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Large scale analysis of amino acid substitutions in bacterial proteomics

Overview of attention for article published in BMC Bioinformatics, November 2016
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Title
Large scale analysis of amino acid substitutions in bacterial proteomics
Published in
BMC Bioinformatics, November 2016
DOI 10.1186/s12859-016-1301-5
Pubmed ID
Authors

Dmitry Ischenko, Dmitry Alexeev, Egor Shitikov, Alexandra Kanygina, Maja Malakhova, Elena Kostryukova, Andrey Larin, Sergey Kovalchuk, Olga Pobeguts, Ivan Butenko, Nikolay Anikanov, Ilya Altukhov, Elena Ilina, Vadim Govorun

Abstract

Proteomics of bacterial pathogens is a developing field exploring microbial physiology, gene expression and the complex interactions between bacteria and their hosts. One of the complications in proteomic approach is micro- and macro-heterogeneity of bacterial species, which makes it impossible to build a comprehensive database of bacterial genomes for identification, while most of the existing algorithms rely largely on genomic data. Here we present a large scale study of identification of single amino acid polymorphisms between bacterial strains. An ad hoc method was developed based on MS/MS spectra comparison without the support of a genomic database. Whole-genome sequencing was used to validate the accuracy of polymorphism detection. Several approaches presented earlier to the proteomics community as useful for polymorphism detection were tested on isolates of Helicobacter pylori, Neisseria gonorrhoeae and Escherichia coli. The developed method represents a perspective approach in the field of bacterial proteomics allowing to identify hundreds of peptides with novel SAPs from a single proteome.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ukraine 1 2%
Thailand 1 2%
Unknown 40 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 24%
Student > Ph. D. Student 9 21%
Student > Bachelor 4 10%
Student > Master 4 10%
Professor > Associate Professor 3 7%
Other 6 14%
Unknown 6 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 33%
Biochemistry, Genetics and Molecular Biology 6 14%
Immunology and Microbiology 6 14%
Computer Science 3 7%
Engineering 2 5%
Other 3 7%
Unknown 8 19%
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 20 July 2017.
All research outputs
#20,353,668
of 22,901,818 outputs
Outputs from BMC Bioinformatics
#6,876
of 7,302 outputs
Outputs of similar age
#270,519
of 312,900 outputs
Outputs of similar age from BMC Bioinformatics
#106
of 125 outputs
Altmetric has tracked 22,901,818 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,302 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 125 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.