A Genetic Algorithm Guided Formation of Spaced Dyads Coupled with an EM Algorithm for Motif Discovery
Previously, we reported GADEM, an efficient de novo motif discovery tool for large-scale genomic sequence data. We present an updated version, v1.3.1, that has improvements and additions. We added a 'seeded' analysis in which a user-specified position weight matrix (PWM) is the starting PWM model. Seeded analyses are at least 10x faster and perhaps more accurate than the already scalable 'unseeded' analyses, and can identify short and less abundant motifs, and variants of dominant motifs. We propose an approach for estimating the number of binding sites in the data, include non-uniform motif priors that take advantage of the high spatial resolution of ChIP-seq data. Finally, runs now report each motif's fold enrichment in input data vs. background/random sequence data. These changes substantially enhance GADEM's functionality and efficiency for motif discovery in large-scale genomic data.
This program was developed by Leping Li at the National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709.
This work is made available under the GPL v3.
Download the source code for the distribution of GADEM along with usage documentation and examples. (gadem_v1.3.1.tar.gz) (482KB) (last update 05/16/2011)
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