GIMSAN: A Gibbs motif finder with significance analysis

GIMSAN

GIMSAN (GIbbsMarkov with Significance ANalysis) is a novel web-server tool for de novo motif discovery:

Hybrid Gibbs sampler for motif finding that uses a Bayesian prior on the percentage of sequences containing sites but uses a maximum likelihood approach on the motif matrix
Biologically realistic and reliable statistical significance analysis
- Using a 3-Gamma approximation scheme
- Factoring local sequence composition information
- Reports an approximate 95% confidence interval of the point estimator of the motif p-value
Can be used to select the optimal width from a range of motif widths based on our significance analysis
Column pair dependency estimated using a Monte-Carlo scheme

It is currently available as a web application at:

GIMSAN @ BioHPC

It is also available as a stand-alone application on Unix and PBS (Portable Batch System) cluster:

Download GIMSAN @ Unix

Commercial use of GIMSAN without written permission from the authors is prohibited. If you use this program in your research, please cite:

Patrick Ng, Uri Keich. GIMSAN: A Gibbs motif finder with significance analysis. Bioinformatics, 24 (19): 2256-2257, 2008.

The motif significance analysis approach is described in detail in:

Patrick Ng, Uri Keich. Factoring local sequence composition in motif significance analysis. Proceedings of the 19th International Conference on Genome Informatics (GIW 2008), In press.
Uri Keich, Patrick Ng. A conservative parametric approach to motif significance analysis. Proceedings of the 18th International Conference on Genome Informatics (GIW), Singapore 2007.

If you use the sequence logos from GIMSAN in your research, please cite WebLogo.

GIMSAN input options

Options	Cmdline	Description
input FASTA	--f	a set of sequences in FASTA format for de novo motif discovery
background model	--bg	file (FASTA format) for background model estimation. For example, this can be a set of S. Cerevisiae intergenic sequences. This data is used to generate null sets of sequences that preserve the dimensions and local GC-content of the input set, as well as estimating the background model for the de novo motif-finding task. Note: It is recommended that the user either "upload your own genomic file" or use "one of our standard genomic files".
motif widths	--w	user can specify a range of motif widths. We previously showed that selecting the optimal width based on our significance analysis can improve the results of de novo motif discovery
size of nullset	--nullset	size of the randomly drawn set to estimate the motif-finder's null distribution based on 3-Gamma approximation. A larger null set would give a more accurate p-value at the expense of longer runtime.
number of processors		the number of processors to allocate on the computer cluster. The specified number of processors are allocated before any execution of the job. Therefore, it is recommended that this parameter should be set to less than 5.

GIMSAN sample output on FHL1 motif

We thank Robert Bukowski for deploying GIMSAN as a web application. GIMSAN is based upon work supported by the National Science Foundation under Grant No. 0644136.

Please send any questions, comments, or suggestions to ppn3@cs.cornell.edu