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A standardized framing for reporting protein identifications in mzIdentML 1.2 |
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| Authors: | Sean L. Seymour Terry Farrah Robert J. Chalkley John S. Cottrell Brian C. Searle David L. Tabb Juan Antonio Vizcaíno Gorka Prieto Julian Uszkoreit Martin Eisenacher Salvador Martínez‐Bartolomé Fawaz Ghali Andrew R. Jones |
| Affiliation: | 1. AB SCIEX, , Redwood City, CA, USA;2. Institute for Systems Biology, , Seattle, WA, USA;3. Department of Pharmaceutical Chemistry, University of California, , San Francisco, CA, USA;4. Matrix Science Ltd, , London, UK;5. Proteome Software, Inc, , Portland, OR, USA;6. Department of Biomedical Informatics, Vanderbilt University Medical Center, , Nashville, TN, USA;7. Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt‐Ingram Cancer Center, , Nashville, TN, USA;8. Department of Biochemistry, Vanderbilt University Medical Center, , Nashville, TN, USA;9. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL‐EBI), Proteomics Services Team, , Cambridge, UK;10. Department of Communications Engineering, University of the Basque Country (UPV/EHU), , Bilbao, Spain;11. Medizinisches Proteom‐Center, Ruhr‐Universit?t Bochum, , Bochum, Germany;12. Centro Nacional de Biotecnología, CSIC, , Madrid, Spain;13. Institute of Integrative Biology, University of Liverpool, , Liverpool, UK |
| Abstract: | Inferring which protein species have been detected in bottom‐up proteomics experiments has been a challenging problem for which solutions have been maturing over the past decade. While many inference approaches now function well in isolation, comparing and reconciling the results generated across different tools remains difficult. It presently stands as one of the greatest barriers in collaborative efforts such as the Human Proteome Project and public repositories such as the PRoteomics IDEntifications (PRIDE) database. Here we present a framework for reporting protein identifications that seeks to improve capabilities for comparing results generated by different inference tools. This framework standardizes the terminology for describing protein identification results, associated with the HUPO‐Proteomics Standards Initiative (PSI) mzIdentML standard, while still allowing for differing methodologies to reach that final state. It is proposed that developers of software for reporting identification results will adopt this terminology in their outputs. While the new terminology does not require any changes to the core mzIdentML model, it represents a significant change in practice, and, as such, the rules will be released via a new version of the mzIdentML specification (version 1.2) so that consumers of files are able to determine whether the new guidelines have been adopted by export software. |
| Keywords: | Bioinformatics Data standards Protein identification Proteomics Standards Initiative Software XML |