Software Designed for Motif and motif module discovery in high-throughput sequencing data sets

SIOMICS is a software developed to de novo identify motifs in large sequence datasets such as those from ChIP-seq experiments. The output of the software is the ranked motifs and motif modules (significantly co-occurring motif combinations). The statistical evaluation of the predicted motifs and motif modules is also provided. There is no limit on the size of input sequence datasets. Even for those large ChIP-seq datasets, SIOMICS can predict motifs and motif modules in a time-efficient way (e.g. For Ctcf dataset with 49114 peaks, SIOMICS is able to output the predictions within 5 hours).

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13 Mouse ES cell 13 ChIP-seq peak sequences used in the paper.

(2/1/2016) Our SIOMICS3 is released! SIOMICS3 enabled a batch of new features: enabled dyad motif identification, enabled length-variable motif prediction, enabled iterative strategy to predict statistically insignificant motifs and many more. For details, please check SIOMICS3.0.

All SIOMICS Versions

Please cite our papers if using SIOMICS:
Ding J, Hu H, Li X. SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data, Nucleic Acids Research. doi: 10.1093/nar/gkt1288, 2013.
Ding J, Dhillon V, Li X, Hu H. Systematic discovery of cofactor motifs from ChIP-seq data by SIOMICS. Methods . 2014; doi:10.1016/j.ymeth.2014.08.006