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Adv Exp Med Biol. 2016;926:65-75.

Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies.

Author information

1
Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway. marc.vaudel@uib.no.
2
KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway. marc.vaudel@uib.no.
3
Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway. marc.vaudel@uib.no.
4
Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway.
5
KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway.
6
Department of Pediatrics, Haukeland University Hospital, Bergen, Norway.

Abstract

Proteogenomic studies ally the omic fields related to gene expression into a combined approach to improve the characterization of biological samples. Part of this consists in mining proteomics datasets for non-canonical sequences of amino acids. These include intergenic peptides, products of mutations, or of RNA editing events hypothesized from genomic, epigenomic, or transcriptomic data. This approach poses new challenges for standard peptide identification workflows. In this chapter, we present the principles behind the use of peptide identification algorithms and highlight the major pitfalls of their application to proteogenomic studies.

KEYWORDS:

Bioinformatics; Proteogenomics; Proteomics

PMID:
27686806
DOI:
10.1007/978-3-319-42316-6_5
[Indexed for MEDLINE]

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