Dataset Information

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

ABSTRACT: 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.

SUBMITTER: Worsley Hunt R

PROVIDER: S-EPMC4082612 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

Worsley Hunt Rebecca R Mathelier Anthony A Del Peso Luis L Wasserman Wyeth W WW

BMC genomics 20140613

<h4>Background</h4>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).<h4>Results</h4>We introduce a set of methods to improve the interpretation of ChIP-Seq data, including ...[more]

PMID: 24927817

Similar Datasets

Project description:BackgroundMotif 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.ResultsWe 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.ConclusionsWe 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.

Dataset Information

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

Publications

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets