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

[1]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[2]  Gad M. Landau,et al.  Introducing efficient parallelism into approximate string matching and a new serial algorithm , 1986, STOC '86.

[3]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[4]  Ricardo A. Baeza-Yates,et al.  Fast and Practical Approximate String Matching , 1992, Inf. Process. Lett..

[5]  Ricardo A. Baeza-Yates,et al.  Fast and Practical Approximate String Matching , 1996, Inf. Process. Lett..

[6]  Dan Gusfield,et al.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[7]  김삼묘,et al.  “Bioinformatics” 특집을 내면서 , 2000 .

[8]  S. Krishnaprasad,et al.  Uses and abuses of Amdahl's law , 2001 .

[9]  Timothy B. Stockwell,et al.  The Sequence of the Human Genome , 2001, Science.

[10]  S. Salzberg,et al.  Versatile and open software for comparing large genomes , 2004, Genome Biology.

[11]  GhemawatSanjay,et al.  The Google file system , 2003 .

[12]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[13]  Michael Q. Zhang,et al.  Using quality scores and longer reads improves accuracy of Solexa read mapping , 2008, BMC Bioinformatics.

[14]  Catherine Shaffer Next-generation sequencing outpaces expectations , 2007, Nature Biotechnology.

[15]  Amitabh Varshney,et al.  High-throughput sequence alignment using Graphics Processing Units , 2007, BMC Bioinformatics.

[16]  J. Lupski,et al.  The complete genome of an individual by massively parallel DNA sequencing , 2008, Nature.

[17]  Ruiqiang Li,et al.  SOAP: short oligonucleotide alignment program , 2008, Bioinform..

[18]  Nancy F. Hansen,et al.  Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry , 2008, Nature.

[19]  R. Durbin,et al.  Mapping Quality Scores Mapping Short Dna Sequencing Reads and Calling Variants Using P

, 2022 .

[20]  Bin Ma,et al.  ZOOM! Zillions of oligos mapped , 2008, Bioinform..

[21]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[22]  Dawei Li,et al.  The diploid genome sequence of an Asian individual , 2008, Nature.