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A parallel method for enumerating amino acid compositions and masses of all theoretical peptides

Overview of attention for article published in BMC Bioinformatics, November 2011
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Mentioned by

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1 tweeter

Citations

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12 Dimensions

Readers on

mendeley
22 Mendeley
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1 CiteULike
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Title
A parallel method for enumerating amino acid compositions and masses of all theoretical peptides
Published in
BMC Bioinformatics, November 2011
DOI 10.1186/1471-2105-12-432
Pubmed ID
Authors

Alexey V Nefedov, Rovshan G Sadygov

Abstract

Enumeration of all theoretically possible amino acid compositions is an important problem in several proteomics workflows, including peptide mass fingerprinting, mass defect labeling, mass defect filtering, and de novo peptide sequencing. Because of the high computational complexity of this task, reported methods for peptide enumeration were restricted to cover limited mass ranges (below 2 kDa). In addition, implementation details of these methods as well as their computational performance have not been provided. The increasing availability of parallel (multi-core) computers in all fields of research makes the development of parallel methods for peptide enumeration a timely topic.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 9%
United States 2 9%
India 1 5%
United Kingdom 1 5%
Unknown 16 73%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 41%
Student > Ph. D. Student 4 18%
Professor > Associate Professor 2 9%
Student > Doctoral Student 1 5%
Student > Bachelor 1 5%
Other 1 5%
Unknown 4 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 32%
Computer Science 6 27%
Biochemistry, Genetics and Molecular Biology 1 5%
Mathematics 1 5%
Social Sciences 1 5%
Other 2 9%
Unknown 4 18%

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 09 November 2011.
All research outputs
#2,016,394
of 3,634,915 outputs
Outputs from BMC Bioinformatics
#1,582
of 2,289 outputs
Outputs of similar age
#27,741
of 71,103 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 99 outputs
Altmetric has tracked 3,634,915 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 21st percentile – i.e., 21% 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 71,103 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.