期刊
PLOS COMPUTATIONAL BIOLOGY
卷 13, 期 4, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1005500
关键词
-
资金
- Wellcome Trust [100585/Z/12/Z]
- National Institute for Health Research Cambridge Biomedical Research Centre
- King Abdullah University of Science and Technology
- National Science Foundation (NSF) [IOS-1340112]
- European Commision [731075]
- Medical Research Council [MC_UU_12012/5/B, MC_UU_12012/5] Funding Source: researchfish
- MRC [MC_UU_12012/5] Funding Source: UKRI
- Wellcome Trust [100585/Z/12/Z] Funding Source: Wellcome Trust
Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据