SIR: Deterministic protein inference from peptides assigned to MS data

• Published in: Journal of proteomics Vol. 75; no. 13; pp. 4176 - 4183
• Main Authors: Matthiesen, Rune, Prieto, Gorka, Amorim, Antonio, Aloria, Kerman, Fullaondo, Asier, Carvalho, Ana S., Arizmendi, Jesus M.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 16.07.2012; Elsevier
• Subjects: Algorithms; analysis; Biological and medical sciences; Computational proteomics; Database dependent search; Databases, Protein; Diverse techniques; Fundamental and applied biological sciences. Psychology; Humans; mass spectrometry; Mass Spectrometry - methods; methods; Molecular and cellular biology; peptides; Protein inference; Protein Isoforms; Protein Isoforms - analysis; proteins; Proteomics; Proteomics - methods; Software; Tandem Mass Spectrometry; Tandem Mass Spectrometry - methods; Computational proteomics; Database dependent search; Protein inference; Peptides; Proteomics; Database; Inference; Data; Protein
• DOI: 10.1016/j.jprot.2012.05.010
• ISSN: 1874-3919

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. ► Explorative protein inference for deterministic protein identification. ► Protein inference based on peptide spectra assignments rather than peptides. ► Calculation of sixteen parameters useful for protein inference. ► Visual tools displaying alignments and peptide–protein network for further exploration. ► First search engine with direct support for advanced protein inference.