ChIPSummitDB: a ChIP-seq-based database of human transcription factor binding sites and the topological arrangements of the proteins bound to them

Abstract

ChIP-seq reveals genomic regions where proteins, e.g. transcription factors (TFs) interact with DNA. A substantial fraction of these regions, however, do not contain the cognate binding site for the TF of interest. This phenomenon might be explained by protein–protein interactions and co-precipitation of interacting gene regulatory elements. We uniformly processed 3727 human ChIP-seq data sets and determined the cistrome of 292 TFs, as well as the distances between the TF binding motif centers and the ChIP-seq peak summits. ChIPSummitDB enables the analysis of ChIP-seq data using multiple approaches. The 292 cistromes and corresponding ChIP-seq peak sets can be browsed in GenomeView. Overlapping SNPs can be inspected in dbSNPView. Most importantly, the MotifView and PairShiftView pages show the average distance between motif centers and overlapping ChIP-seq peak summits and distance distributions thereof, respectively. In addition to providing a comprehensive human TF binding site collection, the ChIPSummitDB database and web interface allows for the examination of the topological arrangement of TF complexes genome-wide. ChIPSummitDB is freely accessible at http://summit.med.unideb.hu/summitdb/. The database will be regularly updated and extended with the newly available human and mouse ChIP-seq data sets.

Integrating Peak Colocalization and Motif Enrichment Analysis for the Discovery of Genome-Wide Regulatory Modules and Transcription Factor Recruitment Rules

“…There are a few main differences between PscanChIP and other methods for the same task. The presence/absence of a motif instance in a region is evaluated with a score, ranging from 0 to 1, instead of a yes/no decision (binding motif present/absent) as for example in recent works (Dergilev et al, 2017;Czipa et al, 2020;Levitsky et al, 2019), which are also focused on the analysis of regions surrounding ChIP-Seq summits. Mean and variance of scores of best motif instances in each of the summit regions are in turn employed by PscanChIP to assess motif enrichment not only with respect to regions flanking peaks (local enrichment), as in similar tools (Zhang et al, 2011;Bailey and MacHanick, 2012), but also with respect to the rest of the genome, providing a more accurate evaluation of their significance.…”

Regulatory Modules of Human Thermogenic Adipocytes: Functional Genomics of Meta-Analyses Derived Marker-Genes

“…data, however, we found no HIF1A or HIF1B (ARNT) chip-seq. experiment in thermogenic adipocytes in neither the ChIP-Atlas (http://dbarchive.biosciencedbc.jp; 27) nor in the ChIPSummitDB database (http://summit.med.unideb.hu;28). For other cell types or conditions, UCP1 was not among the target genes of HIF1A or HIF1B (ChIP-Atlas), or there was no experimental evidence for HIF1A-HIF1B binding to the UCP1 promoter (TSS-5KB) (ChIPSummitDB).…”

“…Chip-Atlas (http://dbarchive.biosciencedbc.jp; 27) and ChIPSummitDB database (University of Debrecen; http://summit.med.unideb.hu; 28) were used to investigate experimental data about HIF1A and other TFS binding to UCP1 and UCP2 promoter. Both online tools collect, organize and visualise the results of the published chip-seq.…”

UniBind: maps of high-confidence direct TF-DNA interactions across nine species

“…For instance, one can apply PWMs to predict TFBSs in open chromatin regions (e.g. derived from DNase-seq or ATAC-seq [10][11][12] ) or TF ChIP-seq peaks [13][14][15] .…”

“…Previous efforts used PWMs to predict TFBSs within ChIP-seq peaks and made the predictions freely available [15][16][17] . These resources are specific to one or two species.…”