MutCombinator: identification of mutated peptides allowing combinatorial mutations using nucleotide-based graph search
Author:
Choi Seunghyuk1,
Paek Eunok1
Affiliation:
1. Department of Computer Science, Hanyang University, Seongdong-gu, Seoul 04763, Republic of Korea
Abstract
Abstract
Motivation
Proteogenomics has proven its utility by integrating genomics and proteomics. Typical approaches use data from next-generation sequencing to infer proteins expressed. A sample-specific protein sequence database is often adopted to identify novel peptides from matched mass spectrometry-based proteomics; nevertheless, there is no software that can practically identify all possible forms of mutated peptides suggested by various genomic information sources.
Results
We propose MutCombinator, which enables us to practically identify mutated peptides from tandem mass spectra allowing combinatorial mutations during the database search. It uses an upgraded version of a variant graph, keeping track of frame information. The variant graph is indexed by nine nucleotides for fast access. Using MutCombinator, we could identify more mutated peptides than previous methods, because combinations of point mutations are considered and also because it can be practically applied together with a large mutation database such as COSMIC. Furthermore, MutCombinator supports in-frame search for coding regions and three-frame search for non-coding regions.
Availability and implementation
https://prix.hanyang.ac.kr/download/mutcombinator.jsp.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
National Research Foundation of Korea
National Research Foundation
Ministry of Education of Korea
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
1 articles.
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