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DistMap: A Toolkit for Distributed Short Read Mapping on a Hadoop Cluster

Overview of attention for article published in PLOS ONE, August 2013
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Title
DistMap: A Toolkit for Distributed Short Read Mapping on a Hadoop Cluster
Published in
PLOS ONE, August 2013
DOI 10.1371/journal.pone.0072614
Pubmed ID
Authors

Ram Vinay Pandey, Christian Schlötterer

Abstract

With the rapid and steady increase of next generation sequencing data output, the mapping of short reads has become a major data analysis bottleneck. On a single computer, it can take several days to map the vast quantity of reads produced from a single Illumina HiSeq lane. In an attempt to ameliorate this bottleneck we present a new tool, DistMap - a modular, scalable and integrated workflow to map reads in the Hadoop distributed computing framework. DistMap is easy to use, currently supports nine different short read mapping tools and can be run on all Unix-based operating systems. It accepts reads in FASTQ format as input and provides mapped reads in a SAM/BAM format. DistMap supports both paired-end and single-end reads thereby allowing the mapping of read data produced by different sequencing platforms. DistMap is available from http://code.google.com/p/distmap/

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 4 6%
Spain 1 1%
Portugal 1 1%
France 1 1%
Unknown 64 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 32%
Student > Ph. D. Student 14 20%
Student > Bachelor 8 11%
Student > Master 7 10%
Lecturer 3 4%
Other 9 13%
Unknown 7 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 37%
Computer Science 19 27%
Biochemistry, Genetics and Molecular Biology 6 8%
Engineering 4 6%
Veterinary Science and Veterinary Medicine 2 3%
Other 6 8%
Unknown 8 11%