Machine and deep learning methods for predicting 3D genome organization
Brydon P. G. Wall My Nguyen J. Chuck Harrell Mikhail G. Dozmorov
Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter
interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B
compartments play critical roles in a wide range of cellular processes by
regulating gene expression. Recent development of chromatin conformation
capture technologies has enabled genome-wide profiling of various 3D
structures, even with single cells. However, current catalogs of 3D structures
remain incomplete and unreliable due to differences in technology, tools, and
low data resolution. Machine learning methods have emerged as an alternative to
obtain missing 3D interactions and/or improve resolution. Such methods
frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA
sequencing information (k-mers, Transcription Factor Binding Site (TFBS)
motifs), and other genomic properties to learn the associations between genomic
features and chromatin interactions. In this review, we discuss computational
tools for predicting three types of 3D interactions (EPIs, chromatin
interactions, TAD boundaries) and analyze their pros and cons. We also point
out obstacles of computational prediction of 3D interactions and suggest future
research directions.