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PubMed2024

TFinder: A Python Web Tool for Predicting Transcription Factor Binding Sites.

Transcription is a key cell process that consists of synthesizing several copies of RNA from a gene DNA sequence. This process is highly regulated and closely linked to the ability of transcription factors to bind specifically to DNA. TFinder is an easy-to-use Python web portal allowing the identification of Individual Motifs (IM) such as Transcription Factor Binding Sites (TFBS). Using the NCBI API, TFinder extracts either promoter or gene terminal regulatory regions, through a simple query of NCBI gene name or ID. It enables simultaneous analysis across five different species for an unlimited number of genes. TFinder searches for Individual Motifs in different formats, including IUPAC codes and JASPAR entries. Moreover, TFinder also allows de novo generations of a Position Weight Matrix (PWM) and the use of already established PWM. Finally, the data are provided in a tabular and a graph format showing the relevance and the P-value of the Individual Motifs found as well as their location relative to the Transcription Start Site (TSS) or the terminal region of the gene. The results are then sent by email to users facilitating the subsequent data analysis and sharing. TFinder is written in Python and freely available on GitHub under the MIT license: https://github.com/Jumitti/TFinder. It can be accessed as a web application implemented in Streamlit at https://tfinder-ipmc.streamlit.app. Resources are available on Streamlit "Resources" tab. TFINDER strength is that it relies on an all-in-one intuitive tool allowing users inexperienced with bioinformatics tools to retrieve gene regulatory regions sequences in multiple species and to search for individual motifs in a huge number of genes.

Transcription Factors↗
PubMed2026

How negative sampling shapes the performance of transcription factor binding site prediction models.

MOTIVATION: Transcription factors (TFs) are key players in gene regulation and development, where they activate and repress gene expression through DNA binding. Predicting transcription factor binding sites (TFBSs) has long been an active area of research, with many deep learning methods developed to tackle this problem. These models are often trained on TF ChIP-seq data, which is generally seen as only providing positive samples. The choice of datasets and negative sampling techniques is a critical yet often overlooked aspect of this work. RESULTS: In this study, we investigate the impact of different negative sampling techniques on TFBS prediction performance. We create high-quality test datasets based on ChIP-seq and ATAC-seq data, where true negatives can be identified as positions that are accessible but not bound by the TF in question. We then train models using various negative sampling techniques, including genomic sampling, shuffling, dinucleotide shuffling, neighborhood sampling, and cell line specific sampling, simulating cases where matching ATAC-seq data is not available. Our results show that, generally, metrics calculated on training datasets give inflated performance scores. Of the tested techniques, genomic sampling of negatives based on similarity to the positives performed by far the best, although still not reaching the performance of baseline models trained on high-quality datasets. Models trained on dinucleotide shuffled negatives performed poorly, despite being a common practice in the field. Our findings highlight the importance of carefully selecting negative sampling techniques for TFBS prediction, as they can significantly impact model performance and the interpretation of results. AVAILABILITY AND IMPLEMENTATION: The code used in this study is available at https://github.com/NatanTourne/TFBS-negatives (DOI: 10.5281/zenodo.18007567).

Binding Sites↗
PubMed2023

H3.3 contributes to chromatin accessibility and transcription factor binding at promoter-proximal regulatory elements in embryonic stem cells.

BACKGROUND: The histone variant H3.3 is enriched at active regulatory elements such as promoters and enhancers in mammalian genomes. These regions are highly accessible, creating an environment that is permissive to transcription factor binding and the recruitment of transcriptional coactivators that establish a unique chromatin post-translational landscape. How H3.3 contributes to the establishment and function of chromatin states at these regions is poorly understood. RESULTS: We perform genomic analyses of features associated with active promoter chromatin in mouse embryonic stem cells (ESCs) and find evidence of subtle yet widespread promoter dysregulation in the absence of H3.3. Loss of H3.3 results in reduced chromatin accessibility and transcription factor (TF) binding at promoters of expressed genes in ESCs. Likewise, enrichment of the transcriptional coactivator p300 and downstream histone H3 acetylation at lysine 27 (H3K27ac) is reduced at promoters in the absence of H3.3, along with reduced enrichment of the acetyl lysine reader BRD4. Despite the observed chromatin dysregulation, H3.3 KO ESCs maintain transcription from ESC-specific genes. However, upon undirected differentiation, H3.3 KO cells retain footprinting of ESC-specific TF motifs and fail to generate footprints of lineage-specific TF motifs, in line with their diminished capacity to differentiate. CONCLUSIONS: H3.3 facilitates DNA accessibility, transcription factor binding, and histone post-translational modification at active promoters. While H3.3 is not required for maintaining transcription in ESCs, it does promote de novo transcription factor binding which may contribute to the dysregulation of cellular differentiation in the absence of H3.3.

Animals↗
PubMed2026

Positional grammar of transcription factor binding partitions developmental and stress-response regulation in plants.

Understanding how transcription factor binding site (TFBS) position influences gene regulation remains a fundamental challenge in plants. Here, we integrate conserved multiDAP TFBS maps for 244 transcription factors (TFs) with single-nucleus chromatin accessibility, cell type-resolved gene expression, and hormone-response datasets across Brassicaceae species to determine how TFBS position relates to regulatory function. Although conserved TFBSs are enriched near transcription start sites (TSSs), TSS-proximal accessibility poorly predicts cell type-specific expression. Instead, cell type-specific expression correlates best with conserved TFBSs embedded in cell type-restricted chromatin, with TF family-specific distributions across distal promoters and introns. In contrast, TSS-proximal TFBSs in broadly accessible chromatin are associated with rapid transcriptional responses to abiotic and biotic stress hormones. Coding sequence TFBSs mark a distinct regulatory context in which the same DNA sequence encodes both amino acid sequence and TF motifs, including evidence that CDS-localized ABR1 binding may contribute to repression during hormone response. Finally, distal upstream regions contain conserved multi-family TF clusters with enhancer-like features overlapping rare cell type-specific accessible chromatin and enriched near genes controlling embryonic, meristematic, and hormone-dependent developmental patterning. Together, these results support a positional grammar in which TFBS position and chromatin context jointly partition developmental, stress-responsive, and repressive regulatory output in plants.

Transcription Factors↗
PubMed2026

Motif-Cluster: Motif driven prioritization of transcription factor binding clusters.

Genome-wide analyses of transcription factor (TF) motif binding sites have largely emphasized individual high-affinity sites, while overlooking the regulatory importance of locally repetitive motif clusters. Such clusters, including combinations of weak and strong binding sites, can collectively enhance TF occupancy and regulatory activity. Here we present Motif-Cluster, an open-source framework for motif-driven prioritization and visualization of TF binding clusters using sequence information alone. Motif-Cluster integrates a density-based clustering strategy with flexible modeling of binding-site gaps and affinity signals, enabling the identification and ranking of candidate regulatory regions without requiring experimental binding data. Through simulations and multiple real-data analyses, we show that combining gap distributions with binding affinity effectively balances cluster size and signal strength while reducing noise from weak sites. Application to ZNF410 successfully recovers the previously characterized binding clusters in the CHD4 promoter, which are conserved between human and mouse. Additional case studies involving PHB1, TWIST1, and EGR1 further demonstrate the general applicability of the method across diverse transcription factors. Motif-Cluster also provides intuitive visualization and reproducible workflows to facilitate interpretation of spatially dense motif patterns. Overall, Motif-Cluster offers a robust and flexible approach for prioritizing transcription factor regulatory regions from genome-wide motif scans, enabling biological discovery and guiding experimental design, particularly in settings where direct genome-wide binding assays are unavailable.

Transcription Factors↗
PubMed2026

SEMPLR: an R package for transcription factor binding prediction.

SUMMARY: SEMPLR is an R package that predicts transcription factor binding and variant effects using SNP Effect Matrices (SEMs), providing efficient, genome-wide scoring, enrichment testing, and visualization tools for comprehensive analysis of regulatory sequences. AVAILABILITY: Available on GitHub at https://github.com/grkenney/SEMPLR and on Bioconductor at https://bioconductor.org/packages/release/bioc/html/SEMPLR.html.

Transcription Factors↗
PubMed2026

Learning a pairwise epigenomic and transcription factor binding association score across the human genome.

MOTIVATION: Identifying pairwise associations between genomic loci is an important challenge for which large and diverse collections of epigenomic and transcription factor (TF) binding data can potentially be informative. RESULTS: We developed Learning Evidence of Pairwise Association from Epigenomic and TF binding data (LEPAE). LEPAE uses neural networks to quantify evidence of association for pairs of genomic windows from large-scale epigenomic and TF binding data along with distance information. We applied LEPAE using thousands of human datasets. We show using additional data that LEPAE captures biologically meaningful pairwise relationships between genomic loci, and we expect LEPAE scores to be a resource. AVAILABILITY AND IMPLEMENTATION: The LEPAE scores and the software are available at https://github.com/ernstlab/LEPAE.

Humans↗
PubMed2026

Genome-wide profiling of histone modifications and transcription factor binding at single-cell resolution by DeChIC-seq.

Mapping of protein-DNA interactions at single-cell resolution remains a central challenge in epigenomics, particularly for transcription factors (TFs), whose sparse binding limits reliable detection. Here, we establish DeChIC-seq (DNA Deaminase-based Chromatin Immuno-Conversion sequencing), a conversion-based strategy that uses a protein A-DddAtox fusion to directly record protein-DNA interactions by inducing localized C-to-U conversions near antibody-bound chromatin. Retaining genome-wide background sequence information without immunoprecipitation, DeChIC-seq enables profiling of histone modifications and sensitive detection of TF binding. Integration with single-cell whole-genome amplification extends DeChIC-seq to single-cell applications (scDeChIC-seq), enabling chromatin profiling of individual cells. Applied to mouse embryogenesis, scDeChIC-seq resolves lineage-specific chromatin states through profiling of H3K4me3, CTCF, and RAD21 and sensitively detects TF binding, including that of NR5A2, TFAP2C, and KLF5, from extremely limited blastomere inputs. This underscores its strong potential for detecting TF-binding sites in scarce biological samples. DeChIC-seq establishes a conversion-based framework for chromatin profiling that enables mechanistic dissection of TF-driven gene regulation across rare cells, developmental systems, and disease contexts.

Animals↗
PubMed2025

A genome-wide survey reveals a diverse array of enhancers coordinate the Drosophila innate immune response.

To defend against microbes, animals regulate a complex immune response. The Drosophila innate immune system deploys a large transcriptional induction of signaling proteins, antimicrobial effectors, and other critical immune factors. This transcriptional response is encoded in enhancers, cis-regulatory sequences that modulate gene expression by binding transcription factors (TFs). While enhancers and transcription factor binding sites (TFBS) have been identified for several immune responsive genes in Drosophila, most enhancers that regulate immune-induced genes are unknown. By identifying enhancers, we can understand how their composition controls expression and contributes to infection outcome. We employed STARR-seq (Self Transcribing Active Regulatory-Region sequencing) in a hemocyte-like cell line to identify immune-specific enhancers across the D. melanogaster genome and performed ATAC-seq in hemocytes extracted from adult flies to assess the chromatin state of these enhancers before and after immune stimulus. We identified thousands of enhancers responsive to IMD stimulation, one of the two primary immune signaling pathways in Drosophila. As expected, immune enhancers are enriched for motifs of Relish, an NF-κB factor, and Kay/Jra, a bZip heterodimer pair, involved in the Imd and JNK pathways respectively, compared to enhancers active in unstimulated cells. However, when grouping enhancers by their target gene's expression timing or functional role or by the enhancers' chromatin accessibility pre- or post-stimulus, different groups of TFBS motifs are enriched, suggesting distinct regulatory logic for different parts of the immune response. Identification and characterization of the diverse array of enhancers that regulate the innate immune response expands our understanding of how animals fight infections.

Drosophila immunity↗
PubMed2026

In silico analysis based on network pharmacology and biomolecular informatics to explore the mechanism of action of Erjing Pills (from Shengji Zonglu) in the treatment of leukotrichia.

This study aimed to explore the core active ingredients and potential molecular mechanisms of Erjing Pills, a prescription in the classic work of Traditional Chinese Medicine, "Shengji Zonglu," in the treatment of leukotrichia by utilizing network pharmacology and biomolecular docking techniques. The chemical components and potential targets of Chinese herbal medicines were analyzed through databases such as the Traditional Chinese Medicine Systems Pharmacology Database. The targets related to leukotrichia were collected using GeneCards. The intersection targets were obtained using RStudio. The protein-protein interaction (PPI) network map and the "drug-component-target-disease" visualization network were generated using Cytoscape and STRING to screen the core components and key targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were carried out using the Database for Annotation, Visualization and Integrated Discovery and RStudio. Finally, molecular docking verification was performed by AutoDock and PyMOL (Schrödinger LLC). The key active ingredients of Erjing Pills in the treatment of leukotrichia are β-sitosterol, quercetin, baicalein, and stigmasterol. The top 5 PPI core target proteins, in order, are AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta. The Gene Ontology enrichment analysis suggests that the biological processes mainly include responses to exogenous stimuli, membrane rafts, and DNA-binding transcription factor binding. The Kyoto Encyclopedia of Genes and Genomes pathways involve signal pathways such as lipid and atherosclerosis, hepatitis B, Kaposi sarcoma virus infection, chemical carcinogenesis, and human cytomegalovirus infection. The molecular docking results indicate that most of the main active ingredients in Erjing Pills have relatively stable binding activities with the key targets, such as AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta, in the PPI network. The active ingredients of Erjing Pills may interfere with the pathological process of leukotrichia by regulating key targets and signal pathways. This study provides a theoretical basis for the clinical application of Erjing Pills and indicates the direction for subsequent experimental research.

Drugs, Chinese Herbal↗
PubMed2026

Oriented binding of transcription factors to nucleosomes remodels chromatin at human promoters.

Transcription factors (TFs) can access nucleosomes via five distinct modes: gyre-spanning, periodic-binding, dyad-binding, and end-binding modes as well as an oriented binding mode, where the TF binding motif shows orientational preference relative to the nucleosome. Here, we report the first structure of an oriented TF:nucleosome complex, where two ELF2 proteins bind to a double motif located at superhelical location +4, unwinding four helical turns of DNA from the nucleosome. We further show that unlike previously described pioneer factors, ELF2 is able to occupy all of its unmethylated, high-affinity double motifs in vivo. Motifs of ELF2 and another oriented nucleosome binder, YY1, are highly enriched downstream of transcription start sites (TSSs) of highly expressed genes, with the motifs oriented in such a way that the TSS becomes accessible upon TF binding. Our results suggest that oriented binding may be generally important for high transcriptional activity.

Nucleosomes↗
PubMed2014

Genome-wide computational analysis reveals cardiomyocyte-specific transcriptional Cis-regulatory motifs that enable efficient cardiac gene therapy.

Gene therapy is a promising emerging therapeutic modality for the treatment of cardiovascular diseases and hereditary diseases that afflict the heart. Hence, there is a need to develop robust cardiac-specific expression modules that allow for stable expression of the gene of interest in cardiomyocytes. We therefore explored a new approach based on a genome-wide bioinformatics strategy that revealed novel cardiac-specific cis-acting regulatory modules (CS-CRMs). These transcriptional modules contained evolutionary-conserved clusters of putative transcription factor binding sites that correspond to a "molecular signature" associated with robust gene expression in the heart. We then validated these CS-CRMs in vivo using an adeno-associated viral vector serotype 9 that drives a reporter gene from a quintessential cardiac-specific α-myosin heavy chain promoter. Most de novo designed CS-CRMs resulted in a >10-fold increase in cardiac gene expression. The most robust CRMs enhanced cardiac-specific transcription 70- to 100-fold. Expression was sustained and restricted to cardiomyocytes. We then combined the most potent CS-CRM4 with a synthetic heart and muscle-specific promoter (SPc5-12) and obtained a significant 20-fold increase in cardiac gene expression compared to the cytomegalovirus promoter. This study underscores the potential of rational vector design to improve the robustness of cardiac gene therapy.

Animals↗
PubMed2025

Non-coding single-nucleotide and structural variants affecting the EYS putative promoter cause autosomal recessive retinitis pigmentosa.

PURPOSE: Variants in untranslated genomic regions are difficult to identify as pathogenic but are capable of causing disease by interfering with gene expression. This study aimed to characterize the effect of variants identified in the 5'-untranslated region of EYS in patients with autosomal recessive retinitis pigmentosa (RP). METHODS: Variant screening included gene panels, Sanger, exome, and genome sequencing. Functional validation included an electrophoretic mobility shift assay and various luciferase assays. RESULTS: Patients with RP from 6 EYS biallelic Arab-Muslim families harbored a 5' noncoding EYS variant, c.-453G>T, and 4 harbored a structural variant affecting the 5' noncoding exons. Electrophoretic mobility shift assay analysis revealed an effect on binding of transcription factors for c.-453G>T and a neighboring variant c.-454G>T. Dual luciferase assays using overexpression of various transcription factors showed distinct effects on expression. c.-453G>T was associated with higher luciferase expression with CRX overexpression and c.-454G>C with OTX2 overexpression. In addition, the 2 variants were found to influence translation by affecting upstream initiation codons. Interestingly, visual function of EYS RP patients who harbor c.-453G>T are better than those with biallelic null EYS variants. CONCLUSION: Our analysis revealed both single-nucleotide and structural variants in the EYS promoter as the cause of autosomal recessive RP. These variants may affect EYS expression via a dual mechanism by altering transcription factor binding affinity at the EYS promoter and by affecting upstream open reading frames.

Humans↗
PubMed2025

Enhancer-targeting CRISPR screens at coronary artery disease loci suggest shared mechanisms of disease risk.

To systematically identify causal genetic mechanisms that confer risk for coronary artery disease (CAD) in GWAS loci, we mapped genome-wide variant-to-enhancer-to-gene (V2E2G) links in vascular smooth muscle cells (SMC). Enhancers identified by active chromatin features, and further prioritized by base-resolution deep learning models of chromatin accessibility in 108 CAD loci, were studied with CRISPRi targeting and Direct-Capture Targeted Perturb-seq (DC-TAP-seq) evaluation of 470 genes. Seventy-six V2E2G links were identified for 59 candidate CAD genes representing gene programs including epithelial-mesenchymal transformation, ubiquitination, and protein folding as well as BMP and TGFB signaling. Similar methods employed with an independent focused screen targeting one candidate locus at 9p21.3 identified 10 enhancers regulating expression of multiple genes at this location. Detailed molecular studies revealed that two enhancers mediating transcription factor binding and transcriptional regulation contribute to ancestry-specific and sex-specific risk for CAD and the surrogate biomarker vascular calcification. Together, these studies advance our identification of GWAS CAD V2E2G links across the genome, and specific mechanisms of risk at the complex 9p21.3 locus.

Journal Article↗
PubMed2026

An expanded codebook of human transcription factor DNA-binding specificity.

Gene expression is regulated by transcription factors (TFs), which recognize specific DNA sequence motifs. Several hundred putative human TFs, identified mainly by an apparent DNA-binding domain, lack known binding motifs1. Furthermore, even for well-characterized TFs, it remains controversial the degree to which motifs accurately reflect binding sites in living cells2. Here we describe a systematic effort ('Codebook') to determine the sequence specificity of 332 putative and poorly characterized human TFs. More than 4,000 independent experiments, encompassing multiple in vitro and in vivo assays, produced motifs for just over half (177; 53%) of the TFs, of which most are associated with only a single protein. These results extend the vocabulary of sequence recognition encoded by human TFs by around 130 distinct motifs. Moreover, binding motifs identified in vitro are strongly enriched in cellular binding sites. Collectively, the data reveal tens of thousands of previously unknown, conserved and direct TF-binding sites across the human genome. These sites are concentrated in promoter regions and are predictive of gene expression. In summary, this new codebook provides an important step forward in decoding the human genome.

Humans↗
PubMed2026

Identification of Rare Noncoding Variants in Familial Nonmedullary Thyroid Carcinoma.

BACKGROUND: Familial nonmedullary thyroid carcinoma (FNMTC) occurs when three or more family members are affected by usually papillary thyroid carcinoma (PTC), the most common form of NMTC. While the heritability to NMTC is among the highest of all cancers, the genetic determinants among NMTC families are not well understood. Here, we aim to understand the contribution of rare noncoding germline variants in the etiology of FNMTC. METHODS: We previously reported whole-genome sequencing (WGS) and linkage analysis in 17 PTC families and reported on 41 protein-coding variants in 40 genes that cosegregated with PTC in 11 of the families. Herein, we further leveraged our WGS data to include noncoding variants in our analysis for all 17 families. We hypothesized that most of the pathogenic noncoding variants would be located in theoretical or empirically determined regulatory regions that demonstrate at a minimum, basal thyroid expression, a positive family linkage score, and co-segregation among PTC-affected individuals. To test this hypothesis, we adopted a unique filtering strategy to identify variants that occurred in known DNA elements and transcription factor binding sites, near regions known to impact on gene expression or splicing in thyroid tissue, and/or in characterized thyroid enhancers. We annotated variants using two analyses (ENCODE and transcription factor binding site) within the BasePlayer software. We separately analyzed (1) expression quantitative trait loci, (2) splicing quantitative trait loci, and (3) thyroid enhancers. We then ranked variants according to predicted pathogenicity and performed Sanger sequencing in all individuals of each family. RESULTS: In total, 121 variants were selected based on in-silico prediction and our custom ranking analysis in each pedigree. Of these, 56 variants showed cosegregation among all PTC-affected individuals and were absent from unaffected individuals. This included candidate variants from five of the six PTC families for whom no protein-coding variants were previously found. CONCLUSION: Our data suggest that noncoding variants are important in the etiology of FNMTC and provide a framework for identifying noncoding germline variants using a novel approach. Further studies are needed to functionally characterize these variants to better understand the molecular mechanism of their pathogenicity.

Humans↗
PubMed2023

Active learning of enhancer and silencer regulatory grammar in photoreceptors.

Cis-regulatory elements (CREs) direct gene expression in health and disease, and models that can accurately predict their activities from DNA sequences are crucial for biomedicine. Deep learning represents one emerging strategy to model the regulatory grammar that relates CRE sequence to function. However, these models require training data on a scale that exceeds the number of CREs in the genome. We address this problem using active machine learning to iteratively train models on multiple rounds of synthetic DNA sequences assayed in live mammalian retinas. During each round of training the model actively selects sequence perturbations to assay, thereby efficiently generating informative training data. We iteratively trained a model that predicts the activities of sequences containing binding motifs for the photoreceptor transcription factor Cone-rod homeobox (CRX) using an order of magnitude less training data than current approaches. The model's internal confidence estimates of its predictions are reliable guides for designing sequences with high activity. The model correctly identified critical sequence differences between active and inactive sequences with nearly identical transcription factor binding sites, and revealed order and spacing preferences for combinations of motifs. Our results establish active learning as an effective method to train accurate deep learning models of cis-regulatory function after exhausting naturally occurring training examples in the genome.

Journal Article↗
PubMed2025

Maternal Immune Activation Disrupts Epigenomic and Functional Maturation of Cortical Excitatory Neurons.

Elevated levels of maternal pro-inflammatory cytokines during gestation can disrupt offspring neural development, increasing the risk of neurodevelopmental disorders. We studied the effects of Poly(I:C)-induced maternal immune activation (PIC-MIA) during mid-gestation on developing cortical excitatory neurons' DNA methylation and transcriptome. PIC-MIA disrupted the developmental regulation of synapse-related genes and of genes implicated in autism spectrum disorders. Genomic regions that gain or lose DNA methylation during normal development were altered following PIC-MIA, including neurodevelopmental transcription factor binding sites. The DNA methylation and transcriptional changes were consistent with a delay in excitatory neuron maturation. Whole-cell recordings showed that PIC-MIA preferentially altered the physiological development of layer 5 excitatory neurons. Taken together, present results suggest that alterations in the epigenome, through the disruption of circuit formation, may drive the long-term consequences of maternal infection during gestation.

DNA methylation↗