Suppr超能文献

揭开非编码 GWAS 变异体的神秘面纱:计算工具和方法概述。

Demystifying non-coding GWAS variants: an overview of computational tools and methods.

机构信息

Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU University Amsterdam, De Boelelaan 1105, Amsterdam 1081HV, The Netherlands.

出版信息

Hum Mol Genet. 2022 Oct 20;31(R1):R73-R83. doi: 10.1093/hmg/ddac198.

Abstract

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

摘要

全基因组关联研究(GWAS)发现大多数与疾病相关的变异是非编码的。对非编码调控景观的绘制进行了重大努力,开发了旨在帮助识别因果变异及其作用机制的工具和方法。在这篇综述中,我们概述了用于分析疾病中非编码 GWAS 变异的当前工具和方法。我们提供了一个工作流程,允许积累计算机模拟证据,从而生成有关疾病潜在机制的新假设,并使用非编码 GWAS 变异对后续研究的目标进行优先级排序。最后,我们讨论了分析非编码变异需要全面的基准和新工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/066c/9585674/a86fc4b8f8d4/ddac198f1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验