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A flexible statistical model for alignment of label-free proteomics data - incorporating ion mobility and product ion information

Overview of attention for article published in BMC Bioinformatics, December 2013
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Mentioned by

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2 tweeters

Citations

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6 Dimensions

Readers on

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33 Mendeley
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Title
A flexible statistical model for alignment of label-free proteomics data - incorporating ion mobility and product ion information
Published in
BMC Bioinformatics, December 2013
DOI 10.1186/1471-2105-14-364
Pubmed ID
Authors

Ashlee M Benjamin, J Will Thompson, Erik J Soderblom, Scott J Geromanos, Ricardo Henao, Virginia B Kraus, M Arthur Moseley, Joseph E Lucas

Abstract

The goal of many proteomics experiments is to determine the abundance of proteins in biological samples, and the variation thereof in various physiological conditions. High-throughput quantitative proteomics, specifically label-free LC-MS/MS, allows rapid measurement of thousands of proteins, enabling large-scale studies of various biological systems. Prior to analyzing these information-rich datasets, raw data must undergo several computational processing steps. We present a method to address one of the essential steps in proteomics data processing--the matching of peptide measurements across samples.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Netherlands 1 3%
Colombia 1 3%
South Africa 1 3%
Unknown 29 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 30%
Student > Ph. D. Student 7 21%
Student > Master 4 12%
Professor 2 6%
Other 2 6%
Other 2 6%
Unknown 6 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 30%
Chemistry 3 9%
Computer Science 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Engineering 2 6%
Other 6 18%
Unknown 7 21%

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 16 December 2013.
All research outputs
#16,645,549
of 21,357,544 outputs
Outputs from BMC Bioinformatics
#5,663
of 6,926 outputs
Outputs of similar age
#217,820
of 304,358 outputs
Outputs of similar age from BMC Bioinformatics
#298
of 363 outputs
Altmetric has tracked 21,357,544 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,926 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 304,358 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 363 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.