Learning a Pairwise Epigenomic and Transcription Factor Binding Association Score Across the Human Genome

Abstract

Identifying pairwise associations between genomic loci is an important challenge for which large and diverse collections of epigenomic and transcription factor (TF) binding data can potentially be informative. We therefore developed Learning Evidence of Pairwise Association from Epigenomic and TF binding data (LEPAE). LEPAE uses neural networks to quantify evidence of association for pairs of genomic windows from large-scale epigenomic and TF binding data along with distance information. We applied LEPAE using thousands of human datasets. We present evidence using additional data that LEPAE captures biologically meaningful pairwise relationships between genomic loci and expect LEPAE scores to be a resource.