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Peptide Identification Using Tandem Mass Spectrometry01:33

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MutCombinator: identification of mutated peptides allowing combinatorial mutations using nucleotide-based graph

Seunghyuk Choi1, Eunok Paek1

  • 1Department of Computer Science, Hanyang University, Seongdong-gu, Seoul 04763, Republic of Korea.

Bioinformatics (Oxford, England)
|July 14, 2020
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Summary

MutCombinator software identifies mutated peptides by considering combinatorial mutations from genomic data. This proteogenomic tool enhances mutated peptide detection in mass spectrometry, improving upon existing methods.

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Area of Science:

  • Proteogenomics
  • Computational Biology
  • Mass Spectrometry

Background:

  • Proteogenomics integrates genomics and proteomics, often using next-generation sequencing data to infer protein expression.
  • Current methods for identifying novel peptides from mass spectrometry data typically rely on sample-specific protein sequence databases.
  • A limitation exists in software capable of identifying all potential mutated peptides suggested by diverse genomic information.

Purpose of the Study:

  • To develop and present MutCombinator, a novel software tool for the practical identification of mutated peptides.
  • To enable the identification of peptides with combinatorial mutations during database searches in mass spectrometry data.
  • To improve the comprehensive detection of mutated peptides in proteogenomic analyses.

Main Methods:

  • MutCombinator utilizes an upgraded variant graph that tracks frame information and is indexed by nine nucleotides for efficient searching.
  • The software supports combinatorial mutations within the database search.
  • It incorporates both in-frame searching for coding regions and three-frame searching for non-coding regions.

Main Results:

  • MutCombinator successfully identified a greater number of mutated peptides compared to previous methodologies.
  • The enhanced identification is attributed to the consideration of combined point mutations.
  • The software demonstrates practical applicability when used with extensive mutation databases like COSMIC.

Conclusions:

  • MutCombinator offers a practical solution for identifying mutated peptides from tandem mass spectra, incorporating combinatorial mutations.
  • The tool significantly advances proteogenomic analysis by enabling more comprehensive detection of mutated peptides.
  • MutCombinator enhances the utility of integrating genomic and proteomic data for biological discovery.