SIR: Deterministic protein inference from peptides assigned to MS data

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Rune Matthiesen (Author) Other; Gorka Prieto (Author) Other; Antonio Amorim (Author) FCUP; Kerman Aloria (Author) Other; Asier Fullaondo (Author) Other; Ana S Carvalho (Author) Other; Jesus M Arizmendi (Author) Other

Journal

Title: Journal of Proteomics

Vol. 75

Pages: 4176-4183

ISSN: 1874-3919

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 21 Citations

Scopus - 22 Citations

Scientific classification

FOS: Natural sciences > Biological sciences

Other information

Authenticus ID: P-002-7YR

DOI: 10.1016/j.jprot.2012.05.010

Abstract (EN): Currently the bottom up approach is the most popular for characterizing protein samples by mass spectrometry. This is mainly attributed to the fact that the bottom up approach has been successfully optimized for high throughput studies. However, the bottom up approach is associated with a number of challenges such as loss of linkage information between peptides. Previous publications have addressed some of these problems which are commonly referred to as protein inference. Nevertheless, all previous publications on the subject are oversimplified and do not represent the full complexity of the proteins identified. To this end we present here SIR (spectra based isoform resolver) that uses a novel transparent and systematic approach for organizing and presenting identified proteins based on peptide spectra assignments. The algorithm groups peptides and proteins into five evidence groups and calculates sixteen parameters for each identified protein that are useful for cases where deterministic protein inference is the goal. The novel approach has been incorporated into SIR which is a user-friendly tool only concerned with protein inference based on imports of Mascot search results. SIR has in addition two visualization tools that facilitate further exploration of the protein inference problem.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8