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      Fast and accurate short read alignment with Burrows–Wheeler transform

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      Bioinformatics

      Oxford University Press

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          Abstract

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.

          Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.

          Availability: http://maq.sourceforge.net

          Contact: rd@ 123456sanger.ac.uk

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          Most cited references 17

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          CloudBurst: highly sensitive read mapping with MapReduce

          Motivation: 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. Results: 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. Using a larger remote compute cloud with 96 cores, CloudBurst improved performance by >100-fold, reducing the running time from hours to mere minutes for typical jobs involving mapping of millions of short reads to the human genome. Availability: CloudBurst is available open-source as a model for parallelizing algorithms with MapReduce at http://cloudburst-bio.sourceforge.net/. Contact: mschatz@umiacs.umd.edu
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            Using quality scores and longer reads improves accuracy of Solexa read mapping

            Background Second-generation sequencing has the potential to revolutionize genomics and impact all areas of biomedical science. New technologies will make re-sequencing widely available for such applications as identifying genome variations or interrogating the oligonucleotide content of a large sample (e.g. ChIP-sequencing). The increase in speed, sensitivity and availability of sequencing technology brings demand for advances in computational technology to perform associated analysis tasks. The Solexa/Illumina 1G sequencer can produce tens of millions of reads, ranging in length from ~25–50 nt, in a single experiment. Accurately mapping the reads back to a reference genome is a critical task in almost all applications. Two sources of information that are often ignored when mapping reads from the Solexa technology are the 3' ends of longer reads, which contain a much higher frequency of sequencing errors, and the base-call quality scores. Results To investigate whether these sources of information can be used to improve accuracy when mapping reads, we developed the RMAP tool, which can map reads having a wide range of lengths and allows base-call quality scores to determine which positions in each read are more important when mapping. We applied RMAP to analyze data re-sequenced from two human BAC regions for varying read lengths, and varying criteria for use of quality scores. RMAP is freely available for downloading at . Conclusion Our results indicate that significant gains in Solexa read mapping performance can be achieved by considering the information in 3' ends of longer reads, and appropriately using the base-call quality scores. The RMAP tool we have developed will enable researchers to effectively exploit this information in targeted re-sequencing projects.
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              ZOOM! Zillions of oligos mapped.

              The next generation sequencing technologies are generating billions of short reads daily. Resequencing and personalized medicine need much faster software to map these deep sequencing reads to a reference genome, to identify SNPs or rare transcripts. We present a framework for how full sensitivity mapping can be done in the most efficient way, via spaced seeds. Using the framework, we have developed software called ZOOM, which is able to map the Illumina/Solexa reads of 15x coverage of a human genome to the reference human genome in one CPU-day, allowing two mismatches, at full sensitivity. ZOOM is freely available to non-commercial users at http://www.bioinfor.com/zoom
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1460-2059
                15 July 2009
                18 May 2009
                18 May 2009
                : 25
                : 14
                : 1754-1760
                Affiliations
                Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: John Quackenbush

                Article
                btp324
                10.1093/bioinformatics/btp324
                2705234
                19451168

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Original Paper
                Sequence Analysis

                Bioinformatics & Computational biology

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