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  Subjects -> COMPUTER SCIENCE (Total: 2002 journals)
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COMPUTER SCIENCE (1160 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: 13)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 73)
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: 13)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 4)
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: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
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: 22)
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: 52)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
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: 2)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 7)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 8)
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: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
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: 33)
Applied Medical Informatics     Open Access   (Followers: 11)
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: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 128)
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: 301)
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: 44)
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: 20)
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: 9)
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: 31)
Computer     Full-text available via subscription   (Followers: 85)
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: 16)
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)
Computer Science Review     Hybrid Journal   (Followers: 10)

        1 2 3 4 5 6 | Last

Journal Cover Bioinformatics
  [SJR: 4.643]   [H-I: 271]   [301 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]
  • fastNGSadmix: admixture proportions and principal component analysis of a
           single NGS sample
    • Authors: Jørsboe E; Hanghøj K, Albrechtsen A.
      Abstract: AbstractMotivationEstimation of admixture proportions and principal component analysis (PCA) are fundamental tools in populations genetics. However, applying these methods to low- or mid-depth sequencing data without taking genotype uncertainty into account can introduce biases.ResultsHere we present fastNGSadmix, a tool to fast and reliably estimate admixture proportions and perform PCA from next generation sequencing data of a single individual. The analyses are based on genotype likelihoods of the input sample and a set of predefined reference populations. The method has high accuracy, even at low sequencing depth and corrects for the biases introduced by small reference populations.Availability and implementationThe admixture estimation method is implemented in C ++ and the PCA method is implemented in R. The code is freely available at http://www.popgen.dk/software/index.php/FastNGSadmixContactemil.jorsboe@bio.ku.dkSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-08-02
       
  • WIsH: who is the host' Predicting prokaryotic hosts from metagenomic
           phage contigs
    • Authors: Galiez C; Siebert M, Enault F, et al.
      Abstract: AbstractSummaryWIsH predicts prokaryotic hosts of phages from their genomic sequences. It achieves 63% mean accuracy when predicting the host genus among 20 genera for 3 kbp-long phage contigs. Over the best current tool, WisH shows much improved accuracy on phage sequences of a few kbp length and runs hundreds of times faster, making it suited for metagenomics studies.Availability and implementationOpenMP-parallelized GPL-licensed C ++ code available at https://github.com/soedinglab/wish.Contactclovis.galiez@mpibpc.mpg.de or soeding@mpibpc.mpg.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-07-13
       
  • ClusterSignificance: a bioconductor package facilitating statistical
           analysis of class cluster separations in dimensionality reduced data
    • Authors: Serviss JT; Gådin JR, Eriksson P, et al.
      Abstract: AbstractSummaryMulti-dimensional data generated via high-throughput experiments is increasingly used in conjunction with dimensionality reduction methods to ascertain if resulting separations of the data correspond with known classes. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package; a set of tools designed to assess the statistical significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non-coding RNA expression in the identity of multiple hematological malignancies.Availability and implementationClusterSignificance is an R package available via Bioconductor (https://bioconductor.org/packages/ClusterSignificance) under GPL-3.Contactdan.grander@ki.seSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-07-03
       
  • A multi-scenario genome-wide medical population genetics simulation
           framework
    • Authors: Mugo JW; Geza E, Defo J, et al.
      Abstract: AbstractMotivationRecent technological advances in high-throughput sequencing and genotyping have facilitated an improved understanding of genomic structure and disease-associated genetic factors. In this context, simulation models can play a critical role in revealing various evolutionary and demographic effects on genomic variation, enabling researchers to assess existing and design novel analytical approaches. Although various simulation frameworks have been suggested, they do not account for natural selection in admixture processes. Most are tailored to a single chromosome or a genomic region, very few capture large-scale genomic data, and most are not accessible for genomic communities.ResultsHere we develop a multi-scenario genome-wide medical population genetics simulation framework called ‘FractalSIM’. FractalSIM has the capability to accurately mimic and generate genome-wide data under various genetic models on genetic diversity, genomic variation affecting diseases and DNA sequence patterns of admixed and/or homogeneous populations. Moreover, the framework accounts for natural selection in both homogeneous and admixture processes. The outputs of FractalSIM have been assessed using popular tools, and the results demonstrated its capability to accurately mimic real scenarios. They can be used to evaluate the performance of a range of genomic tools from ancestry inference to genome-wide association studies.Availability and implementationThe FractalSIM package is available at http://www.cbio.uct.ac.za/FractalSIM.Contactemile.chimusa@uct.ac.zaSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-24
       
  • GfaPy : a flexible and extensible software library for handling sequence
           graphs in Python
    • Authors: Gonnella G; Kurtz S.
      Abstract: AbstractSummaryGFA 1 and GFA 2 are recently defined formats for representing sequence graphs, such as assembly, variation or splicing graphs. The formats are adopted by several software tools. Here, we present GfaPy, a software package for creating, parsing and editing GFA graphs using the programming language Python. GfaPy supports GFA 1 and GFA 2, using the same interface and allows for interconversion between both formats. The software package provides a simple interface for custom record types, which is an important new feature of GFA 2 (compared to GFA 1). This enables new applications of the format.Availability and implementationGfaPy is available open source at https://github.com/ggonnella/gfapy and installable via pip.Contactgonnella@zbh.uni-hamburg.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-22
       
  • Probability Map Viewer: near real-time probability map generator of serial
           block electron microscopy collections
    • Authors: Churas C; Perez AJ, Hakozaki H, et al.
      Abstract: AbstractSummaryTo expedite the review of semi-automated probability maps of organelles and other features from 3D electron microscopy data we have developed Probability Map Viewer, a Java-based web application that enables the computation and visualization of probability map generation results in near real-time as the data are being collected from the microscope. Probability Map Viewer allows the user to select one or more voxel classifiers, apply them on a sub-region of an active collection, and visualize the results as overlays on the raw data via any web browser using a personal computer or mobile device. Thus, Probability Map Viewer accelerates and informs the image analysis workflow by providing a tool for experimenting with and optimizing dataset-specific segmentation strategies during imaging.Availability and implementationhttps://github.com/crbs/probabilitymapviewer.Contactmellisman@ucsd.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-16
       
  • matter: an R package for rapid prototyping with larger-than-memory
           datasets on disk
    • Authors: Bemis KA; Vitek O.
      Abstract: AbstractSummaryWe introduce matter, an R package for direct interactions with larger-than-memory datasets, stored in an arbitrary number of files of any size. matter is primarily designed for datasets in new and rapidly evolving file formats, which may lack extensive software support. matter enables a wide variety of data exploration and manipulation steps and is extensible to many bioinformatics applications. It supports reproducible research by minimizing the need of converting and storing data in multiple formats. We illustrate the performance of matter in conjunction with the Bioconductor package Cardinal for analysis of high-resolution, high-throughput mass spectrometry imaging experiments.Availability and implementationThe package, vignettes and examples of applications in several areas of bioinformatics are available open-source at www.bioconductor.org under the Artistic-2.0 license.Contacto.vitek@neu.edu
      PubDate: 2017-06-15
       
  • The interfacial character of antibody paratopes: analysis of
           antibody–antigen structures
    • Authors: Nguyen MN; Pradhan MR, Verma C, et al.
      Abstract: AbstractSummaryIn this study, computational methods are applied to investigate the general properties of antigen engaging residues of a paratope from a non-redundant dataset of 403 antibody–antigen complexes to dissect the contribution of hydrogen bonds, hydrophobic, van der Waals contacts and ionic interactions, as well as role of water molecules in the antigen–antibody interface. Consistent with previous reports using smaller datasets, we found that Tyr, Trp, Ser, Asn, Asp, Thr, Arg, Gly, His contribute substantially to the interactions between antibody and antigen. Furthermore, antibody–antigen interactions can be mediated by interfacial waters. However, there is no reported comprehensive analysis for a large number of structured waters that engage in higher ordered structures at the antibody–antigen interface. From our dataset, we have found the presence of interfacial waters in 242 complexes. We present evidence that suggests a compelling role of these interfacial waters in interactions of antibodies with a range of antigens differing in shape complementarity. Finally, we carry out 296 835 pairwise 3D structure comparisons of 771 structures of contact residues of antibodies with their interfacial water molecules from our dataset using CLICK method. A heuristic clustering algorithm is used to obtain unique structural similarities, and found to separate into 368 different clusters. These clusters are used to identify structural motifs of contact residues of antibodies for epitope binding.Availability and implementationThis clustering database of contact residues is freely accessible at http://mspc.bii.a-star.edu.sg/minhn/pclick.html.Contactminhn@bii.a-star.edu.sg, chandra@bii.a-star.edu.sg or zhong_pingyu@immunol.a-star.edu.sgSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-15
       
  • omiXcore : a web server for prediction of protein interactions with large
           RNA
    • Authors: Armaos A; Cirillo D, Gaetano Tartaglia G.
      Abstract: AbstractSummaryHere we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites.ResultsomiXcore was trained and tested on enhanced UV Cross-Linking and ImmunoPrecipitation data. The method discriminates interacting and non-interacting protein-RNA pairs and identifies RNA binding sites with Areas under the ROC curve > 0.80, which suggests that the tool is particularly useful to prioritize candidates for further experimental validation.Availability and implementationomiXcore is freely accessed on the web at http://service.tartaglialab.com/grant_submission/omixcore.Contactgian.tartaglia@crg.esSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-15
       
  • kmerPyramid: an interactive visualization tool for nucleobase and k -mer
           frequencies
    • Authors: Kruppa J; van der Vries E, Jo WK, et al.
      Abstract: AbstractSummaryBioinformatics methods often incorporate the frequency distribution of nulecobases or k-mers in DNA or RNA sequences, for example as part of metagenomic or phylogenetic analysis. Because the frequency matrix with sequences in the rows and nucleobases in the columns is multi-dimensional it is hard to visualize. We present the R-package ‘kmerPyramid’ that allows to display each sequence, based on its nucleobase or k-mer distribution projected to the space of principal components, as a point within a 3-dimensional, interactive pyramid. Using the computer mouse, the user can turn the pyramid’s axes, zoom in and out and identify individual points. Additionally, the package provides the k-mer frequency matrices of about 2000 bacteria and 5000 virus reference sequences calculated from the NCBI RefSeq genbank. The ‘kmerPyramid’ can particularly be used for visualization of intra- and inter species differences.Availability and implementationThe R-package ‘kmerPyramid’ is available from the GitHub website at https://github.com/jkruppa/kmerPyramid.Contactklaus.jung@tiho-hannover.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-15
       
  • rqt: an R package for gene-level meta-analysis
    • Authors: Zhbannikov IY; Arbeev KG, Yashin AI.
      Abstract: AbstractMotivationDespite recent advances of modern GWAS methods, it is still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant.ResultsWe developed an R package rqt, which offers gene-level GWAS meta-analysis. The package can be easily included into bioinformatics pipeline or used stand-alone. We applied this tool to the analysis of Alzheimer’s disease data from three datasets CHS, FHS and LOADFS. Test results from meta-analysis of three Alzheimer studies show its applicability for association testing.Availability and implementationThe package rqt is freely available under the following link: https://github.com/izhbannikov/rqt.Contactilya.zhbannikov@duke.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-15
       
  • TIminer: NGS data mining pipeline for cancer immunology and immunotherapy
    • Authors: Tappeiner E; Finotello F, Charoentong P, et al.
      Abstract: AbstractSummaryRecently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity.Availability and implementationTIminer is freely available at http://icbi.i-med.ac.at/software/timiner/timiner.shtml.Contactzlatko.trajanoski@i-med.ac.atSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-15
       
  • MotifHyades: expectation maximization for de novo DNA motif pair discovery
           on paired sequences
    • Authors: Wong K.
      Abstract: AbstractMotivationIn higher eukaryotes, protein–DNA binding interactions are the central activities in gene regulation. In particular, DNA motifs such as transcription factor binding sites are the key components in gene transcription. Harnessing the recently available chromatin interaction data, computational methods are desired for identifying the coupling DNA motif pairs enriched on long-range chromatin-interacting sequence pairs (e.g. promoter–enhancer pairs) systematically.ResultsTo fill the void, a novel probabilistic model (namely, MotifHyades) is proposed and developed for de novo DNA motif pair discovery on paired sequences. In particular, two expectation maximization algorithms are derived for efficient model training with linear computational complexity. Under diverse scenarios, MotifHyades is demonstrated faster and more accurate than the existing ad hoc computational pipeline. In addition, MotifHyades is applied to discover thousands of DNA motif pairs with higher gold standard motif matching ratio, higher DNase accessibility and higher evolutionary conservation than the previous ones in the human K562 cell line. Lastly, it has been run on five other human cell lines (i.e. GM12878, HeLa-S3, HUVEC, IMR90, and NHEK), revealing another thousands of novel DNA motif pairs which are characterized across a broad spectrum of genomic features on long-range promoter–enhancer pairs.Availability and implementationThe matrix-algebra-optimized versions of MotifHyades and the discovered DNA motif pairs can be found in http://bioinfo.cs.cityu.edu.hk/MotifHyades.Contactkc.w@cityu.edu.hkSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-13
       
  • GraphSpace: stimulating interdisciplinary collaborations in network
           biology
    • Authors: Bharadwaj A; Singh DP, Ritz A, et al.
      Abstract: AbstractSummaryNetworks have become ubiquitous in systems biology. Visualization is a crucial component in their analysis. However, collaborations within research teams in network biology are hampered by software systems that are either specific to a computational algorithm, create visualizations that are not biologically meaningful, or have limited features for sharing networks and visualizations. We present GraphSpace, a web-based platform that fosters team science by allowing collaborating research groups to easily store, interact with, layout and share networks.Availability and implementationAnyone can upload and share networks at http://graphspace.org. In addition, the GraphSpace code is available at http://github.com/Murali-group/graphspace if a user wants to run his or her own server.Contactmurali@cs.vt.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-13
       
  • CloudNeo: a cloud pipeline for identifying patient-specific tumor
           neoantigens
    • Authors: Bais P; Namburi S, Gatti DM, et al.
      Abstract: AbstractSummaryWe present CloudNeo, a cloud-based computational workflow for identifying patient-specific tumor neoantigens from next generation sequencing data. Tumor-specific mutant peptides can be detected by the immune system through their interactions with the human leukocyte antigen complex, and neoantigen presence has recently been shown to correlate with anti T-cell immunity and efficacy of checkpoint inhibitor therapy. However computing capabilities to identify neoantigens from genomic sequencing data are a limiting factor for understanding their role. This challenge has grown as cancer datasets become increasingly abundant, making them cumbersome to store and analyze on local servers. Our cloud-based pipeline provides scalable computation capabilities for neoantigen identification while eliminating the need to invest in local infrastructure for data transfer, storage or compute. The pipeline is a Common Workflow Language (CWL) implementation of human leukocyte antigen (HLA) typing using Polysolver or HLAminer combined with custom scripts for mutant peptide identification and NetMHCpan for neoantigen prediction. We have demonstrated the efficacy of these pipelines on Amazon cloud instances through the Seven Bridges Genomics implementation of the NCI Cancer Genomics Cloud, which provides graphical interfaces for running and editing, infrastructure for workflow sharing and version tracking, and access to TCGA data.Availability and implementationThe CWL implementation is at: https://github.com/TheJacksonLaboratory/CloudNeo. For users who have obtained licenses for all internal software, integrated versions in CWL and on the Seven Bridges Cancer Genomics Cloud platform (https://cgc.sbgenomics.com/, recommended version) can be obtained by contacting the authors.Contactjeff.chuang@jax.orgSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-12
       
  • CancerSubtypes: an R/Bioconductor package for molecular cancer subtype
           identification, validation and visualization
    • Authors: Xu T; Le T, Liu L, et al.
      Abstract: AbstractSummaryIdentifying molecular cancer subtypes from multi-omics data is an important step in the personalized medicine. We introduce CancerSubtypes, an R package for identifying cancer subtypes using multi-omics data, including gene expression, miRNA expression and DNA methylation data. CancerSubtypes integrates four main computational methods which are highly cited for cancer subtype identification and provides a standardized framework for data pre-processing, feature selection, and result follow-up analyses, including results computing, biology validation and visualization. The input and output of each step in the framework are packaged in the same data format, making it convenience to compare different methods. The package is useful for inferring cancer subtypes from an input genomic dataset, comparing the predictions from different well-known methods and testing new subtype discovery methods, as shown with different application scenarios in the Supplementary MaterialSupplementary Material.Availability and implementationThe package is implemented in R and available under GPL-2 license from the Bioconductor website (http://bioconductor.org/packages/CancerSubtypes/).Contactthuc.le@unisa.edu.au or jiuyong.li@unisa.edu.auSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-12
       
  • MoDMaps3D: an interactive webtool for the quantification and 3D
           visualization of interrelationships in a dataset of DNA sequences
    • Authors: Karamichalis R; Kari L.
      Abstract: AbstractSummaryMoDMaps3D (Molecular Distance Maps 3D) is an alignment-free, fast, computationally lightweight webtool for computing and visualizing the interrelationships within any dataset of DNA sequences, based on pairwise comparisons between their oligomer compositions. MoDMaps3D is a general-purpose interactive webtool that is free of any requirements on sequence composition, position of the sequences in their respective genomes, presence or absence of similarity or homology, sequence length, or even sequence origin (biological or computer-generated).Availability and implementationMoDMaps3D is open source, cross-platform compatible, and is available under the MIT license at http://moleculardistancemaps.github.io/MoDMaps3D/. The source code is available at https://github.com/moleculardistancemaps/MoDMaps3D/.Contactlila@uwaterloo.caSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-10
       
  • FQC Dashboard: integrates FastQC results into a web-based, interactive,
           and extensible FASTQ quality control tool
    • Authors: Brown J; Pirrung M, McCue L.
      Abstract: AbstractSummaryFQC is software that facilitates quality control of FASTQ files by carrying out a QC protocol using FastQC, parsing results, and aggregating quality metrics into an interactive dashboard designed to richly summarize individual sequencing runs. The dashboard groups samples in dropdowns for navigation among the data sets, utilizes human-readable configuration files to manipulate the pages and tabs, and is extensible with CSV data.Availability and implementationFQC is implemented in Python 3 and Javascript, and is maintained under an MIT license. Documentation and source code is available at: https://github.com/pnnl/fqc.Contactjoseph.brown@pnnl.gov
      PubDate: 2017-06-09
       
  • runBNG: a software package for BioNano genomic analysis on the command
           line
    • Authors: Yuan Y; Bayer PE, Lee H, et al.
      Abstract: AbstractSummaryWe developed runBNG, an open-source software package which wraps BioNano genomic analysis tools into a single script that can be run on the command line. runBNG can complete analyses, including quality control of single molecule maps, optical map de novo assembly, comparisons between different optical maps, super-scaffolding and structural variation detection. Compared to existing software BioNano IrysView and the KSU scripts, the major advantages of runBNG are that the whole pipeline runs on one single platform and it has a high customizability.Availability and implementationrunBNG is written in bash, with the requirement of BioNano IrysSolve packages, GCC, Perl and Python software. It is freely available at https://github.com/appliedbioinformatics/runBNG.Contactdave.edwards@uwa.edu.auSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-09
       
  • DEIsoM: a hierarchical Bayesian model for identifying differentially
           expressed isoforms using biological replicates
    • Authors: Peng H; Yang Y, Zhe S, et al.
      Abstract: AbstractMotivationHigh-throughput mRNA sequencing (RNA-Seq) is a powerful tool for quantifying gene expression. Identification of transcript isoforms that are differentially expressed in different conditions, such as in patients and healthy subjects, can provide insights into the molecular basis of diseases. Current transcript quantification approaches, however, do not take advantage of the shared information in the biological replicates, potentially decreasing sensitivity and accuracy.ResultsWe present a novel hierarchical Bayesian model called Differentially Expressed Isoform detection from Multiple biological replicates (DEIsoM) for identifying differentially expressed (DE) isoforms from multiple biological replicates representing two conditions, e.g. multiple samples from healthy and diseased subjects. DEIsoM first estimates isoform expression within each condition by (1) capturing common patterns from sample replicates while allowing individual differences, and (2) modeling the uncertainty introduced by ambiguous read mapping in each replicate. Specifically, we introduce a Dirichlet prior distribution to capture the common expression pattern of replicates from the same condition, and treat the isoform expression of individual replicates as samples from this distribution. Ambiguous read mapping is modeled as a multinomial distribution, and ambiguous reads are assigned to the most probable isoform in each replicate. Additionally, DEIsoM couples an efficient variational inference and a post-analysis method to improve the accuracy and speed of identification of DE isoforms over alternative methods. Application of DEIsoM to an hepatocellular carcinoma (HCC) dataset identifies biologically relevant DE isoforms. The relevance of these genes/isoforms to HCC are supported by principal component analysis (PCA), read coverage visualization, and the biological literature.Availability and implementationThe software is available at https://github.com/hao-peng/DEIsoMContactpengh@alumni.purdue.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-08
       
  • ISAMBARD: an open-source computational environment for biomolecular
           analysis, modelling and design
    • Authors: Wood CW; Heal JW, Thomson AR, et al.
      Abstract: AbstractMotivationThe rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users.ResultsHere we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the ‘dark matter of protein-fold space’. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology.Availability and implementationA current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper.Contactd.n.woolfson@bristol.ac.uk or chris.wood@bristol.ac.ukSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-05
       
  • Glycan Reader is improved to recognize most sugar types and chemical
           modifications in the Protein Data Bank
    • Authors: Park S; Lee J, Patel DS, et al.
      Abstract: AbstractMotivationGlycans play a central role in many essential biological processes. Glycan Reader was originally developed to simplify the reading of Protein Data Bank (PDB) files containing glycans through the automatic detection and annotation of sugars and glycosidic linkages between sugar units and to proteins, all based on atomic coordinates and connectivity information. Carbohydrates can have various chemical modifications at different positions, making their chemical space much diverse. Unfortunately, current PDB files do not provide exact annotations for most carbohydrate derivatives and more than 50% of PDB glycan chains have at least one carbohydrate derivative that could not be correctly recognized by the original Glycan Reader.ResultsGlycan Reader has been improved and now identifies most sugar types and chemical modifications (including various glycolipids) in the PDB, and both PDB and PDBx/mmCIF formats are supported. CHARMM-GUI Glycan Reader is updated to generate the simulation system and input of various glycoconjugates with most sugar types and chemical modifications. It also offers a new functionality to edit the glycan structures through addition/deletion/modification of glycosylation types, sugar types, chemical modifications, glycosidic linkages, and anomeric states. The simulation system and input files can be used for CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, OpenMM, and CHARMM/OpenMM. Glycan Fragment Database in GlycanStructure.Org is also updated to provide an intuitive glycan sequence search tool for complex glycan structures with various chemical modifications in the PDB.Availability and implementationhttp://www.charmm-gui.org/input/glycan and http://www.glycanstructure.org.Contactwonpil@lehigh.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-05
       
  • JDINAC: joint density-based non-parametric differential interaction
           network analysis and classification using high-dimensional sparse omics
           data
    • Authors: Ji J; He D, Feng Y, et al.
      Abstract: AbstractMotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.ResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.Availability and implementationR scripts available at https://github.com/jijiadong/JDINACContactlxie@iscb.orgSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-05
       
  • COMPASS: the COMPletely Arbitrary Sequence Simulator
    • Authors: Low A; Rodrigue N, Wong A.
      Abstract: AbstractSummarySimulated sequence alignments are frequently used to test bioinformatics tools, but current sequence simulators are limited to defined state spaces. Here, we present the COMPletely Arbitrary Sequence Simulator (COMPASS), which is able to simulate the evolution of absolutely any discrete state space along a tree, for any form of time-reversible model.Availability and implementationCOMPASS is implemented in Python 2.7, and is freely available for all platforms with the Supplementary InformationSupplementary Information, as well as at http://labs.carleton.ca/eme/software-and-data.Contactalex_wong@carleton.caSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-05
       
  • BreakPoint Surveyor: a pipeline for structural variant visualization
    • Authors: Wyczalkowski MA; Wylie KM, Cao S, et al.
      Abstract: AbstractSummaryBreakPoint Surveyor (BPS) is a computational pipeline for the discovery, characterization, and visualization of complex genomic rearrangements, such as viral genome integration, in paired-end sequence data. BPS facilitates interpretation of structural variants by merging structural variant breakpoint predictions, gene exon structure, read depth, and RNA-sequencing expression into a single comprehensive figure.Availability and implementationSource code and sample data freely available for download at https://github.com/ding-lab/BreakPointSurveyor, distributed under the GNU GPLv3 license, implemented in R, Python and BASH scripts, and supported on Unix/Linux/OS X operating systems.Contactlding@wustl.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-06-05
       
  • DeepSite: protein-binding site predictor using 3D-convolutional neural
           networks
    • Authors: Jiménez JJ; Doerr SS, Martínez-Rosell GG, et al.
      Abstract: AbstractMotivationAn important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein.ResultsHere we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies.Availability and implementationDeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface.Contactgianni.defabritiis@upf.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-31
       
  • Hierarchical probabilistic models for multiple gene/variant associations
           based on next-generation sequencing data
    • Authors: Vavoulis DV; Taylor JC, Schuh A.
      Abstract: AbstractMotivationThe identification of genetic variants influencing gene expression (known as expression quantitative trait loci or eQTLs) is important in unravelling the genetic basis of complex traits. Detecting multiple eQTLs simultaneously in a population based on paired DNA-seq and RNA-seq assays employs two competing types of models: models which rely on appropriate transformations of RNA-seq data (and are powered by a mature mathematical theory), or count-based models, which represent digital gene expression explicitly, thus rendering such transformations unnecessary. The latter constitutes an immensely popular methodology, which is however plagued by mathematical intractability.ResultsWe develop tractable count-based models, which are amenable to efficient estimation through the introduction of latent variables and the appropriate application of recent statistical theory in a sparse Bayesian modelling framework. Furthermore, we examine several transformation methods for RNA-seq read counts and we introduce arcsin, logit and Laplace smoothing as preprocessing steps for transformation-based models. Using natural and carefully simulated data from the 1000 Genomes and gEUVADIS projects, we benchmark both approaches under a variety of scenarios, including the presence of noise and violation of basic model assumptions. We demonstrate that an arcsin transformation of Laplace-smoothed data is at least as good as state-of-the-art models, particularly at small samples. Furthermore, we show that an over-dispersed Poisson model is comparable to the celebrated Negative Binomial, but much easier to estimate. These results provide strong support for transformation-based versus count-based (particularly Negative-Binomial-based) models for eQTL mapping.Availability and implementationAll methods are implemented in the free software eQTLseq: https://github.com/dvav/eQTLseqContactdimitris.vavoulis@well.ox.ac.ukSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-31
       
  • Moran’s I quantifies spatio-temporal pattern formation in neural
           imaging data
    • Authors: Schmal C; Myung J, Herzel H, et al.
      Abstract: AbstractMotivationNeural activities of the brain occur through the formation of spatio-temporal patterns. In recent years, macroscopic neural imaging techniques have produced a large body of data on these patterned activities, yet a numerical measure of spatio-temporal coherence has often been reduced to the global order parameter, which does not uncover the degree of spatial correlation. Here, we propose to use the spatial autocorrelation measure Moran’s I, which can be applied to capture dynamic signatures of spatial organization. We demonstrate the application of this technique to collective cellular circadian clock activities measured in the small network of the suprachiasmatic nucleus (SCN) in the hypothalamus.ResultsWe found that Moran’s I is a practical quantitative measure of the degree of spatial coherence in neural imaging data. Initially developed with a geographical context in mind, Moran’s I accounts for the spatial organization of any interacting units. Moran’s I can be modified in accordance with the characteristic length scale of a neural activity pattern. It allows a quantification of statistical significance levels for the observed patterns. We describe the technique applied to synthetic datasets and various experimental imaging time-series from cultured SCN explants. It is demonstrated that major characteristics of the collective state can be described by Moran’s I and the traditional Kuramoto order parameter R in a complementary fashion.Availability and implementationPython 2.7 code of illustrative examples can be found in the Supplementary MaterialSupplementary Material.Contactchristoph.schmal@charite.de or grigory.bordyugov@hu-berlin.deSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-31
       
  • karyoploteR: an R/Bioconductor package to plot customizable genomes
           displaying arbitrary data
    • Authors: Gel B; Serra E.
      Abstract: AbstractMotivationData visualization is a crucial tool for data exploration, analysis and interpretation. For the visualization of genomic data there lacks a tool to create customizable non-circular plots of whole genomes from any species.ResultsWe have developed karyoploteR, an R/Bioconductor package to create linear chromosomal representations of any genome with genomic annotations and experimental data plotted along them. Plot creation process is inspired in R base graphics, with a main function creating karyoplots with no data and multiple additional functions, including custom functions written by the end-user, adding data and other graphical elements. This approach allows the creation of highly customizable plots from arbitrary data with complete freedom on data positioning and representation.Availability and implementationkaryoploteR is released under Artistic-2.0 License. Source code and documentation are freely available through Bioconductor (http://www.bioconductor.org/packages/karyoploteR) and at the examples and tutorial page at https://bernatgel.github.io/karyoploter_tutorial.Contactbgel@igtp.cat
      PubDate: 2017-05-29
       
  • Protein–Sol: a web tool for predicting protein solubility from
           sequence
    • Authors: Hebditch M; Carballo-Amador M, Charonis S, et al.
      Abstract: AbstractMotivationProtein solubility is an important property in industrial and therapeutic applications. Prediction is a challenge, despite a growing understanding of the relevant physicochemical properties.ResultsProtein–Sol is a web server for predicting protein solubility. Using available data for Escherichia coli protein solubility in a cell-free expression system, 35 sequence-based properties are calculated. Feature weights are determined from separation of low and high solubility subsets. The model returns a predicted solubility and an indication of the features which deviate most from average values. Two other properties are profiled in windowed calculation along the sequence: fold propensity, and net segment charge. The utility of these additional features is demonstrated with the example of thioredoxin.Availability and implementationThe Protein–Sol webserver is available at http://protein-sol.manchester.ac.uk.Contactjim.warwicker@manchester.ac.uk
      PubDate: 2017-05-29
       
  • Structurexplor: a platform for the exploration of structural features of
           RNA secondary structures
    • Authors: Glouzon J; Perreault J, Wang S.
      Abstract: AbstractSummaryDiscovering function-related structural features, such as the cloverleaf shape of transfer RNA secondary structures, is essential to understand RNA function. With this aim, we have developed a platform, named Structurexplor, to facilitate the exploration of structural features in populations of RNA secondary structures. It has been designed and developed to help biologists interactively search for, evaluate and select interesting structural features that can potentially explain RNA functions.Availability and implementationStructurxplor is a web application available at http://structurexplor.dinf.usherbrooke.ca. The source code can be found at http://jpsglouzon.github.io/structurexplor/.Contactshengrui.wang@usherbrooke.caSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-29
       
  • Computational modeling of immune system of the fish for a more effective
           vaccination in aquaculture
    • Authors: Madonia A; Melchiorri C, Bonamano S, et al.
      Abstract: AbstractMotivationA computational model equipped with the main immunological features of the sea bass (Dicentrarchus labrax L.) immune system was used to predict more effective vaccination in fish. The performance of the model was evaluated by using the results of two in vivo vaccinations trials against L. anguillarum and P. damselae.ResultsTests were performed to select the appropriate doses of vaccine and infectious bacteria to set up the model. Simulation outputs were compared with the specific antibody production and the expression of BcR and TcR gene transcripts in spleen. The model has shown a good ability to be used in sea bass and could be implemented for different routes of vaccine administration even with more than two pathogens. The model confirms the suitability of in silico methods to optimize vaccine doses and the immune response to them. This model could be applied to other species to optimize the design of new vaccination treatments of fish in aquaculture.Availability and implementationThe method is available at http://www.iac.cnr.it/∼filippo/c-immsim/Contactnromano@unitus.itSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-26
       
  • CWDPRNP: a tool for cervid prion sequence analysis in program R
    • Authors: Miller WL; Walter W.
      Abstract: AbstractSummaryChronic wasting disease is a fatal, neurological disease caused by an infectious prion protein, which affects economically and ecologically important members of the family Cervidae. Single nucleotide polymorphisms within the prion protein gene have been linked to differential susceptibility to the disease in many species. Wildlife managers are seeking to determine the frequencies of disease-associated alleles and genotypes and delineate spatial genetic patterns. The CWDPRNP package, implemented in program R, provides a unified framework for analyzing prion protein gene variability and spatial structure.Availability and implementationThe CWDPRNP package, manual and example data files are available at http://ecosystems.psu.edu/research/labs/walter-lab/additional-labs/population-genetics-lab. This package is available for all commonly used platforms.Contactwlm159psu@gmail.com
      PubDate: 2017-05-26
       
  • DNA sequence+shape kernel enables alignment-free modeling of transcription
           factor binding
    • Authors: Ma W; Yang L, Rohs R, et al.
      Abstract: AbstractMotivationTranscription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites.ResultsWe describe a sequence + shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (i) the k-spectrum + shape model performs better than the classical k-spectrum kernel, particularly for small k values; (ii) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (iii) the di-mismatch + shape kernel performs better than the di-mismatch kernel for intermediate k values.Availability and implementationThe software is available at https://bitbucket.org/wenxiu/sequence-shape.git.Contactrohs@usc.edu or william-noble@uw.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-24
       
  • M2Align: parallel multiple sequence alignment with a multi-objective
           metaheuristic
    • Authors: Zambrano-Vega C; Nebro AJ, García-Nieto J, et al.
      Abstract: AbstractMotivationMultiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such as maximizing the sum-of-pairs, maximizing the totally conserved columns, minimizing the number of gaps, or maximizing structural information based scores such as STRIKE. An approach to deal with MSA problems is to use multi-objective metaheuristics, which are non-exact stochastic optimization methods that can produce high quality solutions to complex problems having two or more objectives to be optimized at the same time. Our motivation is to provide a multi-objective metaheuristic for MSA that can run in parallel taking advantage of multi-core-based computers.ResultsThe software tool we propose, called M2Align (Multi-objective Multiple Sequence Alignment), is a parallel and more efficient version of the three-objective optimizer for sequence alignments MO-SAStrE, able of reducing the algorithm computing time by exploiting the computing capabilities of common multi-core CPU clusters. Our performance evaluation over datasets of the benchmark BAliBASE (v3.0) shows that significant time reductions can be achieved by using up to 20 cores. Even in sequential executions, M2Align is faster than MO-SAStrE, thanks to the encoding method used for the alignments.Availability and implementationM2Align is an open source project hosted in GitHub, where the source code and sample datasets can be freely obtained: https://github.com/KhaosResearch/M2Align.Contactantonio@lcc.uma.esSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-24
       
  • ASAP: a web-based platform for the analysis and interactive visualization
           of single-cell RNA-seq data
    • Authors: Gardeux V; David FA, Shajkofci A, et al.
      Abstract: AbstractMotivationSingle-cell RNA-sequencing (scRNA-seq) allows whole transcriptome profiling of thousands of individual cells, enabling the molecular exploration of tissues at the cellular level. Such analytical capacity is of great interest to many research groups in the world, yet these groups often lack the expertise to handle complex scRNA-seq datasets.ResultsWe developed a fully integrated, web-based platform aimed at the complete analysis of scRNA-seq data post genome alignment: from the parsing, filtering and normalization of the input count data files, to the visual representation of the data, identification of cell clusters, differentially expressed genes (including cluster-specific marker genes), and functional gene set enrichment. This Automated Single-cell Analysis Pipeline (ASAP) combines a wide range of commonly used algorithms with sophisticated visualization tools. Compared with existing scRNA-seq analysis platforms, researchers (including those lacking computational expertise) are able to interact with the data in a straightforward fashion and in real time. Furthermore, given the overlap between scRNA-seq and bulk RNA-seq analysis workflows, ASAP should conceptually be broadly applicable to any RNA-seq dataset. As a validation, we demonstrate how we can use ASAP to simply reproduce the results from a single-cell study of 91 mouse cells involving five distinct cell types.Availability and implementationThe tool is freely available at asap.epfl.ch and R/Python scripts are available at github.com/DeplanckeLab/ASAP.Contactbart.deplancke@epfl.chSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-24
       
  • DIRECTION: a machine learning framework for predicting and characterizing
           DNA methylation and hydroxymethylation in mammalian genomes
    • Authors: Pavlovic M; Ray P, Pavlovic K, et al.
      Abstract: AbstractMotivation5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predicted methylation or hydroxymethylation maps.ResultsWe test our approach on H1 human embryonic stem cells and H1-derived neural progenitor cells. Our predictive model is comparable in accuracy to other state-of-the-art DNA methylation prediction algorithms. We are the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the way for large-scale reconstruction of hydroxymethylation maps in mammalian model systems. We designed a novel, beam-search driven feature selection algorithm to identify the most discriminative predictor variables, and developed a platform for performing integrative analysis and reconstruction of the epigenome. Our toolkit DIRECTION provides predictions at single nucleotide resolution and identifies relevant features based on resource availability. This offers enhanced biological interpretability of results potentially leading to a better understanding of epigenetic gene regulation.Availability and implementationhttp://www.pradiptaray.com/direction, under CC-by-SA license.Contactspradiptaray@gmail.com or mchen@utdallas.edu or michael.zhang@utdallas.eduSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-12
       
  • Application of the cghRA framework to the genomic characterization of
           Diffuse Large B-Cell Lymphoma
    • Authors: Mareschal S; Ruminy P, Alcantara M, et al.
      Abstract: AbstractMotivationAlthough sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyze such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task.ResultsThe cghRA framework combines a user-friendly graphical interface and a powerful object-oriented command-line interface to handle a full aCGH analysis, as is illustrated in an original series of 107 Diffuse Large B-Cell Lymphomas. New algorithms for copy-number calling, polymorphism detection and minimal common region prioritization were also developed and validated. While their performances will only be demonstrated with aCGH, these algorithms could actually prove useful to any copy-number analysis, whatever the technique used.Availability and implementationR package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA.Contactmareschal@ovsa.fr or fabrice.jardin@chb.unicancer.frSupplementary informationSupplementary dataSupplementary data are available at Bioinformatics online.
      PubDate: 2017-05-08
       
 
 
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