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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Ukraine | 1 | 2% |
Thailand | 1 | 2% |
Unknown | 47 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 10 | 20% |
Student > Ph. D. Student | 9 | 18% |
Unspecified | 8 | 16% |
Student > Bachelor | 4 | 8% |
Student > Master | 4 | 8% |
Other | 8 | 16% |
Unknown | 6 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 14 | 29% |
Unspecified | 8 | 16% |
Biochemistry, Genetics and Molecular Biology | 6 | 12% |
Immunology and Microbiology | 6 | 12% |
Computer Science | 3 | 6% |
Other | 4 | 8% |
Unknown | 8 | 16% |