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Bioconductor: open software development for computational biology and bioinformatics.

Genome biology | Oct 5, 2004

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

Pubmed ID: 15461798 RIS Download

Mesh terms: Computational Biology | Internet | Reproducibility of Results | Software

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


R Project for Statistical Computing

A free software environment for statistical computing and graphics that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc) and graphical techniques, and is highly extensible. R provides an Open Source route to participation in that activity. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes: an effective data handling and storage facility; a suite of operators for calculations on arrays, in particular matrices; a large, coherent, integrated collection of intermediate tools for data analysis; graphical facilities for data analysis and display either on-screen or on hardcopy; and a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

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BioPerl

BioPerl is a community effort to produce Perl code which is useful in biology. This toolkit of perl modules is useful in building bioinformatics solutions in Perl. It is built in an object-oriented manner so that many modules depend on each other to achieve a task. The collection of modules in the bioperl-live repository consist of the core of the functionality of bioperl. Additionally auxiliary modules for creating graphical interfaces (bioperl-gui), persistent storage in RDMBS (bioperl-db), running and parsing the results from hundreds of bioinformatics applications (Run package), software to automate bioinformatic analyses (bioperl-pipeline) are all available as Git modules in our repository. The BioPerl toolkit provides a library of hundreds of routines for processing sequence, annotation, alignment, and sequence analysis reports. It often serves as a bridge between different computational biology applications assisting the user to construct analysis pipelines. This chapter illustrates how BioPerl facilitates tasks such as writing scripts summarizing information from BLAST reports or extracting key annotation details from a GenBank sequence record. BioPerl includes modules written by Sohel Merchant of the GO Consortium for parsing and manipulating OBO ontologies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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Bioconductor

A catalog of tools and software packages for the analysis and comprehension of high-throughput genomic data that uses the R statistical programming language. Bioconductor has a development version to which new features and packages are added prior to incorporation in the release. A large number of meta-data packages provide pathway, organism, microarray and other annotations. The broad goals of the Bioconductor project are: to provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data; to facilitate the inclusion of biological metadata in the analysis of genomic data; to provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software; and to train researchers on computational and statistical methods for the analysis of genomic data.

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Biopython

Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics. The source code is made available under the Biopython License, which is extremely liberal and compatible with almost every license in the world. It works along with the Open Bioinformatics Foundation, who generously host it''s website, bug tracker, and mailing lists. Sponsor: This resource is supported by the Open Bioinformatics Foundation. Keywords: Tool, Software, Python, Biological, Computation, Bioinformatics,

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BioJava Project

BioJava is a mature open-source project dedicated to providing a Java framework for processing biological data. It provides analytical and statistical routines, parsers for common file formats and allows the manipulation of sequences and 3D structures. The goal of the biojava project is to facilitate rapid application development for bioinformatics. Sponsor: BioJava is not formally funded by any grants. Through the OBF they have received sponsorship from Sun Microsystems, Apple Computers and NESCent. The initial development of the phylogenetics module was undertaken as a Google Summer of Code 2007 project in collaboration with NESCent.

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GNU.org

Unix-like operating system that is free software. GNU (more precisely, GNU/Linux systems) are entirely free software. The GNU Project was launched in 1984 to develop the GNU system. A Unix-like operating system is a software collection of applications, libraries, and developer tools, plus a program to allocate resources and talk to the hardware, known as a kernel. The Hurd, GNU''s own kernel, is some way from being ready for daily use. Thus, GNU is typically used today with a kernel called Linux. This combination is the GNU/Linux operating system. GNU/Linux is used by millions, though many call it Linux by mistake.

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BioMoby

The MOBY-S system defines an ontology-based messaging standard through which a client will be able to automatically discover and interact with task-appropriate biological data and analytical service providers, without requiring manual manipulation of data formats as data flows from one provider to the next. The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. Sponsors: Funding was provided by Genome Prairie and Genome Alberta A Bioinformatics Platform for Genome Canada''; Canadian Institutes for Health Research; The Natural Sciences and Engineering Research Council of Canada; The Heart and Stroke Foundation for BC and Yukon; The EPSRC through the myGrid (GR/R67743/01, EP/C536444/1, EP/D044324/1, GR/T17457/01) e-Science projects; The Spanish National Institute for Bioinformatics (INB) through Fundacin Genoma Espaa; The Generation Challenge Programme (GCP; http://www.generationcp.org) of the Consultative Group for International Agricultural Research. :Keywords: Ontology, Messaging, Standard, Client, Automatically, Discovery, Biological, Data, ANalytical, Service, Model, Organism, Database, Java, Platform, Semantic, Bioinformatics,

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