PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects
Abstract Predicting the functional impact of point mutations is a critical challenge in genomics. PRESCOTT reconstructs complete mutational landscapes, identifies mutation-sensitive regions, and categorizes missense variants as benign, pathogenic, or variants of uncertain significance. Leveraging pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03581-y |
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| _version_ | 1850277860954079232 |
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| author | Mustafa Tekpinar Laurent David Thomas Henry Alessandra Carbone |
| author_facet | Mustafa Tekpinar Laurent David Thomas Henry Alessandra Carbone |
| author_sort | Mustafa Tekpinar |
| collection | DOAJ |
| description | Abstract Predicting the functional impact of point mutations is a critical challenge in genomics. PRESCOTT reconstructs complete mutational landscapes, identifies mutation-sensitive regions, and categorizes missense variants as benign, pathogenic, or variants of uncertain significance. Leveraging protein sequences, structural models, and population-specific allele frequencies, PRESCOTT surpasses existing methods in classifying ClinVar variants, the ACMG dataset, and over 1800 proteins from the Human Protein Dataset. Its online server facilitates mutation effect predictions for any protein and variant, and includes a database of over 19,000 human proteins, ready for population-specific analyses. Open access to residue-specific scores offers transparency and valuable insights for genomic medicine. |
| format | Article |
| id | doaj-art-18bd023cbe74480d9cdc38e7e6a60a63 |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-18bd023cbe74480d9cdc38e7e6a60a632025-08-20T01:49:43ZengBMCGenome Biology1474-760X2025-05-0126114210.1186/s13059-025-03581-yPRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effectsMustafa Tekpinar0Laurent David1Thomas Henry2Alessandra Carbone3Department of Computational, Quantitative and Synthetic Biology (CQSB), Sorbonne Université, CNRS, IBPS, UMR 7238Department of Computational, Quantitative and Synthetic Biology (CQSB), Sorbonne Université, CNRS, IBPS, UMR 7238Centre International de Recherche en Infectiologie (CIRI), Inserm U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Univ LyonDepartment of Computational, Quantitative and Synthetic Biology (CQSB), Sorbonne Université, CNRS, IBPS, UMR 7238Abstract Predicting the functional impact of point mutations is a critical challenge in genomics. PRESCOTT reconstructs complete mutational landscapes, identifies mutation-sensitive regions, and categorizes missense variants as benign, pathogenic, or variants of uncertain significance. Leveraging protein sequences, structural models, and population-specific allele frequencies, PRESCOTT surpasses existing methods in classifying ClinVar variants, the ACMG dataset, and over 1800 proteins from the Human Protein Dataset. Its online server facilitates mutation effect predictions for any protein and variant, and includes a database of over 19,000 human proteins, ready for population-specific analyses. Open access to residue-specific scores offers transparency and valuable insights for genomic medicine.https://doi.org/10.1186/s13059-025-03581-y |
| spellingShingle | Mustafa Tekpinar Laurent David Thomas Henry Alessandra Carbone PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects Genome Biology |
| title | PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects |
| title_full | PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects |
| title_fullStr | PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects |
| title_full_unstemmed | PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects |
| title_short | PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects |
| title_sort | prescott a population aware epistatic and structural model accurately predicts missense effects |
| url | https://doi.org/10.1186/s13059-025-03581-y |
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