Binding Preferences Of Transcription Factors Research Articles

TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors

Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.

Candidate Genes for IgA Nephropathy in Pediatric Patients: Exome-Wide Association Study.

IgA nephropathy (IgAN) is an autoimmune disorder which is believed to be non-monogenic. We performed an exome-wide association study of 70 children with IgAN and 637 healthy donors. The HLA allele frequencies were compared between the patients and healthy donors from the bone marrow registry of the Pirogov University. We tested 78,020 gene markers for association and performed functional enrichment analysis and transcription factor binding preference detection. We identified 333 genetic variants, employing three inheritance models. The most significant association with the disorder was observed for rs143409664 (PRAG1) in the case of the additive and dominant models (PBONF = 1.808 × 10-15 and PBONF = 1.654 × 10-15, respectively), and for rs13028230 (UBR3) in the case of the recessive model (PBONF = 1.545 × 10-9). Enrichment analysis indicated the strongly overrepresented "immune system" and "kidney development" terms. The HLA-DQA1*01:01:01G allele (p = 0.0076; OR, 2.021 [95% CI, 1.322-3.048]) was significantly the most frequent among IgAN patients. Here, we characterized, for the first time, the genetic background of Russian IgAN patients, identifying the risk alleles typical of the population. The most important signals were detected in previously undescribed loci.

DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants.

Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or mouse data for training, limiting their performance when applied to other species. Furthermore, the limited exploration of many species, particularly in the case of livestock, has led to a scarcity of comprehensive and high-quality epigenetic data, posing challenges in developing reliable deep learning models for decoding their non-coding genomes. The cross-species prediction of the regulatory genome can be achieved by leveraging publicly available data from extensively studied organisms and making use of the conserved DNA binding preferences of transcription factors within the same tissue. In this study, we introduced DeepSATA, a novel deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for the cross-species prediction of chromatin accessibility. By applying DeepSATA to analyze the genomes of pigs, chickens, cattle, humans, and mice, we demonstrated its ability to improve the prediction accuracy of chromatin accessibility and achieve reliable cross-species predictions in animals. Additionally, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits and in increasing the accuracy of genomic predictions. Overall, our study presents a valuable tool to explore the epigenomic landscape of various species and pinpoint regulatory deoxyribonucleic acid (DNA) variants associated with complex traits.

The molecular grammar of protein disorder guiding genome-binding locations.

Intrinsically disordered regions (IDRs) direct transcription factors (TFs) towards selected genomic occurrences of their binding motif, as exemplified by budding yeast's Msn2. However, the sequence basis of IDR-directed TF binding selectivity remains unknown. To reveal this sequence grammar, we analyze the genomic localizations of>100 designed IDR mutants, each carrying up to 122 mutations within this 567-AA region. Our data points at multivalent interactions, carried by hydrophobic-mostly aliphatic-residues dispersed within a disordered environment and independent of linear sequence motifs, as the key determinants of Msn2 genomic localization. The implications of our results for the mechanistic basis of IDR-based TF binding preferences are discussed.

SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength.

Identifying enhancers is a critical task in bioinformatics due to their primary role in regulating gene expression. For this reason, various computational algorithms devoted to enhancer identification have been put forward over the years. More features are extracted from the single DNA sequences to boost the performance. Nevertheless, DNA structural information is neglected, which is an essential factor affecting the binding preferences of transcription factors to regulatory elements like enhancers. Here, we propose SENIES, a DNA shape enhanced deep learning predictor, to identify enhancers and their strength. The predictor consists of two layers where the first layer is for enhancer and non-enhancer identification, and the second layer is for predicting the strength of enhancers. Apart from two common sequence-derived features (i.e., one-hot and k-mer), DNA shape is introduced to describe the 3D structures of DNA sequences. Performance comparison with state-of-the-art methods conducted on public datasets demonstrates the effectiveness and robustness of our predictor. The code implementation of SENIES is publicly available at https://github.com/hlju-liye/SENIES.

Predicting In-Vitro DNA-Protein Binding With a Spatially Aligned Fusion of Sequence and Shape.

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.

Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding

BackgroundWhile methylation of CpG dinucleotides is traditionally considered antagonistic to the DNA-binding activity of most transcription factors (TFs), recent in vitro studies have revealed a more complex picture, suggesting that over a third of TFs may preferentially bind to methylated sequences. Expanding these in vitro observations to in vivo TF binding preferences is challenging since the effect of methylation of individual CpG sites cannot be easily isolated from the confounding effects of DNA accessibility and regional DNA methylation. Thus, in vivo methylation preferences of most TFs remain uncharacterized.ResultsWe introduce joint accessibility-methylation-sequence (JAMS) models, which connect the strength of the binding signal observed in ChIP-seq to the DNA accessibility of the binding site, regional methylation level, DNA sequence, and base-resolution cytosine methylation. We show that JAMS models quantitatively explain TF occupancy, recapitulate cell type-specific TF binding, and have high positive predictive value for identification of TFs affected by intra-motif methylation. Analysis of 2209 ChIP-seq experiments results in high-confidence JAMS models for 260 TFs, revealing a negative association between in vivo TF occupancy and intra-motif methylation for 45% of studied TFs, as well as 16 TFs that are predicted to bind to methylated sites, including 11 novel methyl-binding TFs mostly from the multi-zinc finger family.ConclusionsOur study substantially expands the repertoire of in vivo methyl-binding TFs, but also suggests that most TFs that prefer methylated CpGs in vitro present themselves as methylation agnostic in vivo, potentially due to the balancing effect of competition with other methyl-binding proteins.

An expedient survey and characterization of the soybean JAGGED 1 (GmJAG1) transcription factor binding preference in the soybean genome by modified ChIPmentation on soybean protoplasts

ChIP-seq is widely used for mapping the transcription factor (TF) binding sites throughout the genome in vivo. In this study, we adopted and modified ChIPmentation, a fast, robust, low-input requirement ChIP-seq method, to a transient expression system using soybean protoplasts to expedite the exploration of TF binding sites. To test this new protocol, we expressed a tagged version of a C2H2-type zinc finger TF, JAGGED1 (GmJAG1), in soybean protoplasts and successfully identified its binding sites in the soybean genome. Furthermore, valuable genomic features such as a novel GmJAG1-binding motif, and the epigenetic characteristics as well as an enhancer-like function of GmJBSs were also found via coupling ATAC-seq and H3K27me3 ChIP-seq data. The application of the modified ChIPmentation protocol in this study using soybean protoplasts provided a new approach for rapid elucidation of how a TF binds to the various target genes in the soybean genome, as illustrated here using GmJAG1.

Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity.

Cells use thousands of regulatory sequences to recruit transcription factors (TFs) and produce specific transcriptional outcomes. Since TFs bind degenerate DNA sequences, discriminating functional TF binding sites (TFBSs) from background sequences represents a significant challenge. Here, we show that a Drosophila regulatory element that activates Epidermal Growth Factor signaling requires overlapping, low-affinity TFBSs for competing TFs (Pax2 and Senseless) to ensure cell- and segment-specific activity. Testing available TF binding models for Pax2 and Senseless, however, revealed variable accuracy in predicting such low-affinity TFBSs. To better define parameters that increase accuracy, we developed a method that systematically selects subsets of TFBSs based on predicted affinity to generate hundreds of position-weight matrices (PWMs). Counterintuitively, we found that degenerate PWMs produced from datasets depleted of high-affinity sequences were more accurate in identifying both low- and high-affinity TFBSs for the Pax2 and Senseless TFs. Taken together, these findings reveal how TFBS arrangement can be constrained by competition rather than cooperativity and that degenerate models of TF binding preferences can improve identification of biologically relevant low affinity TFBSs.

Modelling the evolution of transcription factor binding preferences in complex eukaryotes

Transcription factors (TFs) exert their regulatory action by binding to DNA with specific sequence preferences. However, different TFs can partially share their binding sequences due to their common evolutionary origin. This “redundancy” of binding defines a way of organizing TFs in “motif families” by grouping TFs with similar binding preferences. Since these ultimately define the TF target genes, the motif family organization entails information about the structure of transcriptional regulation as it has been shaped by evolution. Focusing on the human TF repertoire, we show that a one-parameter evolutionary model of the Birth-Death-Innovation type can explain the TF empirical repartition in motif families, and allows to highlight the relevant evolutionary forces at the origin of this organization. Moreover, the model allows to pinpoint few deviations from the neutral scenario it assumes: three over-expanded families (including HOX and FOX genes), a set of “singleton” TFs for which duplication seems to be selected against, and a higher-than-average rate of diversification of the binding preferences of TFs with a Zinc Finger DNA binding domain. Finally, a comparison of the TF motif family organization in different eukaryotic species suggests an increase of redundancy of binding with organism complexity.

Motif signatures in stretch enhancers are enriched for disease-associated genetic variants.

BackgroundStretch enhancers (SEs) are large chromatin-defined regulatory elements that are at least 3,000 base pairs (bps) long, in contrast to the median enhancer length of 800 bps. SEs tend to be cell-type specific, regulate cell-type specific gene expression, and are enriched in disease-associated genetic variants in disease-relevant cell types. Transcription factors (TFs) can bind to enhancers to modulate enhancer activity, and their sequence specificity can be represented by motifs. We hypothesize motifs can provide a biological context for how genetic variants contribute to disease.ResultsWe integrated chromatin state, gene expression, and chromatin accessibility [measured as DNase I Hypersensitive Sites (DHSs)] maps across nine different cell types. Motif enrichment analyses of chromatin-defined enhancer sequences identify several known cell-type specific “master” factors. Furthermore, de novo motif discovery not only recovers many of these motifs, but also identifies novel non-canonical motifs, providing additional insight into TF binding preferences. Across the length of SEs, motifs are most enriched in DHSs, though relative enrichment is also observed outside of DHSs. Interestingly, we show that single nucleotide polymorphisms associated with diseases or quantitative traits significantly overlap motif occurrences located in SEs, but outside of DHSs.ConclusionsThese results reinforce the role of SEs in influencing risk for diseases and suggest an expanded regulatory functional role for motifs that occur outside highly accessible chromatin. Furthermore, the motif signatures generated here expand our understanding of the binding preference of well-characterized TFs.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-015-0015-7) contains supplementary material, which is available to authorized users.

Optimally choosing PWM motif databases and sequence scanning approaches based on ChIP-seq data.

BackgroundFor many years now, binding preferences of Transcription Factors have been described by so called motifs, usually mathematically defined by position weight matrices or similar models, for the purpose of predicting potential binding sites. However, despite the availability of thousands of motif models in public and commercial databases, a researcher who wants to use them is left with many competing methods of identifying potential binding sites in a genome of interest and there is little published information regarding the optimality of different choices. Thanks to the availability of large number of different motif models as well as a number of experimental datasets describing actual binding of TFs in hundreds of TF-ChIP-seq pairs, we set out to perform a comprehensive analysis of this matter.ResultsWe focus on the task of identifying potential transcription factor binding sites in the human genome. Firstly, we provide a comprehensive comparison of the coverage and quality of models available in different databases, showing that the public databases have comparable TFs coverage and better motif performance than commercial databases. Secondly, we compare different motif scanners showing that, regardless of the database used, the tools developed by the scientific community outperform the commercial tools. Thirdly, we calculate for each motif a detection threshold optimizing the accuracy of prediction. Finally, we provide an in-depth comparison of different methods of choosing thresholds for all motifs a priori. Surprisingly, we show that selecting a common false-positive rate gives results that are the least biased by the information content of the motif and therefore most uniformly accurate.ConclusionWe provide a guide for researchers working with transcription factor motifs. It is supplemented with detailed results of the analysis and the benchmark datasets at http://bioputer.mimuw.edu.pl/papers/motifs/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0573-5) contains supplementary material, which is available to authorized users.

Covariation between homeodomain transcription factors and the shape of their DNA binding sites

Protein–DNA recognition is a critical component of gene regulatory processes but the underlying molecular mechanisms are not yet completely understood. Whereas the DNA binding preferences of transcription factors (TFs) are commonly described using nucleotide sequences, the 3D DNA structure is recognized by proteins and is crucial for achieving binding specificity. However, the ability to analyze DNA shape in a high-throughput manner made it only recently feasible to integrate structural information into studies of protein–DNA binding. Here we focused on the homeodomain family of TFs and analyzed the DNA shape of thousands of their DNA binding sites, investigating the covariation between the protein sequence and the sequence and shape of their DNA targets. We found distinct homeodomain regions that were more correlated with either the nucleotide sequence or the DNA shape of their preferred binding sites, demonstrating different readout mechanisms through which homeodomains attain DNA binding specificity. We identified specific homeodomain residues that likely play key roles in DNA recognition via shape readout. Finally, we showed that adding DNA shape information when characterizing binding sites improved the prediction accuracy of homeodomain binding specificities. Taken together, our findings indicate that DNA shape information can generally provide new mechanistic insights into TF binding.

MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis

BackgroundTraditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional binding sites because they fail to take into consideration the biological state of the cell. In recent years, genome-wide studies have generated a lot of data that has the potential to improve our ability to identify functional motifs and binding sites, such as information about chromatin accessibility and epigenetic states in different cell types. However, it is not always trivial to make use of this data in combination with existing motif discovery tools, especially for researchers who are not skilled in bioinformatics programming.ResultsHere we present MotifLab, a general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab supports comprehensive motif discovery and analysis by allowing users to integrate several popular motif discovery tools as well as different kinds of additional information, including phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, transcription factor interactions and gene expression. MotifLab offers several data-processing operations that can be used to create, manipulate and analyse data objects, and complete analysis workflows can be constructed and automatically executed within MotifLab, including graphical presentation of the results.ConclusionsWe have developed MotifLab as a flexible workbench for motif analysis in a genomic context. The flexibility and effectiveness of this workbench has been demonstrated on selected test cases, in particular two previously published benchmark data sets for single motifs and modules, and a realistic example of genes responding to treatment with forskolin. MotifLab is freely available at http://www.motiflab.org.

Genome-wide nucleosome positioning during embryonic stem cell development

We determined genome-wide nucleosome occupancies in mouse embryonic stem cells and their neural progenitor and embryonic fibroblast counterparts to assess features associated with nucleosome positioning during lineage commitment. Cell-type- and protein-specific binding preferences of transcription factors to sites with either low (Myc, Klf4 and Zfx) or high (Nanog, Oct4 and Sox2) nucleosome occupancy as well as complex patterns for CTCF were identified. Nucleosome-depleted regions around transcription start and transcription termination sites were broad and more pronounced for active genes, with distinct patterns for promoters classified according to CpG content or histone methylation marks. Throughout the genome, nucleosome occupancy was correlated with certain histone methylation or acetylation modifications. In addition, the average nucleosome repeat length increased during differentiation by 5-7 base pairs, with local variations for specific regions. Our results reveal regulatory mechanisms of cell differentiation that involve nucleosome repositioning.

DNA Sequence Preferences of Transcriptional Activators Correlate More Strongly than Repressors with Nucleosomes

SummaryTranscription factors (TFs) and histone octamers are two abundant classes of DNA binding proteins that coordinate the transcriptional program in cells. Detailed studies of individual TFs have shown that TFs bind to nucleosome-occluded DNA sequences and induce nucleosome disruption/repositioning, while recent global studies suggest this is not the only mechanism used by all TFs. We have analyzed to what extent the intrinsic DNA binding preferences of TFs and histones play a role in determining nucleosome occupancy, in addition to nonintrinsic factors such as the enzymatic activity of chromatin remodelers. The majority of TFs in budding yeast have an intrinsic sequence preference overlapping with nucleosomal histones. TFs with intrinsic DNA binding properties highly correlated with those of histones tend to be associated with gene activation and might compete with histones to bind to genomic DNA. Consistent with this, we show that activators induce more nucleosome disruption upon transcriptional activation than repressors.

Comprehensive Analysis of the DNA-Binding Specificity of an <i>Aspergillus nidulans</i> Transcription Factor, AmyR, Using a Bead Display System

The in vitro DNA binding profile of Aspergillus nidulans transcription factor AmyR was analyzed by a novel approach employing a genetic library of beads and flow cytometry analysis. An artificial library with 22 randomized nucleotides was constructed and subjected to a protein-DNA binding reaction with MalE-tagged AmyR. DNA fragments with potential AmyR-binding sites were labeled with fluorescence-conjugated antibody to be enriched by flow cytometry through 5 rounds of successive selection. Finally, a binding motif with a single CGG triplet was obtained from DNA fragments showing weak AmyR binding, while another motif with dual CGG triplets was discovered with stronger binding fragments. An informative motif, CGGNNNTTTNTCGG, was found to exist only in the promoter region of highly AmyR-dependent genes. These results suggest that this system is a powerful tool for the rapid and comprehensive analysis of the binding preferences of transcription factors.

Genome-wide analysis of the transcription factor binding preference of human bi-directional promoters and functional annotation of related gene pairs

BackgroundBi-directional gene pairs have received considerable attention for their prevalence in vertebrate genomes. However, their biological relevance and exact regulatory mechanism remain less understood. To study the inner properties of this gene organization and the difference between bi- and uni-directional genes, we conducted a genome-wide investigation in terms of their sequence composition, functional association and regulatory motif discovery.ResultsWe identified 1210 bi-directional gene pairs based on the GRCh37 assembly data, accounting for 11.6% of all the human genes owning RNAs. CpG islands were detected in 98.42% of bi-directional promoters and 61.07% of unidirectional promoters. Functional enrichment analysis in GO and GeneGO both revealed that bi-directional genes tend to be associated with housekeeping functions in metabolism pathways and nuclear processes, and 46.84% of the pair members are involved in the same biological function. By fold-enrichment analysis, we characterized 73 and 43 putative transcription factor binding sites(TFBS) that preferentially occur in bi-directional promoters from TRANSFAC and JASPAR database respectively. By text mining, some of them were verified by individual experiments and several novel binding motifs were also identified.ConclusionsBi-directional promoters feature a significant enrichment of CpG-islands as well as a high GC content. We provided insight into the function constraints of bi-directional genes and found that paired genes are biased toward functional similarities. We hypothesized that the functional association underlies the co-expression of bi-directional genes. Furthermore, we proposed a set of putative regulatory motifs in the bi-directional promoters for further experimental studies to investigate transcriptional regulation of bi-directional genes.

High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data.We compare recently developed approaches to determine STAT1 binding sites from ChIP-seq data. Assessing the content of the established consensus sequence for STAT1 binding sites, we find that the usage of “negative control” ChIP-seq data fails to provide substantial advantages. We derive a single refined probabilistic model of STAT1 binding sequences from these ChIP-seq data. Contrary to previous claims, we find no evidence that STAT1 binds to multiple distinct motifs upon interferon-gamma stimulation in vivo. While a large majority of genomic sites with high ChIP-seq signal is associated with a nucleotide sequence ressembling a STAT1 binding site, only a very small subset of the over 5 million potential STAT1 binding sites in the human genome is covered by ChIP-seq data. Furthermore a surprisingly large fraction of the ChIP-seq signal (5%) is absorbed by a small family of repetitive sequences (MER41). The observation of the binding of activated STAT1 protein to a specific repetitive element bolsters similar reports concerning p53 and other TFs, and strengthens the notion of an involvement of repeats in gene regulation. Incidentally MER41 are specific to primates, consequently, regulatory mechanisms in the IFN-STAT pathway might fundamentally differ between primates and rodents. On a methodological aspect, the presence of large numbers of nearly identical binding sites in repetitive sequences may lead to wrong conclusions about intrinsic binding preferences of TF as illustrated by the spacing analysis STAT1 tandem motifs. Therefore, ChIP-seq data should be analyzed independently within repetitive and non-repetitive sequences.