title: Differential motif enrichment analysis of paired ChIP-seq experiments.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.forename: Tom
authors.initials: T
authors.lastname: Lesluyes
authors.forename: James
authors.initials: J
authors.lastname: Johnson
authors.forename: Philip
authors.initials: P
authors.lastname: Machanick
authors.affiliation: Institute for Molecular Bioscience, The University of Queensland, 306 Carmody Road, 4072 Brisbane, Australia. t.bailey@imb.uq.edu.au.
authors.forename: Timothy L
authors.initials: TL
authors.lastname: Bailey
mesh_terms: D001665:Binding Sites; D002460:Cell Line; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D059372:Nucleotide Motifs; D056510:Position-Specific Scoring Matrices; D011401:Promoter Regions, Genetic; D011485:Protein Binding; D013629:Tamoxifen / Q000494:pharmacology; D013997:Time Factors; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors; D013629:Tamoxifen