MEIRLOP: improving score-based motif enrichment by incorporating sequence bias covariates.
ABSTRACT: BACKGROUND:Motif enrichment analysis (MEA) identifies over-represented transcription factor binding (TF) motifs in the DNA sequence of regulatory regions, enabling researchers to infer which transcription factors can regulate transcriptional response to a stimulus, or identify sequence features found near a target protein in a ChIP-seq experiment. Score-based MEA determines motifs enriched in regions exhibiting extreme differences in regulatory activity, but existing methods do not control for biases in GC content or dinucleotide composition. This lack of control for sequence bias, such as those often found in CpG islands, can obscure the enrichment of biologically relevant motifs. RESULTS:We developed Motif Enrichment In Ranked Lists of Peaks (MEIRLOP), a novel MEA method that determines enrichment of TF binding motifs in a list of scored regulatory regions, while controlling for sequence bias. In this study, we compare MEIRLOP against other MEA methods in identifying binding motifs found enriched in differentially active regulatory regions after interferon-beta stimulus, finding that using logistic regression and covariates improves the ability to call enrichment of ISGF3 binding motifs from differential acetylation ChIP-seq data compared to other methods. Our method achieves similar or better performance compared to other methods when quantifying the enrichment of TF binding motifs from ENCODE TF ChIP-seq datasets. We also demonstrate how MEIRLOP is broadly applicable to the analysis of numerous types of NGS assays and experimental designs. CONCLUSIONS:Our results demonstrate the importance of controlling for sequence bias when accurately identifying enriched DNA sequence motifs using score-based MEA. MEIRLOP is available for download from https://github.com/npdeloss/meirlop under the MIT license.
Project description:Advances in high-throughput sequencing have resulted in rapid growth in large, high-quality datasets including those arising from transcription factor (TF) ChIP-seq experiments. While there are many existing tools for discovering TF binding site motifs in such datasets, most web-based tools cannot directly process such large datasets.The MEME-ChIP web service is designed to analyze ChIP-seq 'peak regions'--short genomic regions surrounding declared ChIP-seq 'peaks'. Given a set of genomic regions, it performs (i) ab initio motif discovery, (ii) motif enrichment analysis, (iii) motif visualization, (iv) binding affinity analysis and (v) motif identification. It runs two complementary motif discovery algorithms on the input data--MEME and DREME--and uses the motifs they discover in subsequent visualization, binding affinity and identification steps. MEME-ChIP also performs motif enrichment analysis using the AME algorithm, which can detect very low levels of enrichment of binding sites for TFs with known DNA-binding motifs. Importantly, unlike with the MEME web service, there is no restriction on the size or number of uploaded sequences, allowing very large ChIP-seq datasets to be analyzed. The analyses performed by MEME-ChIP provide the user with a varied view of the binding and regulatory activity of the ChIP-ed TF, as well as the possible involvement of other DNA-binding TFs.MEME-ChIP is available as part of the MEME Suite at http://meme.nbcr.net.
Project description:Chromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-Seq) techniques can reveal DNA regions bound by transcription factors (TF). Analysis of the ChIP-Seq regions is now a central component in gene regulation studies. The need remains strong for methods to improve the interpretation of ChIP-Seq data and the study of specific TF binding sites (TFBS).We introduce a set of methods to improve the interpretation of ChIP-Seq data, including the inference of mediating TFs based on TFBS motif over-representation analysis and the subsequent study of spatial distribution of TFBSs. TFBS over-representation analysis applied to ChIP-Seq data is used to detect which TFBSs arise more frequently than expected by chance. Visualization of over-representation analysis results with new composition-bias plots reveals systematic bias in over-representation scores. We introduce the BiasAway background generating software to resolve the problem. A heuristic procedure based on topological motif enrichment relative to the ChIP-Seq peaks' local maximums highlights peaks likely to be directly bound by a TF of interest. The results suggest that on average two-thirds of a ChIP-Seq dataset's peaks are bound by the ChIP'd TF; the origin of the remaining peaks remaining undetermined. Additional visualization methods allow for the study of both inter-TFBS spatial relationships and motif-flanking sequence properties, as demonstrated in case studies for TBP and ZNF143/THAP11.Topological properties of TFBS within ChIP-Seq datasets can be harnessed to better interpret regulatory sequences. Using GC content corrected TFBS over-representation analysis, combined with visualization techniques and analysis of the topological distribution of TFBS, we can distinguish peaks likely to be directly bound by a TF. The new methods will empower researchers for exploration of gene regulation and TF binding.
Project description:DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.
Project description: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.
Project description:The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated noncoding genetic variants. We present a novel TF binding motif representation, the k-mer set memory (KSM), which consists of a set of aligned k-mers that are overrepresented at TF binding sites, and a new method called KMAC for de novo discovery of KSMs. We find that KSMs more accurately predict in vivo binding sites than position weight matrix (PWM) models and other more complex motif models across a large set of ChIP-seq experiments. Furthermore, KSMs outperform PWMs and more complex motif models in predicting in vitro binding sites. KMAC also identifies correct motifs in more experiments than five state-of-the-art motif discovery methods. In addition, KSM-derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory activities of expression quantitative trait loci (eQTL) alleles. Finally, we have applied KMAC to 1600 ENCODE TF ChIP-seq data sets and created a public resource of KSM and PWM motifs. We expect that the KSM representation and KMAC method will be valuable in characterizing TF binding specificities and in interpreting the effects of noncoding genetic variations.
Project description:Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, discovered 23 cofactor motifs for 127 TFs and revealed six high-confidence novel motifs. The reliability and accuracy of these iPWMs were determined via four independent validation methods, including the detection of experimentally proven binding sites, explanation of effects of characterized SNPs, comparison with previously published motifs and statistical analyses. We also predict previously unreported TF coregulatory interactions (e.g. TF complexes). These iPWMs constitute a powerful tool for predicting the effects of sequence variants in known binding sites, performing mutation analysis on regulatory SNPs and predicting previously unrecognized binding sites and target genes.
Project description: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 resembling 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.
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:BACKGROUND: Cell type and TF specific interactions between Transcription Factors (TFs) and cofactors are essential for transcriptional regulation through recruitment of general transcription machinery to gene promoter regions and their identification heavily reliant on protein interaction assays. RESULTS: Using TF targeted chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) data from Encyclopedia of DNA Elements (ENCODE), we report cell type and TF specific TF-cofactor interactions captured in vivo through enrichments of non target cofactor binding site motifs within ChIP-seq peaks. We observe enrichments in both known and novel cofactor motifs. CONCLUSIONS: Given the regulatory implications which TF and cofactor interactions have on a cell's phenotype, their identification is necessary but challenging. Here we present the findings to our analyses surrounding the investigation of TF-cofactor interactions encoded within TF ChIP-seq peaks. Novel cofactor binding site enrichments observed provides valuable insight into TF and cell type specific interactions driving TF interactions.
Project description:Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.