Guidelines for releasing a variant effect predictor
Abstract Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released,...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03572-z |
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| author | Benjamin J. Livesey Mihaly Badonyi Mafalda Dias Jonathan Frazer Sushant Kumar Kresten Lindorff-Larsen David M. McCandlish Rose Orenbuch Courtney A. Shearer Lara Muffley Julia Foreman Andrew M. Glazer Ben Lehner Debora S. Marks Frederick P. Roth Alan F. Rubin Lea M. Starita Joseph A. Marsh |
| author_facet | Benjamin J. Livesey Mihaly Badonyi Mafalda Dias Jonathan Frazer Sushant Kumar Kresten Lindorff-Larsen David M. McCandlish Rose Orenbuch Courtney A. Shearer Lara Muffley Julia Foreman Andrew M. Glazer Ben Lehner Debora S. Marks Frederick P. Roth Alan F. Rubin Lea M. Starita Joseph A. Marsh |
| author_sort | Benjamin J. Livesey |
| collection | DOAJ |
| description | Abstract Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. |
| format | Article |
| id | doaj-art-5a5f3378d6334c3cbfe4228efab46ae5 |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-5a5f3378d6334c3cbfe4228efab46ae52025-08-20T02:17:47ZengBMCGenome Biology1474-760X2025-04-0126111310.1186/s13059-025-03572-zGuidelines for releasing a variant effect predictorBenjamin J. Livesey0Mihaly Badonyi1Mafalda Dias2Jonathan Frazer3Sushant Kumar4Kresten Lindorff-Larsen5David M. McCandlish6Rose Orenbuch7Courtney A. Shearer8Lara Muffley9Julia Foreman10Andrew M. Glazer11Ben Lehner12Debora S. Marks13Frederick P. Roth14Alan F. Rubin15Lea M. Starita16Joseph A. Marsh17MRC Human Genetics Unit, Institute of Genetics and Cancer, University of EdinburghMRC Human Genetics Unit, Institute of Genetics and Cancer, University of EdinburghCentre for Genomic Regulation (CRG), The Barcelona Institute of Science and TechnologyCentre for Genomic Regulation (CRG), The Barcelona Institute of Science and TechnologyDepartment of Medical Biophysics, University of TorontoDepartment of Biology, Linderstrøm-Lang Centre for Protein Science, University of CopenhagenSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring HarborDepartment of Systems Biology, Harvard Medical SchoolDepartment of Systems Biology, Harvard Medical SchoolDepartment of Genome Sciences, University of Washingtonand the, Brotman Baty Institute for Precision Medicine European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome CampusVanderbilt University Medical CenterWellcome Sanger InstituteDepartment of Systems Biology, Harvard Medical SchoolDepartment of Computational and Systems Biology, University of Pittsburgh School of MedicineBioinformatics Division, Walterand , Eliza Hall Institute of Medical ResearchDepartment of Genome Sciences, University of Washingtonand the, Brotman Baty Institute for Precision Medicine MRC Human Genetics Unit, Institute of Genetics and Cancer, University of EdinburghAbstract Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs.https://doi.org/10.1186/s13059-025-03572-z |
| spellingShingle | Benjamin J. Livesey Mihaly Badonyi Mafalda Dias Jonathan Frazer Sushant Kumar Kresten Lindorff-Larsen David M. McCandlish Rose Orenbuch Courtney A. Shearer Lara Muffley Julia Foreman Andrew M. Glazer Ben Lehner Debora S. Marks Frederick P. Roth Alan F. Rubin Lea M. Starita Joseph A. Marsh Guidelines for releasing a variant effect predictor Genome Biology |
| title | Guidelines for releasing a variant effect predictor |
| title_full | Guidelines for releasing a variant effect predictor |
| title_fullStr | Guidelines for releasing a variant effect predictor |
| title_full_unstemmed | Guidelines for releasing a variant effect predictor |
| title_short | Guidelines for releasing a variant effect predictor |
| title_sort | guidelines for releasing a variant effect predictor |
| url | https://doi.org/10.1186/s13059-025-03572-z |
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