SIOMICS--Systematic Identification Of Motifs In ChIP-Seq data
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.

SIOMICS predictions
SIOMICS Prediction Results on 13 mouse ES cell TF ChIP-seq datasets.
There are 7 results files for each dataset.
  • X.motifs file was the predicted motifs.
  • file was the predicted motif modules, the significant motif combinations of the predicted motifs.
  • X.motifs.JASPAR.pdf file was the comparison results between X.motifs and motif database JASPAR v2010
  • X.motifs.TRANSFAC.pdf file was the comparison results between X.motifs and motif database TRANSFAC v11.3
  • file was the simple interaction format file based on, which can be visualized by cytocape.
  • X.tfbs file was the TFBSs information file for the predicted motifs.
  • running.log was the running parameters the users have specified and the time cost of this running.
See manual for the details of those outputs

Besides, we have also combined the X.motifs.JASPAR.pdf and X.motifs.TRANSFAC.pdf into a single text file so that users can easily find out whether a predicted motifs is similar with known motifs in databases.
comparison of SIOMICS predicted motifs with known TRANSFAC and JASPAR motifs
The predicted motifs were compared to JASPAR 2010 and TRANSFAC V11.3 by using STAMP .

We have developed a Extension Version for SIOMICS, which enables more powerful functions. Please see the following page for details. SIOMICS Extension

Please cite our paper:
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.