bioMRF: Spatial Dependency of Quantitative Gene Expression in the 3D Genome

Hierarchical Markov Random Field model captures spatial dependency in gene expression, demonstrating regulation via the 3D genome

Naihui Zhou, Iddo Friedberg, Mark S. Kaiser

bioRxiv 2019.12.16.878371;

doi: https://doi.org/10.1101/2019.12.16.878371

This repository contains

• scripts to organize and clean HiC and RNAseq data
• scripts to run PhiMRF on RNAseq and HiC data

The R package PhiMRF is the statistical model we developed to detect spatial dependency in count data observed on spatial structures. The source code of the package as well as documentation is available as a separate repository.

Prerequisites:

• PhiMRF
• python3
• R
• bash

This pipeline has four main parts:

• Set up

• Processing RNA-seq data

• Processing HiC data

• Running PhiMRF

The four parts are explained in their separate folders, and should be run sequentially.

This repo also contains code to process sub-groups in a genome

• TAD genes: genes located within Topologically Associating Domains, as well as neighbors isolated by TADs.

• functional genes: genes annotated with certain Gene Ontology term.

All intermediate results from each step of this pipeline are available on figshare:

• IMR90 in 10kb resolution

References

• RNAseq data comes from ENCODE

ENCODE Project Consortium. "An integrated encyclopedia of DNA elements in the human genome." Nature 489.7414 (2012): 57.

• HiC data and normalization method comes from Rao et al, 2014

Rao, Suhas SP, et al. "A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping." Cell 159.7 (2014): 1665-1680.