Ivan V. Kulakovskiy

We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.

Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians.

The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

BACKGROUND: DNA methylation in promoters is closely linked to downstream gene repression. However, whether DNA methylation is a cause or a consequence of gene repression remains an open question. If it is a cause, then DNA methylation may affect the affinity of transcription factors (TFs) for their binding sites (TFBSs). If it is a consequence, then gene repression caused by chromatin modification may be stabilized by DNA methylation. Until now, these two possibilities have been supported only by non-systematic evidence and they have not been tested on a wide range of TFs. An average promoter methylation is usually used in studies, whereas recent results suggested that methylation of individual cytosines can also be important. RESULTS: We found that the methylation profiles of 16.6% of cytosines and the expression profiles of neighboring transcriptional start sites (TSSs) were significantly negatively correlated. We called the CpGs corresponding to such cytosines "traffic lights". We observed a strong selection against CpG "traffic lights" within TFBSs. The negative selection was stronger for transcriptional repressors as compared with transcriptional activators or multifunctional TFs as well as for core TFBS positions as compared with flanking TFBS positions. CONCLUSIONS: Our results indicate that direct and selective methylation of certain TFBS that prevents TF binding is restricted to special cases and cannot be considered as a general regulatory mechanism of transcription.

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.