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Bowtie is an ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of 25 million reads per hour on a typical workstation with 2 gigabytes of memory. Bowtie indexes the genome with a Burrows-Wheeler index to keep its memory footprint small: 1.3 GB for the human genome. It supports alignment policies equivalent to Maq and SOAP but is much faster: about 35x faster than Maq and over 350x faster than SOAP when aligning to the human genome.

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 genome-wide association study (GWAS) publications listed here include only those attempting to assay at least 100,000 single nucleotide polymorphisms (SNPs) in the initial stage. Publications are organized from most to least recent date of publication, indexing from online publication if available. Studies focusing only on candidate genes are excluded from this catalog. Studies are identified through weekly PubMed literature searches, daily NIH-distributed compilations of news and media reports, and occasional comparisons with an existing database of GWAS literature (HuGE Navigator). SNP-trait associations listed here are limited to those with p-values < 1.0 x 10-5. Note that we are now including all identified SNP-trait associations meeting this p-value threshhold. Multipliers of powers of 10 in p-values are rounded to the nearest single digit; odds ratios and allele frequencies are rounded to two decimals. Standard errors are converted to 95 percent confidence intervals where applicable. Allele frequencies, p-values, and odds ratios derived from the largest sample size, typically a combined analysis (initial plus replication studies), are recorded below if reported; otherwise statistics from the initial study sample are recorded. Odds ratios < 1 in the original paper are converted to OR > 1 for the alternate allele. Where results from multiple genetic models are available, we prioritized effect sizes (OR's or beta-coefficients) as follows: 1) genotypic model, per-allele estimate; 2) genotypic model, heterozygote estimate, 3) allelic model, allelic estimate. Gene regions corresponding to SNPs were identified from the UCSC Genome Browser. Gene names are those reported by the authors in the original paper. Only one SNP within a gene or region of high linkage disequilibrium is recorded unless there was evidence of independent association.

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.

bioconductor.org Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data.

This package contains code for use with Short Read DNA Sequencing technologies, and includes packages for ChIP-Seq, Whole Transcriptome Shotgun Sequencing, Whole Genome Shotgun Sequencing, SNP Detection, Transcript expression and file conversion.

This package of tools encompasses many of the common pieces of software required for the analysis of short read sequences produced by the Second Generation DNA sequencing machines. (eg. Illumina/Solexa sequencers, ABI SOLiD and 454). The focus of this project is on post-alignment analysis, thus the input for this process should be the files produced by sequence aligners such as MAQ, Eland or Exonerate. The output should be provided in several formats, including BED and WIG files which are readable by the UCSC Genome Browser.