{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T04:57:12Z","timestamp":1761541032732,"version":"3.38.0"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2080,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: A post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called \u2018blind\u2019) PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments.<\/jats:p><jats:p>Results: To address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We show that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we show that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Additionally, PTMClust markedly outperforms another PTM refinement algorithm, PTMFinder. We demonstrate that our technique is able to reduce false PTM assignments, improve overall detection coverage and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS\/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis.<\/jats:p><jats:p>Availability: Our algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http:\/\/genes.toronto.edu.<\/jats:p><jats:p>Supplementary Information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p><jats:p>Contact: \u00a0frey@psi.utoronto.ca<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr017","type":"journal-article","created":{"date-parts":[[2011,1,23]],"date-time":"2011-01-23T01:16:30Z","timestamp":1295745390000},"page":"797-806","source":"Crossref","is-referenced-by-count":10,"title":["Computational refinement of post-translational modifications predicted from tandem mass spectrometry"],"prefix":"10.1093","volume":"27","author":[{"given":"Clement","family":"Chung","sequence":"first","affiliation":[{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"},{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"},{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Emili","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"},{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brendan J.","family":"Frey","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"},{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"},{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"},{"name":"1 Department of Computer Science, 2Probabilistic and Statistical Inference Group, 3Banting and Best Department of Medical Research, 4Donnelly Centre for Cellular and Biomolecular Research and 5Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2011,1,22]]},"reference":[{"key":"2023012511551301400_B1","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1074\/mcp.M900487-MCP200","article-title":"A novel approach for untargeted post-translational modification identification using integer linear optimization and tandem mass spectrometry","volume":"9","author":"Baliban","year":"2010","journal-title":"Mol. 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