Improved models for transcription factor binding site identification using nonindependent interactions.
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ABSTRACT:
SUBMITTER: Zhao Y
PROVIDER: S-EPMC3389974 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF.
| S-EPMC6037060 | biostudies-literature
Project description:Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).
| S-EPMC6823902 | biostudies-literature
Project description:Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.
| S-EPMC6391789 | biostudies-literature
Project description:The binding motifs of many transcription factors (TFs) comprise a higher degree of complexity than a single position weight matrix model permits. Additional complexity is typically taken into account either as intra-motif dependencies via more sophisticated probabilistic models or as heterogeneities via multiple weight matrices. However, both orthogonal approaches have limitations when learning from in vivo data where binding sites of other factors in close proximity can interfere with motif discovery for the protein of interest. In this work, we demonstrate how intra-motif complexity can, purely by analyzing the statistical properties of a given set of TF-binding sites, be distinguished from complexity arising from an intermix with motifs of co-binding TFs or other artifacts. In addition, we study the related question whether intra-motif complexity is represented more effectively by dependencies, heterogeneities or variants in between. Benchmarks demonstrate the effectiveness of both methods for their respective tasks and applications on motif discovery output from recent tools detect and correct many undesirable artifacts. These results further suggest that the prevalence of intra-motif dependencies may have been overestimated in previous studies on in vivo data and should thus be reassessed.
| S-EPMC6237759 | biostudies-literature
Project description:Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid-DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.
| S-EPMC3526315 | biostudies-literature
Project description:Individual-nucleotide resolution crosslinking and immunoprecipitation (iCLIP) allows the determination of crosslinking sites of RNA-binding proteins (RBPs) on RNAs. iCLIP is based on ultraviolet light crosslinking of RBPs to RNA, reverse transcription and high-throughput sequencing of fragments terminating at the site of crosslinking. As a result, start sites of iCLIP fragments are expected to cluster with a narrow distribution, typically representing the site of direct interaction between the RBP and the RNA. Here we show that for several RBPs (eIF4A3, PTB, SRSF3, SRSF4 and hnRNP L), the start sites of iCLIP fragments show a fragment length-dependent broader distribution that can be shifted to positions upstream of the known RNA-binding site. We developed an analysis tool that identifies these shifts and can improve the positioning of RBP binding sites.
| S-EPMC4918375 | biostudies-literature
Project description:A major mode of gene expression evolution is based on changes in cis-regulatory elements (CREs) whose function critically depends on the presence of transcription factor-binding sites (TFBS). Because CREs experience extensive TFBS turnover even with conserved function, alignment-based studies of CRE sequence evolution are limited to very closely related species. Here, we propose an alternative approach based on a stochastic model of TFBS turnover. We implemented a maximum likelihood model that permits variable turnover rates in different parts of the species tree. This model can be used to detect changes in turnover rate as a proxy for differences in the selective pressures acting on TFBS in different clades. We applied this method to five TFBS in the fungi methionine biosynthesis pathway and three TFBS in the HoxA clusters of vertebrates. We find that the estimated turnover rate is generally high, with half-life ranging between approximately 5 and 150 My and a mode around tens of millions of years. This rate is consistent with the finding that even functionally conserved enhancers can show very low sequence similarity. We also detect statistically significant differences in the equilibrium densities of estrogen- and progesterone-response elements in the HoxA clusters between mammal and nonmammal vertebrates. Even more extreme clade-specific differences were found in the fungal data. We conclude that stochastic models of TFBS turnover enable the detection of shifts in the selective pressures acting on CREs in different organisms. The analysis tool, called CRETO (Cis-Regulatory Element Turn-Over) can be downloaded from http://www.bioinf.uni-leipzig.de/Software/creto/.
| S-EPMC2817405 | biostudies-literature
Project description:Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relationships between TFs and DNA sequences. Moreover, studies such as GWAS and eQTL have verified that most disease-related variants exist in non-coding regions, and highlighted the necessity to identify such variants that cause diseases by interrupting TF binding mechanisms. To do this, it is necessary to build a prediction model that precisely predicts the binding relationships between TFs and DNA sequences. Recently, deep learning based models have been proposed and have shown competitive results on a transcription factor binding site prediction task. However, it is difficult to interpret the prediction results obtained from the previous models. In addition, the previous models assumed all the sequence regions in the input DNA sequence have the same importance for predicting TF-binding, although sequence regions containing TF-binding-associated signals such as TF-binding motifs should be captured more than other regions. To address these challenges, we propose TBiNet, an attention based interpretable deep neural network for predicting transcription factor binding sites. Using the attention mechanism, our method is able to assign more importance on the actual TF binding sites in the input DNA sequence. TBiNet outperforms the current state-of-the-art methods (DeepSea and DanQ) quantitatively in the TF-DNA binding prediction task. Moreover, TBiNet is more effective than the previous models in discovering known TF-binding motifs.
| S-EPMC7414127 | biostudies-literature
Project description:Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ? 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of "pre-sites" or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics.
| S-EPMC4636380 | biostudies-literature
Project description:In protein-DNA interactions, particularly transcription factor (TF) and transcription factor binding site (TFBS) bindings, associated residue variations form patterns denoted as subtypes. Subtypes may lead to changed binding preferences, distinguish conserved from flexible binding residues and reveal novel binding mechanisms. However, subtypes must be studied in the context of core bindings. While solving 3D structures would require huge experimental efforts, recent sequence-based associated TF-TFBS pattern discovery has shown to be promising, upon which a large-scale subtype study is possible and desirable. In this article, we investigate residue-varying subtypes based on associated TF-TFBS patterns. By re-categorizing the patterns with respect to varying TF amino acids, statistically significant (P values ≤ 0.005) subtypes leading to varying TFBS patterns are discovered without using TF family or domain annotations. Resultant subtypes have various biological meanings. The subtypes reflect familial and functional properties and exhibit changed binding preferences supported by 3D structures. Conserved residues critical for maintaining TF-TFBS bindings are revealed by analyzing the subtypes. In-depth analysis on the subtype pair PKVVIL-CACGTG versus PKVEIL-CAGCTG shows the V/E variation is indicative for distinguishing Myc from MRF families. Discovered from sequences only, the TF-TFBS subtypes are informative and promising for more biological findings, complementing and extending recent one-sided subtype and familial studies with comprehensive evidence.
| S-EPMC3479201 | biostudies-literature