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dc.contributor.authorAljarf, Raghad
dc.contributor.authorShen, Mengyuan
dc.contributor.authorPires, Douglas EV
dc.contributor.authorAscher, David B
dc.date.accessioned2022-06-29T19:44:27Z
dc.date.available2022-06-29T19:44:27Z
dc.date.issued2022-06-21
dc.date.submitted2021-09-05
dc.identifier.issn2045-2322
dc.identifier.others41598-022-13508-3
dc.identifier.other13508
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338479
dc.descriptionFunder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265
dc.descriptionFunder: National Health and Medical Research Council; doi: http://dx.doi.org/10.13039/501100000925
dc.descriptionFunder: Wellcome Trust; doi: http://dx.doi.org/10.13039/100010269
dc.description.abstractBRCA1 and BRCA2 are tumour suppressor genes that play a critical role in maintaining genomic stability via the DNA repair mechanism. DNA repair defects caused by BRCA1 and BRCA2 missense variants increase the risk of developing breast and ovarian cancers. Accurate identification of these variants becomes clinically relevant, as means to guide personalized patient management and early detection. Next-generation sequencing efforts have significantly increased data availability but also the discovery of variants of uncertain significance that need interpretation. Experimental approaches used to measure the molecular consequences of these variants, however, are usually costly and time-consuming. Therefore, computational tools have emerged as faster alternatives for assisting in the interpretation of the clinical significance of newly discovered variants. To better understand and predict variant pathogenicity in BRCA1 and BRCA2, various machine learning algorithms have been proposed, however presented limited performance. Here we present BRCA1 and BRCA2 gene-specific models and a generic model for quantifying the functional impacts of single-point missense variants in these genes. Across tenfold cross-validation, our final models achieved a Matthew's Correlation Coefficient (MCC) of up to 0.98 and comparable performance of up to 0.89 across independent, non-redundant blind tests, outperforming alternative approaches. We believe our predictive tool will be a valuable resource for providing insights into understanding and interpreting the functional consequences of missense variants in these genes and as a tool for guiding the interpretation of newly discovered variants and prioritizing mutations for experimental validation.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectArticle
dc.subject/631/114/1305
dc.subject/631/114/663
dc.subject/631/208/212/2301
dc.subjectarticle
dc.titleUnderstanding and predicting the functional consequences of missense mutations in BRCA1 and BRCA2.
dc.typeArticle
dc.date.updated2022-06-29T19:44:26Z
prism.issueIdentifier1
prism.publicationNameSci Rep
prism.volume12
dc.identifier.doi10.17863/CAM.85892
dcterms.dateAccepted2022-05-25
rioxxterms.versionofrecord10.1038/s41598-022-13508-3
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2045-2322
cam.issuedOnline2022-06-21


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