Title |
Brute-Force Approach for Mass Spectrometry-Based Variant Peptide Identification in Proteogenomics without Personalized Genomic Data
|
---|---|
Published in |
Journal of the American Society for Mass Spectrometry, January 2018
|
DOI | 10.1007/s13361-017-1859-9 |
Pubmed ID | |
Authors |
Mark V. Ivanov, Anna A. Lobas, Lev I. Levitsky, Sergei A. Moshkovskii, Mikhail V. Gorshkov |
Abstract |
In a proteogenomic approach based on tandem mass spectrometry analysis of proteolytic peptide mixtures, customized exome or RNA-seq databases are employed for identifying protein sequence variants. However, the problem of variant peptide identification without personalized genomic data is important for a variety of applications. Following the recent proposal by Chick et al. (Nat. Biotechnol. 33, 743-749, 2015) on the feasibility of such variant peptide search, we evaluated two available approaches based on the previously suggested "open" search and the "brute-force" strategy. To improve the efficiency of these approaches, we propose an algorithm for exclusion of false variant identifications from the search results involving analysis of modifications mimicking single amino acid substitutions. Also, we propose a de novo based scoring scheme for assessment of identified point mutations. In the scheme, the search engine analyzes y-type fragment ions in MS/MS spectra to confirm the location of the mutation in the variant peptide sequence. Graphical abstract ᅟ. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 60% |
India | 1 | 20% |
Unknown | 1 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 60% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Members of the public | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 32 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 25% |
Researcher | 7 | 22% |
Student > Master | 3 | 9% |
Student > Doctoral Student | 2 | 6% |
Student > Bachelor | 2 | 6% |
Other | 7 | 22% |
Unknown | 3 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 10 | 31% |
Agricultural and Biological Sciences | 6 | 19% |
Computer Science | 3 | 9% |
Medicine and Dentistry | 2 | 6% |
Chemistry | 2 | 6% |
Other | 4 | 13% |
Unknown | 5 | 16% |