title: Integrating genomic, transcriptomic, and interactome data to improve Peptide and protein identification in shotgun proteomics.
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.
authors.affiliation: Department of Biomedical Informatics, ‡Vanderbilt-Ingram Cancer Center, and §Department of Cancer Biology, Vanderbilt University School of Medicine , Nashville, Tennessee 37232, United States.
authors.forename: Xiaojing
authors.initials: X
authors.lastname: Wang
authors.forename: Bing
authors.initials: B
authors.lastname: Zhang
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling; D006801:Humans; D010449:Peptide Mapping; D025941:Protein Interaction Mapping; D020543:Proteome / Q000235:genetics / Q000378:metabolism*; D040901:Proteomics; D053719:Tandem Mass Spectrometry
chemical_list: D020543:Proteome