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Transcription factors (TFs) are sequence-specific DNA binding proteins, fine-tuning spatiotemporal gene expression. Since genomic occupancy of a TF is highly dynamic, it is crucial to study TF binding sites (TFBSs) in a cell-specific context. To date, thousands of ChIP-seq datasets have portrayed the genomic binding landscapes of numerous TFs in different cell types. Although these datasets can be browsed via several platforms, tools that can operate on that data flow are still lacking. Here, we introduce TFregulomeR (https://github.com/benoukraflab/TFregulomeR), an R-library linked to an up-to-date compendium of cistrome and methylome datasets, implemented with functionalities that facilitate integrative analyses. In particular, TFregulomeR enables the characterization of TF binding partners and cell-specific TFBSs, along with the study of TF's functions in the context of different partnerships and DNA methylation levels. We demonstrated that TFs' target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, we brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized, due to the fact that their position weight matrices were assembled using a mixture of homodimer and heterodimer binding sites. Altogether, analyses of context-specific transcription regulation with TFregulomeR foster our understanding of regulatory network-dependent TF functions.
Pubmed ID: 31754708 RIS Download
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A comprehensive collection of human transcription factor binding sites models. DNA sequences of TF binding regions obtained by both pregenomic and high-throughput methods were collected from existing databases and other public data. The ChIPMunk software was used to construct positional weight matrices. Four motif discovery strategies were tested based on different motif shape priors including flat and periodic priors associated with DNA helix pitch. A quality rating was manually assigned to each model based on known binding preferences. An appropriate TFBS model was selected for each TF, with similar models selected for related TFs. In any case only one model per TF was selected unless there was additional evidence for two distinct binding models or different stable modes of dimerization. All TFBS models and initial binding segments data used for motif discovery were mapped to UniPROT IDs.
View all literature mentionsSoftware tools for Motif Discovery and next-gen sequencing analysis. Used for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets. Collection of command line programs for unix style operating systems written in Perl and C++.
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