CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2014 vol.11 Issue No.05 - Sept.-Oct.
Issue No.05 - Sept.-Oct. (2014 vol.11)
Xiaoqing Liu , School of Software, Dalian University of Technology, Dalian, China
Jun Wu , School of Software, Dalian University of Technology, Dalian, China
Haipeng Gong , School of Software, Dalian University of Technology, Dalian, China
Shengchun Deng , School of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China
Zengyou He , School of Software, Dalian University of Technology, Dalian, China
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 execution. The source code of C-Motif is available at: https://sourceforge.net/ projects/cmotif/.
Peptides, IEEE transactions, Computational biology, Bioinformatics, Amino acids, Proteins, Data mining,data mining, Phosphorylation motif, protein phosphorylation, frequent pattern
Xiaoqing Liu, Jun Wu, Haipeng Gong, Shengchun Deng, Zengyou He, "Mining Conditional Phosphorylation Motifs", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.11, no. 5, pp. 915-927, Sept.-Oct. 2014, doi:10.1109/TCBB.2014.2321400