首页|Computational Assessment of the Expression-modulating Potential for Non-coding Variants

Computational Assessment of the Expression-modulating Potential for Non-coding Variants

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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 supe-rior 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 polymor-phisms(SNPs).CARMEN scales well with the massive datasets,and is available online as a web server at http://carmen.gao-lab.org.

Non-coding variantExpression-modulating variantGene regulationAlgorithmWeb server

Fang-Yuan Shi、Yu Wang、Dong Huang、Yu Liang、Nan Liang、Xiao-Wei Chen、Ge Gao

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State Key Laboratory of Protein and Plant Gene Research,School of Life Sciences,Biomedical Pioneering Innovative Center(BIOPIC)& Beijing Advanced Innovation Center for Genomics(ICG),Center for Bioinformatics(CBI),Peking University,Beijing 100871,China

State Key Laboratory of Membrane Biology,Institute of Molecular Medicine,Peking University,Beijing 100871,China

Human Aging Research Institute,School of Life Science,Nanchang University,Nanchang 330031,China

Peking-Tsinghua Center for Life Sciences,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing 100871,China

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National Key R&D Program of ChinaNational Hightech R&D Program of ChinaState Key Laboratory of Protein and Plant Gene ResearchBeijing Advanced Innovation Center for Genomics(ICG)at Peking UniversityNational Program for Support of Topnotch Young ProfessionalsHighperformance Computing Platform of Peking University

2016YFC09016032015AA020108

2023

10.1016/j.gpb.2021.10.003

基因组蛋白质组与生物信息学报(英文版)
中国科学院北京基因组研究所

基因组蛋白质组与生物信息学报(英文版)

CSTPCDCSCD
影响因子：0.495
ISSN：1672-0229
年,卷(期)：2023.21(3)
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