Work

A comprehensive review of computational prediction of genome-wide features

Year: 2018

Type: article

Abstract: There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bby110

Authors: Tianlei Xu, Xiaoqi Zheng, Ben Li, Peng Jin, Zhaohui Qin, Hao Wu

Institutions: Emory University, Shanghai Normal University

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Institutes of Health, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke

Awards: 基于DNA甲基化的肿瘤异质性分析及应用算法研究, Epigenetic regulation of neurogenesis, Modifiers of FMR1-associated Disorders: Application of High Throughput Technologi, Statistical Methods for Single-Cell RNA-Seq