首页|Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants
Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants
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Non-coding variants in the human genome significantly influence human traits and com-plex diseases via their regulation and modification effects.Hence,an increasing number of compu-tational methods are developed to predict the effects of variants in human non-coding sequences.However,it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods.To solve this issue,we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets:(1)rare germline variants from clin-ical relevant sequence variants(Clin Var),(2)rare somatic variants from Catalogue Of Somatic Mutations In Cancer(COSMIC),(3)common regulatory variants from curated expression quanti-tative trait locus(eQTL)data,and(4)disease-associated common variants from curated genome-wide association studies(GWAS).All 24 tested methods performed differently under various conditions,indicating varying strengths and weaknesses under different scenarios.Importantly,the performance of existing methods was acceptable for rare germline variants from Clin Var with the area under the receiver operating characteristic curve(AUROC)of 0.4481-0.8033 and poor for rare somatic variants from COSMIC(AUROC=0.4984-0.7131),common regulatory variants from curated eQTL data(AUROC=0.4837-0.6472),and disease-associated common variants from curated GWAS(AUROC=0.4766-0.5188).We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder,and found that the combined annotation-dependent depletion(CADD)and context-dependent tolerance score(CDTS)methods showed better performance.Summarily,we assessed the performance of 24 computational methods under diverse scenarios,providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.
Non-coding variantPathogenicity estimationFunctional predictionPerformance assessmentPrediction model
National Clinical Research Centre for Geriatric Disorders,Department of Geriatrics,Xiangya Hospital,Central South University,Changsha 410008,China
Department of Neurology,Xiangya Hospital,Central South University,Changsha 410008,China
Centre for Medical Genetics & Hunan Key Laboratory of Medical Genetics,School of Life Sciences,Central South University,Changsha 410008,China
Reproductive Medicine Center,Xiangya Hospital,Central South University,Changsha 410008,China
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National Natural Science Foundation of ChinaYoung Elite Scientist Sponsorship Program by China Association for Science and TechnologyInnovation-Driven Project of Central South University,ChinaNatural Science Foundation for Young Scientists of Hunan Province,ChinaNatural Science Foundation of Hunan Province for outstanding Young Scholars,China
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