Dataset Information

MCAST: scanning for cis-regulatory motif clusters.

ABSTRACT: Precise regulatory control of genes, particularly in eukaryotes, frequently requires the joint action of multiple sequence-specific transcription factors. A cis-regulatory module (CRM) is a genomic locus that is responsible for gene regulation and that contains multiple transcription factor binding sites in close proximity. Given a collection of known transcription factor binding motifs, many bioinformatics methods have been proposed over the past 15 years for identifying within a genomic sequence candidate CRMs consisting of clusters of those motifs.The MCAST algorithm uses a hidden Markov model with a P-value-based scoring scheme to identify candidate CRMs. Here, we introduce a new version of MCAST that offers improved graphical output, a dynamic background model, statistical confidence estimates based on false discovery rate estimation and, most significantly, the ability to predict CRMs while taking into account epigenomic data such as DNase I sensitivity or histone modification data. We demonstrate the validity of MCAST's statistical confidence estimates and the utility of epigenomic priors in identifying CRMs.MCAST is part of the MEME Suite software toolkit. A web server and source code are available at http://meme-suite.org and http://alternate.meme-suite.orgt.bailey@imb.uq.edu.au or william-noble@uw.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Grant CE

PROVIDER: S-EPMC4907379 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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MCAST: scanning for cis-regulatory motif clusters.

Grant Charles E CE Johnson James J Bailey Timothy L TL Noble William Stafford WS

Bioinformatics (Oxford, England) 20151224 8

<h4>Unlabelled</h4>Precise regulatory control of genes, particularly in eukaryotes, frequently requires the joint action of multiple sequence-specific transcription factors. A cis-regulatory module (CRM) is a genomic locus that is responsible for gene regulation and that contains multiple transcription factor binding sites in close proximity. Given a collection of known transcription factor binding motifs, many bioinformatics methods have been proposed over the past 15 years for identifying with ...[more]

PMID: 26704599

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Project description:BACKGROUND:cis-Regulatory modules (CRMs) of eukaryotic genes often contain multiple binding sites for transcription factors. The phenomenon that binding sites form clusters in CRMs is exploited in many algorithms to locate CRMs in a genome. This gives rise to the problem of calculating the statistical significance of the event that multiple sites, recognized by different factors, would be found simultaneously in a text of a fixed length. The main difficulty comes from overlapping occurrences of motifs. So far, no tools have been developed allowing the computation of p-values for simultaneous occurrences of different motifs which can overlap. RESULTS:We developed and implemented an algorithm computing the p-value that s different motifs occur respectively k1, ..., ks or more times, possibly overlapping, in a random text. Motifs can be represented with a majority of popular motif models, but in all cases, without indels. Zero or first order Markov chains can be adopted as a model for the random text. The computational tool was tested on the set of cis-regulatory modules involved in D. melanogaster early development, for which there exists an annotation of binding sites for transcription factors. Our test allowed us to correctly identify transcription factors cooperatively/competitively binding to DNA. METHOD:The algorithm that precisely computes the probability of simultaneous motif occurrences is inspired by the Aho-Corasick automaton and employs a prefix tree together with a transition function. The algorithm runs with the O(n|Sigma|m|H| + K|sigma|K) Piiki) time complexity, where n is the length of the text |Sigma| is the alphabet size, m is the maximal motif length, |H| is the total number of words in motifs, K is the order of Markov model, and ki is the number of occurrences of the ith motif. CONCLUSION:The primary objective of the program is to assess the likelihood that a given DNA segment is CRM regulated with a known set of regulatory factors. In addition, the program can also be used to select the appropriate threshold for PWM scanning. Another application is assessing similarity of different motifs. AVAILABILITY:Project web page, stand-alone version and documentation can be found at http://bioinform.genetika.ru/AhoPro/

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MCAST: scanning for cis-regulatory motif clusters.

Publications

MCAST: scanning for cis-regulatory motif clusters.

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