Integrating genomic, transcriptomic, and interactome data to improve peptide and protein identification in shotgun proteomics

Xiaojing Wang, Bing Zhang

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

Mass spectrometry (MS)-based shotgun proteomics is an effective technology for global proteome profiling. The ultimate goal is to assign tandem MS spectra to peptides and subsequently infer proteins and their abundance. In addition to database searching and protein assembly algorithms, computational approaches have been developed to integrate genomic, transcriptomic, and interactome information to improve peptide and protein identification. Earlier efforts focus primarily on making databases more comprehensive using publicly available genomic and transcriptomic data. More recently, with the increasing affordability of the Next Generation Sequencing (NGS) technologies, personalized protein databases derived from sample-specific genomic and transcriptomic data have emerged as an attractive strategy. In addition, incorporating interactome data not only improves protein identification but also puts identified proteins into their functional context and thus facilitates data interpretation. In this paper, we survey the major integrative bioinformatics approaches that have been developed during the past decade and discuss their merits and demerits.

Original languageEnglish (US)
Pages (from-to)2715-2723
Number of pages9
JournalJournal of Proteome Research
Volume13
Issue number6
DOIs
StatePublished - Jun 6 2014
Externally publishedYes

Keywords

  • Next Generation Sequencing
  • RNA-Seq
  • data integration
  • peptide identification
  • personalized proteomics
  • protein identification
  • proteogenomics
  • shotgun proteomics

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

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