Background: Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. Description: It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cis MEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cis MEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. Conclusions:cis MEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cis MEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes.
All Science Journal Classification (ASJC) codes
- Structural Biology
- Modelling and Simulation
- Molecular Biology
- Computer Science Applications
- Applied Mathematics