Genome-wide prediction of chromatin accessibility based on gene expression

Research output: Contribution to journalArticlepeer-review

Abstract

Decoding gene regulation in a biological system requires information from both transcriptome and regulome. While multiple high-throughput transcriptome and regulome mapping technologies are available, transcriptome profiling is more widely used. Today, over a million bulk and single-cell gene expression samples are publicly available. This number is orders of magnitude larger than the number of available regulome samples. Most of the gene expression samples do not have corresponding regulome data. However, it is possible to obtain regulome information via prediction. Open chromatin is a hallmark of active regulatory elements. This mini-review discusses recent advances in predicting chromatin accessibility using gene expression data, including both the development of prediction methods and their applications in expanding the regulome catalog, improving regulome analysis, integrating transcriptome and regulome data, and facilitating single-cell analysis of gene regulation. This article is categorized under: Applications of Computational Statistics > Genomics/Proteomics/Genetics Data: Types and Structure > Massive Data Statistical Models > Linear Models.

Original languageEnglish (US)
JournalWiley Interdisciplinary Reviews: Computational Statistics
DOIs
StateAccepted/In press - 2020

Keywords

  • chromatin accessibility
  • gene expression
  • genomics
  • single-cell genomics

ASJC Scopus subject areas

  • Statistics and Probability

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