MEDEA: analysis of transcription factor binding motifs in accessible chromatin.
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ABSTRACT:
SUBMITTER: Mariani L
PROVIDER: S-EPMC7263192 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Transcription factors (TFs) play crucial roles in regulating gene expression through interactions with specific DNA sequences. Recently, the sequence motif of almost 400 human TFs have been identified using high-throughput SELEX sequencing. However, there remain a large number of TFs (?800) with no high-throughput-derived binding motifs. Computational methods capable of associating known motifs to such TFs will avoid tremendous experimental efforts and enable deeper understanding of transcriptional regulatory functions. We present a method to associate known motifs to TFs (MATLAB code is available in Supplementary Materials). Our method is based on a probabilistic framework that not only exploits DNA-binding domains and specificities, but also integrates open chromatin, gene expression and genomic data to accurately infer monomeric and homodimeric binding motifs. Our analysis resulted in the assignment of motifs to 200 TFs with no SELEX-derived motifs, roughly a 50% increase compared to the existing coverage.
| S-EPMC5449588 | biostudies-literature
Project description:BackgroundClassically, models of DNA-transcription factor binding sites (TFBSs) have been based on relatively few known instances and have treated them as sites of fixed length using position weight matrices (PWMs). Various extensions to this model have been proposed, most of which take account of dependencies between the bases in the binding sites. However, some transcription factors are known to exhibit some flexibility and bind to DNA in more than one possible physical configuration. In some cases this variation is known to affect the function of binding sites. With the increasing volume of ChIP-seq data available it is now possible to investigate models that incorporate this flexibility. Previous work on variable length models has been constrained by: a focus on specific zinc finger proteins in yeast using restrictive models; a reliance on hand-crafted models for just one transcription factor at a time; and a lack of evaluation on realistically sized data sets.ResultsWe re-analysed binding sites from the TRANSFAC database and found motivating examples where our new variable length model provides a better fit. We analysed several ChIP-seq data sets with a novel motif search algorithm and compared the results to one of the best standard PWM finders and a recently developed alternative method for finding motifs of variable structure. All the methods performed comparably in held-out cross validation tests. Known motifs of variable structure were recovered for p53, Stat5a and Stat5b. In addition our method recovered a novel generalised version of an existing PWM for Sp1 that allows for variable length binding. This motif improved classification performance.ConclusionsWe have presented a new gapped PWM model for variable length DNA binding sites that is not too restrictive nor over-parameterised. Our comparison with existing tools shows that on average it does not have better predictive accuracy than existing methods. However, it does provide more interpretable models of motifs of variable structure that are suitable for follow-up structural studies. To our knowledge, we are the first to apply variable length motif models to eukaryotic ChIP-seq data sets and consequently the first to show their value in this domain. The results include a novel motif for the ubiquitous transcription factor Sp1.
| S-EPMC2824720 | biostudies-literature
Project description:BackgroundThe use of global gene expression profiling is a well established approach to understand biological processes. One of the major goals of these investigations is to identify sets of genes with similar expression patterns. Such gene signatures may be very informative and reveal new aspects of particular biological processes. A logical and systematic next step is to reduce the identified gene signatures to the regulatory components that induce the relevant gene expression changes. A central issue in this context is to identify transcription factors, or transcription factor binding sites (TFBS), likely to be of importance for the expression of the gene signatures.ResultsWe develop a strategy that efficiently produces TFBS/promoter databases based on user-defined criteria. The resulting databases constitute all genes in the Santa Cruz database and the positions for all TFBS provided by the user as position weight matrices. These databases are then used for two purposes, to identify significant TFBS in the promoters in sets of genes and to identify clusters of co-occurring TFBS. We use two criteria for significance, significantly enriched TFBS in terms of total number of binding sites for the promoters, and significantly present TFBS in terms of the fraction of promoters with binding sites. Significant TFBS are identified by a re-sampling procedure in which the query gene set is compared with typically 10(5) gene lists of similar size randomly drawn from the TFBS/promoter database. We apply this strategy to a large number of published ChIP-Chip data sets and show that the proposed approach faithfully reproduces ChIP-Chip results. The strategy also identifies relevant TFBS when analyzing gene signatures obtained from the MSigDB database. In addition, we show that several TFBS are highly correlated and that co-occurring TFBS define functionally related sets of genes.ConclusionsThe presented approach of promoter analysis faithfully reproduces the results from several ChIP-Chip and MigDB derived gene sets and hence may prove to be an important method in the analysis of gene signatures obtained through ChIP-Chip or global gene expression experiments. We show that TFBS are organized in clusters of co-occurring TFBS that together define highly coherent sets of genes.
| S-EPMC2841680 | biostudies-literature
Project description:Binding of transcription factors to DNA is one of the keystones of gene regulation. The existence of statistical dependencies between binding site positions is widely accepted, while their relevance for computational predictions has been debated. Building probabilistic models of binding sites that may capture dependencies is still challenging, since the most successful motif discovery approaches require numerical optimization techniques, which are not suited for selecting dependency structures. To overcome this issue, we propose sparse local inhomogeneous mixture (Slim) models that combine putative dependency structures in a weighted manner allowing for numerical optimization of dependency structure and model parameters simultaneously. We find that Slim models yield a substantially better prediction performance than previous models on genomic context protein binding microarray data sets and on ChIP-seq data sets. To elucidate the reasons for the improved performance, we develop dependency logos, which allow for visual inspection of dependency structures within binding sites. We find that the dependency structures discovered by Slim models are highly diverse and highly transcription factor-specific, which emphasizes the need for flexible dependency models. The observed dependency structures range from broad heterogeneities to sparse dependencies between neighboring and non-neighboring binding site positions.
| S-EPMC4605289 | biostudies-literature
Project description:Purpose: 224 GSM Samples form GSE32970 and GSE29692 was reanalyzed to find the binding sites of 542 TFs. Methods: 1. iFORM (incorporating Find Occurrence of Regulatory Motifs) is a tool we designed to scan through sequences in search of binding sites that match given motifs. 2. We mapped motif-binding protein information found in TRANSFAC, JASPAR, and UniPROBE databases to 542 coding genes, using GeneCards (Rebhan et al., 1997) and UniProt Knowledgebase (Magrane and Consortium, 2011). The position-specific weight matrices of the 542 TFs, corresponding to 796 motif models, were collected from these databases. 3.We used a uniform set of open chromatin elements (DNaseI hypersensitive sites, DHSs) in 133 ENCODE cell types, based on DNase-seq data produced by the "Open Chromatin" (Duke/UNC/UT-A)(GSE32970) and University of Washington (UW) ENCODE groups (GSE29692)from the project inception in 2007 through the ENCODE January 2011 data freeze. Processing pipeline: Combined all replicates for a given cell type; Subsampled the result at a level of 30 million tags; Ran results through the Stam Lab hotspot pipeline. Hotspots were identified using a relaxed threshold. NarrowPeaks were generated by first thresholding hotspots at FDR 1%, and then locating local maxima of the tag density within the hotspots. The 133 narrowPeaks are base sequences for the given motifs scan. Result: We used our iFORM with a custom library of all 796 motifs to scan for motif instances in 133 ENCODE DHSs cell types separately. Result in 133 files correspond to 133 cell types, each file contains binding sites of 542 TFs in a cell type.
2014-10-12 | GSE53962 | GEO
Project description:We characterize how genomic variants that alter chromatin accessibility influence regulatory factor binding with a new method called DeltaBind that predicts condition specific factor binding more accurately than other methods based on DNase-seq data. Using DeltaBind and DNase-seq experiments we predicted the differential binding of 18 factors in K562 and GM12878 cells with an average precision of 28% at 10% recall, with the prediction of individual factors ranging from 5% to 65% precision. We further found that genome variants that alter chromatin accessibility are not necessarily predictive of altering proximal factor binding. Taken together these findings suggest that DNase-seq or ATAC-seq Quantitative Trait Loci (dsQTLs), while important, must be considered in a broader context to establish causality for phenotypic changes.
| S-EPMC5509100 | biostudies-literature
Project description:Tissue-specific gene expression requires modulation of nucleosomes, allowing transcription factors to occupy cis elements that are accessible only in selected tissues. Master transcription factors control cell-specific genes and define cellular identities, but it is unclear if they possess special abilities to regulate cell-specific chromatin and if such abilities might underlie lineage determination and maintenance. One prevailing view is that several transcription factors enable chromatin access in combination. The homeodomain protein CDX2 specifies the embryonic intestinal epithelium, through unknown mechanisms, and partners with transcription factors such as HNF4A in the adult intestine. We examined enhancer chromatin and gene expression following Cdx2 or Hnf4a excision in mouse intestines. HNF4A loss did not affect CDX2 binding or chromatin, whereas CDX2 depletion modified chromatin significantly at CDX2-bound enhancers, disrupted HNF4A occupancy, and abrogated expression of neighboring genes. Thus, CDX2 maintains transcription-permissive chromatin, illustrating a powerful and dominant effect on enhancer configuration in an adult tissue. Similar, hierarchical control of cell-specific chromatin states is probably a general property of master transcription factors.
| S-EPMC3554120 | biostudies-literature
Project description:To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.
| S-EPMC3509107 | biostudies-literature
Project description:Position-specific scoring matrices (PSSMs) are routinely used to predict transcription factor (TF)-binding sites in genome sequences. However, their reliability to predict novel binding sites can be far from optimum, due to the use of a small number of training sites or the inappropriate choice of parameters when building the matrix or when scanning sequences with it. Measures of matrix quality such as E-value and information content rely on theoretical models, and may fail in the context of full genome sequences. We propose a method, implemented in the program 'matrix-quality', that combines theoretical and empirical score distributions to assess reliability of PSSMs for predicting TF-binding sites. We applied 'matrix-quality' to estimate the predictive capacity of matrices for bacterial, yeast and mouse TFs. The evaluation of matrices from RegulonDB revealed some poorly predictive motifs, and allowed us to quantify the improvements obtained by applying multi-genome motif discovery. Interestingly, the method reveals differences between global and specific regulators. It also highlights the enrichment of binding sites in sequence sets obtained from high-throughput ChIP-chip (bacterial and yeast TFs), and ChIP-seq and experiments (mouse TFs). The method presented here has many applications, including: selecting reliable motifs before scanning sequences; improving motif collections in TFs databases; evaluating motifs discovered using high-throughput data sets.
| S-EPMC3035439 | biostudies-literature
Project description:We study the limits imposed by transcription factor specificity on the maximum number of binding motifs that can coexist in a gene regulatory network, using the SwissRegulon Fantom5 collection of 684 human transcription factor binding sites as a model. We describe transcription factor specificity using regular expressions and find that most human transcription factor binding site motifs are separated in sequence space by one to three motif-discriminating positions. We apply theorems based on the pigeonhole principle to calculate the maximum number of transcription factors that can coexist given this degree of specificity, which is in the order of ten thousand and would fully utilize the space of DNA subsequences. Taking into account an expanded DNA alphabet with modified bases can further raise this limit by several orders of magnitude, at a lower level of sequence space usage. Our results may guide the design of transcription factors at both the molecular and system scale.
| S-EPMC8797260 | biostudies-literature