CMF aims to take advantage of multiple high quality binding datasets to identify subtle regulatory signals, such as context-dependent motifs, within bound sequences. It is specifically designed to discriminate between two sets of bound sequences and takes into account false positive sites when updating PWMs and other model parameters.
Download CMF (Source files), readme file, example data sets and example output file.
Paper 1: Mason M, Plath K and Zhou Q. (2010). Identification of context-dependent motifs by contrasting ChIP binding data. Bioinformatics, 26: 2826-2832.
Paper 2: Lee, Y. and Zhou Q. (2013). Coregulation in embryonic stem cells via context-dependent binding of transcription factors. Bioinformatics, 29: 2162-2168.
R code (with necessary annotation) for liquid association analysis [Li (2002), PNAS, 99: 16875-16880].
CMF is free for academic use. For more information,
please contact zhou@stat.ucla.edu.
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