OPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-Seq datasets.
Ontology highlight
ABSTRACT:
SUBMITTER: Kwon AT
PROVIDER: S-EPMC3429929 | biostudies-literature | 2012 Sep
REPOSITORIES: biostudies-literature
Publications
oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-Seq datasets.
Kwon Andrew T AT Arenillas David J DJ Worsley Hunt Rebecca R Wasserman Wyeth W WW
G3 (Bethesda, Md.) 20120901 9
oPOSSUM-3 is a web-accessible software system for identification of over-represented transcription factor binding sites (TFBS) and TFBS families in either DNA sequences of co-expressed genes or sequences generated from high-throughput methods, such as ChIP-Seq. Validation of the system with known sets of co-regulated genes and published ChIP-Seq data demonstrates the capacity for oPOSSUM-3 to identify mediating transcription factors (TF) for co-regulated genes or co-recovered sequences. oPOSSUM- ...[more]
Similar Datasets
Project description:The identification of over-represented transcription factor binding sites from sets of co-expressed genes provides insights into the mechanisms of regulation for diverse biological contexts. oPOSSUM, an internet-based system for such studies of regulation, has been improved and expanded in this new release. New features include a worm-specific version for investigating binding sites conserved between Caenorhabditis elegans and C. briggsae, as well as a yeast-specific version for the analysis of co-expressed sets of Saccharomyces cerevisiae genes. The human and mouse applications feature improvements in ortholog mapping, sequence alignments and the delineation of multiple alternative promoters. oPOSSUM2, introduced for the analysis of over-represented combinations of motifs in human and mouse genes, has been integrated with the original oPOSSUM system. Analysis using user-defined background gene sets is now supported. The transcription factor binding site models have been updated to include new profiles from the JASPAR database. oPOSSUM is available at http://www.cisreg.ca/oPOSSUM/
| S-EPMC1933229 | biostudies-literature
Project description:ChIP-seq is increasingly used to characterize transcription factor binding and chromatin marks at a genomic scale. Various tools are now available to extract binding motifs from peak data sets. However, most approaches are only available as command-line programs, or via a website but with size restrictions. We present peak-motifs, a computational pipeline that discovers motifs in peak sequences, compares them with databases, exports putative binding sites for visualization in the UCSC genome browser and generates an extensive report suited for both naive and expert users. It relies on time- and memory-efficient algorithms enabling the treatment of several thousand peaks within minutes. Regarding time efficiency, peak-motifs outperforms all comparable tools by several orders of magnitude. We demonstrate its accuracy by analyzing data sets ranging from 4000 to 1,28,000 peaks for 12 embryonic stem cell-specific transcription factors. In all cases, the program finds the expected motifs and returns additional motifs potentially bound by cofactors. We further apply peak-motifs to discover tissue-specific motifs in peak collections for the p300 transcriptional co-activator. To our knowledge, peak-motifs is the only tool that performs a complete motif analysis and offers a user-friendly web interface without any restriction on sequence size or number of peaks.
| S-EPMC3287167 | biostudies-literature
Project description:MOTIVATION:The availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets. RESULTS:We herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes. AVAILABILITY AND IMPLEMENTATION:Source code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms. SUPPLEMENTARY INFORMATION:Supplementary materials are available at Bioinformatics online.
| S-EPMC6853706 | biostudies-literature
Project description:MotivationAdvances 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.ResultsThe 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.AvailabilityMEME-ChIP is available as part of the MEME Suite at http://meme.nbcr.net.
| S-EPMC3106185 | biostudies-literature
Project description:The ChIP-seq technique enables genome-wide mapping of in vivo protein-DNA interactions and chromatin states. Current analytical approaches for ChIP-seq analysis are largely geared towards single-sample investigations, and have limited applicability in comparative settings that aim to identify combinatorial patterns of enrichment across multiple datasets. We describe a novel probabilistic method, jMOSAiCS, for jointly analyzing multiple ChIP-seq datasets. We demonstrate its usefulness with a wide range of data-driven computational experiments and with a case study of histone modifications on GATA1-occupied segments during erythroid differentiation. jMOSAiCS is open source software and can be downloaded from Bioconductor 1.
| S-EPMC4053760 | biostudies-literature
Project description:Motif enrichment analysis of transcription factor ChIP-seq data can help identify transcription factors that cooperate or compete. Previously, little attention has been given to comparative motif enrichment analysis of pairs of ChIP-seq experiments, where the binding of the same transcription factor is assayed under different conditions. Such comparative analysis could potentially identify the distinct regulatory partners/competitors of the assayed transcription factor under different conditions or at different stages of development.We describe a new methodology for identifying sequence motifs that are differentially enriched in one set of DNA or RNA sequences relative to another set, and apply it to paired ChIP-seq experiments. We show that, using paired ChIP-seq data for a single transcription factor, differential motif enrichment analysis identifies all the known key transcription factors involved in the transformation of non-cancerous immortalized breast cells (MCF10A-ER-Src cells) into cancer stem cells whereas non-differential motif enrichment analysis does not. We also show that differential motif enrichment analysis identifies regulatory motifs that are significantly enriched at constrained locations within the bound promoters, and that these motifs are not identified by non-differential motif enrichment analysis. Our methodology differs from other approaches in that it leverages both comparative enrichment and positional enrichment of motifs in ChIP-seq peak regions or in the promoters of genes bound by the transcription factor.We show that differential motif enrichment analysis of paired ChIP-seq experiments offers biological insights not available from non-differential analysis. In contrast to previous approaches, our method detects motifs that are enriched in a constrained region in one set of sequences, but not enriched in the same region in the comparative set. We have enhanced the web-based CentriMo algorithm to allow it to perform the constrained differential motif enrichment analysis described in this paper, and CentriMo's on-line interface (http://meme.ebi.edu.au) provides dozens of databases of DNA- and RNA-binding motifs from a full range of organisms. All data and output files presented here are available at http://research.imb.uq.edu.au/t.bailey/supplementary\_data/Lesluyes2014.
| S-EPMC4167127 | biostudies-literature
Project description:Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.
| S-EPMC3326300 | biostudies-literature
Project description:Thousands of non-coding SNPs have been linked to human diseases in the past. The identification of causal alleles within this pool of disease-associated non-coding SNPs is largely impossible due to the inability to accurately quantify the impact of non-coding variation. To overcome this challenge, we developed a computational model that uses ChIP-seq intensity variation in response to non-coding allelic change as a proxy to the quantification of the biological role of non-coding SNPs. We applied this model to HepG2 enhancers and detected 4796 enhancer SNPs capable of disrupting enhancer activity upon allelic change. These SNPs are significantly over-represented in the binding sites of HNF4 and FOXA families of liver transcription factors and liver eQTLs. In addition, these SNPs are strongly associated with liver GWAS traits, including type I diabetes, and are linked to the abnormal levels of HDL and LDL cholesterol. Our model is directly applicable to any enhancer set for mapping causal regulatory SNPs.
| S-EPMC4288203 | biostudies-literature
Project description:MotivationChIP-seq is a powerful technology to measure the protein binding or histone modification strength in the whole genome scale. Although there are a number of methods available for single ChIP-seq data analysis (e.g. 'peak detection'), rigorous statistical method for quantitative comparison of multiple ChIP-seq datasets with the considerations of data from control experiment, signal to noise ratios, biological variations and multiple-factor experimental designs is under-developed.ResultsIn this work, we develop a statistical method to perform quantitative comparison of multiple ChIP-seq datasets and detect genomic regions showing differential protein binding or histone modification. We first detect peaks from all datasets and then union them to form a single set of candidate regions. The read counts from IP experiment at the candidate regions are assumed to follow Poisson distribution. The underlying Poisson rates are modeled as an experiment-specific function of artifacts and biological signals. We then obtain the estimated biological signals and compare them through the hypothesis testing procedure in a linear model framework. Simulations and real data analyses demonstrate that the proposed method provides more accurate and robust results compared with existing ones.Availability and implementationAn R software package ChIPComp is freely available at http://web1.sph.emory.edu/users/hwu30/software/ChIPComp.html.
| S-EPMC4542775 | biostudies-literature
Project description:Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a "global" optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.
| S-EPMC5468353 | biostudies-literature