BiFET: A Bias-free Transcription Factor Footprint Enrichment Test

Abstract

Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g., hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from human EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC, and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-typespecific loci.

Multi-omics analysis reveals a crucial role for Retinoic Acid in promoting epigenetic and transcriptional competence of anin vitromodel of human Pharyngeal Endoderm

“…To this end, we used the maelstrom tool (Bruse & van Heeringen, 2018) to perform a differential motif enrichment analysis revealing which known TF motifs are specifically enriched in cell type-specific accessible regions. In parallel, the BiFET tool (Youn et al, 2019) was employed to identify TF footprints (FP: less accessible regions within highly accessible regions where a TF motif is found) (Li et al, 2009) enriched in the DARs found in each differential accessibility contrast (). The results of the maelstrom and of the BiFET analyses were integrated by selecting the TF motifs whose differential enrichment trend correlates with the corresponding FP enrichment profile and with the TF expression during the differentiation.…”

GraphPred: An approach to predict multiple DNA motifs from ATAC-seq data using graph neural network and coexisting probability

“…Several tools have been developed to detect footprinting from ATAC-seq data, which are based on multi-omics data and hidden Markov algorithm [1, 10]. ATAC-seq footprinting has been applied to TF network prediction [11], comparison of TF activity [10], and identifying TFs enriched in PBMC-specific peaks [12].…”