Abstract
This chapter describes the Glycan Miner Tool, which is available as a part of the Resource for INformatics of Glycomes at Soka Web site. It implements the α-closed frequent subtree algorithm to find significant subtrees from within a data set of glycan structures, or carbohydrate sugar chains. The results are returned in order of p-value, which is computed based on the probability of the reproducibility of the returned structures. There is also a user-friendly manual that allows users to apply glycan array data from the Consortium for Functional Glycomics. Thus, glycobiologists can take the glycan structures that bind to a particular glycan-binding protein, for example, to retrieve the glycan subtrees that are deemed to be important for the binding to occur.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
References
Hashimoto K, Takigawa I, Shiga M, Kanehisa M, Mamitsuka H (2008) Mining significant tree patterns in carbohydrate sugar chains. Bioinformatics 24:i167–i173
Zaki MJ (2002) Efficiently mining frequent trees in a forest. In: W. Wang and J. Yang (eds.). IEEE Transaction on Knowledge and Data Engineering, Special Issue on Mining Biological Data. Knowledge discovery and data mining. Edmonton, pp. 71–80
Ruckert U, Kramer S (2004) Frequent free tree discovery in graph data. In: H. Jamil and R. Meo (eds.). Proceedings of the 2004 ACM symposium on applied computing. ACM, Nicosia, pp. 564–570
Xiao Y, Yao J.-F (2003) “Efficient data mining for maximal frequent subtrees,” ICDM 2003. Third IEEE International Conference on Data Mining, on November 19--22, pp. 379–386
Chi Y, Muntz R, Nijssen S, Kok J (2005) Frequent subtree mining: an overview. Fundamenta Informaticae 66:161–198
Chi Y, Xia Y, Yang Y, Muntz RR (2005) Mining closed and maximal frequent subtrees from databases of labeled rooted trees. IEEE Trans Knowl Data Eng 17:190–202
Varki A (2009) Essentials of glycobiology. Cold Spring Harbor Laboratory Press, Cold Spring Harbor
Hashimoto K, Goto S, Kawano S, Aoki-Kinoshita K, Ueda N, Hamajima M, Kawasaki T, Kanehisa M (2006) KEGG as a glycome informatics resource. Glycobiology 16:63R–70R
Banin E, Neuberger Y, Altshuler Y, Halevi A, Inbar O, Nir D, Dukler A (2002) A novel Linear Code((R)) nomenclature for complex carbohydrates. Trends in Glycoscience and Glycotechnology. 14:127–137
Raman R, Venkataraman M, Ramakrishnan S, Lang W, Raguram S, Sasisekharan R (2006) Advancing glycomics: implementation strategies at the consortium for functional glycomics. Glycobiology 16:82R–90R
Ranzinger R, Herget S, von der Lieth C-W, Frank M (2011) GlycomeDB—a unified database for carbohydrate structures. Nucleic Acids Res 39:D373–D376
Akune Y, Hosoda M, Kaiya S, Shinmachi D, Aoki-Kinoshita KF (2010) The RINGS resource for glycome informatics analysis and data mining on the Web. OMICS 14:475–486
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this protocol
Cite this protocol
Aoki-Kinoshita, K.F. (2013). Mining Frequent Subtrees in Glycan Data Using the Rings Glycan Miner Tool. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_8
Download citation
DOI: https://doi.org/10.1007/978-1-62703-107-3_8
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-106-6
Online ISBN: 978-1-62703-107-3
eBook Packages: Springer Protocols