article

Mining conditional phosphorylation motifs

Editor: Ying Xu Authors:

Shengchun Deng,

Zengyou HeAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 11, Issue 5

Pages 915 - 927

https://doi.org/10.1109/TCBB.2014.2321400

Published: 01 September 2014 Publication History

Abstract

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/.

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Cited By

Chang XZhu YChen YLi L(2024)DeepNphosComputers in Biology and Medicine10.1016/j.compbiomed.2024.108079170:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108079
He ZGu FZhao CLiu XWu JWang J(2017)Conditional discriminative pattern miningInformation Sciences: an International Journal10.1016/j.ins.2016.09.047375:C(1-15)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.09.047

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Mining conditional phosphorylation motifs

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Information & Contributors

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Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 11, Issue 5

September/October 2014

206 pages

ISSN:1545-5963

Editor:
Ying Xu
Department of Biochemistry, Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 September 2014

Accepted: 21 April 2014

Revised: 04 April 2014

Received: 09 November 2013

Published in TCBB Volume 11, Issue 5

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Cited By

Chang XZhu YChen YLi L(2024)DeepNphosComputers in Biology and Medicine10.1016/j.compbiomed.2024.108079170:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108079
He ZGu FZhao CLiu XWu JWang J(2017)Conditional discriminative pattern miningInformation Sciences: an International Journal10.1016/j.ins.2016.09.047375:C(1-15)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ins.2016.09.047

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