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GenBioWiki is the student home page for the Genetics, Bioinformatics, and Computational Biology (GBCB) program at Virginia Tech. Bioinformatics and computational biology provide a research platform to acquire, manage, analyze, and display large amounts of data, which in turn catalyze a systems approach to understanding biological organisms, as well as making useful predictions about their behavior in response to environmental and other perturbations. Moreover, bioinformatics is the study of biological systems and large biological data sets using analytical methods borrowed from computer science, mathematics, statistics, and the physical sciences. This transdisciplinary approach to research requires graduates with extensive cross-cultural professional and technical training and provides ample employment opportunities for Ph.D. graduates. [1]

CloudBurst: Highly Sensitive Short Read Mapping with MapReduce Michael Schatz Center for Bioinformatics and Computational Biology, University of Maryland Next-generation DNA sequencing machines are generating an enormous amount of sequence data, placing unprecedented demands on traditional single-processor read mapping algorithms. CloudBurst is a new parallel read-mapping algorithm optimized for mapping next-generation sequence data to the human genome and other reference genomes, for use in a variety of biological analyses including SNP discovery, genotyping, and personal genomics. It is modeled after the short read mapping program RMAP, and reports either all alignments or the unambiguous best alignment for each read with any number of mismatches or differences. This level of sensitivity could be prohibitively time consuming, but CloudBurst uses the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. CloudBurst's running time scales linearly with the number of reads mapped, and with near linear speedup as the number of processors increases. In a 24-processor core configuration, CloudBurst is up to 30 times faster than RMAP executing on a single core, while computing an identical set of alignments. In a large remote compute clouds with 96 cores, CloudBurst reduces the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome. CloudBurst is available open-source as a model for parallelizing other bioinformatics algorithms with MapReduce.

The Open Bioinformatics Foundation or O|B|F is a non profit, volunteer run organization focused on supporting open source programming in bioinformatics. The foundation grew out of the volunteer projects BioPerl, BioJava and BioPython and was formally incorporated in order to handle our modest requirements of hardware ownership, domain name management and funding for conferences and workshops.The Foundation does not participate directly in the development or structure of the open source work, but as the members of the foundation are drawn from the member projects, there is clear commonality of direction and purpose. Occasionally the O|B|F directors may make announcements about our direction or purpose (a recent one was on the licensing of academic software) when the board feels there is a need to clarify matters, but in general we prefer to remain simply the administrative support organization for our member projects. Our main activities are: * Underwriting and supporting the BOSC conferences * Organizing and supporting developer-centric "hackathon" events * Managing our servers, colocation facilities, bank account & other assets We are incorporated in the state of Delaware, USA as a not-for-profit company.

We build here a repository of assembled transcript sequences from the contigation (Expressed Sequence Tag (known as EST) & mRNA) in order to discover new genes from already existing data. Publicly available EST & mRNA sequences are clusterised and then contigated with specific bioinformatic tools (see technology).

The Bioinformatics Toolkit is a platform that integrates a great variety of tools for protein sequence analysis. Many tools are developed in-house, and serveral public tools are offered with extended functionality. The toolkit includes, among others: NucleotideBLAST, ProteinBLAST, PSI-BLAST, fastHMMER, HHsenser; ClustalW, MUSCLE, Mafft, ProbCons; HHrep, PCOILS, REPPER; Quick2D; HHpred, Modeller; CLANS, ANCESCON, PHYLIP; Reformat, RetrieveSeq, gi2promoter. For a short description of the tools, click the section tabs.

The Bioclipse project is aimed at creating a Java-based, open source, visual platform for chemo- and bioinformatics based on the Eclipse Rich Client Platform (RCP). Bioclipse, as any RCP application, is based on a plugin architecture that inherits basic functionality and visual interfaces from Eclipse, such as help system, software updates, preferences, cross-platform deployment etc.

FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology

BioMart is a query-oriented data management system developed jointly by the Ontario Institute for Cancer Research (OiCR) and the European Bioinformatics Institute (EBI). The system can be used with any type of data and is particularly suited for providing 'data mining' like searches of complex descriptive data. BioMart comes with an 'out of the box' website that can be installed, configured and customised according to user requirements. Further access is provided by graphical and text based applications or programmatically using web services or API written in Perl and Java. BioMart has built-in support for query optimisation and data federation and in addition can be configured to work as a DAS 1.5 Annotation server. The process of converting a data source into BioMart format is fully automated by the tools included in the package. Currently supported RDBMS platforms are MySQL, Oracle and Postgres. BioMart is completely Open Source, licensed under the LGPL, and freely available to anyone without restrictions.

Hadoop comes bundled with launch scripts to simplify initializing an Amazon Elastic Compute Cloud (EC2) cloud for Hadoop. Once initialized, running CloudBurst is identical to running on a local cluster. If you use EC2 regularly with the same datasets (i.e. the human genome as a reference), you will probably want to copy the data once to Amazon Simple Storage Service (S3) so you can quickly copy the data from S3 to your cloud at low cost.