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  Subjects -> COMPUTER SCIENCE (Total: 1988 journals)
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COMPUTER SCIENCE (1153 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 14)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 68)
ACM Computing Surveys     Hybrid Journal   (Followers: 22)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 11)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 54)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 25)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 1)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 7)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 9)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 32)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 121)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 6)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 9)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 246)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 126)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access  
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 16)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 19)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 53)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 14)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 29)
Computer     Full-text available via subscription   (Followers: 84)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 14)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 11)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 10)

        1 2 3 4 5 6 | Last

Journal Cover Bioinformatics
  [SJR: 4.643]   [H-I: 271]   [246 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1367-4803 - ISSN (Online) 1460-2059
   Published by Oxford University Press Homepage  [370 journals]
  • The null hypothesis of GSEA, and a novel statistical model for competitive
           gene set analysis
    • Authors: Debrabant B.
      First page: 1271
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Competitive gene set analysis intends to assess whether a specific set of genes is more associated with a trait than the remaining genes. However, the statistical models assumed to date to underly these methods do not enable a clear cut formulation of the competitive null hypothesis. This is a major handicap to the interpretation of results obtained from a gene set analysis.<strong>Results:</strong> This work presents a hierarchical statistical model based on the notion of dependence measures, which overcomes this problem. The two levels of the model naturally reflect the modular structure of many gene set analysis methods. We apply the model to show that the popular GSEA method, which recently has been claimed to test the self-contained null hypothesis, actually tests the competitive null if the weight parameter is zero. However, for this result to hold strictly, the choice of the dependence measures underlying GSEA and the estimators used for it is crucial.<strong>Contact:</strong><a href="mailto:'bdebrabant@health.sdu.dk'">bdebrabant@health.sdu.dk</a><strong>Supplementary information:</strong>Supplementary materialSupplementary material is available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-24
      DOI: 10.1093/bioinformatics/btw803
       
  • An informative approach on differential abundance analysis for time-course
           metagenomic sequencing data
    • Authors: Luo D; Ziebell S, An L.
      First page: 1286
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> The advent of high-throughput next generation sequencing technology has greatly promoted the field of metagenomics where previously unattainable information about microbial communities can be discovered. Detecting differentially abundant features (e.g. species or genes) plays a critical role in revealing the contributors (i.e. pathogens) to the biological or medical status of microbial samples. However, currently available statistical methods lack power in detecting differentially abundant features contrasting different biological or medical conditions, in particular, for time series metagenomic sequencing data. We have proposed a novel procedure, metaDprof, which is built upon a spline-based method assuming heterogeneous error, to meet the challenges of detecting differentially abundant features from metagenomic samples by comparing different biological/medical conditions across time. It contains two stages: (i) global detection on features and (ii) time interval detection for significant features. The detection procedures in both stages are based on sound statistical support.<strong>Results:</strong> Compared with existing methods the new method metaDprof shows the best performance in comprehensive simulation studies. Not only can it accurately detect features relating to the biological condition or disease status of samples but it also can accurately detect the starting and ending time points when the differences arise. The proposed method is also applied to a real metagenomic dataset and the results provide an interesting angle to understand the relationship between the microbiota in mouse gut and diet type.<strong>Availability and Implementation:</strong> R code and an example dataset are available at <a href="https://cals.arizona.edu/~anling/sbg/software.htm">https://cals.arizona.edu/∼anling/sbg/software.htm</a><strong>Contact:</strong><a href="mailto:'anling@email.arizona.edu'">anling@email.arizona.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw828
       
  • Development and application of an algorithm to compute weighted multiple
           glycan alignments
    • Authors: Hosoda M; Akune Y, Aoki-Kinoshita KF.
      First page: 1317
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> A glycan consists of monosaccharides linked by glycosidic bonds, has branches and forms complex molecular structures. Databases have been developed to store large amounts of glycan-binding experiments, including glycan arrays with glycan-binding proteins. However, there are few bioinformatics techniques to analyze large amounts of data for glycans because there are few tools that can handle the complexity of glycan structures. Thus, we have developed the MCAW (Multiple Carbohydrate Alignment with Weights) tool that can align multiple glycan structures, to aid in the understanding of their function as binding recognition molecules.<strong>Results:</strong> We have described in detail the first algorithm to perform multiple glycan alignments by modeling glycans as trees. To test our tool, we prepared several data sets, and as a result, we found that the glycan motif could be successfully aligned without any prior knowledge applied to the tool, and the known recognition binding sites of glycans could be aligned at a high rate amongst all our datasets tested. We thus claim that our tool is able to find meaningful glycan recognition and binding patterns using data obtained by glycan-binding experiments. The development and availability of an effective multiple glycan alignment tool opens possibilities for many other glycoinformatics analysis, making this work a big step towards furthering glycomics analysis.<strong>Availability and Implementation: </strong><a href="http://www.rings.t.soka.ac.jp">http://www.rings.t.soka.ac.jp</a><strong>Contact:</strong><a href="mailto:'kkiyoko@soka.ac.jp'">kkiyoko@soka.ac.jp</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-16
      DOI: 10.1093/bioinformatics/btw827
       
  • ntCard: a streaming algorithm for cardinality estimation in genomics data
    • Authors: Mohamadi H; Khan H, Birol I.
      First page: 1324
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Many bioinformatics algorithms are designed for the analysis of sequences of some uniform length, conventionally referred to as <span style="font-style:italic;">k</span>-mers. These include de Bruijn graph assembly methods and sequence alignment tools. An efficient algorithm to enumerate the number of unique <span style="font-style:italic;">k</span>-mers, or even better, to build a histogram of <span style="font-style:italic;">k</span>-mer frequencies would be desirable for these tools and their downstream analysis pipelines. Among other applications, estimated frequencies can be used to predict genome sizes, measure sequencing error rates, and tune runtime parameters for analysis tools. However, calculating a <span style="font-style:italic;">k</span>-mer histogram from large volumes of sequencing data is a challenging task.<strong>Results:</strong> Here, we present ntCard, a streaming algorithm for estimating the frequencies of <span style="font-style:italic;">k</span>-mers in genomics datasets. At its core, ntCard uses the ntHash algorithm to efficiently compute hash values for streamed sequences. It then samples the calculated hash values to build a reduced representation multiplicity table describing the sample distribution. Finally, it uses a statistical model to reconstruct the population distribution from the sample distribution. We have compared the performance of ntCard and other cardinality estimation algorithms. We used three datasets of 480 GB, 500 GB and 2.4 TB in size, where the first two representing whole genome shotgun sequencing experiments on the human genome and the last one on the white spruce genome. Results show ntCard estimates <span style="font-style:italic;">k</span>-mer coverage frequencies >15× faster than the state-of-the-art algorithms, using similar amount of memory, and with higher accuracy rates. Thus, our benchmarks demonstrate ntCard as a potentially enabling technology for large-scale genomics applications.<strong>Availability and Implementation:</strong> ntCard is written in C ++ and is released under the GPL license. It is freely available at <a href="https://github.com/bcgsc/ntCard">https://github.com/bcgsc/ntCard</a>.<strong>Contact:</strong><a href="mailto:'hmohamadi@bcgsc.ca'">hmohamadi@bcgsc.ca</a> or <a href="mailto:'ibirol@bcgsc.ca'">ibirol@bcgsc.ca</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw832
       
  • Protein multiple sequence alignment benchmarking through secondary
           structure prediction
    • Authors: Le Q; Sievers F, Higgins DG.
      First page: 1331
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Multiple sequence alignment (MSA) is commonly used to analyze sets of homologous protein or DNA sequences. This has lead to the development of many methods and packages for MSA over the past 30 years. Being able to compare different methods has been problematic and has relied on gold standard benchmark datasets of ‘true’ alignments or on MSA simulations. A number of protein benchmark datasets have been produced which rely on a combination of manual alignment and/or automated superposition of protein structures. These are either restricted to very small MSAs with few sequences or require manual alignment which can be subjective. In both cases, it remains very difficult to properly test MSAs of more than a few dozen sequences. PREFAB and HomFam both rely on using a small subset of sequences of known structure and do not fairly test the quality of a full MSA.<strong>Results:</strong>In this paper we describe QuanTest, a fully automated and highly scalable test system for protein MSAs which is based on using secondary structure prediction accuracy (SSPA) to measure alignment quality. This is based on the assumption that better MSAs will give more accurate secondary structure predictions when we include sequences of known structure. SSPA measures the quality of an entire alignment however, not just the accuracy on a handful of selected sequences. It can be scaled to alignments of any size but here we demonstrate its use on alignments of either 200 or 1000 sequences. This allows the testing of slow accurate programs as well as faster, less accurate ones. We show that the scores from QuanTest are highly correlated with existing benchmark scores. We also validate the method by comparing a wide range of MSA alignment options and by including different levels of mis-alignment into MSA, and examining the effects on the scores.<strong>Availability and Implementation:</strong> QuanTest is available from <a href="http://www.bioinf.ucd.ie/download/QuanTest.tgz">http://www.bioinf.ucd.ie/download/QuanTest.tgz</a><strong>Contact:</strong><a href="mailto:'quan.le@ucd.ie'">quan.le@ucd.ie</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-16
      DOI: 10.1093/bioinformatics/btw840
       
  • Lineage-specific mutational clustering in protein structures predicts
           evolutionary shifts in function
    • Authors: Adams J; Mansfield MJ, Richard DJ, et al.
      First page: 1338
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Spatially clustered mutations within specific regions of protein structure are thought to result from strong positive selection for altered protein functions and are a common feature of oncoproteins in cancer. Although previous studies have used spatial substitution clustering to identify positive selection between pairs of proteins, the ability of this approach to identify functional shifts in protein phylogenies has not been explored.<strong>Results:</strong> We implemented a previous measure of spatial substitution clustering (the <span style="font-style:italic;">P3D</span> statistic) and extended it to detect spatially clustered substitutions at specific branches of phylogenetic trees. We then applied the analysis to 423 690 phylogenetic branches from 9261 vertebrate protein families, and examined its ability to detect historical shifts in protein function. Our analysis identified 19 607 lineages from 5362 protein families in which substitutions were spatially clustered on protein structures at <span style="font-style:italic;">P3D</span> < 0.01. Spatially clustered substitutions were overrepresented among ligand-binding residues and were significantly enriched among particular protein families and functions including C2H2 transcription factors and protein kinases. A small but significant proportion of branches with spatially clustered substitution also were under positive selection according to the branch-site test. Lastly, exploration of the top-scoring candidates revealed historical substitution events in vertebrate protein families that have generated new functions and protein interactions, including ancient adaptations in <span style="font-style:italic;">SLC7A2, PTEN, and SNAP25</span>. Ultimately, our work shows that lineage-specific, spatially clustered substitutions are a useful feature for identifying functional shifts in protein families, and reveal new candidates for future experimental study.<strong>Availability and Implementation:</strong> Source code and predictions for analyses performed in this study are available at: <a href="https://github.com/doxeylab/evoclust3d">https://github.com/doxeylab/evoclust3d</a><strong>Contact:</strong><a href="mailto:'acdoxey@uwaterloo.ca'">acdoxey@uwaterloo.ca</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-04
      DOI: 10.1093/bioinformatics/btw815
       
  • Sphinx: merging knowledge-based and ab initio approaches to improve
           protein loop prediction
    • Authors: Marks C; Nowak J, Klostermann S, et al.
      First page: 1346
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and <span style="font-style:italic;">ab initio</span>, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines <span style="font-style:italic;">ab initio</span> techniques with the potential extra structural information contained within loops of a different length to improve structure prediction.<strong>Results:</strong> We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the <span style="font-style:italic;">ab initio</span> algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific <span style="font-style:italic;">ab initio</span> methods, both in accuracy and speed.<strong>Availability and Implementation:</strong> Sphinx is available at <a href="http://opig.stats.ox.ac.uk/webapps/sphinx">http://opig.stats.ox.ac.uk/webapps/sphinx</a>.<strong>Contact:</strong><a href="mailto:'deane@stats.ox.ac.uk'">deane@stats.ox.ac.uk</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-16
      DOI: 10.1093/bioinformatics/btw823
       
  • Linearity of network proximity measures: implications for set-based
           queries and significance testing
    • Authors: Maxwell S; Chance MR, Koyutürk M.
      First page: 1354
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> In recent years, various network proximity measures have been proposed to facilitate the use of biomolecular interaction data in a broad range of applications. These applications include functional annotation, disease gene prioritization, comparative analysis of biological systems and prediction of new interactions. In such applications, a major task is the scoring or ranking of the nodes in the network in terms of their proximity to a given set of ‘seed’ nodes (e.g. a group of proteins that are identified to be associated with a disease, or are deferentially expressed in a certain condition). Many different network proximity measures are utilized for this purpose, and these measures are quite diverse in terms of the benefits they offer.<strong>Results:</strong> We propose a unifying framework for characterizing network proximity measures for set-based queries. We observe that many existing measures are linear, in that the proximity of a node to a set of nodes can be represented as an aggregation of its proximity to the individual nodes in the set. Based on this observation, we propose methods for processing of set-based proximity queries that take advantage of sparse local proximity information. In addition, we provide an analytical framework for characterizing the distribution of proximity scores based on reference models that accurately capture the characteristics of the seed set (e.g. degree distribution and biological function). The resulting framework facilitates computation of exact figures for the statistical significance of network proximity scores, enabling assessment of the accuracy of Monte Carlo simulation based estimation methods.<strong>Availability and Implementation:</strong> Implementations of the methods in this paper are available at <a href="https://bioengine.case.edu/crosstalker">https://bioengine.case.edu/crosstalker</a> which includes a robust visualization for results viewing.<strong>Contact</strong>: <a href="mailto:'stm@case.edu'">stm@case.edu</a> or <a href="mailto:'mxk331@case.edu'">mxk331@case.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-18
      DOI: 10.1093/bioinformatics/btw733
       
  • Modeling gene-wise dependencies improves the identification of drug
           response biomarkers in cancer studies
    • Authors: Nikolova O; Moser R, Kemp C, et al.
      First page: 1362
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments.<strong>Results:</strong> We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.<strong>Availability and Implementation</strong>: The code for this work is available at <a href="https://github.com/olganikolova/gbgfa">https://github.com/olganikolova/gbgfa</a><strong>Contact </strong>:<a href="mailto:'nikolova@ohsu.edu'">nikolova@ohsu.edu</a> or <a href="mailto:'margolin@ohsu.edu'">margolin@ohsu.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw836
       
  • PhenoCurve: capturing dynamic phenotype-environment relationships using
           phenomics data
    • Authors: Yang Y; Xu L, Feng Z, et al.
      First page: 1370
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation</strong>: Phenomics is essential for understanding the mechanisms that regulate or influence growth, fitness, and development. Techniques have been developed to conduct high-throughput large-scale phenotyping on animals, plants and humans, aiming to bridge the gap between genomics, gene functions and traits. Although new developments in phenotyping techniques are exciting, we are limited by the tools to analyze fully the massive phenotype data, especially the dynamic relationships between phenotypes and environments.<strong>Results</strong>: We present a new algorithm called PhenoCurve, a knowledge-based curve fitting algorithm, aiming to identify the complex relationships between phenotypes and environments, thus studying both values and trends of phenomics data. The results on both real and simulated data showed that PhenoCurve has the best performance among all the six tested methods. Its application to photosynthesis hysteresis pattern identification reveals new functions of core genes that control photosynthetic efficiency in response to varying environmental conditions, which are critical for understanding plant energy storage and improving crop productivity.<strong>Availability and Implementation:</strong> Software is available at phenomics.uky.edu/PhenoCurve<strong>Contact:</strong><a href="mailto:'chen.jin@uky.edu'">chen.jin@uky.edu</a> or <a href="mailto:'kramerd8@cns.msu.edu'">kramerd8@cns.msu.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-18
      DOI: 10.1093/bioinformatics/btw673
       
  • The genetic map comparator: a user-friendly application to display and
           compare genetic maps
    • Authors: Holtz Y; David J, Ranwez V.
      First page: 1387
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Marker-assisted selection strongly relies on genetic maps to accelerate breeding programs. High-density maps are now available for numerous species. Dedicated tools are required to compare several high-density maps on the basis of their key characteristics, while pinpointing their differences and similarities.<strong>Results:</strong> We developed the Genetic Map Comparator—a web-based application for easy comparison of different maps according to their key statistics and the relative positions of common markers.<strong>Availability and Implementation:</strong> The Genetic Map Comparator is available online at: <a href="http://bioweb.supagro.inra.fr/geneticMapComparator">http://bioweb.supagro.inra.fr/geneticMapComparator</a>. The source code is freely available on GitHub under the under the CeCILL general public license: <a href="https://github.com/holtzy/GenMap-Comparator">https://github.com/holtzy/GenMap-Comparator</a>.<strong>Contact:</strong><a href="mailto:'Holtz@supagro.fr'">Holtz@supagro.fr</a>; <a href="mailto:'Ranwez@supagro.fr'">Ranwez@supagro.fr</a></span>
      PubDate: 2017-01-16
      DOI: 10.1093/bioinformatics/btw816
       
  • VEXOR: an integrative environment for prioritization of functional
           variants in fine-mapping analysis
    • Authors: Lemaçon A; Joly Beauparlant C, Soucy P, et al.
      First page: 1389
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation</strong>: The identification of the functional variants responsible for observed genome-wide association studies (GWAS) signals is one of the most challenging tasks of the post-GWAS research era. Several tools have been developed to annotate genetic variants by their genomic location and potential functional implications. Each of these tools has its own requirements and internal logic, which forces the user to become acquainted with each interface.<strong>Results:</strong> From an awareness of the amount of work needed to analyze a single locus, we have built a flexible, versatile and easy-to-use web interface designed to help in prioritizing variants and predicting their potential functional implications. This interface acts as a single-point of entry linking association results with reference tools and relevant experiments.<strong>Availability and Implementation:</strong> VEXOR is an integrative web application implemented through the Shiny framework and available at: <a href="http://romix.genome.ulaval.ca/vexor">http://romix.genome.ulaval.ca/vexor</a>.<strong>Contact:</strong><a href="mailto:'arnaud.droit@crchuq.ulaval.ca'">arnaud.droit@crchuq.ulaval.ca</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw826
       
  • CircosVCF: circos visualization of whole-genome sequence variations stored
           in VCF files
    • Authors: Drori EE; Levy DD, Smirin-Yosef PP, et al.
      First page: 1392
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> Visualization of whole-genomic variations in a meaningful manner assists researchers in gaining new insights into the underlying data, especially when it comes in the context of whole genome comparisons. CircosVCF is a web based visualization tool for genome-wide variant data described in VCF files, using circos plots. The user friendly interface of CircosVCF supports an interactive design of the circles in the plot, and the integration of additional information such as experimental data or annotations. The provided visualization capabilities give a broad overview of the genomic relationships between genomes, and allow identification of specific meaningful SNPs regions.<strong>Availability and Implementation:</strong> CircosVCF was implemented in JavaScript and is available at <a href="http://www.ariel.ac.il/research/fbl/software">http://www.ariel.ac.il/research/fbl/software</a>.<strong>Contact:</strong><a href="mailto:'malisa@ariel.ac.il'">malisa@ariel.ac.il</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw834
       
  • Edlib: a C/C ++ library for fast, exact sequence alignment using
           edit distance
    • Authors: Šošić M; Šikić M.
      First page: 1394
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> We present Edlib, an open-source C/C ++ library for exact pairwise sequence alignment using edit distance. We compare Edlib to other libraries and show that it is the fastest while not lacking in functionality and can also easily handle very large sequences. Being easy to use, flexible, fast and low on memory usage, we expect it to be easily adopted as a building block for future bioinformatics tools.<strong>Availability and Implementation:</strong> Source code, installation instructions and test data are freely available for download at <a href="https://github.com/Martinsos/edlib">https://github.com/Martinsos/edlib</a>, under the MIT licence. Edlib is implemented in C/C ++ and supported on Linux, MS Windows, and Mac OS.<strong>Contact:</strong><a href="mailto:'mile.sikic@fer.hr'">mile.sikic@fer.hr</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-31
      DOI: 10.1093/bioinformatics/btw753
       
  • AFS: identification and quantification of species composition by
           metagenomic sequencing
    • Authors: Liu Y; Ripp F, Koeppel R, et al.
      First page: 1396
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> DNA-based methods to detect and quantify taxon composition in biological materials are often based on species-specific polymerase chain reaction, limited to detecting species targeted by the assay. Next-generation sequencing overcomes this drawback by untargeted shotgun sequencing of whole metagenomes at affordable cost. Here we present AFS, a software pipeline for quantification of species composition in food. AFS uses metagenomic shotgun sequencing and sequence read counting to infer species proportions. Using Illumina data from a reference sausage comprising four species, we reveal that AFS is independent of the sequencing assay and library preparation protocol. Cost-saving short (50-bp) single-end reads and Nextera<sup>®</sup> library preparation yield reliable results.<strong>Availability and Implementation:</strong> Datasets, binaries and usage instructions are available under <a href="http://all-food-seq.sourceforge.net">http://all-food-seq.sourceforge.net</a>. Raw data is available at NCBI’s SRA with accession number PRJNA271645.<strong>Contact:</strong><a href="mailto:'hankeln@uni-mainz.de'">hankeln@uni-mainz.de</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw822
       
  • fastMitoCalc : an ultra-fast program to estimate mitochondrial DNA copy
           number from whole-genome sequences
    • Authors: Qian Y; Butler TJ, Opsahl-Ong K, et al.
      First page: 1399
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div>Mitochondrial DNA (mtDNA) copy number is tightly regulated in tissues, and is both a critical determinant of mitochondrial function and a potential biomarker for disease. We and other groups have shown that the mtDNA copy number per cell can be directly estimated from whole-genome sequencing. The computation is based on the rationale that sequencing coverage should be proportional to the underlying DNA copy number for autosomal and mitochondrial DNA, and most computing time is spent calculating the average autosomal DNA coverage across ∼3 billion bases. That makes analyzing tens of thousands of available samples very slow. Here we present <span style="font-style:italic;">fastMitoCalc</span>, which takes advantage of the indexing of sequencing alignment files and uses a randomly selected small subset (0.1%) of the nuclear genome to estimate autosomal DNA coverage accurately. It is more than 100 times faster than current programs. <span style="font-style:italic;">fastMitoCalc</span> also provides an option to estimate copy number using a single autosomal chromosome, which could also achieve high accuracy but is slower. Using <span style="font-style:italic;">fastMitoCalc</span>, it becomes much more feasible now to conduct analyses on large-scale consortium data to test for association of mtDNA copy number with quantitative traits or nuclear variants.<strong>Availability and Implementation:</strong><span style="font-style:italic;">fastMitoCalc</span> is available at <a href="https://lgsun.irp.nia.nih.gov/hsgu/software/mitoAnalyzer/index.html">https://lgsun.irp.nia.nih.gov/hsgu/software/mitoAnalyzer/index.html</a><strong>Contact:</strong><a href="mailto:'jun.ding@nih.gov'">jun.ding@nih.gov</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-29
      DOI: 10.1093/bioinformatics/btw835
       
  • MobiDB-lite: fast and highly specific consensus prediction of intrinsic
           disorder in proteins
    • Authors: Necci M; Piovesan D, Dosztányi Z, et al.
      First page: 1402
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains.<strong>Results:</strong> Here, we have focused on providing a single consensus-based prediction, MobiDB-lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases.<strong>Availability and Implementation:</strong> MobiDB-lite is available as part of the MobiDB database from URL: <a href="http://mobidb.bio.unipd.it/">http://mobidb.bio.unipd.it/</a>. An executable can be downloaded from URL: <a href="http://protein.bio.unipd.it/mobidblite/">http://protein.bio.unipd.it/mobidblite/</a>.<strong>Contact:</strong><a href="mailto:'silvio.tosatto@unipd.it'">silvio.tosatto@unipd.it</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-16
      DOI: 10.1093/bioinformatics/btx015
       
  • Primerize-2D: automated primer design for RNA multidimensional chemical
           mapping
    • Authors: Tian S; Das R.
      First page: 1405
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary</strong>: Rapid RNA synthesis of comprehensive single mutant libraries and targeted multiple mutant libraries is enabling new multidimensional chemical approaches to solve RNA structures. PCR assembly of DNA templates and <span style="font-style:italic;">in vitro</span> transcription allow synthesis and purification of hundreds of RNA mutants in a cost-effective manner, with sharing of primers across constructs allowing significant reductions in expense. However, these protocols require organization of primer locations across numerous 96 well plates and guidance for pipetting, non-trivial tasks for which informatics and visualization tools can prevent costly errors. We report here an online tool to accelerate synthesis of large libraries of desired mutants through design and efficient organization of primers. The underlying program and graphical interface have been experimentally tested in our laboratory for RNA domains with lengths up to 300 nucleotides and libraries encompassing up to 960 variants. In addition to the freely available Primerize-2D server, the primer design code is available as a stand-alone Python package for broader applications.<strong>Availability and Implementation</strong>: <a href="http://primerize2d.stanford.edu">http://primerize2d.stanford.edu</a><strong>Contact:</strong><a href="mailto:'rhiju@stanford.edu'">rhiju@stanford.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-04
      DOI: 10.1093/bioinformatics/btw814
       
  • IntegratedMRF: random forest-based framework for integrating prediction
           from different data types
    • Authors: Rahman R; Otridge J, Pal R.
      First page: 1407
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> IntegratedMRF is an open-source R implementation for integrating drug response predictions from various genomic characterizations using univariate or multivariate random forests that includes various options for error estimation techniques. The integrated framework was developed following superior performance of random forest based methods in NCI-DREAM drug sensitivity prediction challenge. The computational framework can be applied to estimate mean and confidence interval of drug response prediction errors based on ensemble approaches with various combinations of genetic and epigenetic characterizations as inputs. The multivariate random forest implementation included in the package incorporates the correlations between output responses in the modeling and has been shown to perform better than existing approaches when the drug responses are correlated. Detailed analysis of the provided features is included in the Supplementary MaterialSupplementary Material.<strong>Availability and Implementation:</strong> The framework has been implemented as a <strong>R</strong> package <span style="font-style:italic;">IntegratedMRF</span>, which can be downloaded from <a href="https://cran.r-project.org/web/packages/IntegratedMRF/index.html">https://cran.r-project.org/web/packages/IntegratedMRF/index.html</a>, where further explanation of the package is available.<strong>Contact:</strong><a href="mailto:'ranadip.pal@ttu.edu'">ranadip.pal@ttu.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-02-06
      DOI: 10.1093/bioinformatics/btw765
       
  • Chainy: an universal tool for standardized relative quantification in
           real-time PCR
    • Authors: Mallona I; Díez-Villanueva A, Martín B, et al.
      First page: 1411
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> Chainy is a cross-platform web tool providing systematic pipelines and steady criteria to process real-time PCR data, including the calculation of efficiencies from raw data by kinetic methods, evaluation of the suitability of multiple references, standardized normalization using one or more references, and group-wise relative quantification statistical testing. We illustrate the utility of Chainy for differential expression and chromatin immunoprecipitation enrichment (ChIP-QPCR) analysis.<strong>Availability and Implementation:</strong> Chainy is open source and freely available at <a href="http://maplab.cat/chainy">http://maplab.cat/chainy</a><strong>Contact:</strong><a href="mailto:'imallona@igtp.cat'">imallona@igtp.cat</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-04-12
      DOI: 10.1093/bioinformatics/btw839
       
  • BioPAXViz: a cytoscape application for the visual exploration of metabolic
           pathway evolution
    • Authors: Psomopoulos FE; Vitsios DM, Baichoo S, et al.
      First page: 1418
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> BioPAXViz is a Cytoscape (version 3) application, providing a comprehensive framework for metabolic pathway visualization. Beyond the basic parsing, viewing and browsing roles, the main novel function that BioPAXViz provides is a visual comparative analysis of metabolic pathway topologies across pre-computed pathway phylogenomic profiles given a species phylogeny. Furthermore, BioPAXViz supports the display of hierarchical trees that allow efficient navigation through sets of variants of a single reference pathway. Thus, BioPAXViz can significantly facilitate, and contribute to, the study of metabolic pathway evolution and engineering.<strong>Availability and Implementation:</strong> BioPAXViz has been developed as a Cytoscape app and is available at: <a href="https://github.com/CGU-CERTH/BioPAX.Viz">https://github.com/CGU-CERTH/BioPAX.Viz</a>. The software is distributed under the MIT License and is accompanied by example files and data. Additional documentation is available at the aforementioned GitHub repository.<strong>Contact:</strong><a href="mailto:'ouzounis@certh.gr'">ouzounis@certh.gr</a>.</span>
      PubDate: 2017-01-25
      DOI: 10.1093/bioinformatics/btw813
       
  • DistributedFBA.jl: high-level, high-performance flux balance analysis in
           Julia
    • Authors: Heirendt L; Thiele I, Fleming RT.
      First page: 1421
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Flux balance analysis and its variants are widely used methods for predicting steady-state reaction rates in biochemical reaction networks. The exploration of high dimensional networks with such methods is currently hampered by software performance limitations.<strong>Results:</strong><span style="font-style:italic;">DistributedFBA.jl</span> is a high-level, high-performance, open-source implementation of flux balance analysis in Julia. It is tailored to solve multiple flux balance analyses on a subset or all the reactions of large and huge-scale networks, on any number of threads or nodes.<strong>Availability and Implementation:</strong> The code is freely available on github.com/opencobra/COBRA.jl. The documentation can be found at opencobra.github.io/COBRA.jl.<strong>Contact:</strong><a href="mailto:'ronan.mt.fleming@gmail.com'">ronan.mt.fleming@gmail.com</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-16
      DOI: 10.1093/bioinformatics/btw838
       
  • MAPPI-DAT: data management and analysis for protein–protein interaction
           data from the high-throughput MAPPIT cell microarray platform
    • Authors: Gupta S; De Puysseleyr V, Van der Heyden J, et al.
      First page: 1424
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> Protein-protein interaction (PPI) studies have dramatically expanded our knowledge about cellular behaviour and development in different conditions. A multitude of high-throughput PPI techniques have been developed to achieve proteome-scale coverage for PPI studies, including the microarray based Mammalian Protein-Protein Interaction Trap (MAPPIT) system. Because such high-throughput techniques typically report thousands of interactions, managing and analysing the large amounts of acquired data is a challenge. We have therefore built the MAPPIT cell microArray Protein Protein Interaction-Data management & Analysis Tool (MAPPI-DAT) as an automated data management and analysis tool for MAPPIT cell microarray experiments. MAPPI-DAT stores the experimental data and metadata in a systematic and structured way, automates data analysis and interpretation, and enables the meta-analysis of MAPPIT cell microarray data across all stored experiments.<strong>Availability and Implementation:</strong> MAPPI-DAT is developed in Python, using R for data analysis and MySQL as data management system. MAPPI-DAT is cross-platform and can be ran on Microsoft Windows, Linux and OS X/macOS. The source code and a Microsoft Windows executable are freely available under the permissive Apache2 open source license at <a href="https://github.com/compomics/MAPPI-DAT">https://github.com/compomics/MAPPI-DAT</a>.<strong>Contact:</strong><a href="mailto:'jan.tavernier@vib-ugent.be'">jan.tavernier@vib-ugent.be</a> or <a href="mailto:'lennart.martens@vib-ugent.be'">lennart.martens@vib-ugent.be</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2017-01-17
      DOI: 10.1093/bioinformatics/btx014
       
  • GDISC: a web portal for integrative analysis of gene–drug
           interaction for survival in cancer
    • Authors: Spainhour J; Lim J, Qiu P.
      First page: 1426
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> Survival analysis has been applied to The Cancer Genome Atlas (TCGA) data. Although drug exposure records are available in TCGA, existing survival analyses typically did not consider drug exposure, partly due to naming inconsistencies in the data. We have spent extensive effort to standardize the drug exposure data, which enabled us to perform survival analysis on drug-stratified subpopulations of cancer patients. Using this strategy, we integrated gene copy number data, drug exposure data and patient survival data to infer gene–drug interactions that impact survival. The collection of all analyzed gene–drug interactions in 32 cancer types are organized and presented in a searchable web-portal called gene–drug Interaction for survival in cancer (GDISC). GDISC allows biologists and clinicians to interactively explore the gene-drug interactions identified in the context of TCGA, and discover interactions associated to their favorite cancer, drug and/or gene of interest. In addition, GDISC provides the standardized drug exposure data, which is a valuable resource for developing new methods for drug-specific analysis.<strong>Availability and Implementation:</strong> GDISC is available at <a href="https://gdisc.bme.gatech.edu/">https://gdisc.bme.gatech.edu/</a>.<strong>Contact:</strong><a href="mailto:'peng.qiu@bme.gatech.edu'">peng.qiu@bme.gatech.edu</a></span>
      PubDate: 2017-01-05
      DOI: 10.1093/bioinformatics/btw830
       
  • Chainy: an universal tool for standardized relative quantification in
           real-time PCR
    • Authors: Mallona I; Díez-Villanueva A, Martín B, et al.
      First page: 1429
      Abstract: <span class="paragraphSection"><span style="font-style:italic;">Bioinformatics</span> (2017) doi: <strong><a href="article.aspx'volume=&page=">10.1093/bioinformatics/btw839<span></span></a></strong></span>
      PubDate: 2017-04-12
      DOI: 10.1093/bioinformatics/btx113
       
  • A new method for decontamination of de novo transcriptomes using a
           hierarchical clustering algorithm
    • Authors: Lafond-Lapalme J; Duceppe M, Wang S, et al.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> The identification of contaminating sequences in a <span style="font-style:italic;">de novo</span> assembly is challenging because of the absence of information on the target species. For sample types where the target organism is impossible to isolate from its matrix, such as endoparasites, endosymbionts and soil-harvested samples, contamination is unavoidable. A few post-assembly decontamination methods are currently available but are based only on alignments to databases, which can lead to poor decontamination.<strong>Results:</strong> We present a new decontamination method based on a hierarchical clustering algorithm called MCSC. This method uses frequent patterns found in sequences to create clusters. These clusters are then linked to the target species or tagged as contaminants using classic alignment tools. The main advantage of this decontamination method is that it allows sequences to be tagged correctly even if they are unknown or misaligned to a database.<strong>Availability and Implementation:</strong> Scripts and documentation about the MCSC decontamination method are available at <a href="https://github.com/Lafond-LapalmeJ/MCSC_Decontamination">https://github.com/Lafond-LapalmeJ/MCSC_Decontamination</a>.<strong>Contact</strong>: <a href="mailto:'benjamin.mimee@agr.gc.ca'">benjamin.mimee@agr.gc.ca</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2016-12-30
       
  • VarMatch: robust matching of small variant datasets using flexible scoring
           schemes
    • Authors: Sun C; Medvedev P.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Small variant calling is an important component of many analyses, and, in many instances, it is important to determine the set of variants which appear in multiple callsets. Variant matching is complicated by variants that have multiple equivalent representations. Normalization and decomposition algorithms have been proposed, but are not robust to different representation of complex variants. Variant matching is also usually done to maximize the number of matches, as opposed to other optimization criteria.<strong>Results:</strong> We present the VarMatch algorithm for the variant matching problem. Our algorithm is based on a theoretical result which allows us to partition the input into smaller subproblems without sacrificing accuracy. VarMatch is robust to different representation of complex variants and is particularly effective in low complexity regions or those dense in variants. VarMatch is able to detect more matches than either the normalization or decomposition algorithms on tested datasets. It also implements different optimization criteria, such as edit distance, that can improve robustness to different variant representations. Finally, the VarMatch software provides summary statistics, annotations and visualizations that are useful for understanding callers’ performance.<strong>Availability and Implementation:</strong> VarMatch is freely available at: <a href="https://github.com/medvedevgroup/varmatch">https://github.com/medvedevgroup/varmatch</a><strong>Contact:</strong><a href="mailto:'chensun@cse.psu.edu'">chensun@cse.psu.edu</a> or <a href="mailto:'pashadag@cse.psu.edu'">pashadag@cse.psu.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2016-12-30
       
  • Image-based correction of continuous and discontinuous non-planar axial
           distortion in serial section microscopy
    • Authors: Hanslovsky P; Bogovic JA, Saalfeld S.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order.<strong>Results:</strong> We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems.<strong>Availability and Implementation:</strong> We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at <a href="http://github.com/saalfeldlab/section-sort">http://github.com/saalfeldlab/section-sort</a>, <a href="http://github.com/saalfeldlab/z-spacing">http://github.com/saalfeldlab/z-spacing</a> and <a href="http://github.com/saalfeldlab/z-spacing-spark">http://github.com/saalfeldlab/z-spacing-spark</a>.<strong>Contact</strong>: <a href="mailto:'saalfelds@janelia.hhmi.org'">saalfelds@janelia.hhmi.org</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online</span>
      PubDate: 2016-12-30
       
  • GppFst : genomic posterior predictive simulations of F ST and d XY for
           identifying outlier loci from population genomic data
    • Authors: Adams RH; Schield D, Card DC, et al.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Summary:</strong> We introduce <span style="font-style:italic;">GppFst</span>, an open source R package that generates posterior predictive distributions of <span style="font-style:italic;">FST</span> and <span style="font-style:italic;">dx</span> under a neutral coalescent model to identify putative targets of selection from genomic data.<strong>Availability and Implementation:</strong><span style="font-style:italic;">GppFst</span> is available at (<a href="https://github.com/radamsRHA/GppFst">https://github.com/radamsRHA/GppFst</a>).<strong>Contact:</strong><a href="mailto:'todd.castoe@uta.edu'">todd.castoe@uta.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2016-12-30
       
  • Wright–Fisher exact solver (WFES): scalable analysis of population
           genetic models without simulation or diffusion theory
    • Authors: Krukov I; de Sanctis B, de Koning A.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> The simplifying assumptions that are used widely in theoretical population genetics may not always be appropriate for empirical population genetics. General computational approaches that do not require the assumptions of classical theory are therefore quite desirable. One such general approach is provided by the theory of absorbing Markov chains, which can be used to obtain exact results by directly analyzing population genetic Markov models, such as the classic bi-allelic Wright–Fisher model. Although these approaches are sometimes used, they are usually forgone in favor of simulation methods, due to the perception that they are too computationally burdensome. Here we show that, surprisingly, direct analysis of virtually any Markov chain model in population genetics can be made quite efficient by exploiting transition matrix sparsity and by solving restricted systems of linear equations, allowing a wide variety of exact calculations (within machine precision) to be easily and rapidly made on modern workstation computers.<strong>Results:</strong> We introduce Wright–Fisher Exact Solver (WFES), a fast and scalable method for direct analysis of Markov chain models in population genetics. WFES can rapidly solve for both long-term and transient behaviours including fixation and extinction probabilities, expected times to fixation or extinction, sojourn times, expected allele age and variance, and others. Our implementation requires only seconds to minutes of runtime on modern workstations and scales to biological population sizes ranging from humans to model organisms.<strong>Availability and Implementation</strong>: The code is available at <a href="https://github.com/dekoning-lab/wfes">https://github.com/dekoning-lab/wfes</a><strong>Contact:</strong><a href="mailto:'jason.dekoning@ucalgary.ca'">jason.dekoning@ucalgary.ca</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2016-12-30
       
  • A general framework for association analysis of microbial communities on a
           taxonomic tree
    • Authors: Tang Z; Chen G, Alekseyenko AV, et al.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation</strong>: Association analysis of microbiome composition with disease-related outcomes provides invaluable knowledge towards understanding the roles of microbes in the underlying disease mechanisms. Proper analysis of sparse compositional microbiome data is challenging. Existing methods rely on strong assumptions on the data structure and fail to pinpoint the associated microbial communities.<strong>Results</strong>: We develop a general framework to: (i) perform robust association tests for the microbial community that exhibits arbitrary inter-taxa dependencies; (ii) localize lineages on the taxonomic tree that are associated with covariates (e.g. disease status); and (iii) assess the overall association of the whole microbial community with the covariates. Unlike existing methods for microbiome association analysis, our framework does not make any distributional assumptions on the microbiome data; it allows for the adjustment of confounding variables and accommodates excessive zero observations; and it incorporates taxonomic information. We perform extensive simulation studies under a wide-range of scenarios to evaluate the new methods and demonstrate substantial power gain over existing methods. The advantages of the proposed framework are further demonstrated with real datasets from two microbiome studies. The relevant R package miLineage is publicly available.<strong>Availability and Implementation</strong>: miLineage package, manual and tutorial are available at <a href="https://medschool.vanderbilt.edu/tang-lab/software/miLineage">https://medschool.vanderbilt.edu/tang-lab/software/miLineage</a>.<strong>Contact:</strong><a href="mailto:'z.tang@vanderbilt.edu'">z.tang@vanderbilt.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2016-12-21
       
  • A mass graph-based approach for the identification of modified proteoforms
           using top-down tandem mass spectra
    • Authors: Kou Q; Wu S, Tolić N, et al.
      Abstract: <span class="paragraphSection"><div class="boxTitle">Abstract</div><strong>Motivation:</strong> Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a ‘bird's eye view’ of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem.<strong>Results:</strong> We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry datasets showed that TopMG outperformed existing methods in identifying complex proteoforms.<strong>Availability and implementation:</strong><a href="http://proteomics.informatics.iupui.edu/software/topmg/">http://proteomics.informatics.iupui.edu/software/topmg/</a><strong>Contact:</strong><a href="mailto:'xwliu@iupui.edu'">xwliu@iupui.edu</a><strong>Supplementary information:</strong>Supplementary dataSupplementary data are available at <span style="font-style:italic;">Bioinformatics</span> online.</span>
      PubDate: 2016-12-21
       
 
 
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