Abstract

BackgroundIn 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.ResultsWe 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.ConclusionOur 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/.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1047) contains supplementary material, which is available to authorized users.

Highlights

  • 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

  • Cooperatively regulate the same regulons in certain cell types by binding to their respective CREs in CRMs, their extended Chromatin immuneprecipitation (ChIP) binding peaks from these cell types should overlap with one another to some extent

  • Overlap of the extended binding peaks of cooperative TFs in the datasets Since D. melanogaster has been long used to study gene transcriptional regulation in metazoans, a relatively large number of its CREs and CRMs have been experimentally characterized, and since a large number of ChIP-chip and ChIP-seq have been generated in the organism in the last few years, we evaluated our algorithm in this organism

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Summary

Introduction

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. It turns out that interpreting a genome is more difficult and challenging than originally thought when a few eukaryotic genomes including the human genome were first released [3,4] With this recognition, the community has taken a more realistic approach by first identifying all the functional sequence elements in the genomes [5,6,7]. While the transcribed sequences specify the potential part list in the cells in an organism, including proteins, various types of RNAs and metabolites, the transcriptional control elements including promoters, enhancers, silencers and insulators together with epigenetic remodeling machineries, determine which protein- or RNA-specifying sequences should be transcribed in each cell during development and under various physiological conditions, thereby specifying the cell’s type during development and specific physiological functions, as it is the dynamic interactions of these components in a cell that determine the cell’s type and specific physiological functions [8]. Once these functional elements are at least partially known, we can move toward to the step to identify dynamic interactions among the functional sequence elements and their products of proteins, RNAs and metabolites in different cell types in the entire life of the organism

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