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

Abstract

Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. Hence, it is critical to locate these TF-DNA interactions to understand transcriptional regulation. The availability of datasets generated by chromatin immunoprecipitation followed by sequencing (ChIP-seq) empowers our efforts to predict the specific locations of TFBSs with greater confidence than previously possible by fusing computational and experimental approaches. In this work, we processed ~10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~44 million TFBSs with experimental and computational evidence for direct TF-DNA interactions for 640 TFs in 1,101 cell lines and tissues. These TFBSs were used to predict >183,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface (https://unibind.uio.no/), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

“…Nevertheless, they provide the necessary background for large-scale analysis and these regions have been identified as TF-bound in biological contexts. Furthermore, the TFBSs stored in UniBind represent evolutionarily conserved elements [25,40] and harbour similar mutational load than protein-coding exons (using TCGA somatic mutation data), supporting their functional relevance [54].…”

“…Genomic regions were lifted, using the liftOver tool from UCSC [72], from the GRCh19 genome assembly over to the GRCh38 version, which is the assembly used in UniBind. Differential enrichment of TFBS sets was performed using the twoSets subcommand of the UniBind enrichment tool (https://unibind.uio.no/enrichment/; https://bitbucket.org/CBGR/unibind_enrichment/) using the collection of TFBS sets from UniBind version 2018 [25,40]. Specifically, the foreground set of regions corresponded to the regions centered around emCpGs or non-correlated CpGs and the combined set of such regions was used as background.…”

Skeletal muscle methylome and transcriptome integration reveals profound sex differences related to muscle function and substrate metabolism

“…Enrichment of TFBSs among the identified DMRs was performed using the enrichment analysis tool in http://unibind.uio.no/ which utilizes the runLOLA function of the R package LOLA [48].…”

“…Given the role of transcription factors (TFs) in regulating chromatin accessibility and thus effecting downstream gene expression [47], as well as the recent studies identifying sex differences in TF targeting patterns [7,14]; we next tested whether DMPs were enriched for the experimentally validated binding sites (TFBSs) of 268 TFs from 518 different cell and tissue types [2,48]. The DMPs were overrepresented in the binding sites of 41 TFs (p-value < 0.005, Figure 2D, Supplementary table 14), including hormone-related TFs such as androgen (AR), estrogen (ESR1), and glucocorticoid (NR3C1) receptors.…”

Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

“…Figure 2 c, h plots the Fisher's exact p values using beeswarm plots (swarmplot function of the seaborn Python package, https://doi.org/10.5281/zenodo. 824567) with annotations for the TFs associated with top 10 most enriched TFBS sets [43].…”