Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models.

A flexible transcription initiation architecture and a uniform regulatory grammar buffer expression variability and the effects of genetic variants on essential genes while a rigid transcription initiation architecture and a diverse regulatory grammar ensure condition-specific responsiveness of other genes.

Transcription Factor Binding Affinities and DNA Shape Readout.

MotivationMachine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-type-specific TF binding sites with base-pair resolution.ResultsWe show that the multitask learning strategy for TF binding prediction is more efficient than the single-task approach due to the increased data availability. NetTIME trains high-dimensional embedding vectors to distinguish TF and cell-type identities. We show that this approach is critical for the success of the multitask learning strategy and allows our model to make accurate transfer predictions within and beyond the training panels of TFs and cell types. We additionally train a linear-chain conditional random field (CRF) to classify binding predictions and show that this CRF eliminates the need for setting a probability threshold and reduces classification noise. We compare our method’s predictive performance with two state-of-the-art methods, Catchitt and Leopard, and show that our method outperforms previous methods under both supervised and transfer learning settings.Availability and implementationNetTIME is freely available at https://github.com/ryi06/NetTIME and the code is also archived at https://doi.org/10.5281/zenodo.6994897.Supplementary informationSupplementary data are available at Bioinformatics online.

Transcription factor (TF) binding at specific DNA sequences is the fundamental step in transcriptional regulation and is highly dependent on the chromatin structure context, which may be affected by specific histone modifications and variants, known as histone marks. The lack of a global binding map for hundreds of TFs means that previous studies have focused mainly on histone marks at binding sites for several specific TFs. We therefore studied 11 histone marks around computationally-inferred and experimentally-determined TF binding sites (TFBSs), based on 164 and 34 TFs, respectively, in human lymphoblastoid cell lines. For H2A.Z, methylation of H3K4, and acetylation of H3K27 and H3K9, the mark patterns exhibited bimodal distributions and strong pairwise correlations in the 600-bp region around enriched TFBSs, suggesting that these marks mainly coexist within the two nucleosomes proximal to the TF sites. TFs competing with nucleosomes to access DNA at most binding sites, contributes to the bimodal distribution, which is a common feature of histone marks for TF binding. Mark H3K79me2 showed a unimodal distribution on one side of TFBSs and the signals extended up to 4000 bp, indicating a longer-distance pattern. Interestingly, H4K20me1, H3K27me3, H3K36me3 and H3K9me3, which were more diffuse and less enriched surrounding TFBSs, showed unimodal distributions around the enriched TFBSs, suggesting that some TFs may bind to nucleosomal DNA. Besides, asymmetrical distributions of H3K36me3 and H3K9me3 indicated that repressors might establish a repressive chromatin structure in one direction to repress gene expression. In conclusion, this study demonstrated the ranges of histone marks associated with TF binding, and the common features of these marks around the binding sites. These findings have epigenetic implications for future analysis of regulatory elements.

Acetylcholinesterase in man is encoded by a single gene, ACHE, located on chromosome 7q22. In this study, the transcription start sites and major DNA promoter elements controlling the expression of this gene have been characterized by structural and functional studies. Immediately upstream of the first untranslated exon of the gene are GC-rich sequences containing consensus binding sites for several transcription factors, including Sp1, EGR-1 and AP2. In vitro transcription studies and RNase protection analyses of mRNA isolated from human NT2/D1 teratocarcinoma cells reveal that two closely spaced transcription cap sites are located at a consensus initiator (Inr) element similar to that found in the terminal transferase gene. Transient transfection of mutant genes shows that removal of three bases of this initiator sequence reduces promoter activity by 98% in NT2/D1 cells. In vitro transcription studies and transient transfection of a series of 5' deletion mutants of the ACHE promoter linked to a luciferase reporter show an Sp1 site at -71 to be essential for promoter activity. Purified Sp1 protein protects this site from DNase cleavage during in vitro footprinting experiments. A conserved AP2 consensus binding site, located between the GC box elements and the Inr, is protected by recombinant AP2 protein in DNase footprinting experiments, induces a mobility shift with AP2 protein and AP2-containing cell extracts, and fosters inhibition of transcription by AP2 as measured by transient transfection in mouse and human cell lines and in in vitro transcription reactions. These results indicate that AP2 functions as a repressor of human ACHE and mouse Ache transcription.

Transcription factor (TF) binding site (TFBS) models are crucial for computational reconstruction of transcription regulatory networks. In existing repositories, a TF often has several models (also called binding profiles or motifs), obtained from different experimental data. Having a single TFBS model for a TF is more pragmatic for practical applications. We show that integration of TFBS data from various types of experiments into a single model typically results in the improved model quality probably due to partial correction of source specific technique bias.We present the Homo sapiens comprehensive model collection (HOCOMOCO, http://autosome.ru/HOCOMOCO/, http://cbrc.kaust.edu.sa/hocomoco/) containing carefully hand-curated TFBS models constructed by integration of binding sequences obtained by both low- and high-throughput methods. To construct position weight matrices to represent these TFBS models, we used ChIPMunk software in four computational modes, including newly developed periodic positional prior mode associated with DNA helix pitch. We selected only one TFBS model per TF, unless there was a clear experimental evidence for two rather distinct TFBS models. We assigned a quality rating to each model. HOCOMOCO contains 426 systematically curated TFBS models for 401 human TFs, where 172 models are based on more than one data source.

Many DNA motif finding algorithms that use Consensus (or any of its variants) in its motif model implicitly impose some restrictive assumptions over transcription factor (TF) binding sites (TFBS). Examples include all binding sites being of equal length, or having exactly one core region with fixed format, etc. In this paper, we have constructed a generalized consensus model (called Mixed-Length-Consensus, or ML-Consensus) without such constraints through multiple sequence alignment of known TFBS. We have extended this model with Information Content (IC) and Pairwise nucleotide correlation Score (PS), and have experimented with using multiple ML-Consensus for a set of binding sites. We have performed leave-one-out cross validation for training and testing of this algorithm over real binding site data of human, mouse, fruit fly, and yeast. We have produced ROC curves (True Positive Rate against False Positive Rate) for these experiments, and have used Wilcoxon Matched-Pair Signed Ranks Test to determine their statistical significance. Our results show that using IC and PS together with ML-Consensus consistently leads to better performance. We have experimented with various scopes for PS, and have found that scope values of 3-5 yield significantly better performance for different configurations. We have also found that using multiple ML-Consensus for one TF significantly improves recognition performance, but single ML-Consensus does better in yeast than in human data. Finally, we have found that using different multiple sequence alignment strategies for ML-Consensus yields varied performance across different species; a naive sorting based multiple sequence alignment outperformed CLUSTAL W2 alignment in yeast data.

Genomic studies have revealed elevated damage and mutation rates in active transcription factor (TF) binding sites in UV-linked cancers. Previous investigations into the relationship between TF activity and UV DNA damage have primarily focused on select TFs or been done in aggregate across large cohorts of TFs at kilobase resolution. While collectively, there is evidence that TFs contribute to UV-induced mutagenesis by both enhancing initial damage formation and attenuating repair, there has yet to be a comprehensive characterization of these mechanisms on a per-TF basis. Using genome-wide maps of UV damage from human skin fibroblasts, we developed a scalable statistical framework to analyze TF-mediated mutagenic mechanisms across hundreds of TFs. We identify numerous previously unreported TFs that significantly enhance and / or inhibit damage formation in their binding sites. A systematic survey of TF-DNA complexes further revealed that positions of UV damage modulation coincide with TF-induced structural distortions that either protect or predispose DNA to photodimer formation. Additionally, we analyzed repair efficiency in TF binding sites with unprecedented resolution, identifying specific TFs and binding site positions likely to compete with repair factors. By comparing these results with skin cancer mutations, we distinguish mutation peaks driven by increased damage susceptibility versus attenuated repair, illustrating that TF-mediated mutagenesis is highly contextual and dependent on the TF, binding site position, and sequence context of the damaged locus. Our approach provides a robust statistical framework for elucidating mechanisms of mutagenic TF-binding and offers novel insights into the complex interplay between protein interactions, DNA damage, and repair.

More accurate and more complete predictions of cis-regulatory modules (CRMs) and constituent transcription factor (TF) binding sites (TFBSs) in genomes can facilitate characterizing functions of regulatory sequences. Here, we developed a database predicted cis-regulatory modules (PCRMS) (https://cci-bioinfo.uncc.edu) that stores highly accurate and unprecedentedly complete maps of predicted CRMs and TFBSs in the human and mouse genomes. The web interface allows the user to browse CRMs and TFBSs in an organism, find the closest CRMs to a gene, search CRMs around a gene and find all TFBSs of a TF. PCRMS can be a useful resource for the research community to characterize regulatory genomes.Database URL: https://cci-bioinfo.uncc.edu/

This work addresses a central question in medical genetics - the distinction between disease-causing SNPs and neutral variations. Unlike previous studies that focused mainly on coding SNPs, our efforts were centered around variations in regulatory regions and specifically within transcription factor (TF) binding sites. We have compiled a comprehensive collection of genome wide TF binding sites and developed computational measures to estimate the effects of binding site variations on the expression profiles of the regulated genes. Applying these measures to binding sites of known TFs, we were able to make predictions that were in line with published experimental evidence and with structural data on DNA-protein interactions. We attempted to generalize the properties of expression-altering substitutions by accumulating statistics from many substitutions across multiple binding sites. We found that in the yeast genome substitutions that abolish a G or a C are on average more severe than substitutions that abolish an A or a T. This may be attributed to the low GC content of the yeast genome, in which G and C may be important for conferring specificity. We found additional factors that are correlated with the severity of a substitution. Such factors can be integrated in order to create a set of rules for the prioritization of regulatory SNPs according to their disease-causing potential.

We performed a systematic analysis of gene expression features in early (10–21 days) development of human vs mouse embryonic cells (hESCs vs mESCs). Many development features were found to be conserved, and a majority of differentially regulated genes have similar expression change in both organisms. The similarity is especially evident, when gene expression profiles are clustered together and properties of clustered groups of genes are compared. First 10 days of mESC development match the features of hESC development within 21 days, in accordance with the differences in population doubling time in human and mouse ESCs. At the same time, several important differences are seen. There is a clear difference in initial expression change of transcription factors and stimulus responsive genes, which may be caused by the difference in experimental procedures. However, we also found that some biological processes develop differently; this can clearly be shown, for example, for neuron and sensory organ development. Some groups of genes show peaks of the expression levels during the development and these peaks cannot be claimed to happen at the same time points in the two organisms, as well as for the same groups of (orthologous) genes. We also detected a larger number of upregulated genes during development of mESCs as compared to hESCs. The differences were quantified by comparing promoters of related genes. Most of gene groups behave similarly and have similar transcription factor (TF) binding sites on their promoters. A few groups of genes have similar promoters, but are expressed differently in two species. Interestingly, there are groups of genes expressed similarly, although they have different promoters, which can be shown by comparing their TF binding sites. Namely, a large group of similarly expressed cell cycle-related genes is found to have discrepant TF binding properties in mouse vs human.

Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods.

BackgroundThe histone variant H3.3 plays a critical role in maintaining the pluripotency of embryonic stem cells (ESCs) by regulating gene expression programs important for lineage specification. H3.3 is deposited by various chaperones at regulatory sites, gene bodies, and certain heterochromatic sites such as telomeres and centromeres. Using Tet-inhibited expression of epitope-tagged H3.3 combined with ChIP-Seq we undertook genome-wide measurements of H3.3 dissociation rates across the ESC genome and examined the relationship between H3.3-nucleosome turnover and ESC-specific transcription factors, chromatin modifiers, and epigenetic marks.ResultsOur comprehensive analysis of H3.3 dissociation rates revealed distinct H3.3 dissociation dynamics at various functional chromatin domains. At transcription start sites, H3.3 dissociates rapidly with the highest rate at nucleosome-depleted regions (NDRs) just upstream of Pol II binding, followed by low H3.3 dissociation rates across gene bodies. H3.3 turnover at transcription start sites, gene bodies, and transcription end sites was positively correlated with transcriptional activity.H3.3 is found decorated with various histone modifications that regulate transcription and maintain chromatin integrity. We find greatly varying H3.3 dissociation rates across various histone modification domains: high dissociation rates at active histone marks and low dissociation rates at heterochromatic marks. Well- defined zones of high H3.3-nucleosome turnover were detected at binding sites of ESC-specific pluripotency factors and chromatin remodelers, suggesting an important role for H3.3 in facilitating protein binding. Among transcription factor binding sites we detected higher H3.3 turnover at distal cis-acting sites compared to proximal genic transcription factor binding sites.Our results imply that fast H3.3 dissociation is a hallmark of interactions between DNA and transcriptional regulators.ConclusionOur study demonstrates that H3.3 turnover and nucleosome stability vary greatly across the chromatin landscape of embryonic stem cells. The presence of high H3.3 turnover at RNA Pol II binding sites at extragenic regions as well as at transcription start and end sites of genes, suggests a specific role for H3.3 in transcriptional initiation and termination. On the other hand, the presence of well-defined zones of high H3.3 dissociation at transcription factor and chromatin remodeler binding sites point to a broader role in facilitating accessibility.Electronic supplementary materialThe online version of this article (doi:10.1186/1756-8935-7-38) contains supplementary material, which is available to authorized users.

With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases.A5Gnetwork is a cloud native, massively connected internet of things (IoT) platform with a huge number of devices hosted on the network now known as massive machine type communication (mMTC). As ultra-low latency is pivotal in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, opposed to older cell association schemes which were based only on the reference signal received power (RSRP). This paper proposes an unsupervised machine learning algorithm, namely hidden Markov model (HMM) learning on the network's telemetry data, which is used to learn network parameters and select the best eNodeB for cell association. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.

SNP2TFBS is a computational resource intended to support researchers investigating the molecular mechanisms underlying regulatory variation in the human genome. The database essentially consists of a collection of text files providing specific annotations for human single nucleotide polymorphisms (SNPs), namely whether they are predicted to abolish, create or change the affinity of one or several transcription factor (TF) binding sites. A SNP's effect on TF binding is estimated based on a position weight matrix (PWM) model for the binding specificity of the corresponding factor. These data files are regenerated at regular intervals by an automatic procedure that takes as input a reference genome, a comprehensive SNP catalogue and a collection of PWMs. SNP2TFBS is also accessible over a web interface, enabling users to view the information provided for an individual SNP, to extract SNPs based on various search criteria, to annotate uploaded sets of SNPs or to display statistics about the frequencies of binding sites affected by selected SNPs. Homepage: http://ccg.vital-it.ch/snp2tfbs/.