DOCSLIB.ORG

Judith Mclaughlin, Nathalie Pochet, Diego Borges-Rivera, Thorin Tabor, Helga Thorvaldsdóttir, Aviv Regev, Jill P

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Judith Mclaughlin, Nathalie Pochet, Diego Borges-Rivera, Thorin Tabor, Helga Thorvaldsdóttir, Aviv Regev, Jill P : An Environment for Frictionless Bioinformatics Michael Reich, Ted Liefeld, Marco Ocana, Dongkeun Jang, Jon Bistline, James Robinson, Peter Carr, Barbara Hill www.genomespace.org Judith McLaughlin, Nathalie Pochet, Diego Borges-Rivera, Thorin Tabor, Helga Thorvaldsdóttir, Aviv Regev, Jill P. Mesirov Background Features Interface Organize and Recipes Driving Biological Projects Customize create groups Genomic research increasingly involves the generation GenomeSpace makes it easy for biologists to use the Easily manage your display of users (e.g. A collection of "recipes" provides quick guides to GenomeSpace development is done in collaboration your files and One-click according to your project Get help with and analysis of data across multiple modalities, e.g. tools they already know to perform analyses and to find directories and launching of the types of team) and add the analysis accomplishing tasks using the GenomeSpace tools: with two Driving Biological Projects (DBPs), which preview files analysis tools analyses you your local tools and sequence variation, gene expression, epigenetics, other tools that can help them extend their research into resident in the on your wish to tools to GenomeSpace provide scientific direction as well as a collection of cloud. datasets. perform. GenomeSpace. itself. proteomics. These efforts are limited however by the new areas. GenomeSpace features include: 1 2 3 4 5 target research scenarios and analytic workflows. Find differentially expressed subnetworks difficulty of analyzing and integrating results from these Seamless transfer of data between tools multiple modalities. Each mode has its own tools, and 6 Dissection of regulatory networks in cancer GenomeSpace automatically converts file Manage your Find differentially expressed genes in Table the tools are seldom designed to work together. account Browser igv RNA-Seq data stem cells by comparative network analysis formats, removing the need to write scripts information. To address these challenges, we have developed 1 with embryonic stem cells. Regulatory and “glue” code. 6 Preprocess and quality check RNA-Seq data GenomeSpace, a lightweight, cloud-based 1 2 3 4 5 networks of embryonic, induced pluripotent, 8 infrastructure to allow genomics tools to share data Easy import of data from public repositories and induced cancer stem cells are compared to ARRAYEXPRESS Identify and visualize expressed transcripts seamlessly. GenomeSpace aims to knock down the Users can transfer data directly from igv in RNA-Seq data find key differing regulatory networks. (Chan barriers between tools, freeing researchers to Lab, Stanford) Web-based resources to their genomics 7 Identify and annotate coding variants from perform analyses and investigate hypotheses that 7 igv tools without the need to download first. whole exome sequencing (WES) data Functional characterization of lincRNAs in previously were too difficult to consider. Context Identify biological functions for genes in copy mammalian genomes by integrating 8 One click Table Connect your own cloud storage accounts menus on Browser MSigDB number variation (CNV) regions Organize and files and launching of 2 epigenomic, transcription, RNA sequencing, Add your own Dropbox, Amazon or (coming manage your directories Share folders or analysis tools on cloud-based just like files with your selected Identify an up- or down-regulated pathway RNAi, WGAS, and other modalities. (Regev Lab, soon) Google Drive accounts easily. project folders desktop individuals, the datasets. MSigDB from expression data just like files on applications. public or specific Broad) Connect Collaborate Combine your desktop. groups of users. Seed Tools New Tools How You Can Participate We are seeking genomic researchers, bioinformatics tool developers, and data repository providers who are interested in joining and expanding the GenomeSpace community. See Cytoscape Galaxy GenePattern Genomica IGV UCSC Browser Cistrome InSilico DB ArrayExpress geWorkbench Synapse - Available soon ISAcreator Reactome - Available soon MSigDB (UCSD) (Penn State University) (Broad Institute) (Weizmann Institute) (Broad Institute) (UCSC) (Dana-Farber) (University of Brussels) (EMBL-EBI) (Columbia University) (Sage Bionetworks) (U. of Oxford) (Ontario Institute for (The Broad Institute) www.genomespace.org Cancer Research) At each step, GenomeSpace performs Finding transcription factor regulators of in Action all data conversions and transfers human hematopoiesis between tools. 4 Galaxy This example GenomeSpace scenario reproduces part of the DMap analysis from the Regev lab paper in Cell, Novershtern et al, 2010 Compute overlaps 3. User loads the lineage-specific a. Upload annotation tracks for the genomic locations of the transcription factors generated regulators, a set of previously published SNPs and a set of 1. User saves the expression data from 2. User performs differential linkage regions from a genome-wide association study. the GO transcription factors to expression using the in GenePattern to Genomica b. Run an overlap analysis to determine the intersection of GenomeSpace. expression data loaded through GenomeSpace. putative regulators, SNPs, and linkage regions from GenomeSpace. Genomica 4. User uploads bed IGV 1 annotation tracks to 5 Extract transcription factors Galaxy and IGV through Visualize data a. Load expression data containing 200 samples and 8000 genes GenomeSpace a. Load annotation tracks for the 3 types of data in step 4 into b. Load a gene set containing Gene Ontology (GO) transcription IGV factors GenePattern Genomica b. View the concordance between the locations of the c. Save the expression data from only the GO transcription 2 3 analytically identified potential regulators and the previously factors to the GenomeSpace Data Manager. Compute differentially expressed Identify module networks published SNPs and linkage regions transcription factors a. Compute module networks to determine coexpressed a. Perform differential expression analysis to determine genes “modules” of genes within the original expression dataset. Hematologic that significantly distinguish human embryonic stem cells b. Load the lineage-specific transcription factors generated by disorders (hESCs) versus differentiated cells. GenePattern c. Use these two datasets to generate a list of potential regulators IGV multi-locus view.
Load more

Recommended publications
  • Cytoscape: the Network Visualization Tool for Genomespace Workflows
    F1000Research 2014, 3:151 Last updated: 21 JUL 2021 SOFTWARE TOOL ARTICLE Cytoscape: the network visualization tool for GenomeSpace workflows [version 2; peer review: 3 approved] Barry Demchak1, Tim Hull1, Michael Reich2, Ted Liefeld2, Michael Smoot3, Trey Ideker1, Jill P. Mesirov2 1Department of Medicine, University of California, San Diego, La Jolla, CA, 92093-0688, USA 2Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA , 02142, USA 3Qualcomm, Inc, San Diego, CA , 92121, USA v2 First published: 01 Jul 2014, 3:151 Open Peer Review https://doi.org/10.12688/f1000research.4492.1 Latest published: 26 Aug 2014, 3:151 https://doi.org/10.12688/f1000research.4492.2 Reviewer Status Invited Reviewers Abstract Modern genomic analysis often requires workflows incorporating 1 2 3 multiple best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms version 2 that create data flows between them. One such tool is Cytoscape 3, a (revision) popular application that enables analysis and visualization of graph- 26 Aug 2014 oriented genomic networks. As Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required version 1 special care in creating a seamless user experience and enabling 01 Jul 2014 report report report appropriate data flows. In this paper, we present the design and operation of the Cytoscape GenomeSpace app, which accomplishes 1. Aris Floratos, Columbia University, New this integration, thereby providing critical analysis and visualization functionality for GenomeSpace users. It has been downloaded over York, NY, USA 850 times since the release of its first version in September, 2013.
    [Show full text]
  • Cytoscape: the Network Visualization Tool for Genomespace Workflows
    UC San Diego UC San Diego Previously Published Works Title Cytoscape: the network visualization tool for GenomeSpace workflows. Permalink https://escholarship.org/uc/item/1qr8t9n8 Authors Demchak, Barry Hull, Tim Reich, Michael et al. Publication Date 2014 DOI 10.12688/f1000research.4492.2 Peer reviewed eScholarship.org Powered by the California Digital Library University of California F1000Research 2014, 3:151 Last updated: 15 SEP 2014 SOFTWARE TOOL Cytoscape: the network visualization tool for GenomeSpace workflows [v2; ref status: indexed, http://f1000r.es/47f] Barry Demchak1, Tim Hull1, Michael Reich2, Ted Liefeld2, Michael Smoot3, 1 2 Trey Ideker , Jill P. Mesirov 1Department of Medicine, University of California, San Diego, La Jolla, 92093-0688, USA 2Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, 02142, USA 3Qualcomm, Inc, San Diego, 92121, USA v2 First published: 01 Jul 2014, 3:151 (doi: 10.12688/f1000research.4492.1) Open Peer Review Latest published: 26 Aug 2014, 3:151 (doi: 10.12688/f1000research.4492.2) Invited Referee Responses Abstract Modern genomic analysis often requires workflows incorporating multiple 1 2 3 best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows version 1 between them. One such tool is Cytoscape 3, a popular application that published report report report 01 Jul 2014 enables analysis and visualization of graph-oriented genomic networks. As 1 1 1 Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows.
    [Show full text]
  • Modeling Genomic Regulatory Networks with Big Data
    TIGS-1108; No. of Pages 10 Review Modeling genomic regulatory networks with big data Hamid Bolouri Division of Human Biology, Fred Hutchinson Cancer Research Center (FHCRC), 1100 Fairview Avenue North, PO Box 19024, Seattle, WA 98109, USA High-throughput sequencing, large-scale data generation throughput sequencing-based technologies and to compu- projects, and web-based cloud computing are changing tational modeling and analysis. At the same time, GRNs how computational biology is performed, who performs are both complex (i.e., can exhibit hard-to-predict/nonline- it, and what biological insights it can deliver. I review here ar behaviors) and complicated (i.e., they are composed of the latest developments in available data, methods, and large numbers of component parts and interactions). For software, focusing on the modeling and analysis of the this reason, mathematical and computational approaches gene regulatory interactions in cells. Three key findings are essential in GRN research. are: (i) although sophisticated computational resources Cellular behaviors have traditionally been character- are increasingly available to bench biologists, tailored ized as being mediated through highly distinct processes ongoing education is necessary to avoid the erroneous (e.g., DNA replication) and pathways (e.g., the canonical use of these resources. (ii) Current models of the regula- WNT signaling pathway). However, because of widespread tion of gene expression are far too simplistic and need interactions among cellular processes and pathways, the updating. (iii) Integrative computational analysis of large- use of unbiased, genome-wide technologies is essential to scale datasets is becoming a fundamental component of the discovery and characterization of GRNs.
    [Show full text]
  • Integrative Genomic Analysis by Interoperation of Bioinformatics Tools in Genomespace
    Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Qu, Kun et al. “Integrative Genomic Analysis by Interoperation of Bioinformatics Tools in GenomeSpace.” Nature Methods 13.3 (2016): 245–247. As Published http://dx.doi.org/10.1038/nmeth.3732 Publisher Nature Publishing Group Version Author's final manuscript Citable link http://hdl.handle.net/1721.1/105733 Terms of Use Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Nat Methods Manuscript Author . Author manuscript; Manuscript Author available in PMC 2016 July 18. Published in final edited form as: Nat Methods. 2016 March ; 13(3): 245–247. doi:10.1038/nmeth.3732. Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace Kun Qu#1, Sara Garamszegi#2, Felix Wu#2, Helga Thorvaldsdottir2, Ted Liefeld2,3, Marco Ocana2,3, Diego Borges-Rivera4, Nathalie Pochet2,5, James T. Robinson2,3, Barry Demchak3, Tim Hull3, Gil Ben-Artzi6,7, Daniel Blankenberg8, Galt P. Barber9, Brian T. Lee9, Robert M. Kuhn9, Anton Nekrutenko8, Eran Segal6, Trey Ideker3, Michael Reich2,3, Aviv Regev2,4,10, Howard Y. Chang1,11, and Jill P. Mesirov2,3 1Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA 2The
    [Show full text]
  • Cytoscape: the Network Visualization Tool for Genomespace Workflows[V2
    F1000Research 2014, 3:151 Last updated: 15 SEP 2014 SOFTWARE TOOL Cytoscape: the network visualization tool for GenomeSpace workflows [v2; ref status: indexed, http://f1000r.es/47f] Barry Demchak1, Tim Hull1, Michael Reich2, Ted Liefeld2, Michael Smoot3, 1 2 Trey Ideker , Jill P. Mesirov 1Department of Medicine, University of California, San Diego, La Jolla, 92093-0688, USA 2Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, 02142, USA 3Qualcomm, Inc, San Diego, 92121, USA v2 First published: 01 Jul 2014, 3:151 (doi: 10.12688/f1000research.4492.1) Open Peer Review Latest published: 26 Aug 2014, 3:151 (doi: 10.12688/f1000research.4492.2) Invited Referee Responses Abstract Modern genomic analysis often requires workflows incorporating multiple 1 2 3 best-of-breed tools. GenomeSpace is a web-based visual workbench that combines a selection of these tools with mechanisms that create data flows version 1 between them. One such tool is Cytoscape 3, a popular application that published report report report 01 Jul 2014 enables analysis and visualization of graph-oriented genomic networks. As 1 1 1 Cytoscape runs on the desktop, and not in a web browser, integrating it into GenomeSpace required special care in creating a seamless user experience and enabling appropriate data flows. In this paper, we present the design and version 2 operation of the Cytoscape GenomeSpace app, which accomplishes this published 26 Aug 2014 integration, thereby providing critical analysis and visualization functionality for GenomeSpace
    [Show full text]
  • Nathalie Pochet, Diego Borges-Rivera, Thorin Tabor, Helga Thorvaldsdóttir, Aviv Regev, Jill P
    : An Environment for Frictionless Bioinformatics Michael Reich, Ted Liefeld, Marco Ocana, Dongkeun Jang, Jon Bistline, James Robinson, Peter Carr, Barbara Hill www.genomespace.org Nathalie Pochet, Diego Borges-Rivera, Thorin Tabor, Helga Thorvaldsdóttir, Aviv Regev, Jill P. Mesirov Features Interface Recipes Using Galaxy with GenomeSpace GenomeSpace Enabled Tools Customize Organize and GenomeSpace makes it easy for researchers to use the Easily manage your display create groups of A collection of "recipes" provides quick guides to Galaxy users can send data easily between Galaxy and your files and One-click according to users (e.g. your Get help with Galaxy (Penn State University) tools they already know to perform analyses and to find directories and launching of the types of project team) the analysis accomplishing tasks using the GenomeSpace tools: their GenomeSpace cloud storage: Scientific workflow and data and analysis preview files analysis tools analyses you and add your tools and platform providing a large number of other tools that can help them extend their research into resident in the on your wish to local tools to GenomeSpace Importing Data from GenomeSpace to Galaxy sequence and genome analysis tools. cloud. datasets. perform. GenomeSpace. itself. new areas. Registration is free and includes 20GB of 1 2 3 4 5 cloud based storage. Preprocess and quality check RNA-Seq data (UCSD) 4 Cytoscape GenomeSpace features include: 6 Visualize and analyze molecular interaction Manage your Identify and annotate coding variants from Data is account igv networks and biological pathways whole exome sequencing (WES) data 2 Register or log in to your imported Seamless transfer of data between tools information.
    [Show full text]
  • Integrative Genomic Analysis by Interoperation Of
    BRIEF COMMUNICATIONS format converters, relieving scientists of the burden of identi- Integrative genomic fying and scripting the conversions. The GenomeSpace Recipe Resource is a growing set of high-utility use cases that demon- analysis by interoperation strate how to leverage multiple tools and serve as quick guides to analysis tasks. The website serves as a knowledge base, newsstand of bioinformatics tools and point of contact and help for the community of users and tool developers. in GenomeSpace Initially seeded by a consortium of biology research labs and development teams of six popular bioinformatics tools Kun Qu1,12, Sara Garamszegi2,12, Felix Wu2,12, (Cytoscape1,2, Galaxy3, GenePattern4, Genomica5, the Integrative Helga Thorvaldsdottir2, Ted Liefeld2,3, Marco Ocana2,3, Genomics Viewer (IGV)6 and the UCSC Table Browser7), Diego Borges-Rivera4, Nathalie Pochet2,5, GenomeSpace now connects 20 tools and data resources. Our James T Robinson2,3, Barry Demchak3, Tim Hull3, consortium labs provided biological projects and analytical needs 6,7 8 9 to drive GenomeSpace design and development. For example, we Gil Ben-Artzi , Daniel Blankenberg , Galt P Barber , 8,9 9 9 8 recapitulated the steps and results of published analyses within Brian T Lee , Robert M Kuhn , Anton Nekrutenko , GenomeSpace (Supplementary Figs. 1 and 2), dissecting and 6 3 2,3 2,4,10 Eran Segal , Trey Ideker , Michael Reich , Aviv Regev , visualizing the gene regulatory networks in human cancer stem Howard Y Chang1,11 & Jill P Mesirov2,3 cells (Supplementary Note 1, Supplementary Figs. 2–5). The study required diverse data types, analytical steps and methods Complex biomedical analyses require the use of multiple and multiple data transfers between tools.
    [Show full text]
  • Cytoscape3 6 0Manual/Pdf
    Cytoscape User Manual Release 3.6.0 The Cytoscape Consortium Nov 16, 2017 Contents 1 Introduction 3 2 Launching Cytoscape 5 2.1 System Requirements..........................................5 2.2 Getting Started..............................................5 3 Command Line Arguments 9 4 Quick Tour of Cytoscape 11 4.1 Starter Panel............................................... 11 4.2 Basic Features.............................................. 12 4.3 The Menus................................................ 14 4.4 Network Management.......................................... 21 4.5 The View Navigator........................................... 24 5 Creating Networks 25 5.1 Import Fixed-Format Network Files................................... 25 5.2 Import Networks from Unformatted Table Files............................. 26 5.3 Import Networks from Public Databases................................ 31 5.4 Create a New Network or Edit One Manually.............................. 37 6 Nested Networks 43 6.1 Creating Nested Networks........................................ 43 6.2 Visualization of Nested Networks.................................... 43 7 Supported Network File Formats 45 7.1 SIF Format................................................ 45 7.2 NNF................................................... 47 7.3 GML Format............................................... 51 7.4 XGMML Format............................................. 51 7.5 SBML (Systems Biology Markup Language) Format.......................... 52 7.6 BioPAX (Biological PAthways
    [Show full text]
  • Genomespace Workshop, FGED 15, Seattle
    Outline • Introduction to GenomeSpace • GenomeSpace Tools and Recipes • GenomeSpace User Interface • Integrative analysis exercise • Other GenomeSpace Tools • GenomeSpace development • Q and A The vision: Integrave Translaonal Genomics GenePattern Cytoscape IGV/UCSC Genomica CMAP iii 1 Differentially Expressed Genes Compendium Load compendium Arrests Show module G2/M 3 map Show i 2 GSEA test network enrichment Network 5 Show Extract 4 module Expand +1 Chromosome Idea (include ii atcgcgtttattcgataagg! neighbors) atcgcgttttttcgataagg! 6 Add ! Transcription Learn p53 Factor track site/score on Alterations Added to GenePattern from UCSC promoter Pathway iv activation v 8 7 Test for Looks close similarity of p53 and gene Expression Conclusion to p53 site location vi Online community to share diverse computational tools Seed Tools Driving Biological Projects Cytoscape Galaxy lincRNAs GenePaern Genomica Cancer stem cells IGV Paent Straficaon UCSC Browser Outreach to new DBPs Outreach to new tools www.genomespace.org GenomeSpace: a connection layer between integrative analysis tools • Support for all types of resource: Web- based, desktop, etc. • Automac conversion of data formats between tools • Easy access to data from any locaon • Ease of entry into the environment GenomeSpace Components GenePattern Galaxy Cytoscape Integrative Genomics Viewer GenomeSpace-Enabled Tools Authentication and Authorization Analysis and Data Manager Tool Manager Genome Space GenomeSpace Server Project Data GenomeSpace Server Register www.genomespace.org Register
    [Show full text]
  • 11 April 2016
    Bioinformatics discovery of new disease drivers from genomic, proteomics, and metabolomics data Andrew P. Hodges, Ph.D. [email protected] SBP 3 May 2016 Viper-KR* • Viper-KR applied to human tissue (obesity / adipose tissue) and cell culture transcriptome. • Data from the Translational Research Institute (TRI) for Metabolism and Diabetes, SBP-Florida Hospital (PI Steven Smith) • The results are promising: multiple computationally predicted gene targets were validated experimentally (7 out of 12) Viper - Virtual Inference of Protein-activity by Enriched Regulon analysis (Mariano J. Alvarez, Federico Giorgi, Andrea Califano). KR – K-nearest neighbor and Random Forest. 5/3/2016 BIOINFORMATIC CORE TOOLS 2 Overview • Pt1 - Introduction/Background • Pt2 - Resources – Public and proprietary data sources – Selected software and underlying algorithms • Pt3 – Example Service: Master regulator/driver predictions – Example models from in vitro and clinical studies – New gene/protein drivers for follow-up confirmation – Integration against knowledge repositories/other resources Pt1 - Introduction Bioinformatics • Major driver in current cancer research • Oftentimes an afterthought • *Should be the driver/guide for your studies! • Quest for drivers/controllers of disease – Not cancer-specific… conceptually any disease – Molecular signatures guiding disease processes and drug responses – Limited by type(s) of data & available samples NCI projects (2015) Major question today: What are the drivers of pancreatic cancer progression in gemcitabine-treated
    [Show full text]
  • Cy3 Welcome Letter V10.Pdf
    February 13, 2014 Welcome Summary Cytoscape v3.1 is now available for immediate download. Relative to v3.0.2, it addresses 236 issues. It is ready for use by all segments of the Cytoscape community. It continues v3.0’s core feature set, specifically: o It contains improvements for all segments of the Cytoscape community. o It is upward compatible and can coexist with previous Cytoscape versions o The most popular plugins from previous versions are available, with more to follow. o Support offerings have been updated and broadened. It includes 10 major feature updates 18 more Apps are available in the App Store as compared to the v3.0.2 release What to do next … Dear Cytoscape User We are proud and pleased to release Cytoscape v3.1. This new release contains both new features and improvements to functionality and documentation, all of which are described in Section 11. Additionally, the App Store contains 28 more Apps than were available when v3.0.0 was released, bringing the total to 41. Since v3.0.2, 18 Apps were added, including: bayelviraApp eXamine MetDisease CoNet Gasoline MetScape CyKEGGParser GenomeSpace Pathway Scoring Application CyTargetLinker GIANT PEPPER CytoNCA HyperModules Reactome FI Plugin Enhanced Graphics KEGGScape WikiPathways If you are a new or casual Cytoscape user, welcome aboard! Feel free to either read on or jump ahead to What to Do Next for a quick start. For clarity, references to “Cytoscape 3.x” apply to all Cytoscape versions 3.0.0 and later. For Cytoscape v2.x Users Previous Cytoscape releases (through v2.8.3) focused on adding numerous annotation, import, layout, analysis, and visualization features, all of which have combined to deliver critical insights into biological processes of interest to Cytoscape’s broad and loyal user community.
    [Show full text]
  • Bioinformatics Support Program
    Bioinformatics Support Program: Bioinformatics Support Program was developed to provide researchers with powerful tools to analyze and understand the biological significance of a variety of data. PEER-REVIEWED JOURNALS OPEN SOURCE SOFTWARE Bioinformatics .NET Bio - A language-neutral bioinformatics toolkit built using the BMC Bioinformatics Microsoft 4.0 .NET Framework to help developers, researchers, and scientists. Platform : .NET Framework. License : Apache License. BMC Genomics BMC Proteomics AMPHORA - Metagenomics analysis software Platform : Linux. Briefings in Bioinformatics License : GNU General Public License. Bulletin of Mathematical Biology Cancer Informatics Anduril - Component-based workflow framework for data analysis Platform : Linux, Mac OS X, Windows. License : GPL. Computational Biology and Chemistry Computational Molecular Biology AutoDock - Suite of automated docking tools Platform : Platform. Computers in Biology and Medicine License : GNU GPL. Database (journal) EMBnet.journal Biochemical Algorithms Library (BALL) - C++ library and framework for molecular modeling and visualization designed for rapid IEEE/ACM Transactions on Computational Biology prototyping Platform : Linux, Mac OS X, Windows. License : LGP and Bioinformatics Journal of Bioinformatics and Computational Bioclipse - Visual platform for chemo- and bioinformatics based on Biology the Eclipse Rich Client Platform (RCP). Platform : Platform. License : Journal of Biomedical Informatics Eclipse Public License. Journal of Computational Biology Bioconductor - R (programming language) language toolkit Platform Journal of Integrative Bioinformatics : Linux, Mac OS X, Windows. License : Artistic License 2.0. Journal of Mathematical Biology Journal of Theoretical Biology BioJava - Java (programming language) Platform : Linux, Mac OS X, Nucleic Acids Research Windows. License : LGPL v2.1. PLoS Computational Biology BioMOBY - Registry of web services Platform : Web. License : Artistic Rapid Communications in Mass Spectrometry License.
    [Show full text]