Xiaoqing Liu , Xiaoqing Liu is with the School of Software, Dalian University of Technology, Dalian, China. (Email: firstname.lastname@example.org).
Phosphorylation motifs represent position-specific amino acid patterns around the phosphorylation sites in the set of phosphopeptides. Several algorithms have been proposed to uncover phosphorylation motifs, whereas the problem of efficiently discovering a set of significant motifs with sufficiently high coverage and non-redundancy still remains unsolved. Here we present a novel notion called conditional phosphorylation motifs. Through this new concept, the motifs whose over-expressiveness mainly benefits from its constituting parts can be filtered out effectively. To discover conditional phosphorylation motifs, we propose an algorithm called C-Motif for a non-redundant identification of significant phosphorylation motifs. C-Motif is implemented under the Apriori framework, and it tests the statistical significance together with the frequency of candidate motifs in a single stage. Experiments demonstrate that C-Motif outperforms some current algorithms such as MMFPh and Motif-All in terms of coverage and non-redundancy of the results and efficiency of the excution. The source code of C-Motif is available at: https://sourceforge.net/projects/cmotif/.
Xiaoqing Liu, Jun Wu, Haipeng Gong, Shengchun Deng, Zengyou He, "Mining conditional phosphorylation motifs", IEEE/ACM Transactions on Computational Biology and Bioinformatics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TCBB.2014.2321400