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- LeDuc, Richard D., et al.
(författare)
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Proteomics Standards Initiative's ProForma 2.0 : Unifying the Encoding of Proteoforms and Peptidoforms br
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:4, s. 1189-1195
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Tidskriftsartikel (refereegranskat)abstract
- It is important for the proteomics community to have a standardizedmanner to represent all possible variations of a protein or peptide primary sequence,including natural, chemically induced, and artifactual modifications. The HumanProteome Organization Proteomics Standards Initiative in collaboration with severalmembers of the Consortium for Top-Down Proteomics (CTDP) has developed astandard notation called ProForma 2.0, which is a substantial extension of the originalProForma notation developed by the CTDP. ProForma 2.0 aims to unify therepresentation of proteoforms and peptidoforms. ProForma 2.0 supports use casesneeded for bottom-up and middle-/top-down proteomics approaches and allows theencoding of highly modified proteins and peptides using a human- and machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated bymass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical) and atomic isotopes.Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification documentand information about software implementations are available athttp://psidev.info/proforma.
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| 2. |
- Luo, Xiyang, et al.
(författare)
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A Comprehensive Evaluation of Consensus Spectrum Generation Methods in Proteomics
- 2022
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 21:6, s. 1566-1574
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Tidskriftsartikel (refereegranskat)abstract
- Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-duster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.
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