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MotifSuite

Fig fail : motifsuite.png
Fig.1 : Overview of functionalities offered in MotifSuite.

MotifSuite

MotifSuite provides a platform for de novo regulatory motif detection using a stochastic motif detection algorithm with various motif assessment tools. The suite revolves around MotifSampler, a de novo motif detection tool based on Gibbs sampling that searches for an overrepresented motif in a set of coregulated input sequences. In addition to the core sampler, MotifSuite provides tools to automatically merge the results of multiple stochastic sampling runs and to perform downstream analyses.

When used as an integrated flow, MotifSuite guides you through the whole process of de novo motif detection going from the selection of a background model, the actual Gibbs sampling, summarizing the output of multiple Gibbs sampling runs, till screening the remainder of the genome with the retrieved motif models (Fig.1, read more in MotifSuite overview). Alternatively, each application within MotifSuite can be used separately and has its own entry page. Submission will initiate the software command on our server where you can download the applications'output files from.
Optionally, a stand-alone executable of each application can be downloaded for use on your own platform.

The suite consists of the following functionalities:

  • generation of a genome-specific background model which can be selected from our database or compiled using CreateBackgroundModel.
  • de novo motif detection by means of MotifSampler. MotifSampler is a Gibbs sampling based de novo motif detection algorithm that performs multiple searches for candidate regulatory motifs. Its output is a long list of redundant and not necessarily all relevant solutions.
  • prioritizing motifs: we provide tools to postprocess the output of multiple runs of a Gibbs sampling algorithm (or any other stochastic motif detection algorithm). MotifRanking reorganizes the multiple motif detection solutions (PWMs) in a shorter list of non-redundant motifs sorted by their motif score. FuzzyClustering constructs consensus motifs by analyzing the multiple motif detection solutions at their instance level.
  • comparing motifs: MotifComparison analyzes if your detected motif (PWM) corresponds to any of previously described motif models reported in curated databases or detected by yourself in previous analyses.
  • genome-wide screening: MotifLocator scans a given set of genome-wide sequences for potential novel instances of your detected motif.

Continue with:Link
Read a more comprehensive description on MotifSuite: Read MotifSuite overview
Run the applications: Run CreateBackgroundModel, Run MotifSampler, Run MotifRanking, Run FuzzyClustering, Run MotifComparison, Run MotifLocator
Read application guidelines: Read CreateBackgroundModel, Read MotifSampler, Read MotifRanking, Read FuzzyClustering, Read MotifComparison, Read MotifLocator
Read requested formats: Read Fasta format, Read PWM format, Read Instances Format, Read Background Model format
Read results of a case study (E.coli RegulonDB): Setup E.coli Benchmark Dataset, Case Study MotifSampler, Case Study MotifRanking, Case Study FuzzyClustering
Download applications: Download Standalones
Use an integrated de novo motif detection workflow: Use Step By Step Approach

Publications & citing MotifSuite:

If you like our software, please use the MotifSuite publication for citing:
MotifSuite: workflow for probabilistic motif detection and assessment. M. Claeys; V. Storms; H. Sun; T. Michoel; K. Marchal. Bioinformatics 2012; 28(14):1931-1932. doi: 10.1093/bioinformatics/bts293

MotifSuite is open source and free to use "as is" for academic and commercial purposes. However, no parts of the source code may be reused in its original or modified form as part of other commercial software projects without the consent of the authors.

Questions & suggestions:

Contact us.

Revisions:

Last revisions :
(webinterface) April/2015 (version 1.3: relocation UG-PSB/UG-Intec).
(software) Jan/2014 (debug in MotifLocator, PSP in MotifSampler),
(databases) Sept/2013 (add Tomato backgroundmodel),
(webinterface) Oct/2012 (version 1.2: relocation KUL/UG).