Constructing tissue-specific transcriptional regulatory networks via a Markov random field.

ABSTRACT:

Background

Recent advances in sequencing technologies have enabled parallel assays of chromatin accessibility and gene expression for major human cell lines. Such innovation provides a great opportunity to decode phenotypic consequences of genetic variation via the construction of predictive gene regulatory network models. However, there still lacks a computational method to systematically integrate chromatin accessibility information with gene expression data to recover complicated regulatory relationships between genes in a tissue-specific manner.

Results

We propose a Markov random field (MRF) model for constructing tissue-specific transcriptional regulatory networks via integrative analysis of DNase-seq and RNA-seq data. Our method, named CSNets (cell-line specific regula

SUBMITTER: Ma S 

PROVIDER: S-EPMC6311931 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature