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Towards a map of cis-regulatory sequences in the human genome.
ABSTRACT: Accumulating evidence indicates that transcription factor (TF) binding sites, or cis-regulatory elements (CREs), and their clusters termed cis-regulatory modules (CRMs) play a more important role than do gene-coding sequences in specifying complex traits in humans, including the susceptibility to common complex diseases. To fully characterize their roles in deriving the complex traits/diseases, it is necessary to annotate all CREs and CRMs encoded in the human genome. However, the current annotations of CREs and CRMs in the human genome are still very limited and mostly coarse-grained, as they often lack the detailed information of CREs in CRMs. Here, we integrated 620 TF ChIP-seq datasets produced by the ENCODE project for 168 TFs in 79 different cell/tissue types and predicted an unprecedentedly completely map of CREs in CRMs in the human genome at single nucleotide resolution. The map includes 305 912 CRMs containing a total of 1 178 913 CREs belonging to 736 unique TF binding motifs. The predicted CREs and CRMs tend to be subject to either purifying selection or positive selection, thus are likely to be functional. Based on the results, we also examined the status of available ChIP-seq datasets for predicting the entire regulatory genome of humans.
Project description:BACKGROUND: In eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task. RESULTS: We have developed a novel graph-theoretic based algorithm DePCRM for genome-wide de novo predictions of CREs and CRMs using a large number of ChIP datasets. DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way. When applied to 168 ChIP datasets of 56 TFs from D. melanogaster, DePCRM identified 184 and 746 overrepresented CRE motifs and their combinatorial patterns, respectively, and predicted a total of 115,932 CRMs in the genome. The predictions recover 77.9% of known CRMs in the datasets and 89.3% of known CRMs containing at least one predicted CRE. We found that the putative CRMs as well as CREs as a whole in a CRM are more conserved than randomly selected sequences. CONCLUSION: Our results suggest that the CRMs predicted by DePCRM are highly likely to be functional. Our algorithm is the first of its kind for de novo genome-wide prediction of CREs and CRMs using larger number of transcription factor ChIP datasets. The algorithm and predictions will hopefully facilitate the elucidation of gene regulatory networks in eukaryotes. All the predicted CREs, CRMs, and their target genes are available at http://bioinfo.uncc.edu/mniu/pcrms/www/.
Project description:In multi-cellular organisms, spatiotemporal activity of cis-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with cis-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (Co-Occurrence Pattern Search), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.
Project description:Mycobacterium tuberculosis (MTB) is a pathogenic bacterium responsible for 12 million active cases of tuberculosis (TB) worldwide. The complexity and critical regulatory components of MTB pathogenicity are still poorly understood despite extensive research efforts. In this study, we constructed the first systems-scale map of transcription factor (TF) binding sites and their regulatory target proteins in MTB. We constructed FLAG-tagged overexpression constructs for 206 TFs in MTB, used ChIP-seq to identify genome-wide binding events and surveyed global transcriptomic changes for each overexpressed TF. Here we present data for the most comprehensive map of MTB gene regulation to date. We also define elaborate quality control measures, extensive filtering steps, and the gene-level overlap between ChIP-seq and microarray datasets. Further, we describe the use of TF overexpression datasets to validate a global gene regulatory network model of MTB and describe an online source to explore the datasets.
Project description:Cis-regulatory modules (CRMs) function by binding sequence specific transcription factors, but the relationship between in vivo physical binding and the regulatory capacity of factor-bound DNA elements remains uncertain. We investigate this relationship for the well-studied Twist factor in Drosophila melanogaster embryos by analyzing genome-wide factor occupancy and testing the functional significance of Twist occupied regions and motifs within regions. Twist ChIP-seq data efficiently identified previously studied Twist-dependent CRMs and robustly predicted new CRM activity in transgenesis, with newly identified Twist-occupied regions supporting diverse spatiotemporal patterns (>74% positive, n = 31). Some, but not all, candidate CRMs require Twist for proper expression in the embryo. The Twist motifs most favored in genome ChIP data (in vivo) differed from those most favored by Systematic Evolution of Ligands by EXponential enrichment (SELEX) (in vitro). Furthermore, the majority of ChIP-seq signals could be parsimoniously explained by a CABVTG motif located within 50 bp of the ChIP summit and, of these, CACATG was most prevalent. Mutagenesis experiments demonstrated that different Twist E-box motif types are not fully interchangeable, suggesting that the ChIP-derived consensus (CABVTG) includes sites having distinct regulatory outputs. Further analysis of position, frequency of occurrence, and sequence conservation revealed significant enrichment and conservation of CABVTG E-box motifs near Twist ChIP-seq signal summits, preferential conservation of ±150 bp surrounding Twist occupied summits, and enrichment of GA- and CA-repeat sequences near Twist occupied summits. Our results show that high resolution in vivo occupancy data can be used to drive efficient discovery and dissection of global and local cis-regulatory logic.
Project description:Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is the most popular assay to identify genomic regions, called ChIP-seq peaks, that are bound in vivo by transcription factors (TFs). These regions are derived from direct TF-DNA interactions, indirect binding of the TF to the DNA (through a co-binding partner), nonspecific binding to the DNA, and noise/bias/artifacts. Delineating the bona fide direct TF-DNA interactions within the ChIP-seq peaks remains challenging. We developed a dedicated software, ChIP-eat, that combines computational TF binding models and ChIP-seq peaks to automatically predict direct TF-DNA interactions. Our work culminated with predicted interactions covering >4% of the human genome, obtained by uniformly processing 1983 ChIP-seq peak data sets from the ReMap database for 232 unique TFs. The predictions were a posteriori assessed using protein binding microarray and ChIP-exo data, and were predominantly found in high quality ChIP-seq peaks. The set of predicted direct TF-DNA interactions suggested that high-occupancy target regions are likely not derived from direct binding of the TFs to the DNA. Our predictions derived co-binding TFs supported by protein-protein interaction data and defined cis-regulatory modules enriched for disease- and trait-associated SNPs. We provide this collection of direct TF-DNA interactions and cis-regulatory modules through the UniBind web-interface (http://unibind.uio.no).
Project description:Understanding the effect of cis-regulatory elements (CRE) and clusters of CREs, which are called cis-regulatory modules (CRM), in eukaryotic gene expression is a challenge of computational biology. We developed two programs that allow simple, fast and reliable analysis of candidate CREs and CRMs that may affect specific gene expression and that determine positional features between individual CREs within a CRM. The first program, "Exploration of Distinctive CREs and CRMs" (EDCC), correlates candidate CREs and CRMs with specific gene expression patterns. For pairs of CREs, EDCC also determines positional preferences of the single CREs in relation to each other and to the transcriptional start site. The second program, "CRM Network Generator" (CNG), prioritizes these positional preferences using a neural network and thus allows unbiased rating of the positional preferences that were determined by EDCC. We tested these programs with data from a microarray study of circadian gene expression in Arabidopsis thaliana. Analyzing more than 1.5 million pairwise CRE combinations, we found 22 candidate combinations, of which several contained known clock promoter elements together with elements that had not been identified as relevant to circadian gene expression before. CNG analysis further identified positional preferences of these CRE pairs, hinting at positional information that may be relevant for circadian gene expression. Future wet lab experiments will have to determine which of these combinations confer daytime specific circadian gene expression.
Project description:Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.
Project description:Deep sequencing of size-selected DNase I-treated chromatin (DNase-seq) allows high-resolution measurement of chromatin accessibility to DNase I cleavage, permitting identification of de novo active cis-regulatory modules (CRMs) and individual transcription factor (TF) binding sites. We adapted DNase-seq to nuclei isolated from C. elegans embryos and L1 arrest larvae to generate high-resolution maps of TF binding. Over half of embryonic DNase I hypersensitive sites (DHSs) were annotated as noncoding, with 24% in intergenic, 12% in promoters, and 28% in introns, with similar statistics observed in L1 arrest larvae. Noncoding DHSs are highly conserved and enriched in marks of enhancer activity and transcription. We validated noncoding DHSs against known enhancers from myo-2, myo-3, hlh-1, elt-2, and lin-26/lir-1 and recapitulated 15 of 17 known enhancers. We then mined DNase-seq data to identify putative active CRMs and TF footprints. Using DNase-seq data improved predictions of tissue-specific expression compared with motifs alone. In a pilot functional test, 10 of 15 DHSs from pha-4, icl-1, and ceh-13 drove reporter gene expression in transgenic C. elegans Overall, we provide experimental annotation of 26,644 putative CRMs in the embryo containing 55,890 TF footprints, as well as 15,841 putative CRMs in the L1 arrest larvae containing 32,685 TF footprints.
Project description:Establishing the architecture of gene regulatory networks (GRNs) relies on chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) methods that provide genome-wide transcription factor binding sites (TFBSs). ChIP-Seq furnishes millions of short reads that, after alignment, describe the genome-wide binding sites of a particular TF. However, in all organisms investigated an average of 40% of reads fail to align to the corresponding genome, with some datasets having as much as 80% of reads failing to align. We describe here the provenance of previously unaligned reads in ChIP-Seq experiments from animals and plants. We show that a substantial portion corresponds to sequences of bacterial and metazoan origin, irrespective of the ChIP-Seq chromatin source. Unforeseen was the finding that 30%-40% of unaligned reads were actually alignable. To validate these observations, we investigated the characteristics of the previously unaligned reads corresponding to TAL1, a human TF involved in lineage specification of hemopoietic cells. We show that, while unmapped ChIP-Seq read datasets contain foreign DNA sequences, additional TFBSs can be identified from the previously unaligned ChIP-Seq reads. Our results indicate that the re-evaluation of previously unaligned reads from ChIP-Seq experiments will significantly contribute to TF target identification and determination of emerging properties of GRNs.
Project description:Cis-regulatory elements (CREs) control gene expression by recruiting transcription factors (TFs) and other DNA binding proteins. We aim to understand how individual nucleotides contribute to the function of CREs. Here we introduce CRE analysis by sequencing (CRE-seq), a high-throughput method for producing and testing large numbers of reporter genes in mammalian cells. We used CRE-seq to assay >1,000 single and double nucleotide mutations in a 52-bp CRE in the Rhodopsin promoter that drives strong and specific expression in mammalian photoreceptors. We find that this particular CRE is remarkably complex. The majority (86%) of single nucleotide substitutions in this sequence exert significant effects on regulatory activity. Although changes in the affinity of known TF binding sites explain some of these expression changes, we present evidence for complex phenomena, including binding site turnover and TF competition. Analysis of double mutants revealed complex, nucleotide-specific interactions between residues in different TF binding sites. We conclude that some mammalian CREs are finely tuned by evolution and function through complex, nonadditive interactions between bound TFs. CRE-seq will be an important tool to uncover the rules that govern these interactions.