Cherie C Y Au-Yeung, Yuen-Ting Cheung, Joshua Y T Cheng, Ken W H Ip, Sau-Dan Lee, Victor Y T Yang, Amy Y T Lau, Chit K C Lee, Peter K H Chong, King Wai Lau, Jurgen T J van Lunenburg, Damon F D Zheng, Brian H M Ho, Crystal Tik, Kingsley K K Ho, Ramesh Rajaby, Chun-Hang Au, Mullin H C Yu, Wing-Kin Sung
{"title":"UniVar: A variant interpretation platform enhancing rare disease diagnosis through robust filtering and unified analysis of SNV, INDEL, CNV and SV.","authors":"Cherie C Y Au-Yeung, Yuen-Ting Cheung, Joshua Y T Cheng, Ken W H Ip, Sau-Dan Lee, Victor Y T Yang, Amy Y T Lau, Chit K C Lee, Peter K H Chong, King Wai Lau, Jurgen T J van Lunenburg, Damon F D Zheng, Brian H M Ho, Crystal Tik, Kingsley K K Ho, Ramesh Rajaby, Chun-Hang Au, Mullin H C Yu, Wing-Kin Sung","doi":"10.1016/j.compbiomed.2024.109560","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Interpreting the pathogenicity of genetic variants associated with rare diseases is a laborious and time-consuming endeavour. To streamline the diagnostic process and lighten the burden of variant interpretation, it is crucial to automate variant annotation and prioritization. Unfortunately, currently available variant interpretation tools lack a unified and comprehensive workflow that can collectively assess the clinical significance of these types of variants together: small nucleotide variants (SNVs), small insertions/deletions (INDELs), copy number variants (CNVs) and structural variants (SVs).</p><p><strong>Results: </strong>The Unified Variant Interpretation Platform (UniVar) is a free web server tool that offers an automated and comprehensive workflow on annotation, filtering and prioritization for SNV, INDEL, CNV and SV collectively to identify disease-causing variants for rare diseases in one interface, ensuring accessibility for users even without programming expertise. To filter common CNVs/SVs, a diverse SV catalogue has been generated, that enables robust filtering of common SVs based on population allele frequency. Through benchmarking our SV catalogue, we showed that it is more complete and accurate than the state-of-the-art SV catalogues. Furthermore, to cope with those patients without detailed clinical information, we have developed a novel computational method that enables variant prioritization from gene panels. Our analysis shows that our approach could prioritize pathogenic variants as effective as using HPO terms assigned by clinicians, which adds value for cases without specific clinically assigned HPO terms. Lastly, through a practical case study of disease-causing compound heterozygous variants across SNV and SV, we demonstrated the uniqueness and effectiveness in variant interpretation of UniVar, edging over any existing interpretation tools.</p><p><strong>Conclusions: </strong>UniVar is a unified and versatile platform that empowers researchers and clinicians to identify and interpret disease-causing variants in rare diseases efficiently through a single holistic interface and without a prerequisite for HPO terms. It is freely available without login and installation at https://univar.live/.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"185 ","pages":"109560"},"PeriodicalIF":7.0000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.compbiomed.2024.109560","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Interpreting the pathogenicity of genetic variants associated with rare diseases is a laborious and time-consuming endeavour. To streamline the diagnostic process and lighten the burden of variant interpretation, it is crucial to automate variant annotation and prioritization. Unfortunately, currently available variant interpretation tools lack a unified and comprehensive workflow that can collectively assess the clinical significance of these types of variants together: small nucleotide variants (SNVs), small insertions/deletions (INDELs), copy number variants (CNVs) and structural variants (SVs).
Results: The Unified Variant Interpretation Platform (UniVar) is a free web server tool that offers an automated and comprehensive workflow on annotation, filtering and prioritization for SNV, INDEL, CNV and SV collectively to identify disease-causing variants for rare diseases in one interface, ensuring accessibility for users even without programming expertise. To filter common CNVs/SVs, a diverse SV catalogue has been generated, that enables robust filtering of common SVs based on population allele frequency. Through benchmarking our SV catalogue, we showed that it is more complete and accurate than the state-of-the-art SV catalogues. Furthermore, to cope with those patients without detailed clinical information, we have developed a novel computational method that enables variant prioritization from gene panels. Our analysis shows that our approach could prioritize pathogenic variants as effective as using HPO terms assigned by clinicians, which adds value for cases without specific clinically assigned HPO terms. Lastly, through a practical case study of disease-causing compound heterozygous variants across SNV and SV, we demonstrated the uniqueness and effectiveness in variant interpretation of UniVar, edging over any existing interpretation tools.
Conclusions: UniVar is a unified and versatile platform that empowers researchers and clinicians to identify and interpret disease-causing variants in rare diseases efficiently through a single holistic interface and without a prerequisite for HPO terms. It is freely available without login and installation at https://univar.live/.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.