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dc.creatorLiu, L
dc.creatorSanderford, MD
dc.creatorPatel, R
dc.creatorChandrashekar, P
dc.creatorGibson, G
dc.creatorKumar, S
dc.date.accessioned2020-12-11T20:13:34Z
dc.date.available2020-12-11T20:13:34Z
dc.date.issued2019-12-01
dc.identifier.issn2041-1723
dc.identifier.issn2041-1723
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4330
dc.identifier.other30659175 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4348
dc.description.abstract© 2019, The Author(s). Computational prediction of the phenotypic propensities of noncoding single nucleotide variants typically combines annotation of genomic, functional and evolutionary attributes into a single score. Here, we evaluate if the claimed excellent accuracies of these predictions translate into high rates of success in addressing questions important in biological research, such as fine mapping causal variants, distinguishing pathogenic allele(s) at a given position, and prioritizing variants for genetic risk assessment. A significant disconnect is found to exist between the statistical modelling and biological performance of predictive approaches. We discuss fundamental reasons underlying these deficiencies and suggest that future improvements of computational predictions need to address confounding of allelic, positional and regional effects as well as imbalance of the proportion of true positive variants in candidate lists.
dc.format.extent330-
dc.language.isoen
dc.relation.haspartNature Communications
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAlgorithms
dc.subjectAnimals
dc.subjectComputational Biology
dc.subjectDisease
dc.subjectEvolution, Molecular
dc.subjectGenome-Wide Association Study
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectMammals
dc.subjectModels, Statistical
dc.subjectPolymorphism, Single Nucleotide
dc.subjectRNA, Untranslated
dc.titleBiological relevance of computationally predicted pathogenicity of noncoding variants
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1038/s41467-018-08270-y
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidKumar, Sudhir|0000-0002-9918-8212
dc.date.updated2020-12-11T20:13:30Z
refterms.dateFOA2020-12-11T20:13:34Z


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