Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors.

We describe an effort ("Codebook") to determine the sequence specificity of 332 putative and largely uncharacterized human transcription factors (TFs), as well as 61 control TFs. Nearly 5,000 independent experiments across multiple and assays produced motifs for just over half of the putative TFs analyzed (177, or 53%), of which most are unique to a single TF. The data highlight the extensive contribution of transposable elements to TF evolution, both in and , and identify tens of thousands of conserved, base-level binding sites in the human genome.

Cross-platform DNA motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors.

A DNA sequence pattern, or "motif", is an essential representation of DNA-binding specificity of a transcription factor (TF). Any particular motif model has potential flaws due to shortcomings of the underlying experimental data and computational motif discovery algorithm. As a part of the Codebook/GRECO-BIT initiative, here we evaluated at large scale the cross-platform recognition performance of positional weight matrices (PWMs), which remain popular motif models in many practical applications.

Extensive binding of uncharacterized human transcription factors to genomic dark matter.

Most of the human genome is thought to be non-functional, and includes large segments often referred to as "dark matter" DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells.

HOCOMOCO in 2024: a rebuild of the curated collection of binding models for human and mouse transcription factors.

We present a major update of the HOCOMOCO collection that provides DNA binding specificity patterns of 949 human transcription factors and 720 mouse orthologs. To make this release, we performed motif discovery in peak sets that originated from 14 183 ChIP-Seq experiments and reads from 2554 HT-SELEX experiments yielding more than 400 thousand candidate motifs. The candidate motifs were annotated according to their similarity to known motifs and the hierarchy of DNA-binding domains of the respective transcription factors.

EpiFactors 2022: expansion and enhancement of a curated database of human epigenetic factors and complexes.

We present an update of EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets, and products which is openly accessible at http://epifactors.autosome.org.

ANANASTRA: annotation and enrichment analysis of allele-specific transcription factor binding at SNPs.

We present ANANASTRA, https://ananastra.autosome.org, a web server for the identification and annotation of regulatory single-nucleotide polymorphisms (SNPs) with allele-specific binding events.

Enhanced C/EBP binding to G·T mismatches facilitates fixation of CpG mutations in cancer and adult stem cells.

Somatic mutations in regulatory sites of human stem cells affect cell identity or cause malignant transformation. By mining the human genome for co-occurrence of mutations and transcription factor binding sites, we show that C/EBP binding sites are strongly enriched with [C > T]G mutations in cancer and adult stem cells, which is of special interest because C/EBPs regulate cell fate and differentiation. In vitro protein-DNA binding assay and structural modeling of the CEBPB-DNA complex show that the G·T mismatch in the core CG dinucleotide strongly enhances affinity of the binding site.

Landscape of allele-specific transcription factor binding in the human genome.

Sequence variants in gene regulatory regions alter gene expression and contribute to phenotypes of individual cells and the whole organism, including disease susceptibility and progression. Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Differential transcription factor binding in heterozygous genomic loci provides a natural source of information on such regulatory variants.

Assessing Ribosome Distribution Along Transcripts with Polarity Scores and Regression Slope Estimates.

During translation, the rate of ribosome movement along mRNA varies. This leads to a non-uniform ribosome distribution along the transcript, depending on local mRNA sequence, structure, tRNA availability, and translation factor abundance, as well as the relationship between the overall rates of initiation, elongation, and termination. Stress, antibiotics, and genetic perturbations affecting composition and properties of translation machinery can alter the ribosome positional distribution dramatically.

A holistic view of mouse enhancer architectures reveals analogous pleiotropic effects and correlation with human disease.

Background: Efforts to elucidate the function of enhancers in vivo are underway but their vast numbers alongside differing enhancer architectures make it difficult to determine their impact on gene activity. By systematically annotating multiple mouse tissues with super- and typical-enhancers, we have explored their relationship with gene function and phenotype.

Results: Though super-enhancers drive high total- and tissue-specific expression of their associated genes, we find that typical-enhancers also contribute heavily to the tissue-specific expression landscape on account of their large numbers in the genome.

Insights gained from a comprehensive all-against-all transcription factor binding motif benchmarking study.

Background: Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets.

What Do Neighbors Tell About You: The Local Context of Cis-Regulatory Modules Complicates Prediction of Regulatory Variants.

Many problems of modern genetics and functional genomics require the assessment of functional effects of sequence variants, including gene expression changes. Machine learning is considered to be a promising approach for solving this task, but its practical applications remain a challenge due to the insufficient volume and diversity of training data. A promising source of valuable data is a saturation mutagenesis massively parallel reporter assay, which quantitatively measures changes in transcription activity caused by sequence variants.

Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data.

Objectives: Mammalian genomics studies, especially those focusing on transcriptional regulation, require information on genomic locations of regulatory regions, particularly, transcription factor (TF) binding sites. There are plenty of published ChIP-Seq data on in vivo binding of transcription factors in different cell types and conditions. However, handling of thousands of separate data sets is often impractical and it is desirable to have a single global map of genomic regions potentially bound by a particular TF in any of studied cell types and conditions.

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.

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.

The single nucleotide variant rs12722489 determines differential estrogen receptor binding and enhancer properties of an IL2RA intronic region.

We studied functional effect of rs12722489 single nucleotide polymorphism located in the first intron of human IL2RA gene on transcriptional regulation. This polymorphism is associated with multiple autoimmune conditions (rheumatoid arthritis, multiple sclerosis, Crohn's disease, and ulcerative colitis). Analysis in silico suggested significant difference in the affinity of estrogen receptor (ER) binding site between alternative allelic variants, with stronger predicted affinity for the risk (G) allele.

Multiple single nucleotide polymorphisms in the first intron of the IL2RA gene affect transcription factor binding and enhancer activity.

IL2RA gene encodes the alpha subunit of a high-affinity receptor for interleukin-2 which is expressed by several distinct populations of lymphocytes involved in autoimmune processes. A large number of polymorphic alleles of the IL2RA locus are associated with the development of various autoimmune diseases. With bioinformatics analysis we the dissected the first intron of the IL2RA gene and selected several single nucleotide polymorphisms (SNPs) that may influence the regulation of the IL2RA gene in cell types relevant to autoimmune pathology.

Early B-cell factor 1 (EBF1) is critical for transcriptional control of SLAMF1 gene in human B cells.

Signaling lymphocytic activation molecule family member 1 (SLAMF1)/CD150 is a co-stimulatory receptor expressed on a variety of hematopoietic cells, in particular on mature lymphocytes activated by specific antigen, costimulation and cytokines. Changes in CD150 expression level have been reported in association with autoimmunity and with B-cell chronic lymphocytic leukemia. We characterized the core promoter for SLAMF1 gene in human B-cell lines and explored binding sites for a number of transcription factors involved in B cell differentiation and activation.

Negative selection maintains transcription factor binding motifs in human cancer.

Background: Somatic mutations in cancer cells affect various genomic elements disrupting important cell functions. In particular, mutations in DNA binding sites recognized by transcription factors can alter regulator binding affinities and, consequently, expression of target genes. A number of promoter mutations have been linked with an increased risk of cancer.

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models.

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.

EpiFactors: a comprehensive database of human epigenetic factors and complexes.

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.

A promoter-level mammalian expression atlas.

Regulated transcription controls the diversity, developmental pathways and spatial organization of the hundreds of cell types that make up a mammal. Using single-molecule cDNA sequencing, we mapped transcription start sites (TSSs) and their usage in human and mouse primary cells, cell lines and tissues to produce a comprehensive overview of mammalian gene expression across the human body. We find that few genes are truly 'housekeeping', whereas many mammalian promoters are composite entities composed of several closely separated TSSs, with independent cell-type-specific expression profiles.

Jaccard index based similarity measure to compare transcription factor binding site models.

Background: Positional weight matrix (PWM) remains the most popular for quantification of transcription factor (TF) binding. PWM supplied with a score threshold defines a set of putative transcription factor binding sites (TFBS), thus providing a TFBS model.TF binding DNA fragments obtained by different experimental methods usually give similar but not identical PWMs.

From binding motifs in ChIP-Seq data to improved models of transcription factor binding sites.

Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) became a method of choice to locate DNA segments bound by different regulatory proteins. ChIP-Seq produces extremely valuable information to study transcriptional regulation. The wet-lab workflow is often supported by downstream computational analysis including construction of models of nucleotide sequences of transcription factor binding sites in DNA, which can be used to detect binding sites in ChIP-Seq data at a single base pair resolution.