{"entity": "researcher", "timestamp": "2026-08-20T20:36:39.506Z", "family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "affiliations": [], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b"}}, "publications": [{"entity": "publication", "iuid": "ed86cc02c63e46d09350d9ba3702350b", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ed86cc02c63e46d09350d9ba3702350b.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ed86cc02c63e46d09350d9ba3702350b"}}, "title": "Open-Source and FAIR Research Software for Proteomics.", "authors": [{"family": "Perez-Riverol", "given": "Yasset", "initials": "Y", "orcid": "0000-0001-6579-6941", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cb4449bad29741b195a7593fc39a769d.json"}}, {"family": "Bittremieux", "given": "Wout", "initials": "W", "orcid": "0000-0002-3105-1359", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/07e740b95ac84bb6ad8f3ff159aece03.json"}}, {"family": "Noble", "given": "William S", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}, {"family": "Martens", "given": "Lennart", "initials": "L", "orcid": "0000-0003-4277-658X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/df9a736ba6d04cbdb881a040480bf6f0.json"}}, {"family": "Bilbao", "given": "Aivett", "initials": "A", "orcid": "0000-0003-2985-8249", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2939469149284dee8f3b20bf40c9e2e9.json"}}, {"family": "Lazear", "given": "Michael R", "initials": "MR", "orcid": "0000-0001-5313-4262", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/ebe2629197bf4b53a53a06244b7f1302.json"}}, {"family": "Gr\u00fcning", "given": "Bjorn", "initials": "B", "orcid": "0000-0002-3079-6586", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b2539c92f7e2437782cf04c0c9a23fc8.json"}}, {"family": "Katz", "given": "Daniel S", "initials": "DS", "orcid": "0000-0001-5934-7525", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b3f98d2fb8384d4684bd7e9e93dc3ef1.json"}}, {"family": "MacCoss", "given": "Michael J", "initials": "MJ", "orcid": "0000-0003-1853-0256", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b57b50e580b940cba50310ac37e89603.json"}}, {"family": "Dai", "given": "Chengxin", "initials": "C"}, {"family": "Eng", "given": "Jimmy K", "initials": "JK", "orcid": "0000-0001-6352-6737", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d748f8f4b03940ac81712a0910415b8b.json"}}, {"family": "Bouwmeester", "given": "Robbin", "initials": "R", "orcid": "0000-0001-6807-7029", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cd945733eeaf46d895f9d8799b430d66.json"}}, {"family": "Shortreed", "given": "Michael R", "initials": "MR", "orcid": "0000-0003-4626-0863", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/cf4bd1d3f2ed44bf94983b952500e04d.json"}}, {"family": "Audain", "given": "Enrique", "initials": "E"}, {"family": "Sachsenberg", "given": "Timo", "initials": "T", "orcid": "0000-0002-2833-6070", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d5425fd7b0434642b4c6f7aef67b5f23.json"}}, {"family": "Van Goey", "given": "Jeroen", "initials": "J"}, {"family": "Wallmann", "given": "Georg", "initials": "G"}, {"family": "Wen", "given": "Bo", "initials": "B"}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Fondrie", "given": "William E", "initials": "WE", "orcid": "0000-0002-1554-3716", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/0605055453fc4d54b057471ae964ca3f.json"}}], "type": "journal article", "published": "2025-05-02", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "24", "issue": "5", "pages": "2222-2234", "issn-l": "1535-3893"}, "abstract": "Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.", "doi": "10.1021/acs.jproteome.4c01079", "pmid": "40267229", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12053954"}], "notes": [], "created": "2026-08-20T08:12:01.116Z", "modified": "2026-08-20T08:12:01.507Z"}, {"entity": "publication", "iuid": "ef47e685f5774fbcb7f34157add464d0", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/ef47e685f5774fbcb7f34157add464d0.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/ef47e685f5774fbcb7f34157add464d0"}}, "title": "How to Train a Postprocessor for Tandem Mass Spectrometry Proteomics Database Search While Maintaining Control of the False Discovery Rate.", "authors": [{"family": "Freestone", "given": "Jack", "initials": "J", "orcid": "0009-0008-2983-6676", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/724eca4dd41a4de8804cd9300644caf9.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}, {"family": "Keich", "given": "Uri", "initials": "U", "orcid": "0000-0002-3209-5011", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/45d275c655e5497c8cfa436ea3b8a32b.json"}}], "type": "journal article", "published": "2025-05-02", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "24", "issue": "5", "pages": "2266-2279", "issn-l": "1535-3893"}, "abstract": "Decoy-based methods are a popular choice for the statistical validation of peptide detection in tandem mass spectrometry and proteomics data. Such methods can achieve a substantial boost in statistical power when coupled with postprocessors such as Percolator that use auxiliary features to learn a better-discriminating scoring function. However, we recently showed that Percolator can struggle to control the false discovery rate (FDR) when reporting the list of discovered peptides. To address this problem, we introduce Percolator-RESET, which is an adaptation of our recently developed RESET meta-procedure to the peptide detection problem. Specifically, Percolator-RESET fuses Percolator's iterative SVM training procedure with RESET's general framework to provide valid false discovery rate control. Percolator-RESET operates in both a standard single-decoy mode and a two-decoy mode, with the latter requiring the generation of two decoys per target. We demonstrate that Percolator-RESET controls the FDR in both modes, both theoretically and empirically, while typically reporting only a marginally smaller number of discoveries than Percolator in the single-decoy mode. The two-decoy mode is marginally more powerful than both Percolator and the single-decoy mode and exhibits less variability than the latter.", "doi": "10.1021/acs.jproteome.4c00742", "pmid": "40163043", "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T08:11:52.931Z", "modified": "2026-08-20T08:11:53.066Z"}, {"entity": "publication", "iuid": "d8d32ba10e7242a7afcabad8153667dd", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/d8d32ba10e7242a7afcabad8153667dd.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/d8d32ba10e7242a7afcabad8153667dd"}}, "title": "Semi-supervised Learning While Controlling the FDR with an Application to Tandem Mass Spectrometry Analysis", "authors": [{"family": "Freestone", "given": "Jack", "initials": "J"}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}, {"family": "Keich", "given": "Uri", "initials": "U", "orcid": "0000-0002-3209-5011", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/45d275c655e5497c8cfa436ea3b8a32b.json"}}], "type": "book-chapter", "published": "2024-00-00", "journal": {"issn": "0302-9743", "pages": "448-453", "issn-l": null}, "abstract": null, "doi": "10.1007/978-1-0716-3989-4_50", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T06:36:35.884Z", "modified": "2026-08-20T06:36:36.046Z"}, {"entity": "publication", "iuid": "9d333934ebfe464798b4df7094016b3d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/9d333934ebfe464798b4df7094016b3d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/9d333934ebfe464798b4df7094016b3d"}}, "title": "How to train a post-processor for tandem mass spectrometry proteomics database search while maintaining control of the false discovery rate", "authors": [{"family": "Freestone", "given": "Jack", "initials": "J", "orcid": "0009-0008-2983-6676", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/724eca4dd41a4de8804cd9300644caf9.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}, {"family": "Keich", "given": "Uri", "initials": "U", "orcid": "0000-0002-3209-5011", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/45d275c655e5497c8cfa436ea3b8a32b.json"}}], "type": "posted-content", "published": "2023-10-31", "journal": {"issn-l": null}, "abstract": null, "doi": "10.1101/2023.10.26.564068", "pmid": null, "labels": [], "xrefs": [], "notes": [], "created": "2026-08-20T10:48:37.881Z", "modified": "2026-08-20T10:48:37.958Z"}, {"entity": "publication", "iuid": "6b3c9d1faf054f2caf91c423ec008722", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/6b3c9d1faf054f2caf91c423ec008722.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/6b3c9d1faf054f2caf91c423ec008722"}}, "title": "Interpretation of the DOME Recommendations for Machine Learning in Proteomics and Metabolomics.", "authors": [{"family": "Palmblad", "given": "Magnus", "initials": "M", "orcid": "0000-0002-5865-8994", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/80053e2256cd4e55afc7ba916821602c.json"}}, {"family": "B\u00f6cker", "given": "Sebastian", "initials": "S", "orcid": "0000-0002-9304-8091", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c71ab14b72a540f3be202f873d6c7dea.json"}}, {"family": "Degroeve", "given": "Sven", "initials": "S"}, {"family": "Kohlbacher", "given": "Oliver", "initials": "O", "orcid": "0000-0003-1739-4598", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9317a8da5d9d4df782158d7555f229eb.json"}}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}, {"family": "Wilhelm", "given": "Mathias", "initials": "M", "orcid": "0000-0002-9224-3258", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2eda8473072c48639cbd09676ed17cfe.json"}}], "type": "journal article", "published": "2022-04-01", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "21", "issue": "4", "pages": "1204-1207", "issn-l": "1535-3893"}, "abstract": "Machine learning is increasingly applied in proteomics and metabolomics to predict molecular structure, function, and physicochemical properties, including behavior in chromatography, ion mobility, and tandem mass spectrometry. These must be described in sufficient detail to apply or evaluate the performance of trained models. Here we look at and interpret the recently published and general DOME (Data, Optimization, Model, Evaluation) recommendations for conducting and reporting on machine learning in the specific context of proteomics and metabolomics.", "doi": "10.1021/acs.jproteome.1c00900", "pmid": "35119864", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8981311"}], "notes": [], "created": "2026-08-20T08:11:33.301Z", "modified": "2026-08-20T08:11:33.445Z"}, {"entity": "publication", "iuid": "2217a0a46f8a48d494c5123c34293e6d", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/2217a0a46f8a48d494c5123c34293e6d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/2217a0a46f8a48d494c5123c34293e6d"}}, "title": "Speeding Up Percolator.", "authors": [{"family": "Halloran", "given": "John T", "initials": "JT"}, {"family": "Zhang", "given": "Hantian", "initials": "H"}, {"family": "Kara", "given": "Kaan", "initials": "K"}, {"family": "Renggli", "given": "C\u00e9dric", "initials": "C"}, {"family": "The", "given": "Matthew", "initials": "M"}, {"family": "Zhang", "given": "Ce", "initials": "C"}, {"family": "Rocke", "given": "David M", "initials": "DM"}, {"family": "K\u00e4ll", "given": "Lukas", "initials": "L", "orcid": "0000-0001-5689-9797", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b4464f2bf868498fa6d149a4a6d60e8b.json"}}, {"family": "Noble", "given": "William Stafford", "initials": "WS", "orcid": "0000-0001-7283-4715", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77bfbf9a1a8242b8955c8dd9f5642a8b.json"}}], "type": "journal article", "published": "2019-09-06", "journal": {"title": "J. Proteome Res.", "issn": "1535-3907", "volume": "18", "issue": "9", "pages": "3353-3359", "issn-l": "1535-3893"}, "abstract": "The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.", "doi": "10.1021/acs.jproteome.9b00288", "pmid": "31407580", "labels": [], "xrefs": [{"db": "mid", "key": "NIHMS1059676"}, {"db": "pmc", "key": "PMC6884961"}], "notes": [], "created": "2026-08-20T08:12:19.090Z", "modified": "2026-08-20T08:12:19.172Z"}]}