|Interface||Command line interface|
|Restrictions to use||None|
|Input data||A Protein Binding Mircoarray (PBM) probe intensity file.|
|Output data||A Hidden Markov Model (HMM) motif model and the most probable path motif logo.|
|Operating system||Unix/Linux, Mac OS, Windows|
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- person_outline Zhaolei Zhang <>
- person_outline Ka-Chun Wong <>
Publication for kmerHMM
2013 Nucleic Acids Res
Department of Computer Science, University of Toronto, Toronto, ON, Canada; Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada; Department of Integrative Biology and Physiology, University of California Los Angeles, Los Angeles, CA, USA; Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, Saudi Arabia; Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
kmerHMM funding source(s)
Supported by Discovery Grant from Natural Sciences and Engineering Research Council, Canada (NSERC), grant number [327612-2009 RGPIN]; Acres Inc. – Joseph Yonan Memorial Fellowship, Kwok Sau Po Scholarship, and International Research and Teaching Assistantship from University of Toronto; Cofunded by NSERC Canada Graduate Scholarship and Ontario Graduate Scholarship.
Excellent Tool for Multiple Motifs Disocovery
kmerHMM is designed for motif discovery on protein binding microarray (PBM) data. Especially, its multiple motif elucidation ability has been developed to such an extent that ad hoc processing is not needed by using Hidden Markov Model (HMM). I believe the software can be used on other data with sufficient modification. Especially, its methodology is very mature and novel (belief propagation and HMM).