PECA: Paired Expression and Chromatin Accessibility modeling

• Version 1.0
• Last updated: April 06, 2017

Reference#

• Zhana Duren, Xi Chen, Rui Jiang, Yong Wang, and Wing Hung Wong. Modeling gene regulation from paired expression and chromatin accessibility data. PNAS, vol. 114 no. 25 E4914-E4923 (2017).

Method#

The rapid increase of genome-wide data sets on gene expression, chromatin states and transcription factor (TF) binding locations offers an exciting opportunity to interpret the information encoded in genomes and epigenomes. This task can be challenging as it requires joint modeling of context specific activation of cis-regulatory elements (RE) and the effects on transcription of associated regulatory factors. To meet this challenge, we propose a statistical approach based on paired expression and chromatin accessibility (PECA) data across diverse cellular contexts. In our approach, we model 1) the localization to REs of chromatin regulators (CR) based on their interaction with sequence-specific TF, 2) the activation of REs due to CRs that are localized to them, 3) the effect of TFs bound to activated REs on the transcription of target genes (TG). The transcriptional regulatory network inferred by PECA provides a detailed view of how trans- and cis-regulatory elements work together to affect gene expression in a context specific manner.

Our analytical approach for learning from this data is to model the distribution of the expression of target genes (TG) conditional on the accessibility of regulatory elements and the expression of transcription factors (TF) and chromatin regulators (CR).

Procedure#

Useful Resource:#

Download 25 bio-sample matched RNA-seq and DNase-seq data from mouse ENCODE project.

Download regulatoryElement-targetGene association.

Download PECA predicted whole network and context specific network for 25 tissues or cell type.

Using PECA Tools:#

PECA-TNet: Matlab tool for context specific network

Input: paired expression of genes (Exp) and chromatin accessibility of regulatory elements (Opn) on your interested cellular context.

Output: context specific network (TissueSpecificNet).

Usage: TissueSpecificNet=PECATool1(Exp,Opn,GeneName,ElementName);

Where GeneName and ElementName are name of gene and regulatory elements.

PECA-Anno: Matlab tool for genomic region annotation by network

Input: Bed file of the genomic region you interested (region.bed’).

Output: annotation network of your region (RegionNet).

Usage: RegionNet=PECATool2(‘region.bed’);

Software#

This is a beta version of the program for preliminary testing. The program is still under development.

• PECA

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Category: Software