General Information
| Description | Scanning for cis-regulatory motif clusters |
| Homepage | http://alternate.meme-suite.org |
Publications
MCAST: scanning for cis-regulatory motif clusters
Summary:
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.
Results:
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.
Availability and implementation:
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.org.
Contact:
t.bailey@imb.uq.edu.au or william-noble@uw.edu
Supplementary information
: Supplementary data are available at Bioinformatics online.
Related Tools
CompleteMOTIFs
Published December 2010
DNA motif discovery platform for transcription factor binding experiments
LedPred
Published December 2015
An R/bioconductor package to predict regulatory sequences using support vector machines
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