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Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0
KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-5401-5553
KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0001-5689-9797
2016 (English)In: Journal of the American Society for Mass Spectrometry, ISSN 1044-0305, E-ISSN 1879-1123, Vol. 27, no 11, 1719-1727 p.Article in journal (Refereed) Published
Abstract [en]

Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as q values and posterior error probabilities, to peptides and peptide-spectrum matches (PSMs) from such experiments. Percolator’s processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore, with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method—grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein—in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542). The source code and Ubuntu, Windows, MacOS, and Fedora binary packages are available from http://percolator.ms/ under an Apache 2.0 license. [Figure not available: see fulltext.]

Place, publisher, year, edition, pages
Springer, 2016. Vol. 27, no 11, 1719-1727 p.
Keyword [en]
Data processing and analysis, Large scale studies, Mass spectrometry - LC-MS/MS, Protein inference, Statistical analysis, Bioinformatics, Data handling, Mass spectrometry, Molecular biology, Peptides, Probability, Statistical methods, Error probabilities, False discovery rate, Large-scale studies, LC-MS/MS, Scalable approach, Shotgun proteomics, Statistical confidence, Proteins
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-195221DOI: 10.1007/s13361-016-1460-7ISI: 000385158400002ScopusID: 2-s2.0-84991105210OAI: oai:DiVA.org:kth-195221DiVA: diva2:1047404
Note

QC 20161117

Available from: 2016-11-17 Created: 2016-11-02 Last updated: 2016-11-17Bibliographically approved

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Gene TechnologyScience for Life Laboratory, SciLifeLab
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