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RegVar:Tissue-specific Prioritization of non-coding Regulatory variants

Genomics, Proteomics & Bioinformatics 2023年第2期21卷 385-395页

作者： Hao Lu Luyu Ma Cheng Quan Lei Li Yiming Lu Gangqiao Zhou Chenggang Zhang Beijing Institute of Radiation Medicine State Key Laboratory of ProteomicsBeijing 100850China

non-coding genomic variants constitute the majority of trait-associated genome variations;however,the identification of functional non-coding variants is still a challenge in human genetics,and a method for systematically assessing the impact of regulatory variants on gene expression and linking these regulatory variants to potential target genes is still lacking.Here,we introduce a deep neural network(DNN)-based computational framework,RegVar,which can accurately predict the tissue-specific impact of non-coding regulatory variants on target genes.We show that by robustly learning the genomic characteristics of massive variant-gene expression associations in a variety of human tissues,RegVar vastly surpasses all current non-coding variant prioritization methods in predicting regulatory variants under different circumstances.The unique features of RegVar make it an excellent framework for assessing the regulatory impact of any variant on its putative target genes in a variety of tissues.

关键词： non-coding variant variantprioritization Expressionregulation Expressionquantitative trait locus Deep neural network

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Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of non-coding variants

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Genomics, Proteomics & Bioinformatics 2023年第3期21卷 649-661页

作者： Zheng Wang Guihu Zhao Bin Li Zhenghuan Fang Qian Chen Xiaomeng Wang Tengfei Luo Yijing Wang Qiao Zhou Kuokuo Li Lu Xia Yi Zhang Xun Zhou Hongxu Pan Yuwen Zhao Yige Wang Lin Wang Jifeng Guo Beisha Tang Kun Xia Jinchen Li National Clinical Research Centre for Geriatric Disorders Department of GeriatricsXiangya HospitalCentral South UniversityChangsha 410008China Department of Neurology Xiangya HospitalCentral South UniversityChangsha 410008China Centre for Medical Genetics&Hunan Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangsha 410008China Reproductive Medicine Center Xiangya HospitalCentral South UniversityChangsha 410008China

non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects.Hence,an increasing number of computational 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 clinical relevant sequence variants(ClinVar),(2)rare somatic variants from Catalogue Of Somatic Mutations In Cancer(COSMIC),(3)common regulatory variants from curated expression quantitative trait locus(eQTL)data,and(4)disease-associated common variants from curated genomewide 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 ClinVar 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.

关键词： non-coding variant Pathogenicity estimation Functional prediction Performance assessment Prediction model

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Computational Assessment of the Expression-modulating Potential for non-coding variants

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Genomics, Proteomics & Bioinformatics 2023年第3期21卷 662-673页

作者： Fang-Yuan Shi Yu Wang Dong Huang Yu Liang Nan Liang Xiao-Wei Chen Ge Gao State Key Laboratory of Protein and Plant Gene Research School of Life SciencesBiomedical Pioneering Innovative Center(BIOPIC)&Beijing Advanced Innovation Center for Genomics(ICG)Center for Bioinformatics(CBI)Peking UniversityBeijing 100871China State Key Laboratory of Membrane Biology Institute of Molecular MedicinePeking UniversityBeijing 100871China Human Aging Research Institute School of Life ScienceNanchang UniversityNanchang 330031China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing 100871China

Large-scale genome-wide association studies(GWAS)and expression quantitative trait locus(eQTL)studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression.However,pinpointing causal variants effectively and efficiently remains a serious challenge.Here,we developed CARMEN,a novel algorithm to identify functional non-coding expression-modulating variants.Multiple evaluations demonstrated CARMEN’s superior performance over state-of-the-art tools.Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms(SNPs).CARMEN scales well with the massive datasets,and is available online as a web server at http://***.

关键词： non-coding variant Expression-modulating variant Gene regulation Algorithm Web server

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