernel regression framework where a linear combination of the kernels is used. We formulate the multiple kernel learning into a quadratically constrained quadratic programming (QCQP) problem, which although yields global optimal solution, is computationally demanding and requires a commercial solver package. We further propose three heuristics based on the principle of kernel-target alignment and predictive accuracy. Empirical results demonstrate that multiple kernel regression can improve accuracy, decrease model complexity by reducing the number of support vectors, and speed up computational performance dramatically. In addition, multiple kernel regression evaluates the importance of constituent kernels, which for the siRNA efficacy prediction problem, compares the relative significance of the design rules. Finally, we give insights into the multiple kernel regression mechanism and point out possible extensions.

Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

KAUST Repository

Foley, Joseph W; Sidow, Arend

2013-01-01

BACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF's direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF's motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.

Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines.

KAUST Repository

Foley, Joseph W

2013-10-20

BACKGROUND: High-occupancy target (HOT) regions are compact genome loci occupied by many different transcription factors (TFs). HOT regions were initially defined in invertebrate model organisms, and we here show that they are a ubiquitous feature of the human gene-regulation landscape. RESULTS: We identified HOT regions by a comprehensive analysis of ChIP-seq data from 96 DNA-associated proteins in 5 human cell lines. Most HOT regions co-localize with RNA polymerase II binding sites, but many are not near the promoters of annotated genes. At HOT promoters, TF occupancy is strongly predictive of transcription preinitiation complex recruitment and moderately predictive of initiating Pol II recruitment, but only weakly predictive of elongating Pol II and RNA transcript abundance. TF occupancy varies quantitatively within human HOT regions; we used this variation to discover novel associations between TFs. The sequence motif associated with any given TF\\'s direct DNA binding is somewhat predictive of its empirical occupancy, but a great deal of occupancy occurs at sites without the TF\\'s motif, implying indirect recruitment by another TF whose motif is present. CONCLUSIONS: Mammalian HOT regions are regulatory hubs that integrate the signals from diverse regulatory pathways to quantitatively tune the promoter for RNA polymerase II recruitment.

Combinatorics of RNA-RNA interaction

DEFF Research Database (Denmark)

Li, Thomas J X; Reidys, Christian

2012-01-01

RNA-RNA binding is an important phenomenon observed for many classes of non-coding RNAs and plays a crucial role in a number of regulatory processes. Recently several MFE folding algorithms for predicting the joint structure of two interacting RNA molecules have been proposed. Here joint structure...... means that in a diagram representation the intramolecular bonds of each partner are pseudoknot-free, that the intermolecular binding pairs are noncrossing, and that there is no so-called "zigzag" configuration. This paper presents the combinatorics of RNA interaction structures including...

AthMethPre: a web server for the prediction and query of mRNA m6A sites in Arabidopsis thaliana.

Science.gov (United States)

Xiang, Shunian; Yan, Zhangming; Liu, Ke; Zhang, Yaou; Sun, Zhirong

2016-10-18

N 6 -Methyladenosine (m 6 A) is the most prevalent and abundant modification in mRNA that has been linked to many key biological processes. High-throughput experiments have generated m 6 A-peaks across the transcriptome of A. thaliana, but the specific methylated sites were not assigned, which impedes the understanding of m 6 A functions in plants. Therefore, computational prediction of mRNA m 6 A sites becomes emergently important. Here, we present a method to predict the m 6 A sites for A. thaliana mRNA sequence(s). To predict the m 6 A sites of an mRNA sequence, we employed the support vector machine to build a classifier using the features of the positional flanking nucleotide sequence and position-independent k-mer nucleotide spectrum. Our method achieved good performance and was applied to a web server to provide service for the prediction of A. thaliana m 6 A sites. The server also provides a comprehensive database of predicted transcriptome-wide m 6 A sites and curated m 6 A-seq peaks from the literature for query and visualization. The AthMethPre web server is the first web server that provides a user-friendly tool for the prediction and query of A. thaliana mRNA m 6 A sites, which is freely accessible for public use at .

Airport Gate Activity Monitoring Tool Suite for Improved Turnaround Prediction, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The objective of this research is to create a suite of tools for monitoring airport gate activities with the objective of improving aircraft turnaround. Airport ramp...

Inferring microRNA regulation of mRNA with partially ordered samples of paired expression data and exogenous prediction algorithms.

Directory of Open Access Journals (Sweden)

Brian Godsey

Full Text Available MicroRNAs (miRs are known to play an important role in mRNA regulation, often by binding to complementary sequences in "target" mRNAs. Recently, several methods have been developed by which existing sequence-based target predictions can be combined with miR and mRNA expression data to infer true miR-mRNA targeting relationships. It has been shown that the combination of these two approaches gives more reliable results than either by itself. While a few such algorithms give excellent results, none fully addresses expression data sets with a natural ordering of the samples. If the samples in an experiment can be ordered or partially ordered by their expected similarity to one another, such as for time-series or studies of development processes, stages, or types, (e.g. cell type, disease, growth, aging, there are unique opportunities to infer miR-mRNA interactions that may be specific to the underlying processes, and existing methods do not exploit this. We propose an algorithm which specifically addresses [partially] ordered expression data and takes advantage of sample similarities based on the ordering structure. This is done within a Bayesian framework which specifies posterior distributions and therefore statistical significance for each model parameter and latent variable. We apply our model to a previously published expression data set of paired miR and mRNA arrays in five partially ordered conditions, with biological replicates, related to multiple myeloma, and we show how considering potential orderings can improve the inference of miR-mRNA interactions, as measured by existing knowledge about the involved transcripts.

Identification of potential prognostic microRNA biomarkers for predicting survival in patients with hepatocellular carcinoma

Directory of Open Access Journals (Sweden)

Liao X

2018-04-01

Full Text Available Xiwen Liao,1 Guangzhi Zhu,1 Rui Huang,2 Chengkun Yang,1 Xiangkun Wang,1 Ketuan Huang,1 Tingdong Yu,1 Chuangye Han,1 Hao Su,1 Tao Peng1 1Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China Background: The aim of the present study was to identify potential prognostic microRNA (miRNA biomarkers for hepatocellular carcinoma (HCC prognosis prediction based on a dataset from The Cancer Genome Atlas (TCGA. Materials and methods: A miRNA sequencing dataset and corresponding clinical parameters of HCC were obtained from TCGA. Genome-wide univariate Cox regression analysis was used to screen prognostic differentially expressed miRNAs (DEMs, and multivariable Cox regression analysis was used for prognostic signature construction. Comprehensive survival analysis was performed to evaluate the prognostic value of the prognostic signature. Results: Five miRNAs were regarded as prognostic DEMs and used for prognostic signature construction. The five-DEM prognostic signature performed well in prognosis prediction (adjusted P < 0.0001, adjusted hazard ratio = 2.249, 95% confidence interval =1.491–3.394, and time-dependent receiver–operating characteristic (ROC analysis showed an area under the curve (AUC of 0.765, 0.745, 0.725, and 0.687 for 1-, 2-, 3-, and 5-year HCC overall survival (OS prediction, respectively. Comprehensive survival analysis of the prognostic signature suggests that the risk score model could serve as an independent factor of HCC and perform better in prognosis prediction than other traditional clinical indicators. Functional assessment of the target genes of hsa-mir-139 and hsa-mir-5003 indicates that they were significantly enriched in multiple biological processes and pathways, including cell proliferation and cell migration

Predicted RNA Binding Proteins Pes4 and Mip6 Regulate mRNA Levels, Translation, and Localization during Sporulation in Budding Yeast.

Science.gov (United States)

Jin, Liang; Zhang, Kai; Sternglanz, Rolf; Neiman, Aaron M

2017-05-01

In response to starvation, diploid cells of Saccharomyces cerevisiae undergo meiosis and form haploid spores, a process collectively referred to as sporulation. The differentiation into spores requires extensive changes in gene expression. The transcriptional activator Ndt80 is a central regulator of this process, which controls many genes essential for sporulation. Ndt80 induces ∼300 genes coordinately during meiotic prophase, but different mRNAs within the NDT80 regulon are translated at different times during sporulation. The protein kinase Ime2 and RNA binding protein Rim4 are general regulators of meiotic translational delay, but how differential timing of individual transcripts is achieved was not known. This report describes the characterization of two related NDT80 -induced genes, PES4 and MIP6 , encoding predicted RNA binding proteins. These genes are necessary to regulate the steady-state expression, translational timing, and localization of a set of mRNAs that are transcribed by NDT80 but not translated until the end of meiosis II. Mutations in the predicted RNA binding domains within PES4 alter the stability of target mRNAs. PES4 and MIP6 affect only a small portion of the NDT80 regulon, indicating that they act as modulators of the general Ime2/Rim4 pathway for specific transcripts. Copyright © 2017 American Society for Microbiology.

A novel method of predicting microRNA-disease associations based on microRNA, disease, gene and environment factor networks.

Science.gov (United States)

Peng, Wei; Lan, Wei; Zhong, Jiancheng; Wang, Jianxin; Pan, Yi

2017-07-15

MicroRNAs have been reported to have close relationship with diseases due to their deregulation of the expression of target mRNAs. Detecting disease-related microRNAs is helpful for disease therapies. With the development of high throughput experimental techniques, a large number of microRNAs have been sequenced. However, it is still a big challenge to identify which microRNAs are related to diseases. Recently, researchers are interesting in combining multiple-biological information to identify the associations between microRNAs and diseases. In this work, we have proposed a novel method to predict the microRNA-disease associations based on four biological properties. They are microRNA, disease, gene and environment factor. Compared with previous methods, our method makes predictions not only by using the prior knowledge of associations among microRNAs, disease, environment factors and genes, but also by using the internal relationship among these biological properties. We constructed four biological networks based on the similarity of microRNAs, diseases, environment factors and genes, respectively. Then random walking was implemented on the four networks unequally. In the walking course, the associations can be inferred from the neighbors in the same networks. Meanwhile the association information can be transferred from one network to another. The results of experiment showed that our method achieved better prediction performance than other existing state-of-the-art methods. Copyright © 2017 Elsevier Inc. All rights reserved.

Escherichia coli promoter sequences predict in vitro RNA polymerase selectivity.

OpenAIRE

Mulligan, M E; Hawley, D K; Entriken, R; McClure, W R

1984-01-01

We describe a simple algorithm for computing a homology score for Escherichia coli promoters based on DNA sequence alone. The homology score was related to 31 values, measured in vitro, of RNA polymerase selectivity, which we define as the product KBk2, the apparent second order rate constant for open complex formation. We found that promoter strength could be predicted to within a factor of +/-4.1 in KBk2 over a range of 10(4) in the same parameter. The quantitative evaluation was linked to ...

TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples.

Science.gov (United States)

Bandyopadhyay, Sanghamitra; Mitra, Ramkrishna

2009-10-15

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training. In this article, we have identified approximately 300 tissue-specific negative examples using a novel approach that involves expression profiling of both miRNAs and mRNAs, miRNA-mRNA structural interactions and seed-site conservation. The newly generated negative examples are validated with pSILAC dataset, which elucidate the fact that the identified non-targets are indeed non-targets.These high-throughput tissue-specific negative examples and a set of experimentally verified positive examples are then used to build a system called TargetMiner, a support vector machine (SVM)-based classifier. In addition to assessing the prediction accuracy on cross-validation experiments, TargetMiner has been validated with a completely independent experimental test dataset. Our method outperforms 10 existing target prediction algorithms and provides a good balance between sensitivity and specificity that is not reflected in the existing methods. We achieve a significantly higher sensitivity and specificity of 69% and 67.8% based on a pool of 90 feature set and 76.5% and 66.1% using a set of 30 selected feature set on the completely independent test dataset. In order to establish the effectiveness of the systematically generated negative examples, the SVM is trained using a different set of negative data generated using the method in Yousef et al. A significantly higher false positive rate (70.6%) is observed when tested on the independent set, while all other factors are kept the

Microbiome Data Accurately Predicts the Postmortem Interval Using Random Forest Regression Models

Directory of Open Access Journals (Sweden)

Aeriel Belk

2018-02-01

Full Text Available Death investigations often include an effort to establish the postmortem interval (PMI in cases in which the time of death is uncertain. The postmortem interval can lead to the identification of the deceased and the validation of witness statements and suspect alibis. Recent research has demonstrated that microbes provide an accurate clock that starts at death and relies on ecological change in the microbial communities that normally inhabit a body and its surrounding environment. Here, we explore how to build the most robust Random Forest regression models for prediction of PMI by testing models built on different sample types (gravesoil, skin of the torso, skin of the head, gene markers (16S ribosomal RNA (rRNA, 18S rRNA, internal transcribed spacer regions (ITS, and taxonomic levels (sequence variants, species, genus, etc.. We also tested whether particular suites of indicator microbes were informative across different datasets. Generally, results indicate that the most accurate models for predicting PMI were built using gravesoil and skin data using the 16S rRNA genetic marker at the taxonomic level of phyla. Additionally, several phyla consistently contributed highly to model accuracy and may be candidate indicators of PMI.

The Los Alamos suite of relativistic atomic physics codes

International Nuclear Information System (INIS)

Fontes, C J; Zhang, H L; Jr, J Abdallah; Clark, R E H; Kilcrease, D P; Colgan, J; Cunningham, R T; Hakel, P; Magee, N H; Sherrill, M E

2015-01-01

The Los Alamos suite of relativistic atomic physics codes is a robust, mature platform that has been used to model highly charged ions in a variety of ways. The suite includes capabilities for calculating data related to fundamental atomic structure, as well as the processes of photoexcitation, electron-impact excitation and ionization, photoionization and autoionization within a consistent framework. These data can be of a basic nature, such as cross sections and collision strengths, which are useful in making predictions that can be compared with experiments to test fundamental theories of highly charged ions, such as quantum electrodynamics. The suite can also be used to generate detailed models of energy levels and rate coefficients, and to apply them in the collisional-radiative modeling of plasmas over a wide range of conditions. Such modeling is useful, for example, in the interpretation of spectra generated by a variety of plasmas. In this work, we provide a brief overview of the capabilities within the Los Alamos relativistic suite along with some examples of its application to the modeling of highly charged ions. (paper)

Integration of RNA-Seq and RPPA data for survival time prediction in cancer patients.

Science.gov (United States)

Isik, Zerrin; Ercan, Muserref Ece

2017-10-01

Integration of several types of patient data in a computational framework can accelerate the identification of more reliable biomarkers, especially for prognostic purposes. This study aims to identify biomarkers that can successfully predict the potential survival time of a cancer patient by integrating the transcriptomic (RNA-Seq), proteomic (RPPA), and protein-protein interaction (PPI) data. The proposed method -RPBioNet- employs a random walk-based algorithm that works on a PPI network to identify a limited number of protein biomarkers. Later, the method uses gene expression measurements of the selected biomarkers to train a classifier for the survival time prediction of patients. RPBioNet was applied to classify kidney renal clear cell carcinoma (KIRC), glioblastoma multiforme (GBM), and lung squamous cell carcinoma (LUSC) patients based on their survival time classes (long- or short-term). The RPBioNet method correctly identified the survival time classes of patients with between 66% and 78% average accuracy for three data sets. RPBioNet operates with only 20 to 50 biomarkers and can achieve on average 6% higher accuracy compared to the closest alternative method, which uses only RNA-Seq data in the biomarker selection. Further analysis of the most predictive biomarkers highlighted genes that are common for both cancer types, as they may be driver proteins responsible for cancer progression. The novelty of this study is the integration of a PPI network with mRNA and protein expression data to identify more accurate prognostic biomarkers that can be used for clinical purposes in the future. Copyright © 2017 Elsevier Ltd. All rights reserved.

Validation suite for MCNP

International Nuclear Information System (INIS)

Mosteller, Russell D.

2002-01-01

Two validation suites, one for criticality and another for radiation shielding, have been defined and tested for the MCNP Monte Carlo code. All of the cases in the validation suites are based on experiments so that calculated and measured results can be compared in a meaningful way. The cases in the validation suites are described, and results from those cases are discussed. For several years, the distribution package for the MCNP Monte Carlo code1 has included an installation test suite to verify that MCNP has been installed correctly. However, the cases in that suite have been constructed primarily to test options within the code and to execute quickly. Consequently, they do not produce well-converged answers, and many of them are physically unrealistic. To remedy these deficiencies, sets of validation suites are being defined and tested for specific types of applications. All of the cases in the validation suites are based on benchmark experiments. Consequently, the results from the measurements are reliable and quantifiable, and calculated results can be compared with them in a meaningful way. Currently, validation suites exist for criticality and radiation-shielding applications.

A framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates.

Science.gov (United States)

Helbling, Damian E; Johnson, David R; Lee, Tae Kwon; Scheidegger, Andreas; Fenner, Kathrin

2015-03-01

The rates at which wastewater treatment plant (WWTP) microbial communities biotransform specific substrates can differ by orders of magnitude among WWTP communities. Differences in taxonomic compositions among WWTP communities may predict differences in the rates of some types of biotransformations. In this work, we present a novel framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. We selected ten WWTPs with substantial variation in their environmental and operational metrics and measured the in situ ammonia biotransformation rate constants in nine of them. We isolated total RNA from samples from each WWTP and analyzed 16S rRNA sequence reads. We then developed multivariate models between the measured abundances of specific bacterial 16S rRNA sequence reads and the ammonia biotransformation rate constants. We constructed model scenarios that systematically explored the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large percentage (greater than 80%) of model scenarios resulted in well-performing and significant models at intermediate SDTs of 0.13-0.14 and 0.26. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then extend the framework by applying it to the biotransformation rate constants of ten micropollutants measured in batch reactors seeded with the ten WWTP communities. We identified phylogenetic groups that were robustly selected into all well-performing and significant models constructed with biotransformation rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic groups can be used as predictive biomarkers of WWTP microbial community activity towards these specific

Bifurcations in the interplay of messenger RNA, protein and nonprotein coding RNA

International Nuclear Information System (INIS)

Zhdanov, Vladimir P

2008-01-01

The interplay of messenger RNA (mRNA), protein, produced via translation of this RNA, and nonprotein coding RNA (ncRNA) may include regulation of the ncRNA production by protein and (i) ncRNA-protein association resulting in suppression of the protein regulatory activity or (ii) ncRNA-mRNA association resulting in degradation of the miRNA-mRNA complex. The kinetic models describing these two scenarios are found to predict bistability provided that protein suppresses the ncRNA formation

A path-based measurement for human miRNA functional similarities using miRNA-disease associations

Science.gov (United States)

Ding, Pingjian; Luo, Jiawei; Xiao, Qiu; Chen, Xiangtao

2016-09-01

Compared with the sequence and expression similarity, miRNA functional similarity is so important for biology researches and many applications such as miRNA clustering, miRNA function prediction, miRNA synergism identification and disease miRNA prioritization. However, the existing methods always utilized the predicted miRNA target which has high false positive and false negative to calculate the miRNA functional similarity. Meanwhile, it is difficult to achieve high reliability of miRNA functional similarity with miRNA-disease associations. Therefore, it is increasingly needed to improve the measurement of miRNA functional similarity. In this study, we develop a novel path-based calculation method of miRNA functional similarity based on miRNA-disease associations, called MFSP. Compared with other methods, our method obtains higher average functional similarity of intra-family and intra-cluster selected groups. Meanwhile, the lower average functional similarity of inter-family and inter-cluster miRNA pair is obtained. In addition, the smaller p-value is achieved, while applying Wilcoxon rank-sum test and Kruskal-Wallis test to different miRNA groups. The relationship between miRNA functional similarity and other information sources is exhibited. Furthermore, the constructed miRNA functional network based on MFSP is a scale-free and small-world network. Moreover, the higher AUC for miRNA-disease prediction indicates the ability of MFSP uncovering miRNA functional similarity.

Escherichia coli promoter sequences predict in vitro RNA polymerase selectivity.

Science.gov (United States)

Mulligan, M E; Hawley, D K; Entriken, R; McClure, W R

1984-01-11

We describe a simple algorithm for computing a homology score for Escherichia coli promoters based on DNA sequence alone. The homology score was related to 31 values, measured in vitro, of RNA polymerase selectivity, which we define as the product KBk2, the apparent second order rate constant for open complex formation. We found that promoter strength could be predicted to within a factor of +/-4.1 in KBk2 over a range of 10(4) in the same parameter. The quantitative evaluation was linked to an automated (Apple II) procedure for searching and evaluating possible promoters in DNA sequence files.

Predicting human miRNA target genes using a novel evolutionary methodology

KAUST Repository

Aigli, Korfiati; Kleftogiannis, Dimitrios A.; Konstantinos, Theofilatos; Spiros, Likothanassis; Athanasios, Tsakalidis; Seferina, Mavroudi

2012-01-01

The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset. © 2012 Springer-Verlag.

Predicting human miRNA target genes using a novel evolutionary methodology

KAUST Repository

Aigli, Korfiati

2012-01-01

The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3\\'untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset. © 2012 Springer-Verlag.

Concepts and introduction to RNA bioinformatics

DEFF Research Database (Denmark)

Gorodkin, Jan; Hofacker, Ivo L.; Ruzzo, Walter L.

2014-01-01

RNA bioinformatics and computational RNA biology have emerged from implementing methods for predicting the secondary structure of single sequences. The field has evolved to exploit multiple sequences to take evolutionary information into account, such as compensating (and structure preserving) base...... for interactions between RNA and proteins.Here, we introduce the basic concepts of predicting RNA secondary structure relevant to the further analyses of RNA sequences. We also provide pointers to methods addressing various aspects of RNA bioinformatics and computational RNA biology....

Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

Science.gov (United States)

Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2012-05-10

RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM

RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

Directory of Open Access Journals (Sweden)

Andronescu Mirela

2008-08-01

Full Text Available Abstract Background The ability to access, search and analyse secondary structures of a large set of known RNA molecules is very important for deriving improved RNA energy models, for evaluating computational predictions of RNA secondary structures and for a better understanding of RNA folding. Currently there is no database that can easily provide these capabilities for almost all RNA molecules with known secondary structures. Results In this paper we describe RNA STRAND – the RNA secondary STRucture and statistical ANalysis Database, a curated database containing known secondary structures of any type and organism. Our new database provides a wide collection of known RNA secondary structures drawn from public databases, searchable and downloadable in a common format. Comprehensive statistical information on the secondary structures in our database is provided using the RNA Secondary Structure Analyser, a new tool we have developed to analyse RNA secondary structures. The information thus obtained is valuable for understanding to which extent and with which probability certain structural motifs can appear. We outline several ways in which the data provided in RNA STRAND can facilitate research on RNA structure, including the improvement of RNA energy models and evaluation of secondary structure prediction programs. In order to keep up-to-date with new RNA secondary structure experiments, we offer the necessary tools to add solved RNA secondary structures to our database and invite researchers to contribute to RNA STRAND. Conclusion RNA STRAND is a carefully assembled database of trusted RNA secondary structures, with easy on-line tools for searching, analyzing and downloading user selected entries, and is publicly available at http://www.rnasoft.ca/strand.

Automated integration of lidar into the LANDFIRE product suite

Science.gov (United States)

Birgit Peterson; Kurtis J. Nelson; Carl Seielstad; Jason Stoker; W. Matt Jolly; Russell Parsons

2015-01-01

Accurate information about three-dimensional canopy structure and wildland fuel across the landscape is necessary for fire behaviour modelling system predictions. Remotely sensed data are invaluable for assessing these canopy characteristics over large areas; lidar data, in particular, are uniquely suited for quantifying three-dimensional canopy structure. Although...

Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.

Science.gov (United States)

Yan, Yumeng; Wen, Zeyu; Zhang, Di; Huang, Sheng-You

2018-05-18

RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA-RNA interactions based on a training set of 97 diverse RNA-RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA-RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA-RNA complexes.

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics.

Science.gov (United States)

Hu, Ruifeng; Sun, Xiaobo

2016-08-01

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

MysiRNA-designer: a workflow for efficient siRNA design.

Directory of Open Access Journals (Sweden)

Mohamed Mysara

Full Text Available The design of small interfering RNA (siRNA is a multi factorial problem that has gained the attention of many researchers in the area of therapeutic and functional genomics. MysiRNA score was previously introduced that improves the correlation of siRNA activity prediction considering state of the art algorithms. In this paper, a new program, MysiRNA-Designer, is described which integrates several factors in an automated work-flow considering mRNA transcripts variations, siRNA and mRNA target accessibility, and both near-perfect and partial off-target matches. It also features the MysiRNA score, a highly ranked correlated siRNA efficacy prediction score for ranking the designed siRNAs, in addition to top scoring models Biopredsi, DISR, Thermocomposition21 and i-Score, and integrates them in a unique siRNA score-filtration technique. This multi-score filtration layer filters siRNA that passes the 90% thresholds calculated from experimental dataset features. MysiRNA-Designer takes an accession, finds conserved regions among its transcript space, finds accessible regions within the mRNA, designs all possible siRNAs for these regions, filters them based on multi-scores thresholds, and then performs SNP and off-target filtration. These strict selection criteria were tested against human genes in which at least one active siRNA was designed from 95.7% of total genes. In addition, when tested against an experimental dataset, MysiRNA-Designer was found capable of rejecting 98% of the false positive siRNAs, showing superiority over three state of the art siRNA design programs. MysiRNA is a freely accessible (Microsoft Windows based desktop application that can be used to design siRNA with a high accuracy and specificity. We believe that MysiRNA-Designer has the potential to play an important role in this area.

The predictive value of microRNA-126 in relation to first line treatment with capecitabine and oxaliplatin in patients with metastatic colorectal cancer

DEFF Research Database (Denmark)

Hansen, Torben Frøstrup; Sørensen, Flemming Brandt; Lindebjerg, Jan

2012-01-01

MicroRNA-126 is the only microRNA (miRNA) known to be endothelial cell-specific influencing angiogenesis in several ways. The aim of the present study was to analyse the possible predictive value of miRNA-126 in relation to first line capecitabine and oxaliplatin (XELOX) in patients with metastatic...

Instant Spring Tool Suite

CERN Document Server

Chiang, Geoff

2013-01-01

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. A tutorial guide that walks you through how to use the features of Spring Tool Suite using well defined sections for the different parts of Spring.Instant Spring Tool Suite is for novice to intermediate Java developers looking to get a head-start in enterprise application development using Spring Tool Suite and the Spring framework. If you are looking for a guide for effective application development using Spring Tool Suite, then this book is for you.

Space suit bioenergetics: framework and analysis of unsuited and suited activity.

Science.gov (United States)

Carr, Christopher E; Newman, Dava J

2007-11-01

Metabolic costs limit the duration and intensity of extravehicular activity (EVA), an essential component of future human missions to the Moon and Mars. Energetics Framework: We present a framework for comparison of energetics data across and between studies. This framework, applied to locomotion, differentiates between muscle efficiency and energy recovery, two concepts often confused in the literature. The human run-walk transition in Earth gravity occurs at the point for which energy recovery is approximately the same for walking and running, suggesting a possible role for recovery in gait transitions. Muscular Energetics: Muscle physiology limits the overall efficiency by which chemical energy is converted through metabolism to useful work. Unsuited Locomotion: Walking and running use different methods of energy storage and release. These differences contribute to the relative changes in the metabolic cost of walking and running as gravity is varied, with the metabolic cost of locomoting at a given velocity changing in proportion to gravity for running and less than in proportion for walking. Space Suits: Major factors affecting the energetic cost of suited movement include suit pressurization, gravity, velocity, surface slope, and space suit configuration. Apollo lunar surface EVA traverse metabolic rates, while unexpectedly low, were higher than other activity categories. The Lunar Roving Vehicle facilitated even lower metabolic rates, thus longer duration EVAs. Muscles and tendons act like springs during running; similarly, longitudinal pressure forces in gas pressure space suits allow spring-like storage and release of energy when suits are self-supporting.

Distill: a suite of web servers for the prediction of one-, two- and three-dimensional structural features of proteins

Directory of Open Access Journals (Sweden)

Walsh Ian

2006-09-01

Full Text Available Abstract Background We describe Distill, a suite of servers for the prediction of protein structural features: secondary structure; relative solvent accessibility; contact density; backbone structural motifs; residue contact maps at 6, 8 and 12 Angstrom; coarse protein topology. The servers are based on large-scale ensembles of recursive neural networks and trained on large, up-to-date, non-redundant subsets of the Protein Data Bank. Together with structural feature predictions, Distill includes a server for prediction of Cα traces for short proteins (up to 200 amino acids. Results The servers are state-of-the-art, with secondary structure predicted correctly for nearly 80% of residues (currently the top performance on EVA, 2-class solvent accessibility nearly 80% correct, and contact maps exceeding 50% precision on the top non-diagonal contacts. A preliminary implementation of the predictor of protein Cα traces featured among the top 20 Novel Fold predictors at the last CASP6 experiment as group Distill (ID 0348. The majority of the servers, including the Cα trace predictor, now take into account homology information from the PDB, when available, resulting in greatly improved reliability. Conclusion All predictions are freely available through a simple joint web interface and the results are returned by email. In a single submission the user can send protein sequences for a total of up to 32k residues to all or a selection of the servers. Distill is accessible at the address: http://distill.ucd.ie/distill/.

An analytical platform for mass spectrometry-based identification and chemical analysis of RNA in ribonucleoprotein complexes.

Science.gov (United States)

Taoka, Masato; Yamauchi, Yoshio; Nobe, Yuko; Masaki, Shunpei; Nakayama, Hiroshi; Ishikawa, Hideaki; Takahashi, Nobuhiro; Isobe, Toshiaki

2009-11-01

We describe here a mass spectrometry (MS)-based analytical platform of RNA, which combines direct nano-flow reversed-phase liquid chromatography (RPLC) on a spray tip column and a high-resolution LTQ-Orbitrap mass spectrometer. Operating RPLC under a very low flow rate with volatile solvents and MS in the negative mode, we could estimate highly accurate mass values sufficient to predict the nucleotide composition of a approximately 21-nucleotide small interfering RNA, detect post-transcriptional modifications in yeast tRNA, and perform collision-induced dissociation/tandem MS-based structural analysis of nucleolytic fragments of RNA at a sub-femtomole level. Importantly, the method allowed the identification and chemical analysis of small RNAs in ribonucleoprotein (RNP) complex, such as the pre-spliceosomal RNP complex, which was pulled down from cultured cells with a tagged protein cofactor as bait. We have recently developed a unique genome-oriented database search engine, Ariadne, which allows tandem MS-based identification of RNAs in biological samples. Thus, the method presented here has broad potential for automated analysis of RNA; it complements conventional molecular biology-based techniques and is particularly suited for simultaneous analysis of the composition, structure, interaction, and dynamics of RNA and protein components in various cellular RNP complexes.

Diagnostic value of microRNA-143 in predicting in-stent restenosis for patients with lower extremity arterial occlusive disease

OpenAIRE

Yu, Zhi-Hai; Wang, Hai-Tao; Tu, Can

2017-01-01

Purpose This study was conducted to explore the diagnostic value of microRNA-143 (miRNA-143) in predicting in-stent restenosis (ISR) of lower extremity arterial occlusive disease (LEAOD). Methods From February 2012 to March 2015, 165 patients (112 males and 53 females) with LEAOD undergoing interventional treatment were enrolled in this study. Serum miRNA-143 expression was detected using quantitative real-time polymerase chain reaction (qRT-PCR). Patients were assigned into the restenosis an...

ARMOUR – A Rice miRNA: mRNA Interaction Resource

OpenAIRE

Neeti Sanan-Mishra; Anita Tripathi; Kavita Goswami; Rohit N. Shukla; Madavan Vasudevan; Hitesh Goswami

2018-01-01

ARMOUR was developed as ARice miRNA:mRNA interaction resource. This informative and interactive database includes the experimentally validated expression profiles of miRNAs under different developmental and abiotic stress conditions across seven Indian rice cultivars. This comprehensive database covers 689 known and 1664 predicted novel miRNAs and their expression profiles in more than 38 different tissues or conditions along with their predicted/known target transcripts. The understanding of...

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

Science.gov (United States)

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

RAJA Performance Suite

Energy Technology Data Exchange (ETDEWEB)

2017-09-01

The RAJA Performance Suite is designed to evaluate performance of the RAJA performance portability library on a wide variety of important high performance computing (HPC) algorithmic lulmels. These kernels assess compiler optimizations and various parallel programming model backends accessible through RAJA, such as OpenMP, CUDA, etc. The Initial version of the suite contains 25 computational kernels, each of which appears in 6 variants: Baseline SequcntiaJ, RAJA SequentiaJ, Baseline OpenMP, RAJA OpenMP, Baseline CUDA, RAJA CUDA. All variants of each kernel perform essentially the same mathematical operations and the loop body code for each kernel is identical across all variants. There are a few kernels, such as those that contain reduction operations, that require CUDA-specific coding for their CUDA variants. ActuaJ computer instructions executed and how they run in parallel differs depending on the parallel programming model backend used and which optimizations are perfonned by the compiler used to build the Perfonnance Suite executable. The Suite will be used primarily by RAJA developers to perform regular assessments of RAJA performance across a range of hardware platforms and compilers as RAJA features are being developed. It will also be used by LLNL hardware and software vendor panners for new defining requirements for future computing platform procurements and acceptance testing. In particular, the RAJA Performance Suite will be used for compiler acceptance testing of the upcoming CORAUSierra machine {initial LLNL delivery expected in late-2017/early 2018) and the CORAL-2 procurement. The Suite will aJso be used to generate concise source code reproducers of compiler and runtime issues we uncover so that we may provide them to relevant vendors to be fixed.

Heat and mass transfer in air-fed pressurised suits

International Nuclear Information System (INIS)

Tesch, K.; Collins, M.W.; Karayiannis, T.G.; Atherton, M.A.; Edwards, P.

2009-01-01

Air-fed pressurised suits are used to protect workers against contamination and hazardous environments. The specific application here is the necessity for regular clean-up maintenance within the torus chamber of fusion reactors. The current design of suiting has been developed empirically. It is, therefore, very desirable to formulate a thermo-fluids model, which will be able to define optimum designs and operating parameters. Two factors indicate that the modelling should be as comprehensive as possible. Firstly, the overall thermo-fluids problem is three-dimensional and includes mass as well as heat transfer. The fluid field is complex, bounded on one side by the human body and on the other by what may be distensible, porous and multi-layer clothing. In this paper, we report firstly the modelling necessary for the additional mass and heat transport processes. This involves the use of Fick's and Fourier's laws and conjugate heat transfer. The results of an initial validation study are presented. Temperatures at the outlet of the suits were obtained experimentally and compared with those predicted by the overall CFD model. Realistic three-dimensional geometries were used for the suit and human body. Calculations were for turbulent flow with single- and two-component (species) models

Characterization of novel precursor miRNAs using next generation sequencing and prediction of miRNA targets in Atlantic halibut.

Directory of Open Access Journals (Sweden)

Teshome Tilahun Bizuayehu

Full Text Available BACKGROUND: microRNAs (miRNAs are implicated in regulation of many cellular processes. miRNAs are processed to their mature functional form in a step-wise manner by multiple proteins and cofactors in the nucleus and cytoplasm. Many miRNAs are conserved across vertebrates. Mature miRNAs have recently been characterized in Atlantic halibut (Hippoglossus hippoglossus L.. The aim of this study was to identify and characterize precursor miRNA (pre-miRNAs and miRNA targets in this non-model flatfish. Discovery of miRNA precursor forms and targets in non-model organisms is difficult because of limited source information available. Therefore, we have developed a methodology to overcome this limitation. METHODS: Genomic DNA and small transcriptome of Atlantic halibut were sequenced using Roche 454 pyrosequencing and SOLiD next generation sequencing (NGS, respectively. Identified pre- miRNAs were further validated with reverse-transcription PCR. miRNA targets were identified using miRanda and RNAhybrid target prediction tools using sequences from public databases. Some of miRNA targets were also identified using RACE-PCR. miRNA binding sites were validated with luciferase assay using the RTS34st cell line. RESULTS: We obtained more than 1.3 M and 92 M sequence reads from 454 genomic DNA sequencing and SOLiD small RNA sequencing, respectively. We identified 34 known and 9 novel pre-miRNAs. We predicted a number of miRNA target genes involved in various biological pathways. miR-24 binding to kisspeptin 1 receptor-2 (kiss1-r2 was confirmed using luciferase assay. CONCLUSION: This study demonstrates that identification of conserved and novel pre-miRNAs in a non-model vertebrate lacking substantial genomic resources can be performed by combining different next generation sequencing technologies. Our results indicate a wide conservation of miRNA precursors and involvement of miRNA in multiple regulatory pathways, and provide resources for further research on miRNA

Apparatus for storing protective suits

International Nuclear Information System (INIS)

Englemann, H.J.; Koller, J.; Schrader, H.R.; Schade, G.; Pedrerol, J.

1975-01-01

Arrangements are described for storing one or more protective suits when contaminated on the outside. In order to permit a person wearing a contaminated suit to leave a contaminated area safely, and without contaminating the environment, it has hitherto been the practice for the suit to be passed through a 'lock' and cleansed under decontaminating showers whilst still being worn. This procedure is time wasting and not always completely effective, and it may be necessary to provide a second suit for use whilst the first suit is being decontaminated. Repeated decontamination may also result in undue wear and tear. The arrangements described provide a 'lock' chamber in which a contaminated suit may be stowed away without its interior becoming contaminated, thus allowing repeated use by persons donning and shedding it. (U.K.)

Spliceman2: a computational web server that predicts defects in pre-mRNA splicing.

Science.gov (United States)

Cygan, Kamil Jan; Sanford, Clayton Hendrick; Fairbrother, William Guy

2017-09-15

Most pre-mRNA transcripts in eukaryotic cells must undergo splicing to remove introns and join exons, and splicing elements present a large mutational target for disease-causing mutations. Splicing elements are strongly position dependent with respect to the transcript annotations. In 2012, we presented Spliceman, an online tool that used positional dependence to predict how likely distant mutations around annotated splice sites were to disrupt splicing. Here, we present an improved version of the previous tool that will be more useful for predicting the likelihood of splicing mutations. We have added industry-standard input options (i.e. Spliceman now accepts variant call format files), which allow much larger inputs than previously available. The tool also can visualize the locations-within exons and introns-of sequence variants to be analyzed and the predicted effects on splicing of the pre-mRNA transcript. In addition, Spliceman2 integrates with RNAcompete motif libraries to provide a prediction of which trans -acting factors binding sites are disrupted/created and links out to the UCSC genome browser. In summary, the new features in Spliceman2 will allow scientists and physicians to better understand the effects of single nucleotide variations on splicing. Freely available on the web at http://fairbrother.biomed.brown.edu/spliceman2 . Website implemented in PHP framework-Laravel 5, PostgreSQL, Apache, and Perl, with all major browsers supported. william_fairbrother@brown.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

Automated identification of RNA 3D modules with discriminative power in RNA structural alignments

DEFF Research Database (Denmark)

Theis, Corinna; Höner zu Siederdissen, Christian; Hofacker, Ivo L.

2013-01-01

Recent progress in predicting RNA structure is moving towards filling the 'gap' in 2D RNA structure prediction where, for example, predicted internal loops often form non-canonical base pairs. This is increasingly recognized with the steady increase of known RNA 3D modules. There is a general...... comparative evidence. Subsequently, the modules, initially represented by a graph, are turned into models for the RMDetect program, which allows to test their discriminative power using real and randomized Rfam alignments. An initial extraction of 22495 3D modules in all PDB files results in 977 internal loop...

Semiautomated improvement of RNA alignments

DEFF Research Database (Denmark)

Andersen, Ebbe Sloth; Lind-Thomsen, Allan; Knudsen, Bjarne

2007-01-01

connects to external tools to provide a flexible semiautomatic editing environment. A new method, Pcluster, is introduced for dividing the sequences of an RNA alignment into subgroups with secondary structure differences. Pcluster was used to evaluate 574 seed alignments obtained from the Rfam database...... and we identified 71 alignments with significant prediction of inconsistent base pairs and 102 alignments with significant prediction of novel base pairs. Four RNA families were used to illustrate how SARSE can be used to manually or automatically correct the inconsistent base pairs detected by Pcluster......: the mir-399 RNA, vertebrate telomase RNA (vert-TR), bacterial transfer-messenger RNA (tmRNA), and the signal recognition particle (SRP) RNA. The general use of the method is illustrated by the ability to accommodate pseudoknots and handle even large and divergent RNA families. The open architecture...

Pharmacy settles suit.

Science.gov (United States)

1998-10-02

A suit was filed by an HIV-positive man against a pharmacy that inadvertently disclosed his HIV status to his ex-wife and children. His ex-wife tried to use the information in a custody battle for their two children. The suit against the pharmacy was settled, but the terms of the settlement remain confidential.

miRNA-mediated 'tug-of-war' model reveals ceRNA propensity of genes in cancers.

Science.gov (United States)

Swain, Arpit Chandan; Mallick, Bibekanand

2018-06-01

Competing endogenous RNA (ceRNA) are transcripts that cross-regulate each other at the post-transcriptional level by competing for shared microRNA response elements (MREs). These have been implicated in various biological processes impacting cell-fate decisions and diseases including cancer. There are several studies that predict possible ceRNA pairs by adopting various machine-learning and mathematical approaches; however, there is no method that enables us to gauge as well as compare the propensity of the ceRNA of a gene and precisely envisages which among a pair exerts a stronger pull on the shared miRNA pool. In this study, we developed a method that uses the 'tug of war of genes' concept to predict and quantify ceRNA potential of a gene for the shared miRNA pool in cancers based on a score represented by SoCeR (score of competing endogenous RNA). The method was executed on the RNA-Seq transcriptional profiles of genes and miRNA available at TCGA along with CLIP-supported miRNA-target sites to predict ceRNA in 32 cancer types which were validated with already reported cases. The proposed method can be used to determine the sequestering capability of the gene of interest as well as in ranking the probable ceRNA candidates of a gene. Finally, we developed standalone applications (SoCeR tool) to aid researchers in easier implementation of the method in analysing different data sets or diseases. © 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

MiRNA expression patterns predict survival in glioblastoma

International Nuclear Information System (INIS)

Niyazi, Maximilian; Belka, Claus; Zehentmayr, Franz; Niemöller, Olivier M; Eigenbrod, Sabina; Kretzschmar, Hans; Osthoff, Klaus-Schulze; Tonn, Jörg-Christian; Atkinson, Mike; Mörtl, Simone

2011-01-01

In order to define new prognostic subgroups in patients with glioblastoma a miRNA screen (> 1000 miRNAs) from paraffin tissues followed by a bio-mathematical analysis was performed. 35 glioblastoma patients treated between 7/2005 - 8/2008 at a single institution with surgery and postoperative radio(chemo)therapy were included in this retrospective analysis. For microarray analysis the febit biochip 'Geniom ® Biochip MPEA homo-sapiens' was used. Total RNA was isolated from FFPE tissue sections and 1100 different miRNAs were analyzed. It was possible to define a distinct miRNA expression pattern allowing for a separation of distinct prognostic subgroups. The defined miRNA pattern was significantly associated with early death versus long-term survival (split at 450 days) (p = 0.01). The pattern and the prognostic power were both independent of the MGMT status. At present, this is the first dataset defining a prognostic role of miRNA expression patterns in patients with glioblastoma. Having defined such a pattern, a prospective validation of this observation is required

Early changes of placenta-derived messenger RNA in maternal plasma – potential value for preeclampsia prediction?

Directory of Open Access Journals (Sweden)

Surugiu Sebastian

2015-12-01

Full Text Available Objective: the pourpose of the study was to determine if there are any differences between placenta derived plasmatic levels of messenger RNA in normal and future preeclamptic pregnancies and if these placental transcripts can predict preeclampsia long before clinical onset

Changes in circulating microRNA-126 during treatment with chemotherapy and bevacizumab predicts treatment response in patients with metastatic colorectal cancer

DEFF Research Database (Denmark)

Hansen, T F; Carlsen, A L; Heegaard, N H H

2015-01-01

BACKGROUND: This study investigated the predictive value of circulating microRNA-126 (cir-miRNA-126) in patients with metastatic colorectal cancer (mCRC) treated with first-line chemotherapy combined with bevacizumab.METHODS: The study included 68 patients. Blood samples (plasma) were collected b...

Fine-grained parallelism accelerating for RNA secondary structure prediction with pseudoknots based on FPGA.

Science.gov (United States)

Xia, Fei; Jin, Guoqing

2014-06-01

PKNOTS is a most famous benchmark program and has been widely used to predict RNA secondary structure including pseudoknots. It adopts the standard four-dimensional (4D) dynamic programming (DP) method and is the basis of many variants and improved algorithms. Unfortunately, the O(N(6)) computing requirements and complicated data dependency greatly limits the usefulness of PKNOTS package with the explosion in gene database size. In this paper, we present a fine-grained parallel PKNOTS package and prototype system for accelerating RNA folding application based on FPGA chip. We adopted a series of storage optimization strategies to resolve the "Memory Wall" problem. We aggressively exploit parallel computing strategies to improve computational efficiency. We also propose several methods that collectively reduce the storage requirements for FPGA on-chip memory. To the best of our knowledge, our design is the first FPGA implementation for accelerating 4D DP problem for RNA folding application including pseudoknots. The experimental results show a factor of more than 50x average speedup over the PKNOTS-1.08 software running on a PC platform with Intel Core2 Q9400 Quad CPU for input RNA sequences. However, the power consumption of our FPGA accelerator is only about 50% of the general-purpose micro-processors.

REDItools: high-throughput RNA editing detection made easy.

Science.gov (United States)

Picardi, Ernesto; Pesole, Graziano

2013-07-15

The reliable detection of RNA editing sites from massive sequencing data remains challenging and, although several methodologies have been proposed, no computational tools have been released to date. Here, we introduce REDItools a suite of python scripts to perform high-throughput investigation of RNA editing using next-generation sequencing data. REDItools are in python programming language and freely available at http://code.google.com/p/reditools/. ernesto.picardi@uniba.it or graziano.pesole@uniba.it Supplementary data are available at Bioinformatics online.

Principles of mRNA transport in yeast.

Science.gov (United States)

Heym, Roland Gerhard; Niessing, Dierk

2012-06-01

mRNA localization and localized translation is a common mechanism by which cellular asymmetry is achieved. In higher eukaryotes the mRNA transport machinery is required for such diverse processes as stem cell division and neuronal plasticity. Because mRNA localization in metazoans is highly complex, studies at the molecular level have proven to be cumbersome. However, active mRNA transport has also been reported in fungi including Saccharomyces cerevisiae, Ustilago maydis and Candida albicans, in which these events are less difficult to study. Amongst them, budding yeast S. cerevisiae has yielded mechanistic insights that exceed our understanding of other mRNA localization events to date. In contrast to most reviews, we refrain here from summarizing mRNA localization events from different organisms. Instead we give an in-depth account of ASH1 mRNA localization in budding yeast. This approach is particularly suited to providing a more holistic view of the interconnection between the individual steps of mRNA localization, from transcriptional events to cytoplasmic mRNA transport and localized translation. Because of our advanced mechanistic understanding of mRNA localization in yeast, the present review may also be informative for scientists working, for example, on mRNA localization in embryogenesis or in neurons.

Atomic-accuracy prediction of protein loop structures through an RNA-inspired Ansatz.

Directory of Open Access Journals (Sweden)

Rhiju Das

Full Text Available Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. Current approaches, such as the protein local optimization protocol or kinematic inversion closure (KIC Monte Carlo, involve stages that coarse-grain proteins, simplifying modeling but precluding a systematic search of all-atom configurations. This article introduces an alternative modeling strategy based on a 'stepwise ansatz', recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include cis-Pro touch turns, loops that pass through tunnels of other side-chains, and loops of lengths up to 24 residues. Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth 'RNA-puzzle' competition. These results establish all-atom enumeration as

RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study.

Science.gov (United States)

Berghoff, Bork A; Karlsson, Torgny; Källman, Thomas; Wagner, E Gerhart H; Grabherr, Manfred G

2017-01-01

Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization. Here, we present a novel method, moose 2 , which predicts invariant genes in silico through a dynamic programming (DP) scheme and applies a quadratic normalization based on this subset. The method allows for specifying a set of known or experimentally validated invariant genes, which guides the DP. We experimentally verified the predictions of this method in the bacterium Escherichia coli , and show how moose 2 is able to (i) estimate the expression value distances between RNA-seq samples, (ii) reduce the variation of expression values across all samples, and (iii) to subsequently reveal new functional groups of genes during the late stages of DNA damage. We further applied the method to three eukaryotic data sets, on which its performance compares favourably to other methods. The software is implemented in C++ and is publicly available from http://grabherr.github.io/moose2/. The proposed RNA-seq normalization method, moose 2 , is a valuable alternative to existing methods, with two major advantages: (i) in silico prediction of invariant genes provides a list of potential reference genes for downstream analyses, and (ii) non-linear artefacts in RNA-seq data

Circulating microRNA levels predict residual beta cell function and glycaemic control in children with type 1 diabetes mellitus

DEFF Research Database (Denmark)

Samandari, Nasim; Mirza, Aashiq H; Nielsen, Lotte B

2017-01-01

AIMS/HYPOTHESIS: We aimed to identify circulating microRNA (miRNA) that predicts clinical progression in a cohort of 123 children with new-onset type 1 diabetes mellitus. METHODS: Plasma samples were prospectively obtained at 1, 3, 6, 12 and 60 months after diagnosis from a subset of 40 children......RNAs revealed significant enrichment for pathways related to gonadotropin-releasing hormone receptor and angiogenesis pathways. CONCLUSIONS/INTERPRETATION: The miRNA hsa-miR-197-3p at 3 months was the strongest predictor of residual beta cell function 1 year after diagnosis in children with type 1 diabetes...... from the Danish Remission Phase Cohort, and profiled for miRNAs. At the same time points, meal-stimulated C-peptide and HbA1c levels were measured and insulin-dose adjusted HbA1c (IDAA1c) calculated. miRNAs that at 3 months after diagnosis predicted residual beta cell function and glycaemic control...

GARN2: coarse-grained prediction of 3D structure of large RNA molecules by regret minimization.

Science.gov (United States)

Boudard, Mélanie; Barth, Dominique; Bernauer, Julie; Denise, Alain; Cohen, Johanne

2017-08-15

Predicting the 3D structure of RNA molecules is a key feature towards predicting their functions. Methods which work at atomic or nucleotide level are not suitable for large molecules. In these cases, coarse-grained prediction methods aim to predict a shape which could be refined later by using more precise methods on smaller parts of the molecule. We developed a complete method for sampling 3D RNA structure at a coarse-grained model, taking a secondary structure as input. One of the novelties of our method is that a second step extracts two best possible structures close to the native, from a set of possible structures. Although our method benefits from the first version of GARN, some of the main features on GARN2 are very different. GARN2 is much faster than the previous version and than the well-known methods of the state-of-art. Our experiments show that GARN2 can also provide better structures than the other state-of-the-art methods. GARN2 is written in Java. It is freely distributed and available at http://garn.lri.fr/. melanie.boudard@lri.fr or johanne.cohen@lri.fr. 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

Statistical Evaluation of Causal Factors Associated with Astronaut Shoulder Injury in Space Suits.

Science.gov (United States)

Anderson, Allison P; Newman, Dava J; Welsch, Roy E

2015-07-01

Shoulder injuries due to working inside the space suit are some of the most serious and debilitating injuries astronauts encounter. Space suit injuries occur primarily in the Neutral Buoyancy Laboratory (NBL) underwater training facility due to accumulated musculoskeletal stress. We quantitatively explored the underlying causal mechanisms of injury. Logistic regression was used to identify relevant space suit components, training environment variables, and anthropometric dimensions related to an increased propensity for space-suited injury. Two groups of subjects were analyzed: those whose reported shoulder incident is attributable to the NBL or working in the space suit, and those whose shoulder incidence began in active duty, meaning working in the suit could be a contributing factor. For both groups, percent of training performed in the space suit planar hard upper torso (HUT) was the most important predictor variable for injury. Frequency of training and recovery between training were also significant metrics. The most relevant anthropometric dimensions were bideltoid breadth, expanded chest depth, and shoulder circumference. Finally, record of previous injury was found to be a relevant predictor for subsequent injury. The first statistical model correctly identifies 39% of injured subjects, while the second model correctly identifies 68% of injured subjects. A review of the literature suggests this is the first work to quantitatively evaluate the hypothesized causal mechanisms of all space-suited shoulder injuries. Although limited in predictive capability, each of the identified variables can be monitored and modified operationally to reduce future impacts on an astronaut's health.

Signatures of RNA binding proteins globally coupled to effective microRNA target sites

DEFF Research Database (Denmark)

Jacobsen, Anders; Wen, Jiayu; Marks, Debora S

2010-01-01

MicroRNAs (miRNAs) and small interfering RNAs (siRNAs), bound to Argonaute proteins (RISC), destabilize mRNAs through base-pairing with the mRNA. However, the gene expression changes after perturbations of these small RNAs are only partially explained by predicted miRNA/siRNA targeting. Targeting...

lncRNA Gene Signatures for Prediction of Breast Cancer Intrinsic Subtypes and Prognosis

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Silu Zhang

2018-01-01

Full Text Available Background: Breast cancer is intrinsically heterogeneous and is commonly classified into four main subtypes associated with distinct biological features and clinical outcomes. However, currently available data resources and methods are limited in identifying molecular subtyping on protein-coding genes, and little is known about the roles of long non-coding RNAs (lncRNAs, which occupies 98% of the whole genome. lncRNAs may also play important roles in subgrouping cancer patients and are associated with clinical phenotypes. Methods: The purpose of this project was to identify lncRNA gene signatures that are associated with breast cancer subtypes and clinical outcomes. We identified lncRNA gene signatures from The Cancer Genome Atlas (TCGA RNAseq data that are associated with breast cancer subtypes by an optimized 1-Norm SVM feature selection algorithm. We evaluated the prognostic performance of these gene signatures with a semi-supervised principal component (superPC method. Results: Although lncRNAs can independently predict breast cancer subtypes with satisfactory accuracy, a combined gene signature including both coding and non-coding genes will give the best clinically relevant prediction performance. We highlighted eight potential biomarkers (three from coding genes and five from non-coding genes that are significantly associated with survival outcomes. Conclusion: Our proposed methods are a novel means of identifying subtype-specific coding and non-coding potential biomarkers that are both clinically relevant and biologically significant.

Durable Suit Bladder with Improved Water Permeability for Pressure and Environment Suits

Science.gov (United States)

Bue, Grant C.; Kuznetz, Larry; Orndoff, Evelyne; Tang, Henry; Aitchison, Lindsay; Ross, Amy

2009-01-01

Water vapor permeability is shown to be useful in rejecting heat and managing moisture accumulation in launch-and-entry pressure suits. Currently this is accomplished through a porous Gortex layer in the Advanced Crew and Escape Suit (ACES) and in the baseline design of the Constellation Suit System Element (CSSE) Suit 1. Non-porous dense monolithic membranes (DMM) that are available offer potential improvements for water vapor permeability with reduced gas leak. Accordingly, three different pressure bladder materials were investigated for water vapor permeability and oxygen leak: ElasthaneTM 80A (thermoplastic polyether urethane) provided from stock polymer material and two custom thermoplastic polyether urethanes. Water vapor, carbon dioxide and oxygen permeability of the DMM's was measured in a 0.13 mm thick stand-alone layer, a 0.08 mm and 0.05 mm thick layer each bonded to two different nylon and polyester woven reinforcing materials. Additional water vapor permeability and mechanical compression measurements were made with the reinforced 0.05 mm thick layers, further bonded with a polyester wicking and overlaid with moistened polyester fleece thermal underwear .This simulated the pressure from a supine crew person. The 0.05 mm thick nylon reinforced sample with polyester wicking layer was further mechanically tested for wear and abrasion. Concepts for incorporating these materials in launch/entry and Extravehicular Activity pressure suits are presented.

CID-miRNA: A web server for prediction of novel miRNA precursors in human genome

International Nuclear Information System (INIS)

Tyagi, Sonika; Vaz, Candida; Gupta, Vipin; Bhatia, Rohit; Maheshwari, Sachin; Srinivasan, Ashwin; Bhattacharya, Alok

2008-01-01

microRNAs (miRNA) are a class of non-protein coding functional RNAs that are thought to regulate expression of target genes by direct interaction with mRNAs. miRNAs have been identified through both experimental and computational methods in a variety of eukaryotic organisms. Though these approaches have been partially successful, there is a need to develop more tools for detection of these RNAs as they are also thought to be present in abundance in many genomes. In this report we describe a tool and a web server, named CID-miRNA, for identification of miRNA precursors in a given DNA sequence, utilising secondary structure-based filtering systems and an algorithm based on stochastic context free grammar trained on human miRNAs. CID-miRNA analyses a given sequence using a web interface, for presence of putative miRNA precursors and the generated output lists all the potential regions that can form miRNA-like structures. It can also scan large genomic sequences for the presence of potential miRNA precursors in its stand-alone form. The web server can be accessed at (http://mirna.jnu.ac.in/cidmirna/)

Integration of Known DNA, RNA and Protein Biomarkers Provides Prediction of Anti-TNF Response in Rheumatoid Arthritis

DEFF Research Database (Denmark)

Folkersen, Lasse; Brynedal, Boel; Marcela Diaz-Gallo, Lina

2016-01-01

OBJECTIVE: In rheumatoid arthritis (RA) several recent efforts have sought to discover means of predicting which patients would benefit from treatment. However, results have been discrepant with few successful replications. Our objective was to build a biobank with DNA, RNA and protein measuremen...

ARMOUR - A Rice miRNA: mRNA Interaction Resource.

Science.gov (United States)

Sanan-Mishra, Neeti; Tripathi, Anita; Goswami, Kavita; Shukla, Rohit N; Vasudevan, Madavan; Goswami, Hitesh

2018-01-01

ARMOUR was developed as A Rice miRNA:mRNA interaction resource. This informative and interactive database includes the experimentally validated expression profiles of miRNAs under different developmental and abiotic stress conditions across seven Indian rice cultivars. This comprehensive database covers 689 known and 1664 predicted novel miRNAs and their expression profiles in more than 38 different tissues or conditions along with their predicted/known target transcripts. The understanding of miRNA:mRNA interactome in regulation of functional cellular machinery is supported by the sequence information of the mature and hairpin structures. ARMOUR provides flexibility to users in querying the database using multiple ways like known gene identifiers, gene ontology identifiers, KEGG identifiers and also allows on the fly fold change analysis and sequence search query with inbuilt BLAST algorithm. ARMOUR database provides a cohesive platform for novel and mature miRNAs and their expression in different experimental conditions and allows searching for their interacting mRNA targets, GO annotation and their involvement in various biological pathways. The ARMOUR database includes a provision for adding more experimental data from users, with an aim to develop it as a platform for sharing and comparing experimental data contributed by research groups working on rice.

Structural basis of genomic RNA (gRNA) dimerization and packaging determinants of mouse mammary tumor virus (MMTV).

Science.gov (United States)

Aktar, Suriya J; Vivet-Boudou, Valérie; Ali, Lizna M; Jabeen, Ayesha; Kalloush, Rawan M; Richer, Delphine; Mustafa, Farah; Marquet, Roland; Rizvi, Tahir A

2014-11-14

One of the hallmarks of retroviral life cycle is the efficient and specific packaging of two copies of retroviral gRNA in the form of a non-covalent RNA dimer by the assembling virions. It is becoming increasingly clear that the process of dimerization is closely linked with gRNA packaging, and in some retroviruses, the latter depends on the former. Earlier mutational analysis of the 5' end of the MMTV genome indicated that MMTV gRNA packaging determinants comprise sequences both within the 5' untranslated region (5' UTR) and the beginning of gag. The RNA secondary structure of MMTV gRNA packaging sequences was elucidated employing selective 2'hydroxyl acylation analyzed by primer extension (SHAPE). SHAPE analyses revealed the presence of a U5/Gag long-range interaction (U5/Gag LRI), not predicted by minimum free-energy structure predictions that potentially stabilizes the global structure of this region. Structure conservation along with base-pair covariations between different strains of MMTV further supported the SHAPE-validated model. The 5' region of the MMTV gRNA contains multiple palindromic (pal) sequences that could initiate intermolecular interaction during RNA dimerization. In vitro RNA dimerization, SHAPE analysis, and structure prediction approaches on a series of pal mutants revealed that MMTV RNA utilizes a palindromic point of contact to initiate intermolecular interactions between two gRNAs, leading to dimerization. This contact point resides within pal II (5' CGGCCG 3') at the 5' UTR and contains a canonical "GC" dyad and therefore likely constitutes the MMTV RNA dimerization initiation site (DIS). Further analyses of these pal mutants employing in vivo genetic approaches indicate that pal II, as well as pal sequences located in the primer binding site (PBS) are both required for efficient MMTV gRNA packaging. Employing structural prediction, biochemical, and genetic approaches, we show that pal II functions as a primary point of contact between

The Apollo Number: space suits, self-support, and the walk-run transition.

Directory of Open Access Journals (Sweden)

Christopher E Carr

Full Text Available BACKGROUND: How space suits affect the preferred walk-run transition is an open question with relevance to human biomechanics and planetary extravehicular activity. Walking and running energetics differ; in reduced gravity (<0.5 g, running, unlike on Earth, uses less energy per distance than walking. METHODOLOGY/PRINCIPAL FINDINGS: The walk-run transition (denoted * correlates with the Froude Number (Fr = v(2/gL, velocity v, gravitational acceleration g, leg length L. Human unsuited Fr* is relatively constant (approximately 0.5 with gravity but increases substantially with decreasing gravity below approximately 0.4 g, rising to 0.9 in 1/6 g; space suits appear to lower Fr*. Because of pressure forces, space suits partially (1 g or completely (lunar-g support their own weight. We define the Apollo Number (Ap = Fr/M as an expected invariant of locomotion under manipulations of M, the ratio of human-supported to total transported mass. We hypothesize that for lunar suited conditions Ap* but not Fr* will be near 0.9, because the Apollo Number captures the effect of space suit self-support. We used the Apollo Lunar Surface Journal and other sources to identify 38 gait events during lunar exploration for which we could determine gait type (walk/lope/run and calculate Ap. We estimated the binary transition between walk/lope (0 and run (1, yielding Fr* (0.36+/-0.11, mean+/-95% CI and Ap* (0.68+/-0.20. CONCLUSIONS/SIGNIFICANCE: The Apollo Number explains 60% of the difference between suited and unsuited Fr*, appears to capture in large part the effects of space suits on the walk-run transition, and provides several testable predictions for space suit locomotion and, of increasing relevance here on Earth, exoskeleton locomotion. The knowledge of how space suits affect gait transitions can be used to optimize space suits for use on the Moon and Mars.

A predictable suite of helminth parasites in the long-billed dowitcher, Limnodromus scolopaceus, from the Chihuahua desert in Texas and Mexico.

Science.gov (United States)

Canaris, Albert G; Ortiz, Rafael; Canaris, Gay J

2010-12-01

Eighty-eight long-billed dowitchers, Limnodromus scolopaceus, were examined for helminth parasites, 62 from Texas and 26 from Mexico. In total, 3,558 helminth parasites were obtained from this host, 2,273 from Texas birds and 1,285 from birds from Mexico. The component communities consisted of 22 species of helminths in Texas, and 19 in Mexico. Of a total of 26 helminth species recorded from the 2 localities, 15 were common to both, 7 found only in Texas, and 4 only in Mexico. Fifty-nine of 62 Texas birds and 25 of 26 birds from Mexico were infected. The most prevalent helminth for Texas was the cestode Shipleya inermis. The cestode Aploparaksis retroversa was the most abundant, accounting for 37% of the total abundance, and was second highest in prevalence. Five species of cestodes, A. retroversa, Aploparaksis diagonalis, Aploparaksis occidentalis, Aploparaksis rissae, and Shipleya inermis accounted for 79% of total abundance. In the sample from Mexico, S. inermis was also highest in prevalence, followed by the nematode Hystrichis tricolor. The cestode A. retroversa was highest in abundance at 50% of the total, and was third highest in prevalence. Mean species richness, diversity, and evenness were similar among the component communities of Texas and Mexico. A predictable suite of aploparaksid cestodes, together with the cestode S. inermis, constituted 79%, and 61%, of total abundance for the component communities of Texas and Mexico, respectively, and were present in all component communities for locality, season, and year. The cestodes, A. retroversa and S. inermis, were the dominant species in all component communities. Differences among component communities and low similarities for all other comparisons were largely caused by less predictable suites of helminth species. A checklist of helminth parasites reported for long-billed dowitchers is included.

Harnessing NGS and Big Data Optimally: Comparison of miRNA Prediction from Assembled versus Non-assembled Sequencing Data--The Case of the Grass Aegilops tauschii Complex Genome.

Science.gov (United States)

Budak, Hikmet; Kantar, Melda

2015-07-01

MicroRNAs (miRNAs) are small, endogenous, non-coding RNA molecules that regulate gene expression at the post-transcriptional level. As high-throughput next generation sequencing (NGS) and Big Data rapidly accumulate for various species, efforts for in silico identification of miRNAs intensify. Surprisingly, the effect of the input genomics sequence on the robustness of miRNA prediction was not evaluated in detail to date. In the present study, we performed a homology-based miRNA and isomiRNA prediction of the 5D chromosome of bread wheat progenitor, Aegilops tauschii, using two distinct sequence data sets as input: (1) raw sequence reads obtained from 454-GS FLX Titanium sequencing platform and (2) an assembly constructed from these reads. We also compared this method with a number of available plant sequence datasets. We report here the identification of 62 and 22 miRNAs from raw reads and the assembly, respectively, of which 16 were predicted with high confidence from both datasets. While raw reads promoted sensitivity with the high number of miRNAs predicted, 55% (12 out of 22) of the assembly-based predictions were supported by previous observations, bringing specificity forward compared to the read-based predictions, of which only 37% were supported. Importantly, raw reads could identify several repeat-related miRNAs that could not be detected with the assembly. However, raw reads could not capture 6 miRNAs, for which the stem-loops could only be covered by the relatively longer sequences from the assembly. In summary, the comparison of miRNA datasets obtained by these two strategies revealed that utilization of raw reads, as well as assemblies for in silico prediction, have distinct advantages and disadvantages. Consideration of these important nuances can benefit future miRNA identification efforts in the current age of NGS and Big Data driven life sciences innovation.

Functional characterization of the Drosophila MRP (mitochondrial RNA processing) RNA gene.

Science.gov (United States)

Schneider, Mary D; Bains, Anupinder K; Rajendra, T K; Dominski, Zbigniew; Matera, A Gregory; Simmonds, Andrew J

2010-11-01

MRP RNA is a noncoding RNA component of RNase mitochondrial RNA processing (MRP), a multi-protein eukaryotic endoribonuclease reported to function in multiple cellular processes, including ribosomal RNA processing, mitochondrial DNA replication, and cell cycle regulation. A recent study predicted a potential Drosophila ortholog of MRP RNA (CR33682) by computer-based genome analysis. We have confirmed the expression of this gene and characterized the phenotype associated with this locus. Flies with mutations that specifically affect MRP RNA show defects in growth and development that begin in the early larval period and end in larval death during the second instar stage. We present several lines of evidence demonstrating a role for Drosophila MRP RNA in rRNA processing. The nuclear fraction of Drosophila MRP RNA localizes to the nucleolus. Further, a mutant strain shows defects in rRNA processing that include a defect in 5.8S rRNA processing, typical of MRP RNA mutants in other species, as well as defects in early stages of rRNA processing.

Impact of target mRNA structure on siRNA silencing efficiency: A large-scale study.

Science.gov (United States)

Gredell, Joseph A; Berger, Angela K; Walton, S Patrick

2008-07-01

The selection of active siRNAs is generally based on identifying siRNAs with certain sequence and structural properties. However, the efficiency of RNA interference has also been shown to depend on the structure of the target mRNA, primarily through studies using exogenous transcripts with well-defined secondary structures in the vicinity of the target sequence. While these studies provide a means for examining the impact of target sequence and structure independently, the predicted secondary structures for these transcripts are often not reflective of structures that form in full-length, native mRNAs where interactions can occur between relatively remote segments of the mRNAs. Here, using a combination of experimental results and analysis of a large dataset, we demonstrate that the accessibility of certain local target structures on the mRNA is an important determinant in the gene silencing ability of siRNAs. siRNAs targeting the enhanced green fluorescent protein were chosen using a minimal siRNA selection algorithm followed by classification based on the predicted minimum free energy structures of the target transcripts. Transfection into HeLa and HepG2 cells revealed that siRNAs targeting regions of the mRNA predicted to have unpaired 5'- and 3'-ends resulted in greater gene silencing than regions predicted to have other types of secondary structure. These results were confirmed by analysis of gene silencing data from previously published siRNAs, which showed that mRNA target regions unpaired at either the 5'-end or 3'-end were silenced, on average, approximately 10% more strongly than target regions unpaired in the center or primarily paired throughout. We found this effect to be independent of the structure of the siRNA guide strand. Taken together, these results suggest minimal requirements for nucleation of hybridization between the siRNA guide strand and mRNA and that both mRNA and guide strand structure should be considered when choosing candidate si

Development of Power Assisting Suit

Science.gov (United States)

Yamamoto, Keijiro; Ishii, Mineo; Hyodo, Kazuhito; Yoshimitsu, Toshihiro; Matsuo, Takashi

In order to realize a wearable power assisting suit for assisting a nurse to carry a patient in her arms, the power supply and control systems of the suit have to be miniaturized, and it has to be wireless and pipeline-less. The new wearable suit consists of shoulders, arms, back, waist and legs units to be fitted on the nurse's body. The arms, waist and legs have new pneumatic rotary actuators driven directly by micro air pumps supplied by portable Ni-Cd batteries. The muscle forces are sensed by a new muscle hardness sensor utilizing a sensing tip mounted on a force sensing film device. An embedded microcomputer is used for the calculations of control signals. The new wearable suit was applied practically to a human body and a series of movement experiments that weights in the arms were held and taken up and down was performed. Each unit of the suit could transmit assisting torque directly to each joint verifying its practicability.

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

Directory of Open Access Journals (Sweden)

Wu Chi-Yeh

2010-01-01

Full Text Available Abstract Background MicroRNAs (miRNAs are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention. Results This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set. Conclusion Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G

Profiled support vector machines for antisense oligonucleotide efficacy prediction

Directory of Open Access Journals (Sweden)

Martín-Guerrero José D

2004-09-01

Full Text Available Abstract Background This paper presents the use of Support Vector Machines (SVMs for prediction and analysis of antisense oligonucleotide (AO efficacy. The collected database comprises 315 AO molecules including 68 features each, inducing a problem well-suited to SVMs. The task of feature selection is crucial given the presence of noisy or redundant features, and the well-known problem of the curse of dimensionality. We propose a two-stage strategy to develop an optimal model: (1 feature selection using correlation analysis, mutual information, and SVM-based recursive feature elimination (SVM-RFE, and (2 AO prediction using standard and profiled SVM formulations. A profiled SVM gives different weights to different parts of the training data to focus the training on the most important regions. Results In the first stage, the SVM-RFE technique was most efficient and robust in the presence of low number of samples and high input space dimension. This method yielded an optimal subset of 14 representative features, which were all related to energy and sequence motifs. The second stage evaluated the performance of the predictors (overall correlation coefficient between observed and predicted efficacy, r; mean error, ME; and root-mean-square-error, RMSE using 8-fold and minus-one-RNA cross-validation methods. The profiled SVM produced the best results (r = 0.44, ME = 0.022, and RMSE= 0.278 and predicted high (>75% inhibition of gene expression and low efficacy (http://aosvm.cgb.ki.se/. Conclusions The SVM approach is well suited to the AO prediction problem, and yields a prediction accuracy superior to previous methods. The profiled SVM was found to perform better than the standard SVM, suggesting that it could lead to improvements in other prediction problems as well.

ncRNA-class Web Tool: Non-coding RNA feature extraction and pre-miRNA classification web tool

KAUST Repository

Kleftogiannis, Dimitrios A.; Theofilatos, Konstantinos A.; Papadimitriou, Stergios; Tsakalidis, Athanasios K.; Likothanassis, Spiridon D.; Mavroudi, Seferina P.

2012-01-01

Until recently, it was commonly accepted that most genetic information is transacted by proteins. Recent evidence suggests that the majority of the genomes of mammals and other complex organisms are in fact transcribed into non-coding RNAs (ncRNAs), many of which are alternatively spliced and/or processed into smaller products. Non coding RNA genes analysis requires the calculation of several sequential, thermodynamical and structural features. Many independent tools have already been developed for the efficient calculation of such features but to the best of our knowledge there does not exist any integrative approach for this task. The most significant amount of existing work is related to the miRNA class of non-coding RNAs. MicroRNAs (miRNAs) are small non-coding RNAs that play a significant role in gene regulation and their prediction is a challenging bioinformatics problem. Non-coding RNA feature extraction and pre-miRNA classification Web Tool (ncRNA-class Web Tool) is a publicly available web tool ( http://150.140.142.24:82/Default.aspx ) which provides a user friendly and efficient environment for the effective calculation of a set of 58 sequential, thermodynamical and structural features of non-coding RNAs, plus a tool for the accurate prediction of miRNAs. © 2012 IFIP International Federation for Information Processing.

Space Suit Joint Torque Testing

Science.gov (United States)

Valish, Dana J.

2011-01-01

In 2009 and early 2010, a test was performed to quantify the torque required to manipulate joints in several existing operational and prototype space suits in an effort to develop joint torque requirements appropriate for a new Constellation Program space suit system. The same test method was levied on the Constellation space suit contractors to verify that their suit design meets the requirements. However, because the original test was set up and conducted by a single test operator there was some question as to whether this method was repeatable enough to be considered a standard verification method for Constellation or other future space suits. In order to validate the method itself, a representative subset of the previous test was repeated, using the same information that would be available to space suit contractors, but set up and conducted by someone not familiar with the previous test. The resultant data was compared using graphical and statistical analysis and a variance in torque values for some of the tested joints was apparent. Potential variables that could have affected the data were identified and re-testing was conducted in an attempt to eliminate these variables. The results of the retest will be used to determine if further testing and modification is necessary before the method can be validated.

Cell-free placental mRNA in maternal plasma to predict placental invasion in patients with placenta accreta.

Science.gov (United States)

El Behery, Manal M; Rasha L, Etewa; El Alfy, Yehya

2010-04-01

To evaluate whether measuring cell-free placental mRNA in maternal plasma improves the diagnostic accuracy of ultrasound and color Doppler in detecting placental invasion in patients at risk for placenta accreta. Thirty-five singleton pregnant women of more than 28 weeks of gestation and at risk for placenta accreta underwent ultrasound and color Doppler assessment. Cell-free placental mRNA in maternal plasma was measured using real-time reverse-transcription polymerase chain reaction. Patients were classified into 2 groups based on the findings at cesarean delivery and histological examination: women with placenta accreta (n=7) and women without placenta accreta (n=28). The median MoM (multiples of the median) value of cell-free placental mRNA was significantly higher in patients with placenta accreta than in those without placenta accreta (6.50 vs 2.60; Pplacental mRNA was significantly elevated in patients with placenta increta and percreta than in those with simple accreta. Six false-positive results were found on ultrasound, all from patients without placenta accreta and an insignificant rise in cell-free placental mRNA levels. Measuring cell-free placental mRNA in maternal plasma may increase the accuracy of ultrasound and color Doppler in prenatal prediction of placental invasion in patients with suspected placenta accreta. Copyright 2009 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

ARMOUR – A Rice miRNA: mRNA Interaction Resource

Directory of Open Access Journals (Sweden)

Neeti Sanan-Mishra

2018-05-01

Full Text Available ARMOUR was developed as ARice miRNA:mRNA interaction resource. This informative and interactive database includes the experimentally validated expression profiles of miRNAs under different developmental and abiotic stress conditions across seven Indian rice cultivars. This comprehensive database covers 689 known and 1664 predicted novel miRNAs and their expression profiles in more than 38 different tissues or conditions along with their predicted/known target transcripts. The understanding of miRNA:mRNA interactome in regulation of functional cellular machinery is supported by the sequence information of the mature and hairpin structures. ARMOUR provides flexibility to users in querying the database using multiple ways like known gene identifiers, gene ontology identifiers, KEGG identifiers and also allows on the fly fold change analysis and sequence search query with inbuilt BLAST algorithm. ARMOUR database provides a cohesive platform for novel and mature miRNAs and their expression in different experimental conditions and allows searching for their interacting mRNA targets, GO annotation and their involvement in various biological pathways. The ARMOUR database includes a provision for adding more experimental data from users, with an aim to develop it as a platform for sharing and comparing experimental data contributed by research groups working on rice.

Species-independent MicroRNA Gene Discovery

KAUST Repository

Kamanu, Timothy K.

2012-12-01

MicroRNA (miRNA) are a class of small endogenous non-coding RNA that are mainly negative transcriptional and post-transcriptional regulators in both plants and animals. Recent studies have shown that miRNA are involved in different types of cancer and other incurable diseases such as autism and Alzheimer’s. Functional miRNAs are excised from hairpin-like sequences that are known as miRNA genes. There are about 21,000 known miRNA genes, most of which have been determined using experimental methods. miRNA genes are classified into different groups (miRNA families). This study reports about 19,000 unknown miRNA genes in nine species whereby approximately 15,300 predictions were computationally validated to contain at least one experimentally verified functional miRNA product. The predictions are based on a novel computational strategy which relies on miRNA family groupings and exploits the physics and geometry of miRNA genes to unveil the hidden palindromic signals and symmetries in miRNA gene sequences. Unlike conventional computational miRNA gene discovery methods, the algorithm developed here is species-independent: it allows prediction at higher accuracy and resolution from arbitrary RNA/DNA sequences in any species and thus enables examination of repeat-prone genomic regions which are thought to be non-informative or ’junk’ sequences. The information non-redundancy of uni-directional RNA sequences compared to information redundancy of bi-directional DNA is demonstrated, a fact that is overlooked by most pattern discovery algorithms. A novel method for computing upstream and downstream miRNA gene boundaries based on mathematical/statistical functions is suggested, as well as cutoffs for annotation of miRNA genes in different miRNA families. Another tool is proposed to allow hypotheses generation and visualization of data matrices, intra- and inter-species chromosomal distribution of miRNA genes or miRNA families. Our results indicate that: miRNA and miRNA

Evaluating Suit Fit Using Performance Degradation

Science.gov (United States)

Margerum, Sarah E.; Cowley, Matthew; Harvill, Lauren; Benson, Elizabeth; Rajulu, Sudhakar

2011-01-01

The Mark III suit has multiple sizes of suit components (arm, leg, and gloves) as well as sizing inserts to tailor the fit of the suit to an individual. This study sought to determine a way to identify the point an ideal suit fit transforms into a bad fit and how to quantify this breakdown using mobility-based physical performance data. This study examined the changes in human physical performance via degradation of the elbow and wrist range of motion of the planetary suit prototype (Mark III) with respect to changes in sizing and as well as how to apply that knowledge to suit sizing options and improvements in suit fit. The methods implemented in this study focused on changes in elbow and wrist mobility due to incremental suit sizing modifications. This incremental sizing was within a range that included both optimum and poor fit. Suited range of motion data was collected using a motion analysis system for nine isolated and functional tasks encompassing the elbow and wrist joints. A total of four subjects were tested with motions involving both arms simultaneously as well as the right arm only. The results were then compared across sizing configurations. The results of this study indicate that range of motion may be used as a viable parameter to quantify at what stage suit sizing causes a detriment in performance; however the human performance decrement appeared to be based on the interaction of multiple joints along a limb, not a single joint angle. The study was able to identify a preliminary method to quantify the impact of size on performance and to develop a means to gauge tolerances around optimal size. More work is needed to improve the assessment of optimal fit and to compensate for multiple joint interactions.

A folding algorithm for extended RNA secondary structures.

Science.gov (United States)

Höner zu Siederdissen, Christian; Bernhart, Stephan H; Stadler, Peter F; Hofacker, Ivo L

2011-07-01

RNA secondary structure contains many non-canonical base pairs of different pair families. Successful prediction of these structural features leads to improved secondary structures with applications in tertiary structure prediction and simultaneous folding and alignment. We present a theoretical model capturing both RNA pair families and extended secondary structure motifs with shared nucleotides using 2-diagrams. We accompany this model with a number of programs for parameter optimization and structure prediction. All sources (optimization routines, RNA folding, RNA evaluation, extended secondary structure visualization) are published under the GPLv3 and available at www.tbi.univie.ac.at/software/rnawolf/.

A probabilistic model of RNA conformational space

DEFF Research Database (Denmark)

Frellsen, Jes; Moltke, Ida; Thiim, Martin

2009-01-01

, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows...... conformations for 9 out of 10 test structures, solely using coarse-grained base-pairing information. In conclusion, the method provides a theoretical and practical solution for a major bottleneck on the way to routine prediction and simulation of RNA structure and dynamics in atomic detail.......The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...

Comprehensive characterization of lncRNA-mRNA related ceRNA network across 12 major cancers

Science.gov (United States)

Feng, Li; Li, Feng; Sun, Zeguo; Wu, Tan; Shi, Xinrui; Li, Jing; Li, Xia

2016-01-01

Recent studies indicate that long noncoding RNAs (lncRNAs) can act as competing endogenous RNAs (ceRNAs) to indirectly regulate mRNAs through shared microRNAs, which represents a novel layer of RNA crosstalk and plays critical roles in the development of tumor. However, the global regulation landscape and characterization of these lncRNA related ceRNA crosstalk in cancers is still largely unknown. Here, we systematically characterized the lncRNA related ceRNA interactions across 12 major cancers and the normal physiological states by integrating multidimensional molecule profiles of more than 5000 samples. Our study suggest the large difference of ceRNA regulation between normal and tumor states and the higher similarity across similar tissue origin of tumors. The ceRNA related molecules have more conserved features in tumor networks and they play critical roles in both the normal and tumorigenesis processes. Besides, lncRNAs in the pan-cancer ceRNA network may be potential biomarkers of tumor. By exploring hub lncRNAs, we found that these conserved key lncRNAs dominate variable tumor hallmark processes across pan-cancers. Network dynamic analysis highlights the critical roles of ceRNA regulation in tumorigenesis. By analyzing conserved ceRNA interactions, we found that miRNA mediate ceRNA regulation showed different patterns across pan-cancer; while analyzing the cancer specific ceRNA interactions reveal that lncRNAs synergistically regulated tumor driver genes of cancer hallmarks. Finally, we found that ceRNA modules have the potential to predict patient survival. Overall, our study systematically dissected the lncRNA related ceRNA networks in pan-cancer that shed new light on understanding the molecular mechanism of tumorigenesis. PMID:27580177

miREE: miRNA recognition elements ensemble

Science.gov (United States)

2011-01-01

Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between

Tumor RNA disruption predicts survival benefit from breast cancer chemotherapy.

Science.gov (United States)

Parissenti, Amadeo M; Guo, Baoqing; Pritzker, Laura B; Pritzker, Kenneth P H; Wang, Xiaohui; Zhu, Mu; Shepherd, Lois E; Trudeau, Maureen E

2015-08-01

In a prior substudy of the CAN-NCIC-MA.22 clinical trial (ClinicalTrials.gov identifier NCT00066443), we observed that neoadjuvant chemotherapy reduced tumor RNA integrity in breast cancer patients, a phenomenon we term "RNA disruption." The purpose of the current study was to assess in the full patient cohort the relationship between mid-treatment tumor RNA disruption and both pCR post-treatment and, subsequently, disease-free survival (DFS) up to 108 months post-treatment. To meet these objectives, we developed the RNA disruption assay (RDA) to quantify RNA disruption and stratify it into 3 response zones of clinical importance. Zone 1 is a level of RNA disruption inadequate for pathologic complete response (pCR); Zone 2 is an intermediate level, while Zone 3 has high RNA disruption. The same RNA disruption cut points developed for pCR response were then utilized for DFS. Tumor RDA identified >fourfold more chemotherapy non-responders than did clinical response by calipers. pCR responders were clustered in RDA Zone 3, irrespective of tumor subtype. DFS was about 2-fold greater for patients with tumors in Zone 3 compared to Zone 1 patients. Kaplan-Meier survival curves corroborated these findings that high tumor RNA disruption was associated with increased DFS. DFS values for patients in zone 3 that did not achieve a pCR were similar to that of pCR recipients across tumor subtypes, including patients with hormone receptor positive tumors that seldom achieve a pCR. RDA appears superior to pCR as a chemotherapy response biomarker, supporting the prospect of its use in response-guided chemotherapy.

Detection of AR-V7 mRNA in whole blood may not predict the effectiveness of novel endocrine drugs for castration-resistant prostate cancer.

Science.gov (United States)

Takeuchi, Takumi; Okuno, Yumiko; Hattori-Kato, Mami; Zaitsu, Masayoshi; Mikami, Koji

2016-01-01

A splice variant of androgen receptor (AR), AR-V7, lacks in androgen-binding portion and leads to aggressive cancer characteristics. Reverse transcription-polymerase chain reactions (PCRs) and subsequent nested PCRs for the amplification of AR-V7 and prostate-specific antigen (PSA) transcripts were done for whole blood of patients with prostate cancer and male controls. With primary reverse transcription PCRs, AR-V7 and PSA were detected in 4.5% and 4.7% of prostate cancer, respectively. With nested PCRs, AR-V7 messenger RNA (mRNA) was positive in 43.8% of castration-sensitive prostate cancer and 48.1% of castration-resistant prostate cancer (CRPC), while PSA mRNA was positive in 6.3% of castration-sensitive prostate cancer and 18.5% of CRPC. Whole-blood samples of controls showed AR-V7 mRNA expression by nested PCR. Based on multivariate analysis, expression of AR-V7 mRNA in whole blood was not significantly correlated with clinical parameters and PSA mRNA in blood, while univariate analysis showed a correlation between AR-V7 mRNA and metastasis at initial diagnosis. Detection of AR-V7 mRNA did not predict the reduction of serum PSA in patients with CRPC following abiraterone and enzalutamide administration. In conclusion, AR-V7 mRNA expression in normal hematopoietic cells may have annihilated the manifestation of aggressiveness of prostate cancer and the prediction of the effectiveness of abiraterone and enzalutamide by the assessment of AR-V7 mRNA in blood.

MicroRNA target finding by comparative genomics.

Science.gov (United States)

Friedman, Robin C; Burge, Christopher B

2014-01-01

MicroRNAs (miRNAs) have been implicated in virtually every metazoan biological process, exerting a widespread impact on gene expression. MicroRNA repression is conferred by relatively short "seed match" sequences, although the degree of repression varies widely for individual target sites. The factors controlling whether, and to what extent, a target site is repressed are not fully understood. As an alternative to target prediction based on sequence alone, comparative genomics has emerged as an invaluable tool for identifying miRNA targets that are conserved by natural selection, and hence likely effective and important. Here we present a general method for quantifying conservation of miRNA seed match sites, separating it from background conservation, controlling for various biases, and predicting miRNA targets. This method is useful not only for generating predictions but also as a tool for empirically evaluating the importance of various target prediction criteria.

Adobe Creative Suite 4 Bible

CERN Document Server

Padova, Ted

2009-01-01

As one of the few books to cover integration and workflow issues between Photoshop, Illustrator, InDesign, GoLive, Acrobat, and Version Cue, this comprehensive reference is the one book that Creative Suite users need; Two well-known and respected authors cover topics such as developing consistent color-managed workflows, moving files among the Creative Suite applications, preparing files for print or the Web, repurposing documents, and using the Creative Suite with Microsoft Office documents; More than 1,200 pages are packed with valuable advice and techniques for tackling common everyday issu

Preparation of Small RNA NGS Libraries from Biofluids.

Science.gov (United States)

Etheridge, Alton; Wang, Kai; Baxter, David; Galas, David

2018-01-01

Next generation sequencing (NGS) is a powerful method for transcriptome analysis. Unlike other gene expression profiling methods, such as microarrays, NGS provides additional information such as splicing variants, sequence polymorphisms, and novel transcripts. For this reason, NGS is well suited for comprehensive profiling of the wide range of extracellular RNAs (exRNAs) in biofluids. ExRNAs are of great interest because of their possible biological role in cell-to-cell communication and for their potential use as biomarkers or for therapeutic purposes. Here, we describe a modified protocol for preparation of small RNA libraries for NGS analysis. This protocol has been optimized for use with low-input exRNA-containing samples, such as plasma or serum, and has modifications designed to reduce the sequence-specific bias typically encountered with commercial small RNA library construction kits.

MicroRNA Gene Regulatory Networks in Peripheral Nerve Sheath Tumors

Science.gov (United States)

2012-09-01

1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202- 4302. Respondents should be aware that notwithstanding any other provision of law , no...Reinhardt F, Benaich N, Calogrias D, Szasz AM, Wang ZC, Brock JE, Richardson AL, Weinberg RA (2009) A pleiotropically acting microRNA, miR-31, inhibits breast

Improved airline-type supplied-air plastic suit

International Nuclear Information System (INIS)

Jolley, L. Jr.; Zippler, D.B.; Cofer, C.H.; Harper, J.A.

1978-06-01

Two piece supplied-air plastic suits are used extensively at the Savannah River Plant for personnel protection against inhalation of airborne plutonium and tritium. Worker comfort and noise level problems gave impetus to development of an improved suit and aid distribution system. The resulting plastic suit and development work are discussed. The plastic suit unit cost is less than $20, the hearing zone noise level is less than 75 dBA, protection factors exceed 10,000, and user comfort is approved. This suit is expected to meet performance requirements for unrestricted use

A comprehensive software suite for protein family construction and functional site prediction.

Directory of Open Access Journals (Sweden)

David Renfrew Haft

Full Text Available In functionally diverse protein families, conservation in short signature regions may outperform full-length sequence comparisons for identifying proteins that belong to a subgroup within which one specific aspect of their function is conserved. The SIMBAL workflow (Sites Inferred by Metabolic Background Assertion Labeling is a data-mining procedure for finding such signature regions. It begins by using clues from genomic context, such as co-occurrence or conserved gene neighborhoods, to build a useful training set from a large number of uncharacterized but mutually homologous proteins. When training set construction is successful, the YES partition is enriched in proteins that share function with the user's query sequence, while the NO partition is depleted. A selected query sequence is then mined for short signature regions whose closest matches overwhelmingly favor proteins from the YES partition. High-scoring signature regions typically contain key residues critical to functional specificity, so proteins with the highest sequence similarity across these regions tend to share the same function. The SIMBAL algorithm was described previously, but significant manual effort, expertise, and a supporting software infrastructure were required to prepare the requisite training sets. Here, we describe a new, distributable software suite that speeds up and simplifies the process for using SIMBAL, most notably by providing tools that automate training set construction. These tools have broad utility for comparative genomics, allowing for flexible collection of proteins or protein domains based on genomic context as well as homology, a capability that can greatly assist in protein family construction. Armed with this new software suite, SIMBAL can serve as a fast and powerful in silico alternative to direct experimentation for characterizing proteins and their functional interactions.

HumanViCe: Host ceRNA network in virus infected cells in human

Directory of Open Access Journals (Sweden)

Suman eGhosal

2014-07-01

Full Text Available Host-virus interaction via host cellular components has been an important field of research in recent times. RNA interference mediated by short interfering RNAs and microRNAs (miRNA, is a widespread anti-viral defence strategy. Importantly, viruses also encode their own miRNAs. In recent times miRNAs were identified as key players in host-virus interaction. Furthermore, viruses were shown to exploit the host miRNA networks to suite their own need. The complex cross-talk between host and viral miRNAs and their cellular and viral targets forms the environment for viral pathogenesis. Apart from protein-coding mRNAs, non-coding RNAs may also be targeted by host or viral miRNAs in virus infected cells, and viruses can exploit the host miRNA mediated gene regulatory network via the competing endogenous RNA effect. A recent report showed that viral U-rich non-coding RNAs called HSUR, expressed in primate virus herpesvirus saimiri (HVS infected T cells, were able to bind to three host miRNAs, causing significant alteration in cellular level for one of the miRNAs. We have predicted protein coding and non protein-coding targets for viral and human miRNAs in virus infected cells. We identified viral miRNA targets within host non-coding RNA loci from AGO interacting regions in three different virus infected cells. Gene ontology (GO and pathway enrichment analysis of the genes comprising the ceRNA networks in the virus infected cells revealed enrichment of key cellular signalling pathways related to cell fate decisions and gene transcription, like Notch and Wnt signalling pathways, as well as pathways related to viral entry, replication and virulence. We identified a vast number of non-coding transcripts playing as potential ceRNAs to the immune response associated genes; e.g. APOBEC family genes, in some virus infected cells. All these information are compiled in HumanViCe, a comprehensive database that provides the potential ceRNA networks in virus

Z-1 Prototype Space Suit Testing Summary

Science.gov (United States)

Ross, Amy

2013-01-01

The Advanced Space Suit team of the NASA-Johnson Space Center performed a series of test with the Z-1 prototype space suit in 2012. This paper discusses, at a summary level, the tests performed and results from those tests. The purpose of the tests were two-fold: 1) characterize the suit performance so that the data could be used in the downselection of components for the Z-2 Space Suit and 2) develop interfaces with the suitport and exploration vehicles through pressurized suit evaluations. Tests performed included isolated and functional range of motion data capture, Z-1 waist and hip testing, joint torque testing, CO2 washout testing, fit checks and subject familiarizations, an exploration vehicle aft deck and suitport controls interface evaluation, delta pressure suitport tests including pressurized suit don and doff, and gross mobility and suitport ingress and egress demonstrations in reduced gravity. Lessons learned specific to the Z-1 prototype and to suit testing techniques will be presented.

Hybrid Enhanced Epidermal SpaceSuit Design Approaches

Science.gov (United States)

Jessup, Joseph M.

A Space suit that does not rely on gas pressurization is a multi-faceted problem that requires major stability controls to be incorporated during design and construction. The concept of Hybrid Epidermal Enhancement space suit integrates evolved human anthropomorphic and physiological adaptations into its functionality, using commercially available bio-medical technologies to address shortcomings of conventional gas pressure suits, and the impracticalities of MCP suits. The prototype HEE Space Suit explored integumentary homeostasis, thermal control and mobility using advanced bio-medical materials technology and construction concepts. The goal was a space suit that functions as an enhanced, multi-functional bio-mimic of the human epidermal layer that works in attunement with the wearer rather than as a separate system. In addressing human physiological requirements for design and construction of the HEE suit, testing regimes were devised and integrated into the prototype which was then subject to a series of detailed tests using both anatomical reproduction methods and human subject.

Bacterial vaginosis, human papilloma virus and herpes viridae do not predict vaginal HIV RNA shedding in women living with HIV in Denmark

DEFF Research Database (Denmark)

Wessman, Maria; Thorsteinsson, Kristina; Jensen, Jørgen S

2017-01-01

in the genital tract despite undetectable HIV RNA plasma viral load. We examined the prevalence and diagnostic predictors of BV and HIV-1 RNA vaginal shedding in women living with HIV (WLWH) in Denmark, taking into account the presence of human papillomavirus (HPV) and herpes viridae. METHODS: WLWH between 18......-51 years were recruited from six Departments of Infectious Diseases in Denmark during enrolment in the SHADE cohort; a prospective cohort study of WLWH attending regular outpatient care. BV was diagnosed by microscopy of vaginal swabs and PCR was used for detection of BV-associated bacteria, HPV, herpes...... RNA. Both before and after adjustment for BV, age, ethnicity, plasma HIV RNA, CD4 cell count, herpes viridae and HPV, we found no significant predictors of HIV RNA vaginal shedding. CONCLUSION: In well-treated WLWH, BV, herpes viridae or HPV do not predict vaginal HIV RNA shedding. This implies...

The predictive and prognostic potential of plasma telomerase reverse transcriptase (TERT) RNA in rectal cancer patients

Science.gov (United States)

Rampazzo, Enrica; Del Bianco, Paola; Bertorelle, Roberta; Boso, Caterina; Perin, Alessandro; Spiro, Giovanna; Bergamo, Francesca; Belluco, Claudio; Buonadonna, Angela; Palazzari, Elisa; Leonardi, Sara; De Paoli, Antonino; Pucciarelli, Salvatore; De Rossi, Anita

2018-01-01

Background: Preoperative chemoradiotherapy (CRT) followed by surgery is the standard care for locally advanced rectal cancer, but tumour response to CRT and disease outcome are variable. The current study aimed to investigate the effectiveness of plasma telomerase reverse transcriptase (TERT) levels in predicting tumour response and clinical outcome. Methods: 176 rectal cancer patients were included. Plasma samples were collected at baseline (before CRT=T0), 2 weeks after CRT was initiated (T1), post-CRT and before surgery (T2), and 4–8 months after surgery (T3) time points. Plasma TERT mRNA levels and total cell-free RNA were determined using real-time PCR. Results: Plasma levels of TERT were significantly lower at T2 (P<0.0001) in responders than in non-responders. Post-CRT TERT levels and the differences between pre- and post-CRT TERT levels independently predicted tumour response, and the prediction model had an area under curve of 0.80 (95% confidence interval (CI) 0.73–0.87). Multiple analysis demonstrated that patients with detectable TERT levels at T2 and T3 time points had a risk of disease progression 2.13 (95% CI 1.10–4.11)-fold and 4.55 (95% CI 1.48–13.95)-fold higher, respectively, than those with undetectable plasma TERT levels. Conclusions: Plasma TERT levels are independent markers of tumour response and are prognostic of disease progression in rectal cancer patients who undergo neoadjuvant therapy. PMID:29449673

A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs.

Science.gov (United States)

Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

2018-02-28

We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.

Automated and fast building of three-dimensional RNA structures.

Science.gov (United States)

Zhao, Yunjie; Huang, Yangyu; Gong, Zhou; Wang, Yanjie; Man, Jianfen; Xiao, Yi

2012-01-01

Building tertiary structures of non-coding RNA is required to understand their functions and design new molecules. Current algorithms of RNA tertiary structure prediction give satisfactory accuracy only for small size and simple topology and many of them need manual manipulation. Here, we present an automated and fast program, 3dRNA, for RNA tertiary structure prediction with reasonable accuracy for RNAs of larger size and complex topology.

Integrative miRNA-Gene Expression Analysis Enables Refinement of Associated Biology and Prediction of Response to Cetuximab in Head and Neck Squamous Cell Cancer

Directory of Open Access Journals (Sweden)

Loris De Cecco

2017-01-01

Full Text Available This paper documents the process by which we, through gene and miRNA expression profiling of the same samples of head and neck squamous cell carcinomas (HNSCC and an integrative miRNA-mRNA expression analysis, were able to identify candidate biomarkers of progression-free survival (PFS in patients treated with cetuximab-based approaches. Through sparse partial least square–discriminant analysis (sPLS-DA and supervised analysis, 36 miRNAs were identified in two components that clearly separated long- and short-PFS patients. Gene set enrichment analysis identified a significant correlation between the miRNA first-component and EGFR signaling, keratinocyte differentiation, and p53. Another significant correlation was identified between the second component and RAS, NOTCH, immune/inflammatory response, epithelial–mesenchymal transition (EMT, and angiogenesis pathways. Regularized canonical correlation analysis of sPLS-DA miRNA and gene data combined with the MAGIA2 web-tool highlighted 16 miRNAs and 84 genes that were interconnected in a total of 245 interactions. After feature selection by a smoothed t-statistic support vector machine, we identified three miRNAs and five genes in the miRNA-gene network whose expression result was the most relevant in predicting PFS (Area Under the Curve, AUC = 0.992. Overall, using a well-defined clinical setting and up-to-date bioinformatics tools, we are able to give the proof of principle that an integrative miRNA-mRNA expression could greatly contribute to the refinement of the biology behind a predictive model.

A probabilistic model of RNA conformational space

DEFF Research Database (Denmark)

Frellsen, Jes; Moltke, Ida; Thiim, Martin

2009-01-01

efficient sampling of RNA conformations in continuous space, and with associated probabilities. We show that the model captures several key features of RNA structure, such as its rotameric nature and the distribution of the helix lengths. Furthermore, the model readily generates native-like 3-D......, the discrete nature of the fragments necessitates the use of carefully tuned, unphysical energy functions, and their non-probabilistic nature impairs unbiased sampling. We offer a solution to the sampling problem that removes these important limitations: a probabilistic model of RNA structure that allows......The increasing importance of non-coding RNA in biology and medicine has led to a growing interest in the problem of RNA 3-D structure prediction. As is the case for proteins, RNA 3-D structure prediction methods require two key ingredients: an accurate energy function and a conformational sampling...

Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus.

Directory of Open Access Journals (Sweden)

Chun Zhao

Full Text Available BACKGROUND: Gestational diabetes mellitus (GDM is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM. METHODOLOGY/PRINCIPAL FINDINGS: We systematically used the TaqMan Low Density Array followed by individual quantitative reverse transcription polymerase chain reaction assays to screen miRNAs in serum collected at 16-19 gestational weeks. The expression levels of three miRNAs (miR-132, miR-29a and miR-222 were significantly decreased in GDM women with respect to the controls in similar gestational weeks in our discovery evaluation and internal validation, and two miRNAs (miR-29a and miR-222 were also consistently validated in two-centric external validation sample sets. In addition, the knockdown of miR-29a could increase Insulin-induced gene 1 (Insig1 expression level and subsequently the level of Phosphoenolpyruvate Carboxy Kinase2 (PCK2 in HepG2 cell lines. CONCLUSIONS/SIGNIFICANCE: Serum miRNAs are differentially expressed between GDM women and controls and could be candidate biomarkers for predicting GDM. The utility of miR-29a, miR-222 and miR-132 as serum-based non-invasive biomarkers warrants further evaluation and optimization.

BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2.

Science.gov (United States)

Lee, Hyungro; Yang, Youngik; Chae, Heejoon; Nam, Seungyoon; Choi, Donghoon; Tangchaisin, Patanachai; Herath, Chathura; Marru, Suresh; Nephew, Kenneth P; Kim, Sun

2012-09-01

MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research. However, the ability to conduct genome-wide microRNA-mRNA (gene) integration currently requires sophisticated, high-end informatics tools, significant expertise in bioinformatics and computer science to carry out the complex integration analysis. In addition, increased computing infrastructure capabilities are essential in order to accommodate large data sets. In this study, we have extended the BioVLAB cloud workbench to develop an environment for the integrated analysis of microRNA and mRNA expression data, named BioVLAB-MMIA. The workbench facilitates computations on the Amazon EC2 and S3 resources orchestrated by the XBaya Workflow Suite. The advantages of BioVLAB-MMIA over the web-based MMIA system include: 1) readily expanded as new computational tools become available; 2) easily modifiable by re-configuring graphic icons in the workflow; 3) on-demand cloud computing resources can be used on an "as needed" basis; 4) distributed orchestration supports complex and long running workflows asynchronously. We believe that BioVLAB-MMIA will be an easy-to-use computing environment for researchers who plan to perform genome-wide microRNA-mRNA (gene) integrated analysis tasks.

Detecting microRNA activity from gene expression data

LENUS (Irish Health Repository)

Madden, Stephen F

2010-05-18

Abstract Background MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions. Results Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance. Conclusions We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

Detecting microRNA activity from gene expression data.

LENUS (Irish Health Repository)

Madden, Stephen F

2010-01-01

BACKGROUND: MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions. RESULTS: Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance. CONCLUSIONS: We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

The BRITNeY Suite Animation Tool

DEFF Research Database (Denmark)

Westergaard, Michael; Lassen, Kristian Bisgaard

2006-01-01

This paper describes the BRITNeY suite, a tool which enables users to create visualizations of formal models. BRITNeY suite is integrated with CPN Tools, and we give an example of how to extend a simple stop-and-wait protocol with a visualization in the form of message sequence charts. We also sh...... examples of animations created during industrial projects to give an impression of what is possible with the BRITNeY suite....

RNA Sequencing and Bioinformatics Analysis Implicate the Regulatory Role of a Long Noncoding RNA-mRNA Network in Hepatic Stellate Cell Activation.

Science.gov (United States)

Guo, Can-Jie; Xiao, Xiao; Sheng, Li; Chen, Lili; Zhong, Wei; Li, Hai; Hua, Jing; Ma, Xiong

2017-01-01

To analyze the long noncoding (lncRNA)-mRNA expression network and potential roles in rat hepatic stellate cells (HSCs) during activation. LncRNA expression was analyzed in quiescent and culture-activated HSCs by RNA sequencing, and differentially expressed lncRNAs verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) were subjected to bioinformatics analysis. In vivo analyses of differential lncRNA-mRNA expression were performed on a rat model of liver fibrosis. We identified upregulation of 12 lncRNAs and 155 mRNAs and downregulation of 12 lncRNAs and 374 mRNAs in activated HSCs. Additionally, we identified the differential expression of upregulated lncRNAs (NONRATT012636.2, NONRATT016788.2, and NONRATT021402.2) and downregulated lncRNAs (NONRATT007863.2, NONRATT019720.2, and NONRATT024061.2) in activated HSCs relative to levels observed in quiescent HSCs, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that changes in lncRNAs associated with HSC activation revealed 11 significantly enriched pathways according to their predicted targets. Moreover, based on the predicted co-expression network, the relative dynamic levels of NONRATT013819.2 and lysyl oxidase (Lox) were compared during HSC activation both in vitro and in vivo. Our results confirmed the upregulation of lncRNA NONRATT013819.2 and Lox mRNA associated with the extracellular matrix (ECM)-related signaling pathway in HSCs and fibrotic livers. Our results detailing a dysregulated lncRNA-mRNA network might provide new treatment strategies for hepatic fibrosis based on findings indicating potentially critical roles for NONRATT013819.2 and Lox in ECM remodeling during HSC activation. © 2017 The Author(s). Published by S. Karger AG, Basel.

Identifying functional cancer-specific miRNA-mRNA interactions in testicular germ cell tumor.

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Sedaghat, Nafiseh; Fathy, Mahmood; Modarressi, Mohammad Hossein; Shojaie, Ali

2016-09-07

Testicular cancer is the most common cancer in men aged between 15 and 35 and more than 90% of testicular neoplasms are originated at germ cells. Recent research has shown the impact of microRNAs (miRNAs) in different types of cancer, including testicular germ cell tumor (TGCT). MicroRNAs are small non-coding RNAs which affect the development and progression of cancer cells by binding to mRNAs and regulating their expressions. The identification of functional miRNA-mRNA interactions in cancers, i.e. those that alter the expression of genes in cancer cells, can help delineate post-regulatory mechanisms and may lead to new treatments to control the progression of cancer. A number of sequence-based methods have been developed to predict miRNA-mRNA interactions based on the complementarity of sequences. While necessary, sequence complementarity is, however, not sufficient for presence of functional interactions. Alternative methods have thus been developed to refine the sequence-based interactions using concurrent expression profiles of miRNAs and mRNAs. This study aims to find functional cancer-specific miRNA-mRNA interactions in TGCT. To this end, the sequence-based predicted interactions are first refined using an ensemble learning method, based on two well-known methods of learning miRNA-mRNA interactions, namely, TaLasso and GenMiR++. Additional functional analyses were then used to identify a subset of interactions to be most likely functional and specific to TGCT. The final list of 13 miRNA-mRNA interactions can be potential targets for identifying TGCT-specific interactions and future laboratory experiments to develop new therapies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of hydrogen and carbon chemical shifts from RNA using database mining and support vector regression

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Brown, Joshua D.; Summers, Michael F. [University of Maryland Baltimore County, Howard Hughes Medical Institute (United States); Johnson, Bruce A., E-mail: bruce.johnson@asrc.cuny.edu [University of Maryland Baltimore County, Department of Chemistry and Biochemistry (United States)

2015-09-15

The Biological Magnetic Resonance Data Bank (BMRB) contains NMR chemical shift depositions for over 200 RNAs and RNA-containing complexes. We have analyzed the {sup 1}H NMR and {sup 13}C chemical shifts reported for non-exchangeable protons of 187 of these RNAs. Software was developed that downloads BMRB datasets and corresponding PDB structure files, and then generates residue-specific attributes based on the calculated secondary structure. Attributes represent properties present in each sequential stretch of five adjacent residues and include variables such as nucleotide type, base-pair presence and type, and tetraloop types. Attributes and {sup 1}H and {sup 13}C NMR chemical shifts of the central nucleotide are then used as input to train a predictive model using support vector regression. These models can then be used to predict shifts for new sequences. The new software tools, available as stand-alone scripts or integrated into the NMR visualization and analysis program NMRViewJ, should facilitate NMR assignment and/or validation of RNA {sup 1}H and {sup 13}C chemical shifts. In addition, our findings enabled the re-calibration a ring-current shift model using published NMR chemical shifts and high-resolution X-ray structural data as guides.

Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer.

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Cascione, Luciano; Gasparini, Pierluigi; Lovat, Francesca; Carasi, Stefania; Pulvirenti, Alfredo; Ferro, Alfredo; Alder, Hansjuerg; He, Gang; Vecchione, Andrea; Croce, Carlo M; Shapiro, Charles L; Huebner, Kay

2013-01-01

Triple negative breast cancer (TNBC) is a heterogeneous disease at the molecular, pathologic and clinical levels. To stratify TNBCs, we determined microRNA (miRNA) expression profiles, as well as expression profiles of a cancer-focused mRNA panel, in tumor, adjacent non-tumor (normal) and lymph node metastatic lesion (mets) tissues, from 173 women with TNBCs; we linked specific miRNA signatures to patient survival and used miRNA/mRNA anti-correlations to identify clinically and genetically different TNBC subclasses. We also assessed miRNA signatures as potential regulators of TNBC subclass-specific gene expression networks defined by expression of canonical signal pathways.Tissue specific miRNAs and mRNAs were identified for normal vs tumor vs mets comparisons. miRNA signatures correlated with prognosis were identified and predicted anti-correlated targets within the mRNA profile were defined. Two miRNA signatures (miR-16, 155, 125b, 374a and miR-16, 125b, 374a, 374b, 421, 655, 497) predictive of overall survival (P = 0.05) and distant-disease free survival (P = 0.009), respectively, were identified for patients 50 yrs of age or younger. By multivariate analysis the risk signatures were independent predictors for overall survival and distant-disease free survival. mRNA expression profiling, using the cancer-focused mRNA panel, resulted in clustering of TNBCs into 4 molecular subclasses with different expression signatures anti-correlated with the prognostic miRNAs. Our findings suggest that miRNAs play a key role in triple negative breast cancer through their ability to regulate fundamental pathways such as: cellular growth and proliferation, cellular movement and migration, Extra Cellular Matrix degradation. The results define miRNA expression signatures that characterize and contribute to the phenotypic diversity of TNBC and its metastasis.

The Mapping of Predicted Triplex DNA:RNA in the Drosophila Genome Reveals a Prominent Location in Development- and Morphogenesis-Related Genes

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Claude Pasquier

2017-07-01

Full Text Available Double-stranded DNA is able to form triple-helical structures by accommodating a third nucleotide strand. A nucleic acid triplex occurs according to Hoogsteen rules that predict the stability and affinity of the third strand bound to the Watson–Crick duplex. The “triplex-forming oligonucleotide” (TFO can be a short sequence of RNA that binds to the major groove of the targeted duplex only when this duplex presents a sequence of purine or pyrimidine bases in one of the DNA strands. Many nuclear proteins are known to bind triplex DNA or DNA:RNA, but their biological functions are unexplored. We identified sequences that are capable of engaging as the “triplex-forming oligonucleotide” in both the pre-lncRNA and pre-mRNA collections of Drosophila melanogaster. These motifs were matched against the Drosophila genome in order to identify putative sequences of triplex formation in intergenic regions, promoters, and introns/exons. Most of the identified TFOs appear to be located in the intronic region of the analyzed genes. Computational prediction of the most targeted genes by TFOs originating from pre-lncRNAs and pre-mRNAs revealed that they are restrictively associated with development- and morphogenesis-related gene networks. The refined analysis by Gene Ontology enrichment demonstrates that some individual TFOs present genome-wide scale matches that are located in numerous genes and regulatory sequences. The triplex DNA:RNA computational mapping at the genome-wide scale suggests broad interference in the regulatory process of the gene networks orchestrated by TFO RNAs acting in association simultaneously at multiple sites.

SRD: a Staphylococcus regulatory RNA database.

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Sassi, Mohamed; Augagneur, Yoann; Mauro, Tony; Ivain, Lorraine; Chabelskaya, Svetlana; Hallier, Marc; Sallou, Olivier; Felden, Brice

2015-05-01

An overflow of regulatory RNAs (sRNAs) was identified in a wide range of bacteria. We designed and implemented a new resource for the hundreds of sRNAs identified in Staphylococci, with primary focus on the human pathogen Staphylococcus aureus. The "Staphylococcal Regulatory RNA Database" (SRD, http://srd.genouest.org/) compiled all published data in a single interface including genetic locations, sequences and other features. SRD proposes novel and simplified identifiers for Staphylococcal regulatory RNAs (srn) based on the sRNA's genetic location in S. aureus strain N315 which served as a reference. From a set of 894 sequences and after an in-depth cleaning, SRD provides a list of 575 srn exempt of redundant sequences. For each sRNA, their experimental support(s) is provided, allowing the user to individually assess their validity and significance. RNA-seq analysis performed on strains N315, NCTC8325, and Newman allowed us to provide further details, upgrade the initial annotation, and identified 159 RNA-seq independent transcribed sRNAs. The lists of 575 and 159 sRNAs sequences were used to predict the number and location of srns in 18 S. aureus strains and 10 other Staphylococci. A comparison of the srn contents within 32 Staphylococcal genomes revealed a poor conservation between species. In addition, sRNA structure predictions obtained with MFold are accessible. A BLAST server and the intaRNA program, which is dedicated to target prediction, were implemented. SRD is the first sRNA database centered on a genus; it is a user-friendly and scalable device with the possibility to submit new sequences that should spread in the literature. © 2015 Sassi et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution

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Zur, Hadas; Tuller, Tamir

2016-01-01

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field. PMID:27591251

Query-dependent banding (QDB for faster RNA similarity searches.

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Eric P Nawrocki

2007-03-01

Full Text Available When searching sequence databases for RNAs, it is desirable to score both primary sequence and RNA secondary structure similarity. Covariance models (CMs are probabilistic models well-suited for RNA similarity search applications. However, the computational complexity of CM dynamic programming alignment algorithms has limited their practical application. Here we describe an acceleration method called query-dependent banding (QDB, which uses the probabilistic query CM to precalculate regions of the dynamic programming lattice that have negligible probability, independently of the target database. We have implemented QDB in the freely available Infernal software package. QDB reduces the average case time complexity of CM alignment from LN(2.4 to LN(1.3 for a query RNA of N residues and a target database of L residues, resulting in a 4-fold speedup for typical RNA queries. Combined with other improvements to Infernal, including informative mixture Dirichlet priors on model parameters, benchmarks also show increased sensitivity and specificity resulting from improved parameterization.

Enabling interoperability in Geoscience with GI-suite

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Boldrini, Enrico; Papeschi, Fabrizio; Santoro, Mattia; Nativi, Stefano

2015-04-01

GI-suite is a brokering framework targeting interoperability of heterogeneous systems in the Geoscience domain. The framework is composed by different brokers each one focusing on a specific functionality: discovery, access and semantics (i.e. GI-cat, GI-axe, GI-sem). The brokering takes place between a set of heterogeneous publishing services and a set of heterogeneous consumer applications: the brokering target is represented by resources (e.g. coverages, features, or metadata information) required to seamlessly flow from the providers to the consumers. Different international and community standards are now supported by GI-suite, making possible the successful deployment of GI-suite in many international projects and initiatives (such as GEOSS, NSF BCube and several EU funded projects). As for the publisher side more than 40 standards and implementations are supported (e.g. Dublin Core, OAI-PMH, OGC W*S, Geonetwork, THREDDS Data Server, Hyrax Server, etc.). The support for each individual standard is provided by means of specific GI-suite components, called accessors. As for the consumer applications side more than 15 standards and implementations are supported (e.g. ESRI ArcGIS, Openlayers, OGC W*S, OAI-PMH clients, etc.). The support for each individual standard is provided by means of specific profiler components. The GI-suite can be used in different scenarios by different actors: - A data provider having a pre-existent data repository can deploy and configure GI-suite to broker it and making thus available its data resources through different protocols to many different users (e.g. for data discovery and/or data access) - A data consumer can use GI-suite to discover and/or access resources from a variety of publishing services that are already publishing data according to well-known standards. - A community can deploy and configure GI-suite to build a community (or project-specific) broker: GI-suite can broker a set of community related repositories and

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

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Marques, Yuri Bento; de Paiva Oliveira, Alcione; Ribeiro Vasconcelos, Ana Tereza; Cerqueira, Fabio Ribeiro

2016-12-15

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

Long noncoding RNA TUG1 alleviates extracellular matrix accumulation via mediating microRNA-377 targeting of PPARγ in diabetic nephropathy.

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Duan, Li-Jun; Ding, Min; Hou, Li-Jun; Cui, Yuan-Tao; Li, Chun-Jun; Yu, De-Min

2017-03-11

Long noncoding RNA taurine-upregulated gene 1 (lncRNA TUG1) has been reported to play a key role in the progression of diabetic nephropathy (DN). However, the role of lncRNA TUG1 in the regulation of diabetic nephropathy remains largely unknown. The aim of the present study is to identify the regulation of lncRNA TUG1 on extracellular matrix accumulation via mediating microRNA-377 targeting of PPARγ, and investigate the underlying mechanisms in progression of DN. Microarray was performed to screen differentially expressed miRNAs in db/db DN mice. Afterwards, computational prediction programs (TargetScan, miRanda, PicTar and miRGen) was applied to predict the target gene of miRNAs. The complementary binding of miRNA and lncRNA was assessed by luciferase assays. Protein and mRNA expression were detected by western blot and real time quantitate PCR. MiRNA-377 was screened by miRNA microarray and differentially up-regulated in db/db DN mice. PPARγ was predicted to be the target of miR-377 and the prediction was verified by luciferase assays. Expression of miR-377 was up-regulated in mesangial cell treated with high glucose (25 mM), and overexpression of miR-377 inhibited PPARγ expression and promoted PAI-1 and TGF-β1 expression. The expression of TUG1 antagonized the effect of miR-377 on the downregulation of its target PPARγ and inhibited extracellular matrix accumulation, including PAI-1, TGF-β1, fibronectin (FN) and collagen IV (Col IV), induced by high glucose. LncRNA TUG1 acts as an endogenous sponge of miR-377 and downregulates miR-377 expression levels, and thereby relieving the inhibition of its target gene PPARγ and alleviates extracellular matrix accumulation of mesangial cells, which provides a novel insight of diabetic nephropathy pathogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

On the importance of cotranscriptional RNA structure formation

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Lai, Daniel; Proctor, Jeff R.; Meyer, Irmtraud M.

2013-01-01

The expression of genes, both coding and noncoding, can be significantly influenced by RNA structural features of their corresponding transcripts. There is by now mounting experimental and some theoretical evidence that structure formation in vivo starts during transcription and that this cotranscriptional folding determines the functional RNA structural features that are being formed. Several decades of research in bioinformatics have resulted in a wide range of computational methods for predicting RNA secondary structures. Almost all state-of-the-art methods in terms of prediction accuracy, however, completely ignore the process of structure formation and focus exclusively on the final RNA structure. This review hopes to bridge this gap. We summarize the existing evidence for cotranscriptional folding and then review the different, currently used strategies for RNA secondary-structure prediction. Finally, we propose a range of ideas on how state-of-the-art methods could be potentially improved by explicitly capturing the process of cotranscriptional structure formation. PMID:24131802

Inference of miRNA targets using evolutionary conservation and pathway analysis

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van Nimwegen Erik

2007-03-01

Full Text Available Abstract Background MicroRNAs have emerged as important regulatory genes in a variety of cellular processes and, in recent years, hundreds of such genes have been discovered in animals. In contrast, functional annotations are available only for a very small fraction of these miRNAs, and even in these cases only partially. Results We developed a general Bayesian method for the inference of miRNA target sites, in which, for each miRNA, we explicitly model the evolution of orthologous target sites in a set of related species. Using this method we predict target sites for all known miRNAs in flies, worms, fish, and mammals. By comparing our predictions in fly with a reference set of experimentally tested miRNA-mRNA interactions we show that our general method performs at least as well as the most accurate methods available to date, including ones specifically tailored for target prediction in fly. An important novel feature of our model is that it explicitly infers the phylogenetic distribution of functional target sites, independently for each miRNA. This allows us to infer species-specific and clade-specific miRNA targeting. We also show that, in long human 3' UTRs, miRNA target sites occur preferentially near the start and near the end of the 3' UTR. To characterize miRNA function beyond the predicted lists of targets we further present a method to infer significant associations between the sets of targets predicted for individual miRNAs and specific biochemical pathways, in particular those of the KEGG pathway database. We show that this approach retrieves several known functional miRNA-mRNA associations, and predicts novel functions for known miRNAs in cell growth and in development. Conclusion We have presented a Bayesian target prediction algorithm without any tunable parameters, that can be applied to sequences from any clade of species. The algorithm automatically infers the phylogenetic distribution of functional sites for each miRNA, and

Carbon Dioxide Washout Testing Using Various Inlet Vent Configurations in the Mark-III Space Suit

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Korona, F. Adam; Norcross, Jason; Conger, Bruce; Navarro, Moses

2014-01-01

Requirements for using a space suit during ground testing include providing adequate carbon dioxide (CO2) washout for the suited subject. Acute CO2 exposure can lead to symptoms including headache, dyspnea, lethargy, and eventually unconsciousness or even death. Symptoms depend on several factors including inspired partial pressure of CO2 (ppCO2), duration of exposure, metabolic rate of the subject, and physiological differences between subjects. Computational Fluid Dynamics (CFD) analysis has predicted that the configuration of the suit inlet vent has a significant effect on oronasal CO2 concentrations. The main objective of this test was to characterize inspired oronasal ppCO2 for a variety of inlet vent configurations in the Mark-III suit across a range of workload and flow rates. Data and trends observed during testing along with refined CFD models will be used to help design an inlet vent configuration for the Z-2 space suit. The testing methodology used in this test builds upon past CO2 washout testing performed on the Z-1 suit, Rear Entry I-Suit, and the Enhanced Mobility Advanced Crew Escape Suit. Three subjects performed two test sessions each in the Mark-III suit to allow for comparison between tests. Six different helmet inlet vent configurations were evaluated during each test session. Suit pressure was maintained at 4.3 psid. Suited test subjects walked on a treadmill to generate metabolic workloads of approximately 2000 and 3000 BTU/hr. Supply airflow rates of 6 and 4 actual cubic feet per minute were tested at each workload. Subjects wore an oronasal mask with an open port in front of the mouth and were allowed to breathe freely. Oronasal ppCO2 was monitored real-time via gas analyzers with sampling tubes connected to the oronasal mask. Metabolic rate was calculated from the CO2 production measured by an additional gas analyzer at the air outlet from the suit. Real-time metabolic rate measurements were used to adjust the treadmill workload to meet

ASDA - Advanced Suit Design Analyzer computer program

Science.gov (United States)

Bue, Grant C.; Conger, Bruce C.; Iovine, John V.; Chang, Chi-Min

1992-01-01

An ASDA model developed to evaluate the heat and mass transfer characteristics of advanced pressurized suit design concepts for low pressure or vacuum planetary applications is presented. The model is based on a generalized 3-layer suit that uses the Systems Integrated Numerical Differencing Analyzer '85 in conjunction with a 41-node FORTRAN routine. The latter simulates the transient heat transfer and respiratory processes of a human body in a suited environment. The user options for the suit encompass a liquid cooled garment, a removable jacket, a CO2/H2O permeable layer, and a phase change layer.

Use MACES IVA Suit for EVA Mobility Evaluations

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Watson, Richard D.

2014-01-01

The use of an Intra-Vehicular Activity (IVA) suit for a spacewalk or Extra-Vehicular Activity (EVA) was evaluated for mobility and usability in the Neutral Buoyancy Lab (NBL) environment. The Space Shuttle Advanced Crew Escape Suit (ACES) has been modified (MACES) to integrate with the Orion spacecraft. The first several missions of the Orion MPCV spacecraft will not have mass available to carry an EVA specific suit so any EVA required will have to be performed by the MACES. Since the MACES was not designed with EVA in mind, it was unknown what mobility the suit would be able to provide for an EVA or if a person could perform useful tasks for an extended time inside the pressurized suit. The suit was evaluated in multiple NBL runs by a variety of subjects including crewmembers with significant EVA experience. Various functional mobility tasks performed included: translation, body positioning, carrying tools, body stabilization, equipment handling, and use of tools. Hardware configurations included with and without TMG, suit with IVA gloves and suit with EVA gloves. Most tasks were completed on ISS mockups with existing EVA tools. Some limited tasks were completed with prototype tools on a simulated rocky surface. Major findings include: demonstration of the ability to weigh-out the suit, understanding the need to have subjects perform multiple runs prior to getting feedback, determination of critical sizing factors, and need for adjustment of suit work envelop. The early testing has demonstrated the feasibility of EVA's limited duration and limited scope. Further testing is required with more flight like tasking and constraints to validate these early results. If the suit is used for EVA, it will require mission specific modifications for umbilical management or PLSS integration, safety tether attachment, and tool interfaces. These evaluations are continuing through calendar year 2014.

Morphing: A Novel Approach to Astronaut Suit Sizing

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Margerum, Sarah; Clowers, Kurt; Rajulu, Sudhakar

2006-01-01

The fitting of a spacesuit to an astronaut is an iterative process consisting of two parts. The first uses anthropometric data to provide an approximation of the suit components that will fit the astronaut. The second part is the subjective fitting, where small adjustments are made based on the astronaut s preference. By providing a better approximation of the correct suit components, the entire fit process time can be reduced significantly. The goals of this project are twofold: (1) To evaluate the effectiveness of the existing sizing algorithm for the Mark III Hybrid suit and (2) to determine what additional components are needed in order to provide adequate sizing for the existing astronaut population. A single subject was scanned using a 3D whole-body scanner (VITUS 3D) in the Mark III suit in eight different poses and four subjects in minimal clothing were also scanned in similar poses. The 3D external body scans of the suit and the subject are overlaid and visually aligned in a customized MATLAB program. The suit components were contracted or expanded linearly along the subjects limbs to match the subjects segmental lengths. Two independent measures were obtained from the morphing program on four subjects and compared with the existing sizing information. Two of the four subjects were in correspondence with the sizing algorithm and morphing results. The morphing outcome for a third subject, incompatible with the suit, suggested that an additional arm element at least 6 inches smaller than the existing smallest suit component would need to be acquired. The morphing result of the fourth subject, deemed incompatible with the suit using the sizing algorithm, indicated a different suit configuration which would be compatible. This configuration matched with the existing suit fit check data.

The integrated analysis of RNA-seq and microRNA-seq depicts miRNA-mRNA networks involved in Japanese flounder (Paralichthys olivaceus) albinism.

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Wang, Na; Wang, Ruoqing; Wang, Renkai; Tian, Yongsheng; Shao, Changwei; Jia, Xiaodong; Chen, Songlin

2017-01-01

Albinism, a phenomenon characterized by pigmentation deficiency on the ocular side of Japanese flounder (Paralichthys olivaceus), has caused significant damage. Limited mRNA and microRNA (miRNA) information is available on fish pigmentation deficiency. In this study, a high-throughput sequencing strategy was employed to identify the mRNA and miRNAs involved in P. olivaceus albinism. Based on P. olivaceus genome, RNA-seq identified 21,787 know genes and 711 new genes by transcripts assembly. Of those, 235 genes exhibited significantly different expression pattern (fold change ≥2 or ≤0.5 and q-value≤0.05), including 194 down-regulated genes and 41 up-regulated genes in albino versus normally pigmented individuals. These genes were enriched to 81 GO terms and 9 KEGG pathways (p≤0.05). Among those, the pigmentation related pathways-Melanogenesis and tyrosine metabolism were contained. High-throughput miRNA sequencing identified a total of 475 miRNAs, including 64 novel miRNAs. Furthermore, 33 differentially expressed miRNAs containing 13 up-regulated and 20 down-regulated miRNAs were identified in albino versus normally pigmented individuals (fold change ≥1.5 or ≤0.67 and p≤0.05). The next target prediction discovered a variety of putative target genes, of which, 134 genes including Tyrosinase (TYR), Tyrosinase-related protein 1 (TYRP1), Microphthalmia-associated transcription factor (MITF) were overlapped with differentially expressed genes derived from RNA-seq. These target genes were significantly enriched to 254 GO terms and 103 KEGG pathways (p<0.001). Of those, tyrosine metabolism, lysosomes, phototransduction pathways, etc., attracted considerable attention due to their involvement in regulating skin pigmentation. Expression patterns of differentially expressed mRNA and miRNAs were validated in 10 mRNA and 10 miRNAs by qRT-PCR. With high-throughput mRNA and miRNA sequencing and analysis, a series of interested mRNA and miRNAs involved in fish

Predictive value of BRCA1/2 mRNA expression for response to neoadjuvant chemotherapy in BRCA-negative breast cancers.

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Xu, Ye; Ouyang, Tao; Li, Jinfeng; Wang, Tianfeng; Fan, Zhaoqing; Fan, Tie; Lin, Benyao; Xie, Yuntao

2018-01-01

It is well known that BRCA1 and BRCA2 play a central role in DNA repair, but the relationship between BRCA1 and BRCA2 mRNA expression and response to neoadjuvant chemotherapy in sporadic breast cancer patients has not been well established. Here, we investigate the association between BRCA1 or BRCA2 mRNA expression levels and pathological response in 674 BRCA1/2 mutation-negative breast cancer patients who received neoadjuvant chemotherapy. BRCA1 and BRCA2 mRNA expression were assessed using quantitative real-time polymerase chain reaction in core biopsy breast cancer tissue obtained prior to the initiation of neoadjuvant chemotherapy. A total 129 patients (19.1%) achieved pathological complete response (pCR) after neoadjuvant chemotherapy. Among patients treated with anthracycline-based chemotherapy (n = 531), BRCA1 mRNA low expression patients had a significantly higher pCR rate than intermediate or high BRCA1 mRNA expression groups (24.6% vs 16.8% or 14.0%, P = .031) and retained borderline significance (OR = 1.54, 95% CI = 0.93-2.56, P = .094) in multivariate analysis. Among the 129 patients who received a taxane-based regimen, pCR rate showed no differences in BRCA1 low, intermediate, and high mRNA level subgroups (19.6%, 26.8% and 21.4%, respectively; P = .71). BRCA2 mRNA level was not associated with pCR rate in the anthracyline-based treated subgroup (P = .60) or the taxane-based regimen subgroup (P = .82). Taken together, our findings suggested that BRCA1 mRNA expression could be used as a predictive marker in BRCA1/2 mutation-negative breast cancer patients who received neoadjuvant anthracycline-based treatment. © 2017 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Homology Modeling and Analysis of Structure Predictions of the Bovine Rhinitis B Virus RNA Dependent RNA Polymerase (RdRp

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Devendra K. Rai

2012-07-01

Full Text Available Bovine Rhinitis B Virus (BRBV is a picornavirus responsible for mild respiratory infection of cattle. It is probably the least characterized among the aphthoviruses. BRBV is the closest relative known to Foot and Mouth Disease virus (FMDV with a ~43% identical polyprotein sequence and as much as 67% identical sequence for the RNA dependent RNA polymerase (RdRp, which is also known as 3D polymerase (3D^pol. In the present study we carried out phylogenetic analysis, structure based sequence alignment and prediction of three-dimensional structure of BRBV 3D^pol using a combination of different computational tools. Model structures of BRBV 3D^pol were verified for their stereochemical quality and accuracy. The BRBV 3D^pol structure predicted by SWISS-MODEL exhibited highest scores in terms of stereochemical quality and accuracy, which were in the range of 2Å resolution crystal structures. The active site, nucleic acid binding site and overall structure were observed to be in agreement with the crystal structure of unliganded as well as template/primer (T/P, nucleotide tri-phosphate (NTP and pyrophosphate (PPi bound FMDV 3D^pol (PDB, 1U09 and 2E9Z. The closest proximity of BRBV and FMDV 3D^pol as compared to human rhinovirus type 16 (HRV-16 and rabbit hemorrhagic disease virus (RHDV 3D^pols is also substantiated by phylogeny analysis and root-mean square deviation (RMSD between C-α traces of the polymerase structures. The absence of positively charged α-helix at C terminal, significant differences in non-covalent interactions especially salt bridges and CH-pi interactions around T/P channel of BRBV 3D^pol compared to FMDV 3D^pol, indicate that despite a very high homology to FMDV 3D^pol, BRBV 3D^pol may adopt a different mechanism for handling its substrates and adapting to physiological requirements. Our findings will be valuable in the

Suited Contingency Ops Food - 2

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Glass, J. W.; Leong, M. L.; Douglas, G. L.

2014-01-01

The contingency scenario for an emergency cabin depressurization event may require crewmembers to subsist in a pressurized suit for up to 144 hours. This scenario requires the capability for safe nutrition delivery through a helmet feed port against a 4 psi pressure differential to enable crewmembers to maintain strength and cognition to perform critical tasks. Two nutritional delivery prototypes were developed and analyzed for compatibility with the helmet feed port interface and for operational effectiveness against the pressure differential. The bag-in-bag (BiB) prototype, designed to equalize the suit pressure with the beverage pouch and enable a crewmember to drink normally, delivered water successfully to three different subjects in suits pressurized to 4 psi. The Boa restrainer pouch, designed to provide mechanical leverage to overcome the pressure differential, did not operate sufficiently. Guidelines were developed and compiled for contingency beverages that provide macro-nutritional requirements, a minimum one-year shelf life, and compatibility with the delivery hardware. Evaluation results and food product parameters have the potential to be used to improve future prototype designs and develop complete nutritional beverages for contingency events. These feeding capabilities would have additional use on extended surface mission EVAs, where the current in-suit drinking device may be insufficient.

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3' UTRs and coding sequences.

Science.gov (United States)

Šulc, Miroslav; Marín, Ray M; Robins, Harlan S; Vaníček, Jiří

2015-07-01

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3' untranslated regions (3' UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3' UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA-mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA-mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Interoperative efficiency in minimally invasive surgery suites.

Science.gov (United States)

van Det, M J; Meijerink, W J H J; Hoff, C; Pierie, J P E N

2009-10-01

Performing minimally invasive surgery (MIS) in a conventional operating room (OR) requires additional specialized equipment otherwise stored outside the OR. Before the procedure, the OR team must collect, prepare, and connect the equipment, then take it away afterward. These extra tasks pose a thread to OR efficiency and may lengthen turnover times. The dedicated MIS suite has permanently installed laparoscopic equipment that is operational on demand. This study presents two experiments that quantify the superior efficiency of the MIS suite in the interoperative period. Preoperative setup and postoperative breakdown times in the conventional OR and the MIS suite in an experimental setting and in daily practice were analyzed. In the experimental setting, randomly chosen OR teams simulated the setup and breakdown for a standard laparoscopic cholecystectomy (LC) and a complex laparoscopic sigmoid resection (LS). In the clinical setting, the interoperative period for 66 LCs randomly assigned to the conventional OR or the MIS suite were analyzed. In the experimental setting, the setup and breakdown times were significantly shorter in the MIS suite. The difference between the two types of OR increased for the complex procedure: 2:41 min for the LC (p < 0.001) and 10:47 min for the LS (p < 0.001). In the clinical setting, the setup and breakdown times as a whole were not reduced in the MIS suite. Laparoscopic setup and breakdown times were significantly shorter in the MIS suite (mean difference, 5:39 min; p < 0.001). Efficiency during the interoperative period is significantly improved in the MIS suite. The OR nurses' tasks are relieved, which may reduce mental and physical workload and improve job satisfaction and patient safety. Due to simultaneous tasks of other disciplines, an overall turnover time reduction could not be achieved.

Combining miRNA and mRNA Expression Profiles in Wilms Tumor Subtypes

Directory of Open Access Journals (Sweden)

Nicole Ludwig

2016-03-01

Full Text Available Wilms tumor (WT is the most common childhood renal cancer. Recent findings of mutations in microRNA (miRNA processing proteins suggest a pivotal role of miRNAs in WT genesis. We performed miRNA expression profiling of 36 WTs of different subtypes and four normal kidney tissues using microarrays. Additionally, we determined the gene expression profile of 28 of these tumors to identify potentially correlated target genes and affected pathways. We identified 85 miRNAs and 2107 messenger RNAs (mRNA differentially expressed in blastemal WT, and 266 miRNAs and 1267 mRNAs differentially expressed in regressive subtype. The hierarchical clustering of the samples, using either the miRNA or mRNA profile, showed the clear separation of WT from normal kidney samples, but the miRNA pattern yielded better separation of WT subtypes. A correlation analysis of the deregulated miRNA and mRNAs identified 13,026 miRNA/mRNA pairs with inversely correlated expression, of which 2844 are potential interactions of miRNA and their predicted mRNA targets. We found significant upregulation of miRNAs-183, -301a/b and -335 for the blastemal subtype, and miRNAs-181b, -223 and -630 for the regressive subtype. We found marked deregulation of miRNAs regulating epithelial to mesenchymal transition, especially in the blastemal subtype, and miRNAs influencing chemosensitivity, especially in regressive subtypes. Further research is needed to assess the influence of preoperative chemotherapy and tumor infiltrating lymphocytes on the miRNA and mRNA patterns in WT.

The Variable Vector Countermeasure Suit (V2Suit for Space Habitation and Exploration

Directory of Open Access Journals (Sweden)

Kevin R Duda

2015-04-01

Full Text Available The Variable Vector Countermeasure Suit (V2Suit for Space Habitation and Exploration is a novel system concept that provides a platform for integrating sensors and actuators with daily astronaut intravehicular activities to improve health and performance, while reducing the mass and volume of the physiologic adaptation countermeasure systems, as well as the required exercise time during long-duration space exploration missions. The V2Suit system leverages wearable kinematic monitoring technology and uses inertial measurement units (IMUs and control moment gyroscopes (CMGs within miniaturized modules placed on body segments to provide a viscous resistance during movements against a specified direction of down – initially as a countermeasure to the sensorimotor adaptation performance decrements that manifest themselves while living and working in microgravity and during gravitational transitions during long-duration spaceflight, including post-flight recovery and rehabilitation. Several aspects of the V2Suit system concept were explored and simulated prior to developing a brassboard prototype for technology demonstration. This included a system architecture for identifying the key components and their interconnects, initial identification of key human-system integration challenges, development of a simulation architecture for CMG selection and parameter sizing, and the detailed mechanical design and fabrication of a module. The brassboard prototype demonstrates closed-loop control from down initialization through CMG actuation, and provides a research platform for human performance evaluations to mitigate sensorimotor adaptation, as well as a tool for determining the performance requirements when used as a musculoskeletal deconditioning countermeasure. This type of countermeasure system also has Earth benefits, particularly in gait or movement stabilization and rehabilitation.

Innovative technology summary report: Sealed-seam sack suits

International Nuclear Information System (INIS)

1998-09-01

Sealed-seam sack suits are an improved/innovative safety and industrial hygiene technology designed to protect workers from dermal exposure to contamination. Most of these disposable, synthetic-fabric suits are more protective than cotton suits, and are also water-resistant and gas permeable. Some fabrics provide a filter to aerosols, which is important to protection against contamination, while allowing air to pass, increasing comfort level of workers. It is easier to detect body-moisture breakthrough with the disposable suits than with cotton, which is also important to protecting workers from contamination. These suits present a safe and cost-effective (6% to 17% less expensive than the baseline) alternative to traditional protective clothing. This report covers the period from October 1996 to August 1997. During that time, sealed-seam sack suits were demonstrated during daily activities under normal working conditions at the C Reactor and under environmentally controlled conditions at the Los Alamos National Laboratory (LANL)

dPORE-miRNA: Polymorphic regulation of microRNA genes

KAUST Repository

Schmeier, Sebastian; Schaefer, Ulf; MacPherson, Cameron R.; Bajic, Vladimir B.

2011-01-01

Background: MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed at http://cbrc.kaust.edu.sa/dpore and http://apps.sanbi.ac.za/dpore/. Its use is free for academic and non-profit users. © 2011 Schmeier et al.

dPORE-miRNA: Polymorphic regulation of microRNA genes

KAUST Repository

Schmeier, Sebastian

2011-02-04

Background: MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed at http://cbrc.kaust.edu.sa/dpore and http://apps.sanbi.ac.za/dpore/. Its use is free for academic and non-profit users. © 2011 Schmeier et al.

The ZPIC educational code suite

Science.gov (United States)

Calado, R.; Pardal, M.; Ninhos, P.; Helm, A.; Mori, W. B.; Decyk, V. K.; Vieira, J.; Silva, L. O.; Fonseca, R. A.

2017-10-01

Particle-in-Cell (PIC) codes are used in almost all areas of plasma physics, such as fusion energy research, plasma accelerators, space physics, ion propulsion, and plasma processing, and many other areas. In this work, we present the ZPIC educational code suite, a new initiative to foster training in plasma physics using computer simulations. Leveraging on our expertise and experience from the development and use of the OSIRIS PIC code, we have developed a suite of 1D/2D fully relativistic electromagnetic PIC codes, as well as 1D electrostatic. These codes are self-contained and require only a standard laptop/desktop computer with a C compiler to be run. The output files are written in a new file format called ZDF that can be easily read using the supplied routines in a number of languages, such as Python, and IDL. The code suite also includes a number of example problems that can be used to illustrate several textbook and advanced plasma mechanisms, including instructions for parameter space exploration. We also invite contributions to this repository of test problems that will be made freely available to the community provided the input files comply with the format defined by the ZPIC team. The code suite is freely available and hosted on GitHub at https://github.com/zambzamb/zpic. Work partially supported by PICKSC.

Comparison of efficacy, safety, and predictability of laser in situ keratomileusis using two laser suites

Directory of Open Access Journals (Sweden)

Meidani A

2016-08-01

Full Text Available Alexandra Meidani,1–3 Chara Tzavara3 1Hypervision Laser Centre, 2Eye Day Clinic, 3Department of Hygiene, University of Athens Medical School, Centre for Health Services Research, Epidemiology and Medical Statistics, Athens, Greece Purpose: The main aim of this study was to compare the efficacy, safety, and predictability of femtosecond laser-assisted in situ keratomileusis performed by two different laser suites in the treatment of myopia for up to 6 months.Methods: In this two-site retrospective nonrandomized study, myopic eyes that underwent laser-assisted in situ keratomileusis using IntraLase FS 60 kHz formed group 1 and those using WaveLight FS200 femtosecond laser system formed group 2. Ablation was performed with Visx Star S4 IR and WaveLight EX500 Excimer lasers, respectively, in groups 1 and 2. Both groups were well matched for age, sex, and mean level of preoperative refractive spherical equivalent (MRSE. Uncorrected distance visual acuity, corrected distance visual acuity, and MRSE were evaluated preoperatively and at 1 week, 1 month, and 6 months after treatment.Results: Fifty-six eyes of 28 patients were included in the study. At 6-month follow-up postop, 78.6% of eyes in group 1 and 92.8% of eyes in group 2 achieved an uncorrected distance visual acuity of 20/20 or better (P=0.252. 35.7% and 50% in group 1 and group 2, respectively, gained one line (P=0.179. No eye lost lines of corrected distance visual acuity. Twenty-five eyes in group 1 (92.7% and 27 eyes in group 2 (96.3% had MRSE within ±0.5 D in the 6-month follow-up (P>0.999. The mean efficacy index at 6 months was similar in group 1 and group 2 (mean 1.10±0.12 [standard deviation] vs 1.10±0.1 (P=0.799. The mean safety index was similar in group 1 and group 2 (mean 1.10±0.10 [standard deviation] vs 1.10±0.09 (P=0.407.Conclusion: The outcomes were excellent between the two laser suites. There were no significant differences at 6-month follow-up postop between the two

[Antigravity suit used for neurosurgical operations in sitting position].

Science.gov (United States)

Szpiro-Zurkowska, A; Milczarek, Z; Marchel, A; Jagielski, J

1996-01-01

The aviator's antigravity suit (G-suit) was used for 40 operations on neurosurgical patients operated on in sitting position. The G-suit was filled with air to 0.2 atmosphere (20 kPa) pressure in 26 cases, and 0.3 atm. (30 kPa) in 14 cases. In all cases G-suit filling was followed by central venous pressure rise and mean arterial pressure rise. Venous air embolism was found in 5 (12.5%) patients. No other complications connected with the use of G-suit were observed.

Evolutionary Transitions of MicroRNA-Target Pairs

KAUST Repository

Nozawa, Masafumi; Fujimi, Mai; Iwamoto, Chie; Onizuka, Kanako; Fukuda, Nana; Ikeo, Kazuho; Gojobori, Takashi

2016-01-01

How newly generated microRNA (miRNA) genes are integrated into gene regulatory networks during evolution is fundamental in understanding the molecular and evolutionary bases of robustness and plasticity in gene regulation. A recent model proposed that after the birth of a miRNA, the miRNA is generally integrated into the network by decreasing the number of target genes during evolution. However, this decreasing model remains to be carefully examined by considering in vivo conditions. In this study, we therefore compared the number of target genes among miRNAs with different ages, combining experiments with bioinformatics predictions. First, we focused on three Drosophila miRNAs with different ages. As a result, we found that an older miRNA has a greater number of target genes than a younger miRNA, suggesting the increasing number of targets for each miRNA during evolution (increasing model). To further confirm our results, we also predicted all target genes for all miRNAs in D. melanogaster, considering co-expression of miRNAs and mRNAs in vivo. The results obtained also do not support the decreasing model but are reasonably consistent with the increasing model of miRNA-target pairs. Furthermore, our large-scale analyses of currently available experimental data of miRNA-target pairs also showed a weak but the same trend in humans. These results indicate that the current decreasing model of miRNA-target pairs should be reconsidered and the increasing model may be more appropriate to explain the evolutionary transitions of miRNA-target pairs in many organisms.

Evolutionary Transitions of MicroRNA-Target Pairs

KAUST Repository

Nozawa, Masafumi

2016-04-27

How newly generated microRNA (miRNA) genes are integrated into gene regulatory networks during evolution is fundamental in understanding the molecular and evolutionary bases of robustness and plasticity in gene regulation. A recent model proposed that after the birth of a miRNA, the miRNA is generally integrated into the network by decreasing the number of target genes during evolution. However, this decreasing model remains to be carefully examined by considering in vivo conditions. In this study, we therefore compared the number of target genes among miRNAs with different ages, combining experiments with bioinformatics predictions. First, we focused on three Drosophila miRNAs with different ages. As a result, we found that an older miRNA has a greater number of target genes than a younger miRNA, suggesting the increasing number of targets for each miRNA during evolution (increasing model). To further confirm our results, we also predicted all target genes for all miRNAs in D. melanogaster, considering co-expression of miRNAs and mRNAs in vivo. The results obtained also do not support the decreasing model but are reasonably consistent with the increasing model of miRNA-target pairs. Furthermore, our large-scale analyses of currently available experimental data of miRNA-target pairs also showed a weak but the same trend in humans. These results indicate that the current decreasing model of miRNA-target pairs should be reconsidered and the increasing model may be more appropriate to explain the evolutionary transitions of miRNA-target pairs in many organisms.

Predictions of RNA-binding ability and aggregation propensity of proteins

OpenAIRE

Agostini, Federico, 1985-

2014-01-01

RNA-binding proteins (RBPs) control the fate of a multitude of coding and non-coding transcripts. Formation of ribonucleoprotein (RNP) complexes fine-tunes regulation of post-transcriptional events and influences gene expression. Recently, it has been observed that non-canonical proteins with RNA-binding ability are enriched in structurally disordered and low-complexity regions that are generally involved in functional and dysfunctional associations. Therefore, it is possible that interaction...

Oracle SOA Suite 11g performance cookbook

CERN Document Server

Brasier, Matthew; Wright, Nicholas

2013-01-01

This is a Cookbook with interesting, hands-on recipes, giving detailed descriptions and lots of practical walkthroughs for boosting the performance of your Oracle SOA Suite.This book is for Oracle SOA Suite 11g administrators, developers, and architects who want to understand how they can maximise the performance of their SOA Suite infrastructure. The recipes contain easy to follow step-by-step instructions and include many helpful and practical tips. It is suitable for anyone with basic operating system and application server administration experience.

G-cimp status prediction of glioblastoma samples using mRNA expression data.

Science.gov (United States)

Baysan, Mehmet; Bozdag, Serdar; Cam, Margaret C; Kotliarova, Svetlana; Ahn, Susie; Walling, Jennifer; Killian, Jonathan K; Stevenson, Holly; Meltzer, Paul; Fine, Howard A

2012-01-01

Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.

Thermodynamic heuristics with case-based reasoning: combined insights for RNA pseudoknot secondary structure.

Science.gov (United States)

Al-Khatib, Ra'ed M; Rashid, Nur'Aini Abdul; Abdullah, Rosni

2011-08-01

The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.

Oxfold: Kinetic Folding of RNA using Stochastic Context-Free Grammars and Evolutionary Information

DEFF Research Database (Denmark)

Anderson, James W.J.; Haas, Pierre A.; Mathieson, Leigh-Anne

2013-01-01

Motivation: Many computational methods for RNA secondary structure prediction, and, in particular, for the prediction of a consensus structure of an alignment of RNA sequences, have been developed. Most methods however ignore biophysical factors such as the kinetics of RNA folding; no current...... implementation considers both evolutionary information and folding kinetics, thus losing information which, when considered, might lead to better predictions. Results: We present an iterative algorithm, Oxfold, in the framework of stochastic context-free grammars, that emulates the kinetics of RNA folding...

Safety in the use of pressurized suits

International Nuclear Information System (INIS)

1984-01-01

This Code of Practice describes the procedures relating to the safe operation of Pressurized Suit Areas and their supporting services. It is directed at personnel responsible for the design and/or operation of Pressurized Suit Areas. (author)

Freiburg RNA Tools: a web server integrating INTARNA, EXPARNA and LOCARNA.

Science.gov (United States)

Smith, Cameron; Heyne, Steffen; Richter, Andreas S; Will, Sebastian; Backofen, Rolf

2010-07-01

The Freiburg RNA tools web server integrates three tools for the advanced analysis of RNA in a common web-based user interface. The tools IntaRNA, ExpaRNA and LocARNA support the prediction of RNA-RNA interaction, exact RNA matching and alignment of RNA, respectively. The Freiburg RNA tools web server and the software packages of the stand-alone tools are freely accessible at http://rna.informatik.uni-freiburg.de.

Suites of dwarfs around Nearby giant galaxies

International Nuclear Information System (INIS)

Karachentsev, Igor D.; Kaisina, Elena I.; Makarov, Dmitry I.

2014-01-01

The Updated Nearby Galaxy Catalog (UNGC) contains the most comprehensive summary of distances, radial velocities, and luminosities for 800 galaxies located within 11 Mpc from us. The high density of observables in the UNGC makes this sample indispensable for checking results of N-body simulations of cosmic structures on a ∼1 Mpc scale. The environment of each galaxy in the UNGC was characterized by a tidal index Θ 1 , depending on the separation and mass of the galaxy's main disturber (MD). We grouped UNGC galaxies with a common MD in suites, and ranked suite members according to their Θ 1 . All suite members with positive Θ 1 are assumed to be physical companions of the MD. About 58% of the sample are members of physical groups. The distribution of suites by the number of members, n, follows a relation N(n) ∼ n –2 . The 20 most populated suites contain 468 galaxies, i.e., 59% of the UNGC sample. The fraction of MDs among the brightest galaxies is almost 100% and drops to 50% at M B = –18 m . We discuss various properties of MDs, as well as galaxies belonging to their suites. The suite abundance practically does not depend on the morphological type, linear diameter, or hydrogen mass of the MD, the tightest correlation being with the MD dynamical mass. Dwarf galaxies around MDs exhibit well-known segregation effects: the population of the outskirts has later morphological types, richer H I contents, and higher rates of star formation activity. Nevertheless, there are some intriguing cases where dwarf spheroidal galaxies occur at the far periphery of the suites, as well as some late-type dwarfs residing close to MDs. Comparing simulation results with galaxy groups, most studies assume the Local Group is fairly typical. However, we recognize that the nearby groups significantly differ from each other and there is considerable variation in their properties. The suites of companions around the Milky Way and M31, consisting of the Local Group, do not

Space Suit Joint Torque Measurement Method Validation

Science.gov (United States)

Valish, Dana; Eversley, Karina

2012-01-01

In 2009 and early 2010, a test method was developed and performed to quantify the torque required to manipulate joints in several existing operational and prototype space suits. This was done in an effort to develop joint torque requirements appropriate for a new Constellation Program space suit system. The same test method was levied on the Constellation space suit contractors to verify that their suit design met the requirements. However, because the original test was set up and conducted by a single test operator there was some question as to whether this method was repeatable enough to be considered a standard verification method for Constellation or other future development programs. In order to validate the method itself, a representative subset of the previous test was repeated, using the same information that would be available to space suit contractors, but set up and conducted by someone not familiar with the previous test. The resultant data was compared using graphical and statistical analysis; the results indicated a significant variance in values reported for a subset of the re-tested joints. Potential variables that could have affected the data were identified and a third round of testing was conducted in an attempt to eliminate and/or quantify the effects of these variables. The results of the third test effort will be used to determine whether or not the proposed joint torque methodology can be applied to future space suit development contracts.

An overview of recent projects to study thermal protection in life rafts, lifeboats and immersion suits

Energy Technology Data Exchange (ETDEWEB)

Mak, L.; DuCharme, M. B.; Farnworth, B.; Wissler, E. H.; Brown, R.; Kuczora, A. [Maritime and Arctic Survival Scientific and Engineering Ressearch Team (Canada)

2011-07-01

Survival during a marine evacuation in cold regions is very challenging. However international regulations do not require specific thermal protection or ventilation performance criteria for lifeboats. In the same way, the testing methods for approval testing of immersion suits are not standardised. This paper investigated recent projects completed or on-going to study thermal protection in life rafts, lifeboats and immersion suits. An overview of several projects from the Maritime and Arctic Survival Scientific and Engineering Research Team (MASSERT) was conducted. This review provided the necessary knowledge to advance international standards and develop the thermal protection requirements for survival in the Arctic. The results showed the MASSERT correlated thermal insulation values between human subjects and thermal manikins in life rafts and in immersion suits. It was found that the manikins are a valuable evaluation tool, as well as the computerised models used as prediction tools.

Je, a versatile suite to handle multiplexed NGS libraries with unique molecular identifiers.

Science.gov (United States)

Girardot, Charles; Scholtalbers, Jelle; Sauer, Sajoscha; Su, Shu-Yi; Furlong, Eileen E M

2016-10-08

The yield obtained from next generation sequencers has increased almost exponentially in recent years, making sample multiplexing common practice. While barcodes (known sequences of fixed length) primarily encode the sample identity of sequenced DNA fragments, barcodes made of random sequences (Unique Molecular Identifier or UMIs) are often used to distinguish between PCR duplicates and transcript abundance in, for example, single-cell RNA sequencing (scRNA-seq). In paired-end sequencing, different barcodes can be inserted at each fragment end to either increase the number of multiplexed samples in the library or to use one of the barcodes as UMI. Alternatively, UMIs can be combined with the sample barcodes into composite barcodes, or with standard Illumina® indexing. Subsequent analysis must take read duplicates and sample identity into account, by identifying UMIs. Existing tools do not support these complex barcoding configurations and custom code development is frequently required. Here, we present Je, a suite of tools that accommodates complex barcoding strategies, extracts UMIs and filters read duplicates taking UMIs into account. Using Je on publicly available scRNA-seq and iCLIP data containing UMIs, the number of unique reads increased by up to 36 %, compared to when UMIs are ignored. Je is implemented in JAVA and uses the Picard API. Code, executables and documentation are freely available at http://gbcs.embl.de/Je . Je can also be easily installed in Galaxy through the Galaxy toolshed.

Accelerated probabilistic inference of RNA structure evolution

Directory of Open Access Journals (Sweden)

Holmes Ian

2005-03-01

Full Text Available Abstract Background Pairwise stochastic context-free grammars (Pair SCFGs are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered. Results We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database. Conclusion A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License.

Single-Cell mRNA-Seq Using the Fluidigm C1 System and Integrated Fluidics Circuits.

Science.gov (United States)

Gong, Haibiao; Do, Devin; Ramakrishnan, Ramesh

2018-01-01

Single-cell mRNA-seq is a valuable tool to dissect expression profiles and to understand the regulatory network of genes. Microfluidics is well suited for single-cell analysis owing both to the small volume of the reaction chambers and easiness of automation. Here we describe the workflow of single-cell mRNA-seq using C1 IFC, which can isolate and process up to 96 cells. Both on-chip procedure (lysis, reverse transcription, and preamplification PCR) and off-chip sequencing library preparation protocols are described. The workflow generates full-length mRNA information, which is more valuable compared to 3' end counting method for many applications.

Sibelius. Karelia Suite, Op. 11 / Robert Layton

Index Scriptorium Estoniae

Layton, Robert

1996-01-01

Uuest heliplaadist "Sibelius. Karelia Suite, Op. 11. Luonnotar, Op. 70 a. Andante festivo. The Oceanides, Op. 73. King Christian II, Op. 27-Suite. Finlandia, Op. 26a. Gothenburg Symphony Orchester, Neeme Järvi" DG 447 760-2GH (72 minutes: DDD)

Advanced EVA Suit Camera System Development Project

Science.gov (United States)

Mock, Kyla

2016-01-01

The National Aeronautics and Space Administration (NASA) at the Johnson Space Center (JSC) is developing a new extra-vehicular activity (EVA) suit known as the Advanced EVA Z2 Suit. All of the improvements to the EVA Suit provide the opportunity to update the technology of the video imagery. My summer internship project involved improving the video streaming capabilities of the cameras that will be used on the Z2 Suit for data acquisition. To accomplish this, I familiarized myself with the architecture of the camera that is currently being tested to be able to make improvements on the design. Because there is a lot of benefit to saving space, power, and weight on the EVA suit, my job was to use Altium Design to start designing a much smaller and simplified interface board for the camera's microprocessor and external components. This involved checking datasheets of various components and checking signal connections to ensure that this architecture could be used for both the Z2 suit and potentially other future projects. The Orion spacecraft is a specific project that may benefit from this condensed camera interface design. The camera's physical placement on the suit also needed to be determined and tested so that image resolution can be maximized. Many of the options of the camera placement may be tested along with other future suit testing. There are multiple teams that work on different parts of the suit, so the camera's placement could directly affect their research or design. For this reason, a big part of my project was initiating contact with other branches and setting up multiple meetings to learn more about the pros and cons of the potential camera placements we are analyzing. Collaboration with the multiple teams working on the Advanced EVA Z2 Suit is absolutely necessary and these comparisons will be used as further progress is made for the overall suit design. This prototype will not be finished in time for the scheduled Z2 Suit testing, so my time was

Increased Expression of microRNA-17 Predicts Poor Prognosis in Human Glioma

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Shengkui Lu

2012-01-01

Full Text Available Aim. To investigate the clinical significance of microRNA-17 (miR-17 expression in human gliomas. Methods. Quantitative real-time polymerase chain reaction (qRT-PCR analysis was used to characterize the expression patterns of miR-17 in 108 glioma and 20 normal brain tissues. The associations of miR-17 expression with clinicopathological factors and prognosis of glioma patients were also statistically analyzed. Results. Compared with normal brain tissues, miR-17 expression was significantly higher in glioma tissues (P<0.001. In addition, the increased expression of miR-17 in glioma was significantly associated with advanced pathological grade (P=0.006 and low Karnofsky performance score (KPS, P=0.01. Moreover, Kaplan-Meier survival and Cox regression analyses showed that miR-17 overexpression (P=0.008 and advanced pathological grade (P=0.02 were independent factors predicting poor prognosis for gliomas. Furthermore, subgroup analyses showed that miR-17 expression was significantly associated with poor overall survival in glioma patients with high pathological grades (for grade III~IV: P<0.001. Conclusions. Our data offer the convinced evidence that the increased expression of miR-17 may have potential value for predicting poor prognosis in glioma patients with high pathological grades, indicating that miR-17 may contribute to glioma progression and be a candidate therapeutic target for this disease.

Open architecture of smart sensor suites

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Müller, Wilmuth; Kuwertz, Achim; Grönwall, Christina; Petersson, Henrik; Dekker, Rob; Reinert, Frank; Ditzel, Maarten

2017-10-01

Experiences from recent conflicts show the strong need for smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors. Smart sensor suites should be part of a smart sensor network - a network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations such as situation assessment, intelligence, surveillance, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Within the study at hand, an open system architecture was developed in order to increase the efficiency of sensor suites. The open system architecture for smart sensor suites, based on a system-of-systems approach, enables combining different sensors in multiple physical configurations, such as distributed sensors, co-located sensors combined in a single package, tower-mounted sensors, sensors integrated in a mobile platform, and trigger sensors. The architecture was derived from a set of system requirements and relevant scenarios. Its mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc. The presented open architecture is designed in accordance with the NATO Architecture Framework (NAF). The architecture allows smart sensor suites to be part of a surveillance network, linked e.g. to a sensor planning system and a C4ISR center, and to be used in combination with future RPAS (Remotely Piloted Aircraft Systems) for supporting a more flexible dynamic configuration of RPAS payloads.

Conformational Selection and Induced Fit for RNA Polymerase and RNA/DNA Hybrid Backtracked Recognition

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Haifeng eChen

2015-11-01

Full Text Available RNA polymerase catalyzes transcription with a high fidelity. If DNA/RNA mismatch or DNA damage occurs downstream, a backtracked RNA polymerase can proofread this situation. However, the backtracked mechanism is still poorly understood. Here we have performed multiple explicit-solvent molecular dynamics (MD simulations on bound and apo DNA/RNA hybrid to study backtracked recognition. MD simulations at room temperature suggest that specific electrostatic interactions play key roles in the backtracked recognition between the polymerase and DNA/RNA hybrid. Kinetics analysis at high temperature shows that bound and apo DNA/RNA hybrid unfold via a two-state process. Both kinetics and free energy landscape analyses indicate that bound DNA/RNA hybrid folds in the order of DNA/RNA contracting, the tertiary folding and polymerase binding. The predicted Φ-values suggest that C7, G9, dC12, dC15 and dT16 are key bases for the backtracked recognition of DNA/RNA hybrid. The average RMSD values between the bound structures and the corresponding apo ones and Kolmogorov-Smirnov (KS P test analyses indicate that the recognition between DNA/RNA hybrid and polymerase might follow an induced fit mechanism for DNA/RNA hybrid and conformation selection for polymerase. Furthermore, this method could be used to relative studies of specific recognition between nucleic acid and protein.

PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants.

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Vieira, Lucas Maciel; Grativol, Clicia; Thiebaut, Flavia; Carvalho, Thais G; Hardoim, Pablo R; Hemerly, Adriana; Lifschitz, Sergio; Ferreira, Paulo Cavalcanti Gomes; Walter, Maria Emilia M T

2017-03-04

Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane ( Saccharum spp.) and in maize ( Zea mays ). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.

PlantRNA_Sniffer: A SVM-Based Workflow to Predict Long Intergenic Non-Coding RNAs in Plants

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Lucas Maciel Vieira

2017-03-01

Full Text Available Non-coding RNAs (ncRNAs constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs, which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM. We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp. and in maize (Zea mays. From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.

Advanced Sensor Platform to Evaluate Manloads For Exploration Suit Architectures

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McFarland, Shane; Pierce, Gregory

2016-01-01

Space suit manloads are defined as the outer bounds of force that the human occupant of a suit is able to exert onto the suit during motion. They are defined on a suit-component basis as a unit of maximum force that the suit component in question must withstand without failure. Existing legacy manloads requirements are specific to the suit architecture of the EMU and were developed in an iterative fashion; however, future exploration needs dictate a new suit architecture with bearings, load paths, and entry capability not previously used in any flight suit. No capability currently exists to easily evaluate manloads imparted by a suited occupant, which would be required to develop requirements for a flight-rated design. However, sensor technology has now progressed to the point where an easily-deployable, repeatable and flexible manloads measuring technique could be developed leveraging recent advances in sensor technology. INNOVATION: This development positively impacts schedule, cost and safety risk associated with new suit exploration architectures. For a final flight design, a comprehensive and accurate man loads requirements set must be communicated to the contractor; failing that, a suit design which does not meet necessary manloads limits is prone to failure during testing or worse, during an EVA, which could cause catastrophic failure of the pressure garment posing risk to the crew. This work facilitates a viable means of developing manloads requirements using a range of human sizes & strengths. OUTCOME / RESULTS: Performed sensor market research. Highlighted three viable options (primary, secondary, and flexible packaging option). Designed/fabricated custom bracket to evaluate primary option on a single suit axial. Manned suited manload testing completed and general approach verified.

Correction factors for assessing immersion suits under harsh conditions.

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Power, Jonathan; Tikuisis, Peter; Ré, António Simões; Barwood, Martin; Tipton, Michael

2016-03-01

Many immersion suit standards require testing of thermal protective properties in calm, circulating water while these suits are typically used in harsher environments where they often underperform. Yet it can be expensive and logistically challenging to test immersion suits in realistic conditions. The goal of this work was to develop a set of correction factors that would allow suits to be tested in calm water yet ensure they will offer sufficient protection in harsher conditions. Two immersion studies, one dry and the other with 500 mL of water within the suit, were conducted in wind and waves to measure the change in suit insulation. In both studies, wind and waves resulted in a significantly lower immersed insulation value compared to calm water. The minimum required thermal insulation for maintaining heat balance can be calculated for a given mean skin temperature, metabolic heat production, and water temperature. Combining the physiological limits of sustainable cold water immersion and actual suit insulation, correction factors can be deduced for harsh conditions compared to calm. The minimum in-situ suit insulation to maintain thermal balance is 1.553-0.0624·TW + 0.00018·TW(2) for a dry calm condition. Multiplicative correction factors to the above equation are 1.37, 1.25, and 1.72 for wind + waves, 500 mL suit wetness, and both combined, respectively. Calm water certification tests of suit insulation should meet or exceed the minimum in-situ requirements to maintain thermal balance, and correction factors should be applied for a more realistic determination of minimum insulation for harsh conditions. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Modeling the Impact of Space Suit Components and Anthropometry on the Center of Mass of a Seated Crewmember

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Rajulu, Sudhakar; Blackledge, Christopher; Ferrer, Mike; Margerum, Sarah

2009-01-01

The designers of the Orion Crew Exploration Vehicle (CEV) utilize an intensive simulation program in order to predict the launch and landing characteristics of the Crew Impact Attenuation System (CIAS). The CIAS is the energy absorbing strut concept that dampens loads to levels sustainable by the crew during landing and consists of the crew module seat pallet that accommodates four to six seated astronauts. An important parameter required for proper dynamic modeling of the CIAS is knowledge of the suited center of mass (COM) variations within the crew population. Significant center of mass variations across suited crew configurations would amplify the inertial effects of the pallet and potentially create unacceptable crew loading during launch and landing. Established suited, whole-body, and posture-based mass properties were not available due to the uncertainty of the final CEV seat posture and suit hardware configurations. While unsuited segmental center of mass values can be obtained via regression equations from previous studies, building them into a model that was posture dependent with custom anthropometry and integrated suit components proved cumbersome and time consuming. Therefore, the objective of this study was to quantify the effects of posture, suit components, and the expected range of anthropometry on the center of mass of a seated individual. Several elements are required for the COM calculation of a suited human in a seated position: anthropometry; body segment mass; suit component mass; suit component location relative to the body; and joint angles defining the seated posture. Anthropometry and body segment masses used in this study were taken from a selection of three-dimensional human body models, called boundary manikins, which were developed in a previous project. These boundary manikins represent the critical anthropometric dimension extremes for the anticipated astronaut population. Six male manikins and 6 female manikins, representing a

Z-2 Prototype Space Suit Development

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Ross, Amy; Rhodes, Richard; Graziosi, David; Jones, Bobby; Lee, Ryan; Haque, Bazle Z.; Gillespie, John W., Jr.

2014-01-01

NASA's Z-2 prototype space suit is the highest fidelity pressure garment from both hardware and systems design perspectives since the Space Shuttle Extravehicular Mobility Unit (EMU) was developed in the late 1970's. Upon completion the Z-2 will be tested in the 11 foot human-rated vacuum chamber and the Neutral Buoyancy Laboratory (NBL) at the NASA Johnson Space Center to assess the design and to determine applicability of the configuration to micro-, low- (asteroid), and planetary- (surface) gravity missions. This paper discusses the 'firsts' that the Z-2 represents. For example, the Z-2 sizes to the smallest suit scye bearing plane distance for at least the last 25 years and is being designed with the most intensive use of human models with the suit model.

Sequence analysis of L RNA of Lassa virus

International Nuclear Information System (INIS)

Vieth, Simon; Torda, Andrew E.; Asper, Marcel; Schmitz, Herbert; Guenther, Stephan

2004-01-01

The L RNA of three Lassa virus strains originating from Nigeria, Ghana/Ivory Coast, and Sierra Leone was sequenced and the data subjected to structure predictions and phylogenetic analyses. The L gene products had 2218-2221 residues, diverged by 18% at the amino acid level, and contained several conserved regions. Only one region of 504 residues (positions 1043-1546) could be assigned a function, namely that of an RNA polymerase. Secondary structure predictions suggest that this domain is very similar to RNA-dependent RNA polymerases of known structure encoded by plus-strand RNA viruses, permitting a model to be built. Outside the polymerase region, there is little structural data, except for regions of strong alpha-helical content and probably a coiled-coil domain at the N terminus. No evidence for reassortment or recombination during Lassa virus evolution was found. The secondary structure-assisted alignment of the RNA polymerase region permitted a reliable reconstruction of the phylogeny of all negative-strand RNA viruses, indicating that Arenaviridae are most closely related to Nairoviruses. In conclusion, the data provide a basis for structural and functional characterization of the Lassa virus L protein and reveal new insights into the phylogeny of negative-strand RNA viruses

miRQuest: integration of tools on a Web server for microRNA research.

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Aguiar, R R; Ambrosio, L A; Sepúlveda-Hermosilla, G; Maracaja-Coutinho, V; Paschoal, A R

2016-03-28

This report describes the miRQuest - a novel middleware available in a Web server that allows the end user to do the miRNA research in a user-friendly way. It is known that there are many prediction tools for microRNA (miRNA) identification that use different programming languages and methods to realize this task. It is difficult to understand each tool and apply it to diverse datasets and organisms available for miRNA analysis. miRQuest can easily be used by biologists and researchers with limited experience with bioinformatics. We built it using the middleware architecture on a Web platform for miRNA research that performs two main functions: i) integration of different miRNA prediction tools for miRNA identification in a user-friendly environment; and ii) comparison of these prediction tools. In both cases, the user provides sequences (in FASTA format) as an input set for the analysis and comparisons. All the tools were selected on the basis of a survey of the literature on the available tools for miRNA prediction. As results, three different cases of use of the tools are also described, where one is the miRNA identification analysis in 30 different species. Finally, miRQuest seems to be a novel and useful tool; and it is freely available for both benchmarking and miRNA identification at http://mirquest.integrativebioinformatics.me/.

Non-coding RNA in Deinococcus radiodurans

International Nuclear Information System (INIS)

Chen Zhongzhong; Wang Liangyan; Lin Jun; Tian Bing; Hua Yuejin

2006-01-01

Researches on DNA damage and repair pathways of Deinococcus radiodurans show its extreme resistance to ionizing radiation, ultraviolet radiation and reactive oxygen species. Non-coding (ncRNA) RNAs are involved in a variety of processes such as transcriptional regulations, RNA processing and modification, mRNA translation, protein transportation and stability. The conserved secondary structures of intergenic regions of Deinococcus radiodurans R1 were predicted using Stochastic Context Free Grammar (SCFG) scan strategy. Results showed that 28 ncRNA families were present in the non-coding regions of the genome of Deinococcus radiodurans R1. Among these families, IRE is the largest family, followed by Histone3, tRNA, SECIS. DicF, ctRNA-pGA1 and tmRNA are one discovered in bacteria. Results from the comparison with other organisms showed that these ncRNA can be applied to the study of biological function of Deinococcus radiodurans and supply reference for the further study of DNA damage and repair mechanisms of this bacterium. (authors)

Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming.

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Robinson, J M; Henderson, W A

2018-01-12

We report a method using functional-molecular databases and network modelling to identify hypothetical mRNA-miRNA interaction networks regulating intestinal epithelial barrier function. The model forms a data-analysis component of our cell culture experiments, which produce RNA expression data from Nanostring Technologies nCounter ® system. The epithelial tight-junction (TJ) and actin cytoskeleton interact as molecular components of the intestinal epithelial barrier. Upstream regulation of TJ-cytoskeleton interaction is effected by the Rac/Rock/Rho signaling pathway and other associated pathways which may be activated or suppressed by extracellular signaling from growth factors, hormones, and immune receptors. Pathway activations affect epithelial homeostasis, contributing to degradation of the epithelial barrier associated with osmotic dysregulation, inflammation, and tumor development. The complexity underlying miRNA-mRNA interaction networks represents a roadblock for prediction and validation of competing-endogenous RNA network function. We developed a network model to identify hypothetical co-regulatory motifs in a miRNA-mRNA interaction network related to epithelial function. A mRNA-miRNA interaction list was generated using KEGG and miRWalk2.0 databases. R-code was developed to quantify and visualize inherent network structures. We identified a sub-network with a high number of shared, targeting miRNAs, of genes associated with cellular proliferation and cancer, including c-MYC and Cyclin D.

Bacterial vaginosis, human papilloma virus and herpes viridae do not predict vaginal HIV RNA shedding in women living with HIV in Denmark.

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Wessman, Maria; Thorsteinsson, Kristina; Jensen, Jørgen S; Storgaard, Merete; Rönsholt, Frederikke F; Johansen, Isik S; Pedersen, Gitte; Nørregård Nielsen, Lars; Bonde, Jesper; Katzenstein, Terese L; Weis, Nina; Lebech, Anne-Mette

2017-05-31

Bacterial vaginosis (BV) has been found to be associated with HIV acquisition and transmission. This is suggested to be due to higher HIV RNA levels in cervicovaginal fluids in women living with HIV (WLWH) with BV, as bacteria associated with BV may induce viral replication and shedding in the genital tract despite undetectable HIV RNA plasma viral load. We examined the prevalence and diagnostic predictors of BV and HIV-1 RNA vaginal shedding in women living with HIV (WLWH) in Denmark, taking into account the presence of human papillomavirus (HPV) and herpes viridae. WLWH between 18-51 years were recruited from six Departments of Infectious Diseases in Denmark during enrolment in the SHADE cohort; a prospective cohort study of WLWH attending regular outpatient care. BV was diagnosed by microscopy of vaginal swabs and PCR was used for detection of BV-associated bacteria, HPV, herpes viridae, and vaginal HIV viral load. Median age of the 150 included women was 41 years; ethnicity was predominantly White (35%) or Black (47%). The majority (96%) was on ART and had undetectable (85%) plasma HIV RNA (<40 copies/mL). BV was diagnosed in 32%. Overall, 11% had detectable vaginal HIV RNA. Both before and after adjustment for BV, age, ethnicity, plasma HIV RNA, CD4 cell count, herpes viridae and HPV, we found no significant predictors of HIV RNA vaginal shedding. In well-treated WLWH, BV, herpes viridae or HPV do not predict vaginal HIV RNA shedding. This implies that HIV shedding does not seem to be increased by BV.

G-cimp status prediction of glioblastoma samples using mRNA expression data.

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Mehmet Baysan

Full Text Available Glioblastoma Multiforme (GBM is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.

Structural modeling of tissue-specific mitochondrial alanyl-tRNA synthetase (AARS2 defects predicts differential effects on aminoacylation

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Liliya eEuro

2015-02-01

Full Text Available The accuracy of mitochondrial protein synthesis is dependent on the coordinated action of nuclear-encoded mitochondrial aminoacyl-tRNA synthetases (mtARSs and the mitochondrial DNA-encoded tRNAs. The recent advances in whole-exome sequencing have revealed the importance of the mtARS proteins for mitochondrial pathophysiology since nearly every nuclear gene for mtARS (out of 19 is now recognized as a disease gene for mitochondrial disease. Typically, defects in each mtARS have been identified in one tissue-specific disease, most commonly affecting the brain, or in one syndrome. However, mutations in the AARS2 gene for mitochondrial alanyl-tRNA synthetase (mtAlaRS have been reported both in patients with infantile-onset cardiomyopathy and in patients with childhood to adulthood-onset leukoencephalopathy. We present here an investigation of the effects of the described mutations on the structure of the synthetase, in an effort to understand the tissue-specific outcomes of the different mutations.The mtAlaRS differs from the other mtARSs because in addition to the aminoacylation domain, it has a conserved editing domain for deacylating tRNAs that have been mischarged with incorrect amino acids. We show that the cardiomyopathy phenotype results from a single allele, causing an amino acid change p.R592W in the editing domain of AARS2, whereas the leukodystrophy mutations are located in other domains of the synthetase. Nevertheless, our structural analysis predicts that all mutations reduce the aminoacylation activity of the synthetase, because all mtAlaRS domains contribute to tRNA binding for aminoacylation. According to our model, the cardiomyopathy mutations severely compromise aminoacylation whereas partial activity is retained by the mutation combinations found in the leukodystrophy patients. These predictions provide a hypothesis for the molecular basis of the distinct tissue-specific phenotypic outcomes.

Problem of Office Suite Training at the University

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Natalia A. Nastashchuk

2013-01-01

Full Text Available Te paper considers the problem of office suite applications training, caused by a rapid change of their versions, variety of software developers and a rapid development of software and hardware platforms. The content of office suite applications training, based on the system of office suite notions, its basic functional and standards of information technologies development (OpenDocument Format Standard, ISO 26300-200Х is presented.

Shielding the messenger (RNA): microRNA-based anticancer therapies

Science.gov (United States)

Sotillo, Elena; Thomas-Tikhonenko, Andrei

2011-01-01

It has been a decade since scientists realized that microRNAs (miRNAs) are not an oddity invented by worms to regulate gene expression at post-transcriptional levels. Rather, many of these 21–22-nucleotide-short RNAs exist in invertebrates and vertebrates alike and some of them are in fact highly conserved. miRNAs are now recognized as an important class of non-coding small RNAs that inhibit gene expression by targeting mRNA stability and translation. In the last ten years, our knowledge of the miRNAs world was expanding at vertiginous speed, propelled by the development of computational engines for miRNA identification and target prediction, biochemical tools and techniques to modulate miRNA activity, and last but not least, the emergence of miRNA-centric animal models. One important conclusion that has emerged from this effort is that many microRNAs and their cognate targets are strongly implicated in cancer, either as oncogenes or tumor and metastasis suppressors. In this review we will discuss the diverse role that miRNAs play in cancer initiation and progression and also the tools with which miRNA expression could be corrected in vivo. While the idea of targeting microRNAs towards therapeutic ends is getting considerable traction, basic, translational, and clinical research done in the next few years will tell whether this promise is well-founded. PMID:21514318

46 CFR 199.214 - Immersion suits and thermal protective aids.

Science.gov (United States)

2010-10-01

... 46 Shipping 7 2010-10-01 2010-10-01 false Immersion suits and thermal protective aids. 199.214... Passenger Vessels § 199.214 Immersion suits and thermal protective aids. (a) Each passenger vessel must... an immersion suit. (c) The immersion suits and thermal protective aids required under paragraphs (a...

PLEASE: The Python Low-energy Electron Analysis SuitE – Enabling Rapid Analysis of LEEM and LEED Data

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Maxwell Grady

2018-02-01

Full Text Available PLEASE, the Python Low-energy Electron Analysis SuitE, provides an open source and cross-platform graphical user interface (GUI for rapid analysis and visualization of low energy electron microscopy (LEEM data sets. LEEM and the associated technique, selected area micro-spot low energy electron diffraction (μ-LEED, are powerful tools for analysis of the surface structure for many novel materials. Specifically, these tools are uniquely suited for the characterization of two-dimensional materials. PLEASE offers a user-friendly point-and-click method for extracting intensity-voltage curves from LEEM and LEED data sets. Analysis of these curves provides insight into the atomic structure of the target material surface with unparalleled resolution.

SCRAM: a pipeline for fast index-free small RNA read alignment and visualization.

Science.gov (United States)

Fletcher, Stephen J; Boden, Mikael; Mitter, Neena; Carroll, Bernard J

2018-03-15

Small RNAs play key roles in gene regulation, defense against viral pathogens and maintenance of genome stability, though many aspects of their biogenesis and function remain to be elucidated. SCRAM (Small Complementary RNA Mapper) is a novel, simple-to-use short read aligner and visualization suite that enhances exploration of small RNA datasets. The SCRAM pipeline is implemented in Go and Python, and is freely available under MIT license. Source code, multiplatform binaries and a Docker image can be accessed via https://sfletc.github.io/scram/. s.fletcher@uq.edu.au. Supplementary data are available at Bioinformatics online.

An efficient genetic algorithm for structural RNA pairwise alignment and its application to non-coding RNA discovery in yeast

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Taneda Akito

2008-12-01

Full Text Available Abstract Background Aligning RNA sequences with low sequence identity has been a challenging problem since such a computation essentially needs an algorithm with high complexities for taking structural conservation into account. Although many sophisticated algorithms for the purpose have been proposed to date, further improvement in efficiency is necessary to accelerate its large-scale applications including non-coding RNA (ncRNA discovery. Results We developed a new genetic algorithm, Cofolga2, for simultaneously computing pairwise RNA sequence alignment and consensus folding, and benchmarked it using BRAliBase 2.1. The benchmark results showed that our new algorithm is accurate and efficient in both time and memory usage. Then, combining with the originally trained SVM, we applied the new algorithm to novel ncRNA discovery where we compared S. cerevisiae genome with six related genomes in a pairwise manner. By focusing our search to the relatively short regions (50 bp to 2,000 bp sandwiched by conserved sequences, we successfully predict 714 intergenic and 1,311 sense or antisense ncRNA candidates, which were found in the pairwise alignments with stable consensus secondary structure and low sequence identity (≤ 50%. By comparing with the previous predictions, we found that > 92% of the candidates is novel candidates. The estimated rate of false positives in the predicted candidates is 51%. Twenty-five percent of the intergenic candidates has supports for expression in cell, i.e. their genomic positions overlap those of the experimentally determined transcripts in literature. By manual inspection of the results, moreover, we obtained four multiple alignments with low sequence identity which reveal consensus structures shared by three species/sequences. Conclusion The present method gives an efficient tool complementary to sequence-alignment-based ncRNA finders.

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3′ UTRs and coding sequences

Science.gov (United States)

Šulc, Miroslav; Marín, Ray M.; Robins, Harlan S.; Vaníček, Jiří

2015-01-01

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3′ untranslated regions (3′ UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3′ UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA–mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA–mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats. PMID:25948580

Viral IRES prediction system - a web server for prediction of the IRES secondary structure in silico.

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Jun-Jie Hong

Full Text Available The internal ribosomal entry site (IRES functions as cap-independent translation initiation sites in eukaryotic cells. IRES elements have been applied as useful tools for bi-cistronic expression vectors. Current RNA structure prediction programs are unable to predict precisely the potential IRES element. We have designed a viral IRES prediction system (VIPS to perform the IRES secondary structure prediction. In order to obtain better results for the IRES prediction, the VIPS can evaluate and predict for all four different groups of IRESs with a higher accuracy. RNA secondary structure prediction, comparison, and pseudoknot prediction programs were implemented to form the three-stage procedure for the VIPS. The backbone of VIPS includes: the RNAL fold program, aimed to predict local RNA secondary structures by minimum free energy method; the RNA Align program, intended to compare predicted structures; and pknotsRG program, used to calculate the pseudoknot structure. VIPS was evaluated by using UTR database, IRES database and Virus database, and the accuracy rate of VIPS was assessed as 98.53%, 90.80%, 82.36% and 80.41% for IRES groups 1, 2, 3, and 4, respectively. This advance useful search approach for IRES structures will facilitate IRES related studies. The VIPS on-line website service is available at http://140.135.61.250/vips/.

A Robust and Efficient Numerical Method for RNA-Mediated Viral Dynamics

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Vladimir Reinharz

2017-10-01

Full Text Available The multiscale model of hepatitis C virus (HCV dynamics, which includes intracellular viral RNA (vRNA replication, has been formulated in recent years in order to provide a new conceptual framework for understanding the mechanism of action of a variety of agents for the treatment of HCV. We present a robust and efficient numerical method that belongs to the family of adaptive stepsize methods and is implicit, a Rosenbrock type method that is highly suited to solve this problem. We provide a Graphical User Interface that applies this method and is useful for simulating viral dynamics during treatment with anti-HCV agents that act against HCV on the molecular level.

Novel modeling of combinatorial miRNA targeting identifies SNP with potential role in bone density.

Directory of Open Access Journals (Sweden)

Claudia Coronnello

Full Text Available MicroRNAs (miRNAs are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting, a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential

Rational Design and Tuning of Functional RNA Switch to Control an Allosteric Intermolecular Interaction.

Science.gov (United States)

Endoh, Tamaki; Sugimoto, Naoki

2015-08-04

Conformational transitions of biomolecules in response to specific stimuli control many biological processes. In natural functional RNA switches, often called riboswitches, a particular RNA structure that has a suppressive or facilitative effect on gene expression transitions to an alternative structure with the opposite effect upon binding of a specific metabolite to the aptamer region. Stability of RNA secondary structure (-ΔG°) can be predicted based on thermodynamic parameters and is easily tuned by changes in nucleobases. We envisioned that tuning of a functional RNA switch that causes an allosteric interaction between an RNA and a peptide would be possible based on a predicted switching energy (ΔΔG°) that corresponds to the energy difference between the RNA secondary structure before (-ΔG°before) and after (-ΔG°after) the RNA conformational transition. We first selected functional RNA switches responsive to neomycin with predicted ΔΔG° values ranging from 5.6 to 12.2 kcal mol(-1). We then demonstrated a simple strategy to rationally convert the functional RNA switch to switches responsive to natural metabolites thiamine pyrophosphate, S-adenosyl methionine, and adenine based on the predicted ΔΔG° values. The ΔΔG° values of the designed RNA switches proportionally correlated with interaction energy (ΔG°interaction) between the RNA and peptide, and we were able to tune the sensitivity of the RNA switches for the trigger molecule. The strategy demonstrated here will be generally applicable for construction of functional RNA switches and biosensors in which mechanisms are based on conformational transition of nucleic acids.

Allele-Specific Alternative mRNA processing (ASARP) | Informatics Technology for Cancer Research (ITCR)

Science.gov (United States)

A software pipeline for prediction of allele-specific alternative RNA processing events using single RNA-seq data. The current version focuses on prediction of alternative splicing and alternative polyadenylation modulated by genetic variants.

A new device for the inflation of the antigravity suit.

Science.gov (United States)

Brodrick, P M

1986-02-01

The 'Schuco' orthopaedic tourniquet inflator can be simply converted into a suitable device for inflating an antigravity suit (G-suit). The antigravity suit may be used on neurosurgical patients undergoing procedures in the sitting position to help prevent hypotension and air embolism. The availability of this device may encourage the more widespread use of an antigravity suit in neuro-anaesthetic practice.

Effectiveness comparison of partially executed t-way test suite based generated by existing strategies

Science.gov (United States)

Othman, Rozmie R.; Ahmad, Mohd Zamri Zahir; Ali, Mohd Shaiful Aziz Rashid; Zakaria, Hasneeza Liza; Rahman, Md. Mostafijur

2015-05-01

Consuming 40 to 50 percent of software development cost, software testing is one of the most resource consuming activities in software development lifecycle. To ensure an acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations. Due to combinatorial explosion problem, considering all exhaustive testing is practically impossible. Resource constraints, costing factors as well as strict time-to-market deadlines are amongst the main factors that inhibit such consideration. Earlier work suggests that sampling strategy (i.e. based on t-way parameter interaction or called as t-way testing) can be effective to reduce number of test cases without effecting the fault detection capability. However, for a very large system, even t-way strategy will produce a large test suite that need to be executed. In the end, only part of the planned test suite can be executed in order to meet the aforementioned constraints. Here, there is a need for test engineers to measure the effectiveness of partially executed test suite in order for them to assess the risk they have to take. Motivated by the abovementioned problem, this paper presents the effectiveness comparison of partially executed t-way test suite generated by existing strategies using tuples coverage method. Here, test engineers can predict the effectiveness of the testing process if only part of the original test cases is executed.

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.

Prognostic and predictive roles of microRNA-383 in colorectal cancer

Directory of Open Access Journals (Sweden)

Alavieh Fateh

2016-08-01

Full Text Available MicroRNAs (miRNAs are impressive regulators of gene expression that have a critical role in the pathogenesis of colorectal cancer (CRC. With respect to the aberrant expression of miRNA-383 (miR-383 in some types of human malignancy, this prospective study characterized its contribution to CRC tumorigenesis. The real-time reverse transcriptionpolymerase chain reaction was used to examine miR-383 expression levels prospectively in 40 sample pairs of CRC tissues and adjacent noncancerous tissues (>2 cm from cancer tissue. No significant relationship was found between miR-383 expression levels and clinicopathological features. The ability of miR-383 to function as a tumor marker was also examined. Showing significant changes overall, miR-383 expression levels were significantly down regulated in the group of CRC samples compared with matched noncancerous tissue samples. A receiver-operating characteristic (ROC curve also showed ROC area of 70% for miR-383 with 68 and 75% sensitivity and specificity, respectively. Therefore, miR-383 can be considered as a tumor marker in CRC and help as a potential predictive biomarker in the diagnosis of colorectal cancer.

Development on smart suit for dairy work assistance.

Science.gov (United States)

Nara, Hiroyuki; Kusaka, Takashi; Tanaka, Takayuki; Yamagishi, Takayuki; Ogura, Shotaroh

2013-01-01

Our purpose in this study is to achieve an independent life and a social involvement for the elderly using KEIROKA Technology(fatigue-reduction) which makes it possible to improve the quality of chores and occupations by removing excessive strain and tiredness. The authors have developed power assist suits named "smart suit". The authors have evaluated the effect that the purpose of dairy work assistance, to measure EMG of the worker, compared to the potential of the surface of the non-wearing and wearing "smart suit".

MicroRNA transfection and AGO-bound CLIP-seq data sets reveal distinct determinants of miRNA action

DEFF Research Database (Denmark)

Wen, Jiayu; Parker, Brian J; Jacobsen, Anders

2011-01-01

the predictive effect of target flanking features. We observe distinct target determinants between expression-based and CLIP-based data. Target flanking features such as flanking region conservation are an important AGO-binding determinant-we hypothesize that CLIP experiments have a preference for strongly bound......Microarray expression analyses following miRNA transfection/inhibition and, more recently, Argonaute cross-linked immunoprecipitation (CLIP)-seq assays have been used to detect miRNA target sites. CLIP and expression approaches measure differing stages of miRNA functioning-initial binding of the mi...... miRNP-target interactions involving adjacent RNA-binding proteins that increase the strength of cross-linking. In contrast, seed-related features are major determinants in expression-based studies, but less so for CLIP-seq studies, and increased miRNA concentrations typical of transfection studies...

Engineering Software Suite Validates System Design

Science.gov (United States)

2007-01-01

EDAptive Computing Inc.'s (ECI) EDAstar engineering software tool suite, created to capture and validate system design requirements, was significantly funded by NASA's Ames Research Center through five Small Business Innovation Research (SBIR) contracts. These programs specifically developed Syscape, used to capture executable specifications of multi-disciplinary systems, and VectorGen, used to automatically generate tests to ensure system implementations meet specifications. According to the company, the VectorGen tests considerably reduce the time and effort required to validate implementation of components, thereby ensuring their safe and reliable operation. EDASHIELD, an additional product offering from ECI, can be used to diagnose, predict, and correct errors after a system has been deployed using EDASTAR -created models. Initial commercialization for EDASTAR included application by a large prime contractor in a military setting, and customers include various branches within the U.S. Department of Defense, industry giants like the Lockheed Martin Corporation, Science Applications International Corporation, and Ball Aerospace and Technologies Corporation, as well as NASA's Langley and Glenn Research Centers

Parallel mRNA, proteomics and miRNA expression analysis in cell line models of the intestine.

Science.gov (United States)

O'Sullivan, Finbarr; Keenan, Joanne; Aherne, Sinead; O'Neill, Fiona; Clarke, Colin; Henry, Michael; Meleady, Paula; Breen, Laura; Barron, Niall; Clynes, Martin; Horgan, Karina; Doolan, Padraig; Murphy, Richard

2017-11-07

To identify miRNA-regulated proteins differentially expressed between Caco2 and HT-29: two principal cell line models of the intestine. Exponentially growing Caco-2 and HT-29 cells were harvested and prepared for mRNA, miRNA and proteomic profiling. mRNA microarray profiling analysis was carried out using the Affymetrix GeneChip Human Gene 1.0 ST array. miRNA microarray profiling analysis was carried out using the Affymetrix Genechip miRNA 3.0 array. Quantitative Label-free LC-MS/MS proteomic analysis was performed using a Dionex Ultimate 3000 RSLCnano system coupled to a hybrid linear ion trap/Orbitrap mass spectrometer. Peptide identities were validated in Proteome Discoverer 2.1 and were subsequently imported into Progenesis QI software for further analysis. Hierarchical cluster analysis for all three parallel datasets (miRNA, proteomics, mRNA) was conducted in the R software environment using the Euclidean distance measure and Ward's clustering algorithm. The prediction of miRNA and oppositely correlated protein/mRNA interactions was performed using TargetScan 6.1. GO biological process, molecular function and cellular component enrichment analysis was carried out for the DE miRNA, protein and mRNA lists via the Pathway Studio 11.3 Web interface using their Mammalian database. Differential expression (DE) profiling comparing the intestinal cell lines HT-29 and Caco-2 identified 1795 Genes, 168 Proteins and 160 miRNAs as DE between the two cell lines. At the gene level, 1084 genes were upregulated and 711 were downregulated in the Caco-2 cell line relative to the HT-29 cell line. At the protein level, 57 proteins were found to be upregulated and 111 downregulated in the Caco-2 cell line relative to the HT-29 cell line. Finally, at the miRNAs level, 104 were upregulated and 56 downregulated in the Caco-2 cell line relative to the HT-29 cell line. Gene ontology (GO) analysis of the DE mRNA identified cell adhesion, migration and ECM organization, cellular lipid

Probing binding hot spots at protein-RNA recognition sites.

Science.gov (United States)

Barik, Amita; Nithin, Chandran; Karampudi, Naga Bhushana Rao; Mukherjee, Sunandan; Bahadur, Ranjit Prasad

2016-01-29

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Immersion Suit Usage Within the RAAF

Science.gov (United States)

1992-01-01

IMMERSION SUIT USED UVIC QDIS HOLDINGS 202. in 12 Sizes, held by ALSS 492SQN REQUIREMENTS No comment USAGE POLICY REFERENCE DIRAF) AAP 7215.004-1 (P3C...held by ALSS 492SQN. REQUIREMENTS No comment ISACE POLICY REFERENCE DIIAF) AAP 7215.004-1 (P3C Flight Manual) RAAF Supplement No 92 USAGE POUICY UVIC...TYPE P3C REFERENCE Telecon FLTLT Toft I I SQNfRESO AVMED Dated 22 Mar 91 IMMERSION SUIT USED UVIC QDIS HOLDINGS No comment REQUIREMENTS No comment USAGE

Advanced Sensor Platform to Evaluate Manloads for Exploration Suit Architectures

Data.gov (United States)

National Aeronautics and Space Administration — Space suit manloads are defined as the outer bounds of force that the human occupant of a suit is able to exert onto the suit during motion. They are defined on a...

Alterations in MAST suit pressure with changes in ambient temperature.

Science.gov (United States)

Sanders, A B; Meislin, H W; Daub, E

1983-01-01

A study was undertaken to test the hypothesis that change in ambient air temperature has an effect on MAST suit pressure according to the ideal gas law. Two different MAST suits were tested on Resusci-Annie dummies. The MAST suits were applied in a cold room at 4.4 degrees C and warmed to 44 degrees C. Positive linear correlations were found in nine trials, but the two suits differed in their rate of increase in pressure. Three trials using humans were conducted showing increased pressure with temperature but at a lesser rate than with dummies. A correlation of 0.5 to 1.0 mm Hg increase in MAST suit pressure for each 1.0 degrees C increase in ambient temperature was found. Implications are discussed for the use of the MAST suit in environmental conditions where the temperature changes.

Transcriptome dynamics of the microRNA inhibition response

DEFF Research Database (Denmark)

Wen, Jiayu; Leucci, Elenora; Vendramin, Roberto

2015-01-01

We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line-the first such dynamic study of the microRNA inhibition response-revealing both general and specific aspects of the physiological response. We show...... validate the key observations with independent time series qPCR and we experimentally validate key predicted miR-9 targets. Methodologically, we developed sensitive functional data analytic predictive methods to analyse the weak response inherent in microRNA inhibition experiments. The methods...... of this study will be applicable to similar high-resolution time series transcriptome analyses and provides the context for more accurate experimental design and interpretation of future microRNA inhibition studies....

Safety Precautions and Operating Procedures in an (A)BSL-4 Laboratory: 1. Biosafety Level 4 Suit Laboratory Suite Entry and Exit Procedures.

Science.gov (United States)

Janosko, Krisztina; Holbrook, Michael R; Adams, Ricky; Barr, Jason; Bollinger, Laura; Newton, Je T'aime; Ntiforo, Corrie; Coe, Linda; Wada, Jiro; Pusl, Daniela; Jahrling, Peter B; Kuhn, Jens H; Lackemeyer, Matthew G

2016-10-03

Biosafety level 4 (BSL-4) suit laboratories are specifically designed to study high-consequence pathogens for which neither infection prophylaxes nor treatment options exist. The hallmarks of these laboratories are: custom-designed airtight doors, dedicated supply and exhaust airflow systems, a negative-pressure environment, and mandatory use of positive-pressure ("space") suits. The risk for laboratory specialists working with highly pathogenic agents is minimized through rigorous training and adherence to stringent safety protocols and standard operating procedures. Researchers perform the majority of their work in BSL-2 laboratories and switch to BSL-4 suit laboratories when work with a high-consequence pathogen is required. Collaborators and scientists considering BSL-4 projects should be aware of the challenges associated with BSL-4 research both in terms of experimental technical limitations in BSL-4 laboratory space and the increased duration of such experiments. Tasks such as entering and exiting the BSL-4 suit laboratories are considerably more complex and time-consuming compared to BSL-2 and BSL-3 laboratories. The focus of this particular article is to address basic biosafety concerns and describe the entrance and exit procedures for the BSL-4 laboratory at the NIH/NIAID Integrated Research Facility at Fort Detrick. Such procedures include checking external systems that support the BSL-4 laboratory, and inspecting and donning positive-pressure suits, entering the laboratory, moving through air pressure-resistant doors, and connecting to air-supply hoses. We will also discuss moving within and exiting the BSL-4 suit laboratories, including using the chemical shower and removing and storing positive-pressure suits.

Antagonism pattern detection between microRNA and target expression in Ewing's sarcoma.

Directory of Open Access Journals (Sweden)

Loredana Martignetti

Full Text Available MicroRNAs (miRNAs have emerged as fundamental regulators that silence gene expression at the post-transcriptional and translational levels. The identification of their targets is a major challenge to elucidate the regulated biological processes. The overall effect of miRNA is reflected on target mRNA expression, suggesting the design of new investigative methods based on high-throughput experimental data such as miRNA and transcriptome profiles. We propose a novel statistical measure of non-linear dependence between miRNA and mRNA expression, in order to infer miRNA-target interactions. This approach, which we name antagonism pattern detection, is based on the statistical recognition of a triangular-shaped pattern in miRNA-target expression profiles. This pattern is observed in miRNA-target expression measurements since their simultaneously elevated expression is statistically under-represented in the case of miRNA silencing effect. The proposed method enables miRNA target prediction to strongly rely on cellular context and physiological conditions reflected by expression data. The procedure has been assessed on synthetic datasets and tested on a set of real positive controls. Then it has been applied to analyze expression data from Ewing's sarcoma patients. The antagonism relationship is evaluated as a good indicator of real miRNA-target biological interaction. The predicted targets are consistently enriched for miRNA binding site motifs in their 3'UTR. Moreover, we reveal sets of predicted targets for each miRNA sharing important biological function. The procedure allows us to infer crucial miRNA regulators and their potential targets in Ewing's sarcoma disease. It can be considered as a valid statistical approach to discover new insights in the miRNA regulatory mechanisms.

Prokaryotic Argonautes - variations on the RNA interference theme

Science.gov (United States)

van der Oost, John; Swarts, Daan C.; Jore, Matthijs M.

2014-01-01

The discovery of RNA interference (RNAi) has been a major scientific breakthrough. This RNA-guided RNA interference system plays a crucial role in a wide range of regulatory and defense mechanisms in eukaryotes. The key enzyme of the RNAi system is Argonaute (Ago), an endo-ribonuclease that uses a small RNA guide molecule to specifically target a complementary RNA transcript. Two functional classes of eukaryotic Ago have been described: catalytically active Ago that cleaves RNA targets complementary to its guide, and inactive Ago that uses its guide to bind target RNA to down-regulate translation efficiency. A recent comparative genomics study has revealed that Argonaute-like proteins are also encoded by prokaryotic genomes. Interestingly, there is a lot of variation among these prokaryotic Argonaute (pAgo) proteins with respect to domain architecture: some resemble the eukaryotic Ago (long pAgo) containing a complete or disrupted catalytic site, while others are truncated versions (short pAgo) that generally contain an incomplete catalytic site. Prokaryotic Agos with an incomplete catalytic site often co-occur with (predicted) nucleases. Based on this diversity, and on the fact that homologs of other RNAi-related protein components (such as Dicer nucleases) have never been identified in prokaryotes, it has been predicted that variations on the eukaryotic RNAi theme may occur in prokaryotes. PMID:28357239

An intergenic non-coding rRNA correlated with expression of the rRNA and frequency of an rRNA single nucleotide polymorphism in lung cancer cells.

Directory of Open Access Journals (Sweden)

Yih-Horng Shiao

Full Text Available BACKGROUND: Ribosomal RNA (rRNA is a central regulator of cell growth and may control cancer development. A cis noncoding rRNA (nc-rRNA upstream from the 45S rRNA transcription start site has recently been implicated in control of rRNA transcription in mouse fibroblasts. We investigated whether a similar nc-rRNA might be expressed in human cancer epithelial cells, and related to any genomic characteristics. METHODOLOGY/PRINCIPAL FINDINGS: Using quantitative rRNA measurement, we demonstrated that a nc-rRNA is transcribed in human lung epithelial and lung cancer cells, starting from approximately -1000 nucleotides upstream of the rRNA transcription start site (+1 and extending at least to +203. This nc-rRNA was significantly more abundant in the majority of lung cancer cell lines, relative to a nontransformed lung epithelial cell line. Its abundance correlated negatively with total 45S rRNA in 12 of 13 cell lines (P = 0.014. During sequence analysis from -388 to +306, we observed diverse, frequent intercopy single nucleotide polymorphisms (SNPs in rRNA, with a frequency greater than predicted by chance at 12 sites. A SNP at +139 (U/C in the 5' leader sequence varied among the cell lines and correlated negatively with level of the nc-rRNA (P = 0.014. Modelling of the secondary structure of the rRNA 5'-leader sequence indicated a small increase in structural stability due to the +139 U/C SNP and a minor shift in local configuration occurrences. CONCLUSIONS/SIGNIFICANCE: The results demonstrate occurrence of a sense nc-rRNA in human lung epithelial and cancer cells, and imply a role in regulation of the rRNA gene, which may be affected by a +139 SNP in the 5' leader sequence of the primary rRNA transcript.

CRISPRTarget: bioinformatic prediction and analysis of crRNA targets

NARCIS (Netherlands)

Biswas, A.; Gagnon, J.N.; Brouns, S.J.J.; Fineran, P.C.; Brown, C.M.

2013-01-01

The bacterial and archaeal CRISPR/Cas adaptive immune system targets specific protospacer nucleotide sequences in invading organisms. This requires base pairing between processed CRISPR RNA and the target protospacer. For type I and II CRISPR/Cas systems, protospacer adjacent motifs (PAM) are

Protein-driven inference of miRNA-disease associations

DEFF Research Database (Denmark)

Mørk, Søren; Pletscher-Frankild, Sune; Palleja, Albert

2014-01-01

MicroRNAs (miRNAs) are a highly abundant class of non-coding RNA genes involved in cellular regulation and thus also diseases. Despite miRNAs being important disease factors, miRNA-disease associations remain low in number and of variable reliability. Furthermore, existing databases and prediction...

A tale of two sequences: microRNA-target chimeric reads.

Science.gov (United States)

Broughton, James P; Pasquinelli, Amy E

2016-04-04

In animals, a functional interaction between a microRNA (miRNA) and its target RNA requires only partial base pairing. The limited number of base pair interactions required for miRNA targeting provides miRNAs with broad regulatory potential and also makes target prediction challenging. Computational approaches to target prediction have focused on identifying miRNA target sites based on known sequence features that are important for canonical targeting and may miss non-canonical targets. Current state-of-the-art experimental approaches, such as CLIP-seq (cross-linking immunoprecipitation with sequencing), PAR-CLIP (photoactivatable-ribonucleoside-enhanced CLIP), and iCLIP (individual-nucleotide resolution CLIP), require inference of which miRNA is bound at each site. Recently, the development of methods to ligate miRNAs to their target RNAs during the preparation of sequencing libraries has provided a new tool for the identification of miRNA target sites. The chimeric, or hybrid, miRNA-target reads that are produced by these methods unambiguously identify the miRNA bound at a specific target site. The information provided by these chimeric reads has revealed extensive non-canonical interactions between miRNAs and their target mRNAs, and identified many novel interactions between miRNAs and noncoding RNAs.

Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities.

Science.gov (United States)

Hagio, Taichi; Sakuraba, Shun; Iwakiri, Junichi; Mori, Ryota; Asai, Kiyoshi

2018-02-19

It is known that functional RNAs often switch their functions by forming different secondary structures. Popular tools for RNA secondary structures prediction, however, predict the single 'best' structures, and do not produce alternative structures. There are bioinformatics tools to predict suboptimal structures, but it is difficult to detect which alternative secondary structures are essential. We proposed a new computational method to detect essential alternative secondary structures from RNA sequences by decomposing the base-pairing probability matrix. The decomposition is calculated by a newly implemented software tool, RintW, which efficiently computes the base-pairing probability distributions over the Hamming distance from arbitrary reference secondary structures. The proposed approach has been demonstrated on ROSE element RNA thermometer sequence and Lysine RNA ribo-switch, showing that the proposed approach captures conformational changes in secondary structures. We have shown that alternative secondary structures are captured by decomposing base-paring probabilities over Hamming distance. Source code is available from http://www.ncRNA.org/RintW .

Satellite Ocean Heat Content Suite

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This collection contains an operational Satellite Ocean Heat Content Suite (SOHCS) product generated by NOAA National Environmental Satellite, Data, and Information...

EVA Suit Microbial Leakage Investigation

Data.gov (United States)

National Aeronautics and Space Administration — The objective of this project is to collect microbial samples from various EVA suits to determine how much microbial contamination is typically released during...

Study of the suit inflation effect on crew safety during landing using a full-pressure IVA suit for new-generation reentry space vehicles

Science.gov (United States)

Wataru, Suzuki

Recently, manned space capsules have been recognized as beneficial and reasonable human space vehicles again. The Dragon capsule already achieved several significant successes. The Orion capsule is going to be sent to a high-apogee orbit without crews for experimental purposes in September 2014. For such human-rated space capsules, the study of acceleration impacts against the human body during splashdown is essential to ensure the safety of crews. Moreover, it is also known that wearing a full pressure rescue suit significantly increases safety of a crew, compared to wearing a partial pressure suit. This is mainly because it enables the use of a personal life support system independently in addition to that which installed in the space vehicle. However, it is unclear how the inflation of the full pressure suit due to pressurization affects the crew safety during splashdown, especially in the case of the new generation manned space vehicles. Therefore, the purpose of this work is to investigate the effect of the suit inflation on crew safety against acceleration impact during splashdown. For this objective, the displacements of the safety harness in relation with the suit, a human surrogate, and the crew seats during pressurizing the suit in order to determine if the safety and survivability of a crew can be improved by wearing a full pressure suit. For these tests, the DL/H-1 full pressure IVA suit, developed by Pablo de Leon and Gary L. Harris, will be used. These tests use image analysis techniques to determine the displacements. It is expected, as a result of these tests, that wearing a full pressure suit will help to mitigate the impacts and will increase the safety and survivability of a crew during landing since it works as a buffer to mitigate impact forces during splashdown. This work also proposes a future plan for sled test experiments using a sled facility such as the one in use by the Civil Aerospace Medical Institute (CAMI) for experimental validation

Circular RNA Signature Predicts Gemcitabine Resistance of Pancreatic Ductal Adenocarcinoma

Directory of Open Access Journals (Sweden)

Feng Shao

2018-06-01

Full Text Available Gemcitabine resistance is currently the main problem of chemotherapy for advanced pancreatic cancer patients. The resistance is thought to be caused by altered drug metabolism or reduced apoptosis of cancer cells. However, the underlying mechanism of Gemcitabine resistance in pancreatic cancer remains unclear. In this study, we established Gemcitabine resistant PANC-1 (PANC-1-GR cell lines and compared the circular RNAs (circRNAs profiles between PANC-1 cells and PANC-1-GR cells by RNA sequencing. Differentially expressed circRNAs were demonstrated using scatter plot and cluster heatmap analysis. Gene ontology and pathway analysis were performed to systemically map the genes which are functionally associated to those differentially expressed circRNAs identified from our data. The expression of the differentially expressed circRNAs picked up by RNAseq in PANC-1-GR cells was further validated by qRT-PCR and two circRNAs were eventually identified as the most distinct targets. Consistently, by analyzing plasma samples form pancreatic ductal adenocarcinoma (PDAC patients, the two circRNAs showed more significant expression in the Gemcitabine non-responsive patients than the responsive ones. In addition, we found that silencing of the two circRNAs could restore the sensitivity of PANC-1-GR cells to Gemcitabine treatment, while over-expression of them could increase the resistance of normal PANC-1 and MIA PACA-2 cells, suggesting that they might serve as drug targets for Gemcitabine resistance. Furthermore, the miRNA interaction networks were also explored based on the correlation analysis of the target microRNAs of these two circRNAs. In conclusion, we successfully established new PANC-1-GR cells, systemically characterized the circRNA and miRNA profiles, and identified two circRNAs as novel biomarkers and potential therapeutic targets for Gemcitabine non-responsive PDAC patients.

Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite.

Science.gov (United States)

Peng, Hui; Lan, Chaowang; Zheng, Yi; Hutvagner, Gyorgy; Tao, Dacheng; Li, Jinyan

2017-03-24

MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi-disease associated co-functional microRNAs that regulate their common dysfunctional target genes cooperatively in the development of multiple diseases. The research is potentially useful for human disease studies at the transcriptional level and for the study of multi-purpose microRNA therapeutics. We designed a computational method to detect multi-disease associated co-functional microRNA pairs and conducted cross disease analysis on a reconstructed disease-gene-microRNA (DGR) tripartite network. The construction of the DGR tripartite network is by the integration of newly predicted disease-microRNA associations with those relationships of diseases, microRNAs and genes maintained by existing databases. The prediction method uses a set of reliable negative samples of disease-microRNA association and a pre-computed kernel matrix instead of kernel functions. From this reconstructed DGR tripartite network, multi-disease associated co-functional microRNA pairs are detected together with their common dysfunctional target genes and ranked by a novel scoring method. We also conducted proof-of-concept case studies on cancer-related co-functional microRNA pairs as well as on non-cancer disease-related microRNA pairs. With the prioritization of the co-functional microRNAs that relate to a series of diseases, we found that the co-function phenomenon is not unusual. We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases.

miRNA signatures can predict acute liver failure in hepatitis E infected pregnant females

Directory of Open Access Journals (Sweden)

Nirupma Trehanpati

2017-04-01

Full Text Available Background: Acute viral hepatitis E (AVH-E can often result in acute liver failure (ALF during pregnancy. microRNAs serve as mediators in drug induced liver failure. We investigated their role as a biomarker in predicting ALF due to HEV (ALF-E. Methods: We performed next generation sequencing and subsequent validation studies in PBMCs of pregnant (P self limiting AVH-E, ALF due to HEV (ALF-E and compared with AVH-E in non-pregnant (NP females and healthy controls. Findings: Eleven microRNAs were significantly expressed in response to HEV infection; importantly, miR- 431, 654, 1468 and 4435, were distinctly expressed in pregnant self-limiting AVH-E and healthy females (p = 0.0005, but not in ALF-E. Sixteen exclusive microRNAs differentiated ALF-E from self limiting AVH-E in pregnant females. miR-450b which affects cellular proliferation and metabolic processes through RNF20 and SECB was predominanlty upregulated and correlated with poor outcome (ROC 0.958, p = 0.001. Interpretation: Our results reveal that a specific microRNA profile can predict fatality in ALF-E in pregnancy. These microRNAs could be exploited as prognostic biomarkers and help in the development of new therapeutic interventions. Keywords: Health sciences, Virology

EVA Physiology and Medical Considerations Working in the Suit

Science.gov (United States)

Parazynski, Scott

2012-01-01

This "EVA Physiology and Medical Considerations Working in the Suit" presentation covers several topics related to the medical implications and physiological effects of suited operations in space from the perspective of a physician with considerable first-hand Extravehicular Activity (EVA) experience. Key themes include EVA physiology working in a pressure suit in the vacuum of space, basic EVA life support and work support, Thermal Protection System (TPS) inspections and repairs, and discussions of the physical challenges of an EVA. Parazynski covers the common injuries and significant risks during EVAs, as well as physical training required to prepare for EVAs. He also shares overall suit physiological and medical knowledge with the next generation of Extravehicular Mobility Unit (EMU) system designers.

Correlation analyses revealed global microRNA-mRNA expression associations in human peripheral blood mononuclear cells.

Science.gov (United States)

Wang, Lan; Zhu, Jiang; Deng, Fei-Yan; Wu, Long-Fei; Mo, Xing-Bo; Zhu, Xiao-Wei; Xia, Wei; Xie, Fang-Fei; He, Pei; Bing, Peng-Fei; Qiu, Ying-Hua; Lin, Xiang; Lu, Xin; Zhang, Lei; Yi, Neng-Jun; Zhang, Yong-Hong; Lei, Shu-Feng

2018-02-01

MicroRNAs (miRNAs) can regulate gene expression through binding to complementary sites in the 3'-untranslated regions of target mRNAs, which will lead to existence of correlation in expression between miRNA and mRNA. However, the miRNA-mRNA correlation patterns are complex and remain largely unclear yet. To establish the global correlation patterns in human peripheral blood mononuclear cells (PBMCs), multiple miRNA-mRNA correlation analyses and expression quantitative trait locus (eQTL) analysis were conducted in this study. We predicted and achieved 861 miRNA-mRNA pairs (65 miRNAs, 412 mRNAs) using multiple bioinformatics programs, and found global negative miRNA-mRNA correlations in PBMC from all 46 study subjects. Among the 861 pairs of correlations, 19.5% were significant (P correlation network was complex and highlighted key miRNAs/genes in PBMC. Some miRNAs, such as hsa-miR-29a, hsa-miR-148a, regulate a cluster of target genes. Some genes, e.g., TNRC6A, are regulated by multiple miRNAs. The identified genes tend to be enriched in molecular functions of DNA and RNA binding, and biological processes such as protein transport, regulation of translation and chromatin modification. The results provided a global view of the miRNA-mRNA expression correlation profile in human PBMCs, which would facilitate in-depth investigation of biological functions of key miRNAs/mRNAs and better understanding of the pathogenesis underlying PBMC-related diseases.

Results from Carbon Dioxide Washout Testing Using a Suited Manikin Test Apparatus with a Space Suit Ventilation Test Loop

Science.gov (United States)

Chullen, Cinda; Conger, Bruce; McMillin, Summer; Vonau, Walt; Kanne, Bryan; Korona, Adam; Swickrath, Mike

2016-01-01

NASA is developing an advanced portable life support system (PLSS) to meet the needs of a new NASA advanced space suit. The PLSS is one of the most critical aspects of the space suit providing the necessary oxygen, ventilation, and thermal protection for an astronaut performing a spacewalk. The ventilation subsystem in the PLSS must provide sufficient carbon dioxide (CO2) removal and ensure that the CO2 is washed away from the oronasal region of the astronaut. CO2 washout is a term used to describe the mechanism by which CO2 levels are controlled within the helmet to limit the concentration of CO2 inhaled by the astronaut. Accumulation of CO2 in the helmet or throughout the ventilation loop could cause the suited astronaut to experience hypercapnia (excessive carbon dioxide in the blood). A suited manikin test apparatus (SMTA) integrated with a space suit ventilation test loop was designed, developed, and assembled at NASA in order to experimentally validate adequate CO2 removal throughout the PLSS ventilation subsystem and to quantify CO2 washout performance under various conditions. The test results from this integrated system will be used to validate analytical models and augment human testing. This paper presents the system integration of the PLSS ventilation test loop with the SMTA including the newly developed regenerative Rapid Cycle Amine component used for CO2 removal and tidal breathing capability to emulate the human. The testing and analytical results of the integrated system are presented along with future work.

The BRITNeY Suite: A Platfor for Experiments

DEFF Research Database (Denmark)

Westergaard, Michael

2006-01-01

This paper describes a platform, the BRITNeY Suite, for experimenting with Coloured Petri nets. The BRITNeY Suite provides access to data-structures and a simulator for Coloured Petri nets via a powerful scripting language and plug-in-mechanism, thereby making it easy to perform customized...

EDL Sensor Suite, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Optical Air Data Systems (OADS) L.L.C. proposes a LIDAR based remote measurement sensor suite capable of satisfying a significant number of the desired sensing...

Full-length mRNA sequencing uncovers a widespread coupling between transcription initiation and mRNA processing.

Science.gov (United States)

Anvar, Seyed Yahya; Allard, Guy; Tseng, Elizabeth; Sheynkman, Gloria M; de Klerk, Eleonora; Vermaat, Martijn; Yin, Raymund H; Johansson, Hans E; Ariyurek, Yavuz; den Dunnen, Johan T; Turner, Stephen W; 't Hoen, Peter A C

2018-03-29

The multifaceted control of gene expression requires tight coordination of regulatory mechanisms at transcriptional and post-transcriptional level. Here, we studied the interdependence of transcription initiation, splicing and polyadenylation events on single mRNA molecules by full-length mRNA sequencing. In MCF-7 breast cancer cells, we find 2700 genes with interdependent alternative transcription initiation, splicing and polyadenylation events, both in proximal and distant parts of mRNA molecules, including examples of coupling between transcription start sites and polyadenylation sites. The analysis of three human primary tissues (brain, heart and liver) reveals similar patterns of interdependency between transcription initiation and mRNA processing events. We predict thousands of novel open reading frames from full-length mRNA sequences and obtained evidence for their translation by shotgun proteomics. The mapping database rescues 358 previously unassigned peptides and improves the assignment of others. By recognizing sample-specific amino-acid changes and novel splicing patterns, full-length mRNA sequencing improves proteogenomics analysis of MCF-7 cells. Our findings demonstrate that our understanding of transcriptome complexity is far from complete and provides a basis to reveal largely unresolved mechanisms that coordinate transcription initiation and mRNA processing.

Use of tiling array data and RNA secondary structure predictions to identify noncoding RNA genes

DEFF Research Database (Denmark)

Weile, Christian; Gardner, Paul P; Hedegaard, Mads M

2007-01-01

neuroblastoma cell line SK-N-AS. Using this strategy, we identify thousands of human candidate RNA genes. To further verify the expression of these genes, we focused on candidate genes that had a stable hairpin structures or a high level of covariance. Using northern blotting, we verify the expression of 2 out...

Integrated Suit Test 1 - A Study to Evaluate Effects of Suit Weight, Pressure, and Kinematics on Human Performance during Lunar Ambulation

Science.gov (United States)

Gernhardt, Michael L.; Norcross, Jason; Vos, Jessica R.

2008-01-01

In an effort to design the next generation Lunar suit, NASA has initiated a series of tests aimed at understanding the human physiological and biomechanical affects of space suits under a variety of conditions. The first of these tests was the EVA Walkback Test (ICES 2007-01-3133). NASA-JSC assembled a multi-disciplinary team to conduct the second test of the series, titled Integrated Suit Test 1 (IST-1), from March 6 through July 24, 2007. Similar to the Walkback Test, this study was performed with the Mark III (MKIII) EVA Technology Demonstrator suit, a treadmill, and the Partial Gravity Simulator in the Space Vehicle Mock-Up Facility at Johnson Space Center. The data collected for IST-1 included metabolic rates, ground reaction forces, biomechanics, and subjective workload and controllability feedback on both suited and unsuited (shirt-sleeve) astronaut subjects. For IST-1 the center of gravity was controlled to a nearly perfect position while the weight, pressure and biomechanics (waist locked vs. unlocked) were varied individually to evaluate the effects of each on the ability to perform level (0 degree incline) ambulation in simulated Lunar gravity. The detailed test methodology and preliminary key findings of IST-1 are summarized in this report.

Evolutionary modeling and prediction of non-coding RNAs in Drosophila.

Directory of Open Access Journals (Sweden)

Robert K Bradley

2009-08-01

Full Text Available We performed benchmarks of phylogenetic grammar-based ncRNA gene prediction, experimenting with eight different models of structural evolution and two different programs for genome alignment. We evaluated our models using alignments of twelve Drosophila genomes. We find that ncRNA prediction performance can vary greatly between different gene predictors and subfamilies of ncRNA gene. Our estimates for false positive rates are based on simulations which preserve local islands of conservation; using these simulations, we predict a higher rate of false positives than previous computational ncRNA screens have reported. Using one of the tested prediction grammars, we provide an updated set of ncRNA predictions for D. melanogaster and compare them to previously-published predictions and experimental data. Many of our predictions show correlations with protein-coding genes. We found significant depletion of intergenic predictions near the 3' end of coding regions and furthermore depletion of predictions in the first intron of protein-coding genes. Some of our predictions are colocated with larger putative unannotated genes: for example, 17 of our predictions showing homology to the RFAM family snoR28 appear in a tandem array on the X chromosome; the 4.5 Kbp spanned by the predicted tandem array is contained within a FlyBase-annotated cDNA.

RCrane: semi-automated RNA model building

International Nuclear Information System (INIS)

Keating, Kevin S.; Pyle, Anna Marie

2012-01-01

RCrane is a new tool for the partially automated building of RNA crystallographic models into electron-density maps of low or intermediate resolution. This tool helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems

A novel knowledge-based potential for RNA 3D structure evaluation

Science.gov (United States)

Yang, Yi; Gu, Qi; Zhang, Ben-Gong; Shi, Ya-Zhou; Shao, Zhi-Gang

2018-03-01

Ribonucleic acids (RNAs) play a vital role in biology, and knowledge of their three-dimensional (3D) structure is required to understand their biological functions. Recently structural prediction methods have been developed to address this issue, but a series of RNA 3D structures are generally predicted by most existing methods. Therefore, the evaluation of the predicted structures is generally indispensable. Although several methods have been proposed to assess RNA 3D structures, the existing methods are not precise enough. In this work, a new all-atom knowledge-based potential is developed for more accurately evaluating RNA 3D structures. The potential not only includes local and nonlocal interactions but also fully considers the specificity of each RNA by introducing a retraining mechanism. Based on extensive test sets generated from independent methods, the proposed potential correctly distinguished the native state and ranked near-native conformations to effectively select the best. Furthermore, the proposed potential precisely captured RNA structural features such as base-stacking and base-pairing. Comparisons with existing potential methods show that the proposed potential is very reliable and accurate in RNA 3D structure evaluation. Project supported by the National Science Foundation of China (Grants Nos. 11605125, 11105054, 11274124, and 11401448).

The Bacillus subtilis and Bacillus halodurans Aspartyl-tRNA Synthetases Retain Recognition of tRNA(Asn).

Science.gov (United States)

Nair, Nilendra; Raff, Hannah; Islam, Mohammed Tarek; Feen, Melanie; Garofalo, Denise M; Sheppard, Kelly

2016-02-13

Synthesis of asparaginyl-tRNA (Asn-tRNA(Asn)) in bacteria can be formed either by directly ligating Asn to tRNA(Asn) using an asparaginyl-tRNA synthetase (AsnRS) or by synthesizing Asn on the tRNA. In the latter two-step indirect pathway, a non-discriminating aspartyl-tRNA synthetase (ND-AspRS) attaches Asp to tRNA(Asn) and the amidotransferase GatCAB transamidates the Asp to Asn on the tRNA. GatCAB can be similarly used for Gln-tRNA(Gln) formation. Most bacteria are predicted to use only one route for Asn-tRNA(Asn) formation. Given that Bacillus halodurans and Bacillus subtilis encode AsnRS for Asn-tRNA(Asn) formation and Asn synthetases to synthesize Asn and GatCAB for Gln-tRNA(Gln) synthesis, their AspRS enzymes were thought to be specific for tRNA(Asp). However, we demonstrate that the AspRSs are non-discriminating and can be used with GatCAB to synthesize Asn. The results explain why B. subtilis with its Asn synthetase genes knocked out is still an Asn prototroph. Our phylogenetic analysis suggests that this may be common among Firmicutes and 30% of all bacteria. In addition, the phylogeny revealed that discrimination toward tRNA(Asp) by AspRS has evolved independently multiple times. The retention of the indirect pathway in B. subtilis and B. halodurans likely reflects the ancient link between Asn biosynthesis and its use in translation that enabled Asn to be added to the genetic code. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Variable Vector Countermeasure Suit for Space Habitation and Exploration

Data.gov (United States)

National Aeronautics and Space Administration — The "Variable Vector Countermeasure Suit (V2Suit) for Space Habitation and Exploration" is a visionary system concept that will revolutionize space missions by...

JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures.

Science.gov (United States)

Shi, Jieming; Li, Xi; Dong, Min; Graham, Mitchell; Yadav, Nehul; Liang, Chun

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html.

JNSViewer—A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures

Science.gov (United States)

Dong, Min; Graham, Mitchell; Yadav, Nehul

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html. PMID:28582416

JNSViewer-A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures.

Directory of Open Access Journals (Sweden)

Jieming Shi

Full Text Available Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html.

Tissue-specific RNA expression marks distant-acting developmental enhancers.

Directory of Open Access Journals (Sweden)

Han Wu

2014-09-01

Full Text Available Short non-coding transcripts can be transcribed from distant-acting transcriptional enhancer loci, but the prevalence of such enhancer RNAs (eRNAs within the transcriptome, and the association of eRNA expression with tissue-specific enhancer activity in vivo remain poorly understood. Here, we investigated the expression dynamics of tissue-specific non-coding RNAs in embryonic mouse tissues via deep RNA sequencing. Overall, approximately 80% of validated in vivo enhancers show tissue-specific RNA expression that correlates with tissue-specific enhancer activity. Globally, we identified thousands of tissue-specifically transcribed non-coding regions (TSTRs displaying various genomic hallmarks of bona fide enhancers. In transgenic mouse reporter assays, over half of tested TSTRs functioned as enhancers with reproducible activity in the predicted tissue. Together, our results demonstrate that tissue-specific eRNA expression is a common feature of in vivo enhancers, as well as a major source of extragenic transcription, and that eRNA expression signatures can be used to predict tissue-specific enhancers independent of known epigenomic enhancer marks.

Affinity labeling of Escherichia coli phenylalanyl-tRNA synthetase at the binding site for tRNA

International Nuclear Information System (INIS)

Hountondji, C.; Schmitter, J.M.; Beauvallet, C.; Blanquet, S.

1987-01-01

Periodate-oxidized tRNA/sup Phe/ (tRNA/sub ox//sup Phe/) behaves as a specific affinity label of tetrameric Escherichia coli phenylalanyl-tRNA synthetase (PheRS). Reaction of the α 2 β 2 enzyme with tRNA/sub ox//sup Phe/ results in the loss of tRNA/sup Phe/ aminoacylation activity with covalent attachment of 2 mol of tRNA dialdehyde/mol of enzyme, in agreement with the stoichiometry of tRNA binding. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of the PheRS-[ 14 C]tRNA/sub ox//sup Phe/ covalent complex indicates that the large (α, M/sub r/ 87K) subunit of the enzyme interacts with the 3'-adenosine of tRNA/sub ox//sup Phe/. The [ 14 C]tRNA-labeled chymotryptic peptides of PheRS were purified by both gel filtration and reverse-phase high-performance liquid chromatography. The radioactivity was almost equally distributed among three peptides: Met-Lys[Ado]-Phe, Ala-Asp-Lys[Ado]-Leu, and Lys-Ile-Lys[Ado]-Ala. These sequences correspond to residues 1-3, 59-62, and 104-107, respectively, in the N-terminal region of the 795 amino acid sequence of the α subunit. It is noticeable that the labeled peptide Ala-Asp-Lys-Leu is adjacent to residues 63-66 (Arg-Val-Thr-Lys). The latter sequence was just predicted to resemble the proposed consensus tRNA CCA binding region Lys-Met-Ser-Lys-Ser, as deduced from previous affinity labeling studies on E. coli methionyl- and tyrosyl-tRNA synthetases

The modeled structure of the RNA dependent RNA polymerase of GBV-C Virus suggests a role for motif E in Flaviviridae RNA polymerases

Directory of Open Access Journals (Sweden)

Dutartre Hélène

2005-10-01

Full Text Available Abstract Background The Flaviviridae virus family includes major human and animal pathogens. The RNA dependent RNA polymerase (RdRp plays a central role in the replication process, and thus is a validated target for antiviral drugs. Despite the increasing structural and enzymatic characterization of viral RdRps, detailed molecular replication mechanisms remain unclear. The hepatitis C virus (HCV is a major human pathogen difficult to study in cultured cells. The bovine viral diarrhea virus (BVDV is often used as a surrogate model to screen antiviral drugs against HCV. The structure of BVDV RdRp has been recently published. It presents several differences relative to HCV RdRp. These differences raise questions about the relevance of BVDV as a surrogate model, and cast novel interest on the "GB" virus C (GBV-C. Indeed, GBV-C is genetically closer to HCV than BVDV, and can lead to productive infection of cultured cells. There is no structural data for the GBV-C RdRp yet. Results We show in this study that the GBV-C RdRp is closest to the HCV RdRp. We report a 3D model of the GBV-C RdRp, developed using sequence-to-structure threading and comparative modeling based on the atomic coordinates of the HCV RdRp structure. Analysis of the predicted structural features in the phylogenetic context of the RNA polymerase family allows rationalizing most of the experimental data available. Both available structures and our model are explored to examine the catalytic cleft, allosteric and substrate binding sites. Conclusion Computational methods were used to infer evolutionary relationships and to predict the structure of a viral RNA polymerase. Docking a GTP molecule into the structure allows defining a GTP binding pocket in the GBV-C RdRp, such as that of BVDV. The resulting model suggests a new proposition for the mechanism of RNA synthesis, and may prove useful to design new experiments to implement our knowledge on the initiation mechanism of RNA

NetSuite OneWorld Implementation 2011 R2

CERN Document Server

Foydel, Thomas

2011-01-01

This book is a focused, step-by step tutorial that shows you how to successfully implement NetSuite OneWorld into your organization. It is written in an easy-to-read style, with a strong emphasis on real-world, practical examples with step-by-step explanations. The book focuses on NetSuite OneWorld 2011 R1. If you are an application administrator, business analyst, project team member or business process owner who wants to implement NetSuite OneWorld into your organization, then this book is for you. This book might also be useful if you are a business manager considering a new system for your

Prokofieff: Krieg und Frieden (Sinfonische Suite), Die Verlobung im Kloster (Sommernacht-Suite), Russische Overtüre. Philharmonia Orchestra, Neeme Järvi / G. W.

Index Scriptorium Estoniae

G. W.

1993-01-01

Uuest heliplaadist "Prokofieff: Krieg und Frieden (Sinfonische Suite), Die Verlobung im Kloster (Sommernacht-Suite), Russische Overtüre. Philharmonia Orchestra, Neeme Järvi. (AD: 1991). Chandos/Koch CD 9096

Designing synthetic RNA for delivery by nanoparticles

International Nuclear Information System (INIS)

Jedrzejczyk, Dominika; Pawlowska, Roza; Chworos, Arkadiusz; Gendaszewska-Darmach, Edyta

2017-01-01

The rapid development of synthetic biology and nanobiotechnology has led to the construction of various synthetic RNA nanoparticles of different functionalities and potential applications. As they occur naturally, nucleic acids are an attractive construction material for biocompatible nanoscaffold and nanomachine design. In this review, we provide an overview of the types of RNA and nucleic acid’s nanoparticle design, with the focus on relevant nanostructures utilized for gene-expression regulation in cellular models. Structural analysis and modeling is addressed along with the tools available for RNA structural prediction. The functionalization of RNA-based nanoparticles leading to prospective applications of such constructs in potential therapies is shown. The route from the nanoparticle design and modeling through synthesis and functionalization to cellular application is also described. For a better understanding of the fate of targeted RNA after delivery, an overview of RNA processing inside the cell is also provided. (topical review)

BioVLAB-MMIA-NGS: microRNA-mRNA integrated analysis using high-throughput sequencing data.

Science.gov (United States)

Chae, Heejoon; Rhee, Sungmin; Nephew, Kenneth P; Kim, Sun

2015-01-15

It is now well established that microRNAs (miRNAs) play a critical role in regulating gene expression in a sequence-specific manner, and genome-wide efforts are underway to predict known and novel miRNA targets. However, the integrated miRNA-mRNA analysis remains a major computational challenge, requiring powerful informatics systems and bioinformatics expertise. The objective of this study was to modify our widely recognized Web server for the integrated mRNA-miRNA analysis (MMIA) and its subsequent deployment on the Amazon cloud (BioVLAB-MMIA) to be compatible with high-throughput platforms, including next-generation sequencing (NGS) data (e.g. RNA-seq). We developed a new version called the BioVLAB-MMIA-NGS, deployed on both Amazon cloud and on a high-performance publicly available server called MAHA. By using NGS data and integrating various bioinformatics tools and databases, BioVLAB-MMIA-NGS offers several advantages. First, sequencing data is more accurate than array-based methods for determining miRNA expression levels. Second, potential novel miRNAs can be detected by using various computational methods for characterizing miRNAs. Third, because miRNA-mediated gene regulation is due to hybridization of an miRNA to its target mRNA, sequencing data can be used to identify many-to-many relationship between miRNAs and target genes with high accuracy. http://epigenomics.snu.ac.kr/biovlab_mmia_ngs/. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Reexamining microRNA site accessibility in Drosophila: a population genomics study.

Directory of Open Access Journals (Sweden)

Kevin Chen

Full Text Available Kertesz et al. (Nature Genetics 2008 described PITA, a miRNA target prediction algorithm based on hybridization energy and site accessibility. In this note, we used a population genomics approach to reexamine their data and found that the PITA algorithm had lower specificity than methods based on evolutionary conservation at comparable levels of sensitivity.We also showed that deeply conserved miRNAs tend to have stronger hybridization energies to their targets than do other miRNAs. Although PITA had higher specificity in predicting targets than a naïve seed-match method, this signal was primarily due to the use of a single cutoff score for all miRNAs and to the observed correlation between conservation and hybridization energy. Overall, our results clarify the accuracy of different miRNA target prediction algorithms in Drosophila and the role of site accessibility in miRNA target prediction.

TRANSAT-- method for detecting the conserved helices of functional RNA structures, including transient, pseudo-knotted and alternative structures.

Science.gov (United States)

Wiebe, Nicholas J P; Meyer, Irmtraud M

2010-06-24

The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular

Metabolic and Subjective Results Review of the Integrated Suit Test Series

Science.gov (United States)

Norcross, J.R.; Stroud, L.C.; Klein, J.; Desantis, L.; Gernhardt, M.L.

2009-01-01

Crewmembers will perform a variety of exploration and construction activities on the lunar surface. These activities will be performed while inside an extravehicular activity (EVA) spacesuit. In most cases, human performance is compromised while inside an EVA suit as compared to a crewmember s unsuited performance baseline. Subjects completed different EVA type tasks, ranging from ambulation to geology and construction activities, in different lunar analog environments including overhead suspension, underwater and 1-g lunar-like terrain, in both suited and unsuited conditions. In the suited condition, the Mark III (MKIII) EVA technology demonstrator suit was used and suit pressure and suit weight were parameters tested. In the unsuited conditions, weight, mass, center of gravity (CG), terrain type and navigation were the parameters. To the extent possible, one parameter was varied while all others were held constant. Tests were not fully crossed, but rather one parameter was varied while all others were left in the most nominal setting. Oxygen consumption (VO2), modified Cooper-Harper (CH) ratings of operator compensation and ratings of perceived exertion (RPE) were measured for each trial. For each variable, a lower value correlates to more efficient task performance. Due to a low sample size, statistical significance was not attainable. Initial findings indicate that suit weight, CG and the operational environment can have a large impact on human performance during EVA. Systematic, prospective testing series such as those performed to date will enable a better understanding of the crucial interactions of the human and the EVA suit system and their environment. However, work remains to be done to confirm these findings. These data have been collected using only unsuited subjects and one EVA suit prototype that is known to fit poorly on a large demographic of the astronaut population. Key findings need to be retested using an EVA suit prototype better suited to a

Regulatory RNA design through evolutionary computation and strand displacement.