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Bioinformatics 2006 22(14):e25-e34; doi:10.1093/bioinformatics/btl244
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains

Kiyoko F. Aoki-Kinoshita *,{dagger}, Nobuhisa Ueda , Hiroshi Mamitsuka and Minoru Kanehisa

Bioinformatics Center, Institute for Chemical Research, Kyoto University

*To whom correspondence should be addressed.

Motivation: Carbohydrate sugar chains, or glycans, are considered the third major class of biomolecules after DNA and proteins. They consist of branching monosaccharides, starting from a single monosaccharide. They are extremely vital to the development and functioning of multicellular organisms because they are recognized by various proteins to allow them to perform specific functions. Our motivation is to study this recognition mechanism using informatics techniques from the data available. Previously, we introduced a probabilistic sibling-dependent tree Markov model (PSTMM), which we showed could be efficiently trained on sibling-dependent tree structures and return the most likely state paths. However, it had some limitations in that the extra dependency between siblings caused overfitting problems. The retrieval of the patterns from the trained model also involved manually extracting the patterns from the most likely state paths. Thus we introduce a profilePSTMM model which avoids these problems, incorporating a novel concept of different types of state transitions to handle parent-child and sibling dependencies differently.

Results: Our new algorithms are more efficient and able to extract the patterns more easily. We tested the profilePSTMM model on both synthetic (controlled) data as well as glycan data from the KEGG GLYCAN database. Additionally, we tested it on glycans which are known to be recognized and bound to proteins at various binding affinities, and we show that our results correlate with results published in the literature.

Contact: kkiyoko{at}t.soka.ac.jp


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K. Hashimoto, I. Takigawa, M. Shiga, M. Kanehisa, and H. Mamitsuka
Mining significant tree patterns in carbohydrate sugar chains
Bioinformatics, August 15, 2008; 24(16): i167 - i173.
[Abstract] [Full Text] [PDF]


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