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Functional divergence of transcription factors (TFs) has driven cellular and organismal complexity throughout evolution, but its mechanistic drivers remain poorly understood. Here we test for new mechanisms using CORONA (CNA) and PHABULOSA (PHB), two functionally diverged paralogs in the CLASS III HOMEODOMAIN LEUCINE ZIPPER (HD-ZIPIII) family of TFs. We show that virtually all genes bound by PHB ( ~ 99%) are also bound by CNA, ruling out occupation of distinct sets of genes as a mechanism of functional divergence. Further, genes bound and regulated by both paralogs are almost always regulated in the same direction, ruling out opposite regulation of shared targets as a mechanistic driver. Functional divergence of CNA and PHB instead results from differential usage of shared binding sites, with hundreds of uniquely regulated genes emerging from a commonly bound genetic network. Regulation of a given gene by CNA or PHB is thus a function of whether a bound site is considered 'responsive' versus 'non-responsive' by each paralog. Discrimination between responsive and non-responsive sites is controlled, at least in part, by their lipid binding START domain. This suggests a model in which HD-ZIPIII TFs use information integrated by their START domain to generate paralog-specific transcriptional outcomes from a shared network architecture. Taken together, our study identifies a mechanism of HD-ZIPIII TF paralog divergence and proposes the ubiquitously distributed START evolutionary module as a driver of functional divergence., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs-in our case NFkB-at the protein level., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Gerbaldo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Many enhancers control gene expression by assembling regulatory factor clusters, also referred to as condensates. This process is vital for facilitating enhancer communication and establishing cellular identity. However, how DNA sequence and transcription factor (TF) binding instruct the formation of high regulatory factor environments remains poorly understood. Here we developed a new approach leveraging enhancer-centric chromatin accessibility quantitative trait loci (caQTLs) to nominate regulatory factor clusters genome-wide. By analyzing TF-binding signatures within the context of caQTLs and comparing episomal versus endogenous enhancer activities, we discovered a class of regulators, 'context-only' TFs, that amplify the activity of cell type-specific caQTL-binding TFs, that is, 'context-initiator' TFs. Similar to super-enhancers, enhancers enriched for context-only TF-binding sites display high coactivator binding and sensitivity to bromodomain-inhibiting molecules. We further show that binding sites for context-only and context-initiator TFs underlie enhancer coordination, providing a mechanistic rationale for how a loose TF syntax confers regulatory specificity., (© 2024. The Author(s).)

Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, a novel Nucleotide-Level Deep Neural Network (NLDNN) is first proposed to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task, which takes DNA sequences as input and directly predicts experimental coverage values. Beyond predictive performance, it also assesses model performance by locating potential TF binding regions, discriminating TF-specific single-nucleotide polymorphisms (SNPs), and identifying causal disease-associated SNPs. The experimental results show that NLDNN outperforms the competing methods in these tasks. Then, a dual-path framework is designed for adversarial training of NLDNN to further improve the cross-species prediction performance by pulling the domain space of human and mouse species closer. Through comparison and analysis, it finds that adversarial training not only can improve the cross-species prediction performance between humans and mice but also enhance the ability to locate TF binding regions and discriminate TF-specific SNPs. By visualizing the predictions, it is figured out that the framework corrects some mispredictions by amplifying the coverage values of incorrectly predicted peaks., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Transposable elements (TEs) comprise ~50% of our genome, but knowledge of how TEs affect genome evolution remains incomplete. Leveraging ENCODE4 data, we provide the most comprehensive study to date of TE contributions to the regulatory genome. We find 236,181 (~25%) human candidate cis-regulatory elements (cCREs) are TE-derived, with over 90% lineage-specific since the human-mouse split, accounting for 8-36% of lineage-specific cCREs. Except for SINEs, cCRE-associated transcription factor (TF) motifs in TEs are derived from ancestral TE sequence more than expected by chance. We show that TEs may adopt similar regulatory activities of elements near their integration site. Since human-mouse divergence, TEs have contributed 3-56% of TF binding site turnover events across 30 examined TFs. Finally, TE-derived cCREs are similar to non-TE cCREs in terms of MPRA activity and GWAS variant enrichment. Overall, our results substantiate the notion that TEs have played an important role in shaping the human regulatory genome., (© 2024. The Author(s).)

Cell-type-Specific Chromatin Loops (CSCLs) are crucial for gene regulation and cell fate determination. However, the mechanisms governing their establishment remain elusive. Here, we present SpecLoop, a network regularization-based machine learning framework, to investigate the role of transcription factors (TFs) cooperation in CSCL formation. SpecLoop integrates multi-omics data, including gene expression, chromatin accessibility, sequence, protein-protein interaction, and TF binding motif data, to predict CSCLs and identify TF cooperations. Using high resolution Hi-C data as the gold standard, SpecLoop accurately predicts CSCL in GM12878, IMR90, HeLa-S3, K562, HUVEC, HMEC, and NHEK seven cell types, with the AUROC values ranging from 0.8645 to 0.9852 and AUPR values ranging from 0.8654 to 0.9734. Notably SpecLoop demonstrates improved accuracy in predicting long-distance CSCLs and identifies TF complexes with strong predictive ability. Our study systematically explores the TFs and TF pairs associated with CSCL through effective integration of diverse omics data. SpecLoop is freely available at https://github.com/AMSSwanglab/SpecLoop., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models., (© The Author(s) 2024. Published by Oxford University Press.)

The effects of transcription factor binding sites (TFBSs) on the activity of a cis-regulatory element (CRE) depend on the local sequence context. In rod photoreceptors, binding sites for the transcription factor (TF) Cone-rod homeobox (CRX) occur in both enhancers and silencers, but the sequence context that determines whether CRX binding sites contribute to activation or repression of transcription is not understood. To investigate the context-dependent activity of CRX sites, we fit neural network-based models to the activities of synthetic CREs composed of photoreceptor TFBSs. The models revealed that CRX binding sites consistently make positive, independent contributions to CRE activity, while negative homotypic interactions between sites cause CREs composed of multiple CRX sites to function as silencers. The effects of negative homotypic interactions can be overcome by the presence of other TFBSs that either interact cooperatively with CRX sites or make independent positive contributions to activity. The context-dependent activity of CRX sites is thus determined by the balance between positive heterotypic interactions, independent contributions of TFBSs, and negative homotypic interactions. Our findings explain observed patterns of activity among genomic CRX-bound enhancers and silencers, and suggest that enhancers may require diverse TFBSs to overcome negative homotypic interactions between TFBSs., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: BAC is on the scientific advisory board of Patch Biosciences. Neither any reagent nor any funding from this organizations was used in this study. Other co-authors have no competing interests to declare., (Copyright: © 2024 Loell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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. Next, the motifs underwent human expert curation to stratify distinct motif subtypes and remove non-informative patterns and common artifacts. Finally, the curated subset of 100 thousand motifs was supplied to the automated benchmarking to select the best-performing motifs for each transcription factor. The resulting HOCOMOCO v12 core collection contains 1443 verified position weight matrices, including distinct subtypes of DNA binding motifs for particular transcription factors. In addition to the core collection, HOCOMOCO v12 provides motif sets optimized for the recognition of binding sites in vivo and in vitro, and for annotation of regulatory sequence variants. HOCOMOCO is available at https://hocomoco12.autosome.org and https://hocomoco.autosome.org., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Evolution of gene expression mediated by cis-regulatory changes is thought to be an important contributor to organismal adaptation, but identifying adaptive cis-regulatory changes is challenging due to the difficulty in knowing the expectation under no positive selection. A new approach for detecting positive selection on transcription factor binding sites (TFBSs) was recently developed, thanks to the application of machine learning in predicting transcription factor (TF) binding affinities of DNA sequences. Given a TFBS sequence from a focal species and the corresponding inferred ancestral sequence that differs from the former at n sites, one can predict the TF-binding affinities of many n-step mutational neighbors of the ancestral sequence and obtain a null distribution of the derived binding affinity, which allows testing whether the binding affinity of the real derived sequence deviates significantly from the null distribution. Applying this test genomically to all experimentally identified binding sites of 3 TFs in humans, a recent study reported positive selection for elevated binding affinities of TFBSs. Here, we show that this genomic test suffers from an ascertainment bias because, even in the absence of positive selection for strengthened binding, the binding affinities of known human TFBSs are more likely to have increased than decreased in evolution. We demonstrate by computer simulation that this bias inflates the false positive rate of the selection test. We propose several methods to mitigate the ascertainment bias and show that almost all previously reported positive selection signals disappear when these methods are applied., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Transcription factors (TFs) bind to specific regions of DNA known as transcription factor binding sites (TFBSs) and modulate gene expression by interacting with the transcriptional machinery. TFBSs are typically located upstream of target genes, within a few thousand base pairs of the transcription start site. The binding of TFs to TFBSs influences the recruitment of the transcriptional machinery, thereby regulating gene transcription in a precise and specific manner. This chapter provides practical examples and case studies demonstrating the extraction of upstream gene regions from the genome, identification of TFBSs using PWMEnrich R/Bioconductor package, interpretation of results, and preparation of publication-ready figures and tables. The EOMES promoter is used as a case study for single DNA sequence analysis, revealing potential regulation by the LHX9-FOXP1 complex during embryonic development. Additionally, an example is presented on how to investigate TFBSs in the upstream regions of a group of genes, using a case study of differentially expressed genes in response to human parainfluenza virus type 1 (HPIV1) infection and interferon-beta. Key regulators identified in this context include the STAT1:STAT2 heterodimer and interferon regulatory factor family proteins. The presented protocol is designed to be accessible to individuals with basic computer literacy. Understanding the interactions between TFs and TFBSs provides insights into the complex transcriptional regulatory networks that govern gene expression, with broad implications for several fields such as developmental biology, immunology, and disease research., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Transcription factor binding across the genome is regulated by DNA sequence and chromatin features. However, it is not yet possible to quantify the impact of chromatin context on transcription factor binding affinities. Here, we report a method called binding affinities to native chromatin by sequencing (BANC-seq) to determine absolute apparent binding affinities of transcription factors to native DNA across the genome. In BANC-seq, a concentration range of a tagged transcription factor is added to isolated nuclei. Concentration-dependent binding is then measured per sample to quantify apparent binding affinities across the genome. BANC-seq adds a quantitative dimension to transcription factor biology, which enables stratification of genomic targets based on transcription factor concentration and prediction of transcription factor binding sites under non-physiological conditions, such as disease-associated overexpression of (onco)genes. Notably, whereas consensus DNA binding motifs for transcription factors are important to establish high-affinity binding sites, these motifs are not always strictly required to generate nanomolar-affinity interactions in the genome., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Transcription factors (TF) play a crucial role in the regulation of gene transcription; alterations of their activity and binding to DNA areas are strongly involved in cancer and other disease onset and development. For proper biomedical investigation, it is hence essential to correctly trace TF dense DNA areas, having multiple bindings of distinct factors, and select DNA high occupancy target (HOT) zones, showing the highest accumulation of such bindings. Indeed, systematic and replicable analysis of HOT zones in a large variety of cells and tissues would allow further understanding of their characteristics and could clarify their functional role., Results: Here, we propose, thoroughly explain and discuss a full computational procedure to study in-depth DNA dense areas of transcription factor accumulation and identify HOT zones. This methodology, developed as a computationally efficient parametric algorithm implemented in an R/Bioconductor package, uses a systematic approach with two alternative methods to examine transcription factor bindings and provide comparative and fully-reproducible assessments. It offers different resolutions by introducing three distinct types of accumulation, which can analyze DNA from single-base to region-oriented levels, and a moving window, which can estimate the influence of the neighborhood for each DNA base under exam., Conclusions: We quantitatively assessed the full procedure by using our implemented software package, named TFHAZ, in two example applications of biological interest, proving its full reliability and relevance., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Background: Transcription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored., Results: Here, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types., Conclusions: Our study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Transcription factors (TFs) are proteins that affect gene expression by binding to regulatory regions of DNA in a sequence specific manner. The binding of TFs to DNA is controlled by many factors, including the DNA sequence, concentration of TF, chromatin accessibility and co-factors. Here, we systematically investigated the binding mechanism of hundreds of TFs by analysing ChIP-seq data with our explainable statistical model, ChIPanalyser. This tool uses as inputs the DNA sequence binding motif; the capacity to distinguish between strong and weak binding sites; the concentration of TF; and chromatin accessibility. We found that approximately one third of TFs are predicted to bind the genome in a DNA accessibility independent fashion, which includes TFs that can open the chromatin, their co-factors and TFs with similar motifs. Our model predicted this to be the case when the TF binds to its strongest binding regions in the genome, and only a small number of TFs have the capacity to bind dense chromatin at their weakest binding regions, such as CTCF, USF2 and CEBPB. Our study demonstrated that the binding of hundreds of human and mouse TFs is predicted by ChIPanalyser with high accuracy and showed that many TFs can bind dense chromatin., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Human nuclear receptors (NRs) are a superfamily of ligand-responsive transcription factors that have central roles in cellular function. Their malfunction is linked to numerous diseases, and the ability to modulate their activity with synthetic ligands has yielded 16% of all FDA-approved drugs. NRs regulate distinct gene networks, however they often function from genomic sites that lack known binding motifs. Here, to annotate genomic binding sites of known and unexamined NRs more accurately, we use high-throughput SELEX to comprehensively map DNA binding site preferences of all full-length human NRs, in complex with their ligands. Furthermore, to identify non-obvious binding sites buried in DNA-protein interactomes, we develop MinSeq Find, a search algorithm based on the MinTerm concept from electrical engineering and digital systems design. The resulting MinTerm sequence set (MinSeqs) reveal a constellation of binding sites that more effectively annotate NR-binding profiles in cells. MinSeqs also unmask binding sites created or disrupted by 52,106 single-nucleotide polymorphisms associated with human diseases. By implicating druggable NRs as hidden drivers of multiple human diseases, our results not only reveal new biological roles of NRs, but they also provide a resource for drug-repurposing and precision medicine., (© 2023. The Author(s).)

Transcription factors (TF) recognize specific motifs in the genome that are typically 6-12 bp long to regulate various aspects of the cellular machinery. Presence of binding motifs and favorable genome accessibility are key drivers for a consistent TF-DNA interaction. Although these pre-requisites may occur thousands of times in the genome, there seems to be a high degree of selectivity for the sites that are actually bound. Here, we present a deep-learning framework that identifies and characterizes the upstream and downstream genetic elements to the binding motif, for their role in enforcing the mentioned selectivity. The proposed framework is based on an interpretable recurrent neural network architecture that enables for the relative analysis of sequence context features. We apply the framework to model twenty-six transcription factors and score the TF-DNA binding at a base-pair resolution. We find significant differences in activations of DNA context features for bound and unbound sequences. In addition to standardized evaluation protocols, we offer outstanding interpretability that enables us to identify and annotate DNA sequence with possible elements that modulate TF-DNA binding. Also, differences in data processing have a huge influence on the overall model performance. Overall, the proposed framework allows for novel insights on the non-coding genetic elements and their role in facilitating a stable TF-DNA interaction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The gene regulatory code and grammar remain largely unknown, precluding our ability to link phenotype to genotype in regulatory sequences. Here, using a massively parallel reporter assay (MPRA) of 209,440 sequences, we examine all possible pair and triplet combinations, permutations and orientations of eighteen liver-associated transcription factor binding sites (TFBS). We find that TFBS orientation and order have a major effect on gene regulatory activity. Corroborating these results with genomic analyses, we find clear human promoter TFBS orientation biases and similar TFBS orientation and order transcriptional effects in an MPRA that tested 164,307 liver candidate regulatory elements. Additionally, by adding TFBS orientation to a model that predicts expression from sequence we improve performance by 7.7%. Collectively, our results show that TFBS orientation and order have a significant effect on gene regulatory activity and need to be considered when analyzing the functional effect of variants on the activity of these sequences., (© 2023. The Author(s).)

Somatic mutations are highly enriched at transcription factor (TF) binding sites, with the strongest trend being observed for ultraviolet light (UV)-induced mutations in melanomas. One of the main mechanisms proposed for this hypermutation pattern is the inefficient repair of UV lesions within TF-binding sites, caused by competition between TFs bound to these lesions and the DNA repair proteins that must recognize the lesions to initiate repair. However, TF binding to UV-irradiated DNA is poorly characterized, and it is unclear whether TFs maintain specificity for their DNA sites after UV exposure. We developed UV-Bind, a high-throughput approach to investigate the impact of UV irradiation on protein-DNA binding specificity. We applied UV-Bind to ten TFs from eight structural families, and found that UV lesions significantly altered the DNA-binding preferences of all the TFs tested. The main effect was a decrease in binding specificity, but the precise effects and their magnitude differ across factors. Importantly, we found that despite the overall reduction in DNA-binding specificity in the presence of UV lesions, TFs can still compete with repair proteins for lesion recognition, in a manner consistent with their specificity for UV-irradiated DNA. In addition, for a subset of TFs, we identified a surprising but reproducible effect at certain nonconsensus DNA sequences, where UV irradiation leads to a high increase in the level of TF binding. These changes in DNA-binding specificity after UV irradiation, at both consensus and nonconsensus sites, have important implications for the regulatory and mutagenic roles of TFs in the cell.

Recent comparative genomic studies have identified many human accelerated elements (HARs) with elevated substitution rates in the human lineage. However, it remains unknown to what extent transcription factor binding sites (TFBSs) are under accelerated evolution in humans and other primates. Here, we introduce two pooling-based phylogenetic methods with dramatically enhanced sensitivity to examine accelerated evolution in TFBSs. Using these new methods, we show that more than 6000 TFBSs annotated in the human genome have experienced accelerated evolution in Hominini, apes, and Old World monkeys. Although these TFBSs individually show relatively weak signals of accelerated evolution, they collectively are more abundant than HARs. Also, we show that accelerated evolution in Pol III binding sites may be driven by lineage-specific positive selection, whereas accelerated evolution in other TFBSs might be driven by nonadaptive evolutionary forces. Finally, the accelerated TFBSs are enriched around developmental genes, suggesting that accelerated evolution in TFBSs may drive the divergence of developmental processes between primates., (© 2023. The Author(s).)

Here we describe a competitive genome editing method that measures the effect of mutations on molecular functions, based on precision CRISPR editing using template libraries with either the original or altered sequence, and a sequence tag, enabling direct comparison between original and mutated cells. Using the example of the MYC oncogene, we identify important transcriptional targets and show that E-box mutations at MYC target gene promoters reduce cellular fitness., (© 2022. The Author(s).)

Motivation: Cell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TF's intrinsic sequence preferences, cooperative interactions with co-factors, cell-type-specific chromatin landscapes and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied., Results: In this article, we propose two computational approaches for predicting and characterizing cell-type-specific and shared binding sites by integrating multiple types of features, in which one is based on XGBoost and another is based on convolutional neural network (CNN). To validate the performance of our proposed approaches, ChIP-seq datasets of 10 binding factors were collected from the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines, each of which was further categorized into cell-type-specific (GM12878- and K562-specific) and shared binding sites. Then, multiple types of features for these binding sites were integrated to train the XGBoost- and CNN-based models. Experimental results show that our proposed approaches significantly outperform other competing methods on three classification tasks. Moreover, we identified independent feature contributions for cell-type-specific and shared sites through SHAP values and explored the ability of the CNN-based model to predict cell-type-specific and shared binding sites by excluding or including DNase signals. Furthermore, we investigated the generalization ability of our proposed approaches to different binding factors in the same cellular environment., Availability and Implementation: The source code is available at: https://github.com/turningpoint1988/CSSBS., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Background: Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results., Results: We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences., Conclusion: TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Parab et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Cell type-specific gene expression relies on transcription factors (TFs) binding DNA sequence motifs embedded in chromatin. Understanding how motifs are accessed in chromatin is crucial to comprehend differential transcriptional responses and the phenotypic impact of sequence variation. Chromatin obstacles to TF binding range from DNA methylation to restriction of DNA access by nucleosomes depending on their position, composition and modification. In vivo and in vitro approaches now enable the study of TF binding in chromatin at unprecedented resolution. Emerging insights suggest that TFs vary in their ability to navigate chromatin states. However, it remains challenging to link binding and transcriptional outcomes to molecular characteristics of TFs or the local chromatin substrate. Here, we discuss our current understanding of how TFs access DNA in chromatin and novel techniques and directions towards a better understanding of this critical step in genome regulation., (© 2022. Springer Nature Limited.)

Sequence-specific recognition of transcription factor (TF) binding motifs in the target site of DNA over the vast amount of non-target DNA is of primary importance for the transcriptional regulation of gene expression by the TFs. Binding of TFs to the target site of DNA relies not only on the direct contact formation but also on the structural and conformational features of DNA. Recognition of DNA structural features or shape readout by proteins is an important factor in the context of TF-DNA interaction. Based on the atomistic molecular simulation, here we report the sequence-dependent unique structural features, solvation, and ion-binding properties of biologically relevant AT- and GC-rich human TF binding motifs in DNA. Counterion and water distribution around the motif is found to be sensitive to the motif sequence, which is accompanied with the DNA shape features. The motif sequence affects the electrostatic potential along the grooves, and cytosine methylation alters the DNA shape features. Characteristic solvation properties of TF binding motif DNA fragments infer that an ionic environment and hydration influences are essential to describe TF-DNA interactions.

Discovery of transcription factor binding sites (TFBSs) is of primary importance for understanding the underlying binding mechanic and gene regulation process. Growing evidence indicates that apart from the primary DNA sequences, DNA shape landscape has a significant influence on transcription factor binding preference. To effectively model the co-influence of sequence and shape features, we emphasize the importance of position information of sequence motif and shape pattern. In this paper, we propose a novel deep learning-based architecture, named hybridShape eDeepCNN, for TFBS prediction which integrates DNA sequence and shape information in a spatially aligned manner. Our model utilizes the power of the multi-layer convolutional neural network and constructs an independent subnetwork to adapt for the distinct data distribution of heterogeneous features. Besides, we explore the usage of continuous embedding vectors as the representation of DNA sequences. Based on the experiments on 20 in-vitro datasets derived from universal protein binding microarrays (uPBMs), we demonstrate the superiority of our proposed method and validate the underlying design logic.

The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generation sequencing data have been developed to improve the performance of pattern matching. However, such tools have not yet been comprehensively evaluated in plants. Hence, we developed a new streamlined method aiming at predicting CREs and target genes of plant TFs in specific organs or conditions. Our approach implements a supervised machine learning strategy, which allows decision rule models to be learnt using TF ChIP-chip/seq experimental data. Different layers of genomic features were integrated in predictive models: the position on the gene, the DNA sequence conservation, the chromatin state and various CRE footprints. Among the tested features, the chromatin features were crucial for improving the accuracy of the method. Furthermore, we evaluated the transferability of predictive models across TFs, organs and species. Finally, we validated our method by correctly inferring the target genes of key TFs controlling metabolite biosynthesis at the organ level in Arabidopsis. We developed a tool-Wimtrap-to reproduce our approach in plant species and conditions/organs for which ChIP-chip/seq data are available. Wimtrap is a user-friendly R package that supports an R Shiny web interface and is provided with pre-built models that can be used to quickly get predictions of CREs and TF gene targets in different organs or conditions in Arabidopsis thaliana, Solanum lycopersicum, Oryza sativa and Zea mays., (© The Author(s) 2022. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Transcription factors (TFs) regulate transcription by binding to the specific sequences at the promoter region. However, the mechanisms and functions of TFs binding within the coding sequences (CDS) remain largely elusive in prokaryotes. To this end, we collected 409 data sets for bacterial TFs, including 104 chromatin immunoprecipitation sequencing (ChIP-seq) assays and 305 data sets from the systematic evolution of ligands by exponential enrichment (SELEX) in seven model bacteria. Interestingly, these TFs displayed the same binding capabilities for both coding and intergenic regions. Subsequent biochemical and genetic experiments demonstrated that several TFs bound to the coding regions and regulated the transcription of the binding or adjacent genes. Strand-specific RNA sequencing revealed that these CDS-binding TFs regulated the activity of the cryptic promoters, resulting in the altered transcription of the corresponding antisense RNA. TF RhpR hindered the transcriptional elongation of a subgenic transcript within a CDS. A ChIP-seq and Ribo-seq coanalysis revealed that RhpR influenced the translational efficiency of binding genes. Taken together, the present study reveals three regulatory mechanisms of CDS-bound TFs within individual genes, operons, and antisense RNAs, which demonstrate the variability of the regulatory mechanisms of TFs and expand upon the complexity of bacterial transcriptomes. IMPORTANCE Although bacterial TFs regulate transcription by binding to specific sequences at the promoter region, little is known about the mechanisms and functions of TFs binding within the CDS. In this study, we show that bacterial TFs have same binding pattern in both CDS and promoter regions, and we reveal three regulatory mechanisms of CDS-bound TF that together demonstrate the complexity of the regulatory mechanisms of bacterial TFs and the wide spread of internal cryptic promoters in CDS.

Chromatin features can reveal tissue-specific TF-DNA binding, which leads to a better understanding of many critical physiological processes. Accurately identifying TF-DNA bindings and constructing their relationships with chromatin features is a long-standing goal in the bioinformatic field. However, this has remained elusive due to the complex binding mechanisms and heterogeneity among inputs. Here, we have developed the GHTNet (General Hybrid Transformer Network), a transformer-based model to predict TF-DNA binding specificity. The GHTNet decodes the relationship between tissue-specific TF-DNA binding and chromatin features via a specific input scheme of alternative inputs and reveals important gene regions and tissue-specific motifs. Our experiments show that the GHTNet has excellent performance, achieving about a 5% absolute improvement over existing methods. The TF-DNA binding mechanism analysis shows that the importance of TF-DNA binding features varies across tissues. The best predictor is based on the DNA sequence, followed by epigenomics and shape. In addition, cross-species studies address the limited data, thus providing new ideas in this case. Moreover, the GHTNet is applied to interpret the relationship among TFs, chromatin features, and diseases associated with AD46 tissue. This paper demonstrates that the GHTNet is an accurate and robust framework for deciphering tissue-specific TF-DNA binding and interpreting non-coding regions., Competing Interests: The authors declare no conflict of interest.

The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. On the task of retrieving the motifs of expressed TFs in a given cell type, BindVAE is competitive with existing motif discovery approaches., (© 2022. The Author(s).)

While great advances in predicting the effects of coding variants have been made, the assessment of non-coding variants remains challenging. This is especially problematic for variants within promoter regions which can lead to over-expression of a gene or reduce or even abolish its expression. The binding of transcription factors to the DNA can be predicted using position weight matrices (PWMs). More recently, transcription factor flexible models (TFFMs) have been introduced and shown to be more accurate than PWMs. TFFMs are based on hidden Markov models and can account for complex positional dependencies. Our new web-based application FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants affect the binding of 1387 different human transcription factors. For each variant and transcription factor, the software combines the results of different models for a final prediction of the resulting binding-affinity change. The software is written in C++ for speed but variants can be entered through a web interface. Alternatively, a VCF file can be uploaded to assess variants identified by high-throughput sequencing. The search can be restricted to variants in the vicinity of candidate genes. FABIAN-variant is available freely at https://www.genecascade.org/fabian/., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Chromatin immunoprecipitation (ChIP) is an important technique for characterizing protein-DNA binding in vivo. One drawback of ChIP-based techniques is the lack of cell type-specificity when profiling complex tissues. To overcome this limitation, we developed SpyChIP to identify cell type-specific transcription factor (TF) binding sites in native physiological contexts without tissue dissociation or nuclei sorting. SpyChIP takes advantage of a specific covalent isopeptide bond that rapidly forms between the 15-amino acid SpyTag and the 17-kDa protein SpyCatcher. In SpyChIP, the target TF is fused with SpyTag by genome engineering, and an epitope tagged SpyCatcher is expressed in cell populations of interest, where it covalently binds to SpyTag-TF. Cell type-specific ChIP is obtained by immunoprecipitating chromatin prepared from whole tissues using antibodies directed against the epitope-tagged SpyCatcher. Using SpyChIP, we identified the genome-wide binding profiles of the Hox protein Ultrabithorax (Ubx) in two distinct cell types of the Drosophila haltere imaginal disc. Our results revealed extensive region-specific Ubx-DNA binding events, highlighting the significance of cell type-specific ChIP and the limitations of whole-tissue ChIP approaches. Analysis of Ubx::SpyChIP results provided insights into the relationship between chromatin accessibility and Ubx-DNA binding, as well as different mechanisms Ubx employs to regulate its downstream cis -regulatory modules. In addition to SpyChIP, we suggest that SpyTag-SpyCatcher technology, as well as other protein pairs that form covalent isopeptide bonds, will facilitate many additional in vivo applications that were previously impractical.

Background: Transcription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive., Methodology/principal Findings: This study presents a computational tool, named "Grit", for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student's t-test statistical method that detects high-scoring binding sites utilizing conservation information among species. The program performs sequence scanning at a rate of 3.2 Mbp/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It significantly outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets., Significance: Grit is a good alternative to current available motif scanning tools., Competing Interests: The authors have declared that no competing interests exist.

Background: Transcription factors (TFs) bind regulatory DNA regions with sequence specificity, form complexes and regulate gene expression. In cooperative TF-TF binding, two transcription factors bind onto a shared DNA binding site as a pair. Previous work has demonstrated pairwise TF-TF-DNA interactions with position weight matrices (PWMs), which may however not sufficiently take into account the complexity and flexibility of pairwise binding., Results: We propose two random forest (RF) methods for joint TF-TF binding site prediction: ComBind and JointRF. We train models with previously published large-scale CAP-SELEX DNA libraries, which comprise DNA sequences enriched for binding of a selected TF pair. JointRF builds a random forest with sub-sequences selected from CAP-SELEX DNA reads with previously proposed pairwise PWM. JointRF outperforms (area under receiver operating characteristics curve, AUROC, 0.75) the current state-of-the-art method i.e. orientation and spacing specific pairwise PWMs (AUROC 0.59). Thus, JointRF may be utilized to improve prediction accuracy for pre-determined binding preferences. However, pairwise TF binding is currently considered flexible; a pair may bind DNA with different orientations and amounts of dinucleotide gaps or overlap between the two motifs. Thus, we developed ComBind, which utilizes random forests by considering simultaneously multiple orientations and spacings of the two factors. Our approach outperforms (AUROC 0.78) PWMs, as well as JointRF (p<0.00195). ComBind provides an approach for predicting TF-TF binding sites without prior knowledge on pairwise binding preferences. However, more research is needed to assess ComBind eligibility for practical applications., Conclusions: Random forest is well suited for modeling pairwise TF-TF-DNA binding specificities, and ComBind provides an improvement to pairwise binding site prediction accuracy., (© 2022. The Author(s).)

Over a thousand different transcription factors (TFs) bind with varying occupancy across the human genome. Chromatin immunoprecipitation (ChIP) can assay occupancy genome-wide, but only one TF at a time, limiting our ability to comprehensively observe the TF occupancy landscape, let alone quantify how it changes across conditions. We developed TF occupancy profiler (TOP), a Bayesian hierarchical regression framework, to profile genome-wide quantitative occupancy of numerous TFs using data from a single chromatin accessibility experiment (DNase- or ATAC-seq). TOP is supervised, and its hierarchical structure allows it to predict the occupancy of any sequence-specific TF, even those never assayed with ChIP. We used TOP to profile the quantitative occupancy of hundreds of sequence-specific TFs at sites throughout the genome and examined how their occupancies changed in multiple contexts: in approximately 200 human cell types, through 12 h of exposure to different hormones, and across the genetic backgrounds of 70 individuals. TOP enables cost-effective exploration of quantitative changes in the landscape of TF binding., (© 2022 Luo et al.; Published by Cold Spring Harbor Laboratory Press.)

Tudor domain containing 1 (TDRD1) is a member of the TDRD family and plays an important role in embryogenesis and gametogenesis. A detailed study of the characteristics of chicken TDRD1 can lay a foundation for the study of chicken spermatogonia stem cell formation and spermatogenesis. We cloned 2117 bp upstream fragment of TDRD1 promoter and constructed a series of recombinant vectors with different length deletions. The dual-luciferase experiments reveal that the upstream region of - 161 to 0 bp was its core transcription promoter region. Bioinformatics analysis predicted the possible binding of Transcription Factor 7 Like 2 (TCF7L2) and Zinc Finger E-Box-Binding Homeobox 1(ZEB1) transcription factors in the core region. The transcriptional activity of TDRD1 was significantly decreased after mutation of TCF7L2-binding site, while that of TDRD1 was significantly increased after mutation of ZEB1-binding site. Further, ChIP experiments verified that TCF7L2 enriched in the TDRD1 core transcriptional initiation region, suggesting that TCF7L2 and ZEB1 play an important role in the regulation of TDRD1. In summary, the region from - 161 to 0 bp is the core promoter region of TDRD1; ZEB1 and TCF7L2 bind to the TDRD1 promoter region and TCF7L2 activates the transcription of TDRD1 gene., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters., Results: A new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach., Conclusion: This work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq., (© 2022. The Author(s).)

DNA can determine where and when genes are expressed, but the full set of sequence determinants that control gene expression is unknown. Here, we measured the transcriptional activity of DNA sequences that represent an ~100 times larger sequence space than the human genome using massively parallel reporter assays (MPRAs). Machine learning models revealed that transcription factors (TFs) generally act in an additive manner with weak grammar and that most enhancers increase expression from a promoter by a mechanism that does not appear to involve specific TF-TF interactions. The enhancers themselves can be classified into three types: classical, closed chromatin and chromatin dependent. We also show that few TFs are strongly active in a cell, with most activities being similar between cell types. Individual TFs can have multiple gene regulatory activities, including chromatin opening and enhancing, promoting and determining transcription start site (TSS) activity, consistent with the view that the TF binding motif is the key atomic unit of gene expression., (© 2022. The Author(s).)

Deep learning has been successfully applied to surprisingly different domains. Researchers and practitioners are employing trained deep learning models to enrich our knowledge. Transcription factors (TFs)are essential for regulating gene expression in all organisms by binding to specific DNA sequences. Here, we designed a deep learning model named SemanticCS (Semantic ChIP-seq)to predict TF binding specificities. We trained our learning model on an ensemble of ChIP-seq datasets (Multi-TF-cell)to learn useful intermediate features across multiple TFs and cells. To interpret these feature vectors, visualization analysis was used. Our results indicate that these learned representations can be used to train shallow machines for other tasks. Using diverse experimental data and evaluation metrics, we show that SemanticCS outperforms other popular methods. In addition, from experimental data, SemanticCS can help to identify the substitutions that cause regulatory abnormalities and to evaluate the effect of substitutions on the binding affinity for the RXR transcription factor. The online server for SemanticCS is freely available at http://qianglab.scst.suda.edu.cn/semanticCS/.

The nuclear receptors (NRs) are an evolutionarily related family of transcription factors, which share certain common structural characteristics and regulate the expressions of various genes by recognizing different response elements. NRs play important roles in cell differentiation, proliferation, survival and apoptosis, rendering them indispensable in many physiological activities including growth and metabolism. As a result, dysfunctions of NRs are closely related to a variety of diseases, such as diabetes, obesity, infertility, inflammation, the Alzheimer's disease, cardiovascular diseases, prostate and breast cancers. Meanwhile, small-molecule drugs directly targeting NRs have been widely used in the treatment of above diseases. Here we summarize recent progress in the structural biology studies of NR family proteins. Compared with the dozens of structures of isolated DNA-binding domains (DBDs) and the striking more than a thousand of structures of isolated ligand-binding domains (LBDs) accumulated in the Protein Data Bank (PDB) over thirty years, by now there are only a small number of multi-domain NR complex structures, which reveal the integration of different NR domains capable of the allosteric signal transduction, or the detailed interactions between NR and various coregulator proteins. On the other hand, the structural information about several orphan NRs is still totally unavailable, hindering the further understanding of their functions. The fast development of new technologies in structural biology will certainly help us gain more comprehensive information of NR structures, inspiring the discovery of novel NR-targeting drugs with a new binding site beyond the classic LBD pockets and/or a new mechanism of action.

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that "the position weight matrices of most transcription factors lack sufficient predictive power" if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Boytsov A et al.)

The position weight matrix, also called the position-specific scoring matrix, is the commonly accepted model to quantify the specificity of transcription factor binding to DNA. Position weight matrices are used in thousands of projects and software tools in regulatory genomics, including computational prediction of the regulatory impact of single-nucleotide variants. Yet, recently Yan et al. reported that "the position weight matrices of most transcription factors lack sufficient predictive power" if applied to the analysis of regulatory variants studied with a newly developed experimental method, SNP-SELEX. Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can adequately quantify transcription factor binding to alternative alleles., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Boytsov A et al.)

The commonly accepted model to quantify the specificity of transcription factor binding to DNA is the position weight matrix, also called the position-specific scoring matrix. Position weight matrices are used in thousands of projects and computational tools in regulatory genomics, including prediction of the regulatory potential of single-nucleotide variants. Yet, recently Yan et al . presented new experimental method for analysis of regulatory variants and, based on its results, reported that "the position weight matrices of most transcription factors lack sufficient predictive power". Here, we re-analyze the rich experimental dataset obtained by Yan et al . and show that appropriately selected position weight matrices in fact can successfully quantify transcription factor binding to alternative alleles., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Boytsov A et al.)

The human genome contains ∼2000 transcriptional regulatory proteins, including ∼1600 DNA-binding transcription factors (TFs) recognizing characteristic sequence motifs to exert regulatory effects on gene expression. The binding specificities of these factors have been profiled both in vitro, using techniques such as HT-SELEX, and in vivo, using techniques including ChIP-seq. We previously developed Factorbook, a TF-centric database of annotations, motifs, and integrative analyses based on ChIP-seq data from Phase II of the ENCODE Project. Here we present an update to Factorbook which significantly expands the breadth of cell type and TF coverage. The update includes an expanded motif catalog derived from thousands of ENCODE Phase II and III ChIP-seq experiments and HT-SELEX experiments; this motif catalog is integrated with the ENCODE registry of candidate cis-regulatory elements to annotate a comprehensive collection of genome-wide candidate TF binding sites. The database also offers novel tools for applying the motif models within machine learning frameworks and using these models for integrative analysis, including annotation of variants and disease and trait heritability. Factorbook is publicly available at www.factorbook.org; we will continue to expand the resource as ENCODE Phase IV data are released., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Osteosarcoma (OS) is the most common primary malignant bone tumor which has unclear pathobiology. Hence, enlightening the exact molecular mechanism underlying osteosarcoma progression is crucial for developing new treatment strategies. One member of the ARID family of DNA binding proteins is ARID3A that is implicated in osteosarcoma pathogenesis. ARID3A could bind E2F1 and regulate the transcription of E2F1 targets. At the same time, BECN1 is a well-characterized autophagy regulator gene that is a direct target of E2F1. The present study aimed to investigate the effect of ARID3A on the expression of BECN1 in osteosarcoma cells. First, we determined gene expression levels of ARID3A, BECN1, and E2F1 in U-2 OS by qPCR and confirmed with online datasets from GEO database. In addition, the prognostic value of these genes was also evaluated from Kaplan-Meier plotter database. Next, ARID3A was overexpressed and silenced in order to investigate the effect of ARID3A on BECN1 expression and proliferation of U-2 OS cells. Our results demonstrated that BECN1 was negatively correlated with E2F1 and positively correlated with ARID3A based on initial expression and prognostic effect in OS. Overexpression of ARID3A upregulated BECN1 while silenced ARID3A downregulated BECN1 expression in U-2 OS cells. Additionally, silencing of ARID3A promoted colony formation and proliferation, whereas overexpression of ARID3A suppressed colony formation and proliferation of U-2 OS cells. Taken together, these results indicate that ARID3A could function as tumor suppressor and affect the expression level of BECN1 in U-2 OS cells., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The development of high-throughput genomic sequencing coupled with chromatin immunoprecipitation technologies allows studying the binding sites of the protein transcription factors (TF) in the genome scale. The growth of data volume on the experimentally determined binding sites raises qualitatively new problems for the analysis of gene expression regulation, prediction of transcription factors target genes, and regulatory gene networks reconstruction. Genome regulation remains an insufficiently studied though plants have complex molecular regulatory mechanisms of gene expression and response to environmental stresses. It is important to develop new software tools for the analysis of the TF binding sites location and their clustering in the plant genomes, visualization, and the following statistical estimates. This study presents application of the analysis of multiple TF binding profiles in three evolutionarily distant model plant organisms. The construction and analysis of non-random ChIP-seq binding clusters of the different TFs in mammalian embryonic stem cells were discussed earlier using similar bioinformatics approaches. Such clusters of TF binding sites may indicate the gene regulatory regions, enhancers and gene transcription regulatory hubs. It can be used for analysis of the gene promoters as well as a background for transcription networks reconstruction. We discuss the statistical estimates of the TF binding sites clusters in the model plant genomes. The distributions of the number of different TFs per binding cluster follow same power law distribution for all the genomes studied. The binding clusters in Arabidopsis thaliana genome were discussed here in detail., (© 2021 Arthur I. Dergilev et al., published by De Gruyter, Berlin/Boston.)