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Combining Parallel Gibbs Sampling with Maximal Cliques for hunting DNA Motif GSMC

Provides an accurate method for DNA motif discovery, especially for detecting cofactor motifs in better large-scale ChIP-Seq data. GSMC uses Gibbs sampling to generate initial motifs and then employs maximal cliques to cluster them under Similarity with Position Information Contents (SPIC) and finally regards the first motif for each cluster as an output motif.

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GSMC forum

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GSMC classification

GSMC specifications

Software type:
Application/Script
Restrictions to use:
None
Programming languages:
C++
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux
Computer skills:
Advanced
Maintained:
Yes

GSMC distribution

versioning

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No versioning.

GSMC support

Maintainer

  • Shu-Lin Wang <>

Credits

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Publications

Institution(s)

College of Computer Science and Electronics Engineering, Hunan University, Changsha, China; Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA

Funding source(s)

Supported by the grants of the National Science Foundation of China (Grant Nos. 61472467, 61672011, and 61471169) and the Collaboration and Innovation Center for Digital Chinese Medicine of 2011 Project of Colleges and Universities in Hunan Province.

Link to literature

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