CMfinder is a tool to predict RNA motifs in unaligned sequences. It is an expectation maximization algorithm using covariance models for motif description, featuring novel integration of multiple techniques for effective search of motif space, and a Bayesian framework that blends mutual information-based and folding energy-based approaches to predict structure in a principled way.
Zizhen Yao, Zasha Weinberg, Walter L. Ruzzo , The Computational & Synthetic Biology group – COMPUTER SCIENCE & ENGINEERING at UNIVERSITY OF WASHINGTON
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Methods Mol Biol. 2014;1097:303-18. doi: 10.1007/978-1-62703-709-9_15.
De novo discovery of structured ncRNA motifs in genomic sequences.
Ruzzo WL， Gorodkin J.
Z. Yao, Z. Weinberg, W. Ruzzo, “CMfinder–a covariance model based RNA motif finding algorithm”, Bioinformatics, vol. 22 (2006) 445-52. Pubmed 16357030.