Journal article
MapReduce implementation of a hybrid spectral library-database search method for large-scale peptide identification
Bioinformatics (Oxford, England), Vol.27(21), pp.3072-3073
11/01/2011
Handle:
https://hdl.handle.net/2376/106893
PMCID: PMC3198583
PMID: 21926122
Abstract
A MapReduce-based implementation called
MR-MSPolygraph
for parallelizing peptide identification from mass spectrometry data is presented. The underlying serial method,
MSPolygraph
, uses a novel hybrid approach to match an experimental spectrum against a combination of a protein sequence database and a spectral library. Our MapReduce implementation can run on any Hadoop cluster environment. Experimental results demonstrate that, relative to the serial version,
MR-MSPolygraph
reduces the time to solution from weeks to hours, for processing tens of thousands of experimental spectra. Speedup and other related performance studies are also reported on a 400-core Hadoop cluster using spectral datasets from environmental microbial communities as inputs.
Availability:
The source code along with user documentation are available on
http://compbio.eecs.wsu.edu/MR-MSPolygraph
.
Contact:
ananth@eecs.wsu.edu
;
william.cannon@pnnl.gov
Supplementary Information:
Supplementary data
are available at
Bioinformatics
online.
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Details
- Title
- MapReduce implementation of a hybrid spectral library-database search method for large-scale peptide identification
- Creators
- Ananth Kalyanaraman - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752William R Cannon - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752Benjamin Latt - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752Douglas J Baxter - School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164-2752
- Publication Details
- Bioinformatics (Oxford, England), Vol.27(21), pp.3072-3073
- Academic Unit
- Electrical Engineering and Computer Science, School of
- Publisher
- Oxford University Press
- Identifiers
- 99900546846001842
- Language
- English
- Resource Type
- Journal article