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MapReduce implementation of a hybrid spectral library-database search method for large-scale peptide identification

Overview of attention for article published in Bioinformatics, September 2011
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Article details
Title
MapReduce implementation of a hybrid spectral library-database search method for large-scale peptide identification
Published in
Bioinformatics, September 2011
DOI 10.1093/bioinformatics/btr523
Pubmed ID
Authors
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.

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X Demographics

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 2 4%
Turkey 1 2%
India 1 2%
France 1 2%
Belgium 1 2%
Unknown 41 87%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 14 30%
Researcher 13 28%
Other 4 9%
Professor 4 9%
Student > Master 2 4%
Other 6 13%
Unknown 4 9%
Readers by discipline
Readers by discipline Count As %
Computer Science 18 38%
Agricultural and Biological Sciences 12 26%
Biochemistry, Genetics and Molecular Biology 4 9%
Chemistry 2 4%
Engineering 2 4%
Other 4 9%
Unknown 5 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 September 2011.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from Bioinformatics
#10,569
of 12,809 outputs
Outputs of similar age
#92,171
of 129,987 outputs
Outputs of similar age from Bioinformatics
#80
of 104 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 129,987 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 104 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.