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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.

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.

The purpose of this wiki is to provide a home for community contributions from across Europe, to create a dynamic and updatable picture of open access activity in the EU.

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]

PircBot is a Java framework for writing IRC bots quickly and easily. Its features include an event-driven architecture to handle common IRC events, flood protection, DCC resuming support, ident support, and more. Its comprehensive logfile format is suitable for use with pisg to generate channel statistics. Full documentation is included, and this page contains a 5-minute step-by-step guide to making your first IRC bot. PircBot allows you to perform a variety of fun tasks on IRC, but it is also used for more serious applications by the US Navy, the US Air Force, the CIA (unconfirmed), several national defence agencies, and inside the Azureus bittorrent client. But don't let that put you off - it's still easy to use!

This site makes the best talks and performances from TED available to the public, for free. More than 200 talks from our archive are now available, with more added each week. These videos are released under a Creative Commons license, so they can be freely shared and reposted.