Journal
JOURNAL OF PROTEOME RESEARCH
Volume 14, Issue 7, Pages 2988-2997Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.5b00121
Keywords
Protein inference; search engine combination; protein identification; peptide identification; identification analysis; database search; mass spectrometry
Categories
Funding
- PURE (Protein Unit for Research in Europe), a project of North Rhine-Westphalia, Germany
- BMBF grant de.NBI - German Network for Bioinformatics Infrastructure [FKZ 031 A 534A]
- German Research Foundation [FOR1228]
- BBSRC [BB/K01997X/1]
- federal state North Rhine-Westphalia [Az.: 131/1.08-031]
- Biotechnology and Biological Sciences Research Council [BB/K01997X/1, BB/K020145/1] Funding Source: researchfish
- BBSRC [BB/K020145/1, BB/K01997X/1] Funding Source: UKRI
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Protein inference connects the peptide spectrum matches (PSMs) obtained from database search engines back to proteins, which are typically at the heart of most proteomics studies. Different Search engines yield different PSMs and thus different protein lists. Analysis of results from one or multiple search engines is often hampered by different data exchange formats and lack of convenient and intuitive user interfaces. We present PIA, a flexible software suite for combining PSMs from different search engine runs and turning these into consistent results: PIA can be integrated into proteomics data analysis workflows in several ways. A user-friendly graphical user interface can be run either locally or (e.g., for larger core facilities) from a central server. For automated data processing, stand-alone tools are available. PIA implements several established protein inference algorithms and can combine results from different search engines seamlessly. On several benchmark data sets, we show that PIA can identify a larger number of proteins at the same protein FDR when compared to that using inference based on a single search engine. PIA supports the majority of established search engines and data in the mzIdentML standard format. It is implemented in Java and freely available at https://github.com/mpc-bioinformatics/pia.
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