Department of Biostatistics, Virginia Commonwealth University, Richmond, VA 23298, USA.
Bioinformatics. 2017 Oct 15;33(20):3323-3330. doi: 10.1093/bioinformatics/btx414.
One of the goals of functional genomics is to understand the regulatory implications of experimentally obtained genomic regions of interest (ROIs). Most sequencing technologies now generate ROIs distributed across the whole genome. The interpretation of these genome-wide ROIs represents a challenge as the majority of them lie outside of functionally well-defined protein coding regions. Recent efforts by the members of the International Human Epigenome Consortium have generated volumes of functional/regulatory data (reference epigenomic datasets), effectively annotating the genome with epigenomic properties. Consequently, a wide variety of computational tools has been developed utilizing these epigenomic datasets for the interpretation of genomic data.
The purpose of this review is to provide a structured overview of practical solutions for the interpretation of ROIs with the help of epigenomic data. Starting with epigenomic enrichment analysis, we discuss leading tools and machine learning methods utilizing epigenomic and 3D genome structure data. The hierarchy of tools and methods reviewed here presents a practical guide for the interpretation of genome-wide ROIs within an epigenomic context.
mikhail.dozmorov@vcuhealth.org.
Supplementary data are available at Bioinformatics online.
功能基因组学的目标之一是了解实验获得的基因组感兴趣区域 (ROI) 的调控含义。大多数测序技术现在生成分布在整个基因组中的 ROI。由于大多数 ROI 位于功能明确的蛋白编码区域之外，因此对这些全基因组 ROI 的解释是一个挑战。国际人类表观基因组联合会成员最近的努力已经产生了大量的功能/调控数据（参考表观基因组数据集），有效地利用表观基因组特性对基因组进行注释。因此，已经开发了各种各样的计算工具，利用这些表观基因组数据集来解释基因组数据。
本综述的目的是提供一个结构化的概述，介绍在表观基因组数据的帮助下解释 ROI 的实用解决方案。从表观基因组富集分析开始，我们讨论了利用表观基因组和 3D 基因组结构数据的领先工具和机器学习方法。这里回顾的工具和方法层次结构为在表观基因组背景下解释全基因组 ROI 提供了实用指南。
mikhail.dozmorov@vcuhealth.org.
补充数据可在“Bioinformatics”在线获取。
