Journal
JOURNAL OF PROTEOME RESEARCH
Volume 18, Issue 2, Pages 741-747Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jproteome.8b00723
Keywords
protein inference; spectrum identification; protein isoforms; standard formats; workflows; computational proteomics
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Funding
- de.NBI, a project of the German Federal Ministry of Education and Research (Bundesministerium fur Bildung and Forschung BMBF) [FKZ 031 A 534A]
- PURE, a project of North Rhine-Westphalia, a federal German state
- BBSRC [BB/L024225/1]
- NIH [R24 GM127667-01]
- Two ELIXIR Implementation Studies
- Ruhr University Research School PLUS - Germany's Excellence Initiative [DFG GSC 98/3]
- BBSRC [BB/L024225/1] Funding Source: UKRI
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Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass-spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here proteins are digested, which leads to the identification and quantification of peptides instead. 'While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample. In this work, we highlight some of the previously published and newly added features of the tool PIA - Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published data set for isoform detection. PIA is open source and available for download on GitHub (https://github.com/mpc-bioinformatics/pia) or directly via the community extensions inside the KNIME analytics platform.
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