1. Glycome Informatics for the Practical Application of Computational Models
Kiyoko F. Aoki-Kinoshita
Dept. of Bioinformatics, Soka University
(formerly of Bioinformatics University)
Frontiers in Glycomics: Bioinformatics and Biomarkers in Disease
September 11-13, 2005

2. Another vocabulary term
Glycome Informatics
Algorithms, methods and computational models for the study of the glycome
(Glycome: the repertoire of glycans in a cell, tissue, or organism)

3. Informatics Techniques for Glycomics
Mass spec prediction/annotation
StrOligo (M. Ethier et al, Methods Mol Biol., 2006)
Cartoonist (D. Goldberg et al, Proteomics, 2005)
H. Tang et al, Bioinformatics, 2005
Method using glycan arrays
Structure prediction from glycosyltransferase expression data (S. Kawano et al, Bioinformatics, 2005)

4. Algorithmic Techniques for Glycome Informatics
Computer Theoretic Algorithms for Trees
KCaM: K.F. Aoki et al, NAR, 2004
Score matrix for glycan linkages, K.F. Aoki et al, Bioinformatics, 2005
Least common supertree approximation algorithm for reconstructing glycans from spectral data, K.F. Aoki-Kinoshita et al, ISAAC 2006
Probabilistic Models
PSTMM, N. Ueda et al, TKDE, 2005
Profile PSTMM, K.F. Aoki-Kinoshita et al, ISMB 2006
OTMM, Hashimoto et al, KDD 2006
Kernel Methods
Leukemia marker detection, Y. Hizukuri et al, Carbohydrate Research, 2005
General purpose marker detection, T. Kuboyama et al, GIW 2006 (submitted)
Proteins
Glycans
Smith-Waterman
KCaM
PAM/ BLOSUM
Glycan Score Matrix
(Profile) HMM
(Profile) PSTMM

5. Applications of Probabilistic Models
Statistically compute the common patterns in tree structures
Profile PSTMM (Probabilistic Sibling-dependent Tree Markov Model)
Provided binding affinity data for a specific lectin, compute the most likely structure being recognized
Statistically compute the key patterns of sulfation in GAGs based on various biological measurements (i.e. inhibition)

6. Kernel Methods Machine learning method
Machine learning method
e.g. Support Vector Machines (SVM)
Can handle features in high-dimensions
e.g. Expression data, pathway information, localization information, etc.
Statistically computes commonalities by reducing the dimensions of the data
Data classification
Feature extraction

7. Leukemia-specific features
Hizukuri et al, Carbohydr. Res. 340, (2005).
Used KEGG GLYCAN data:
Entries whose CarbBank annotations were related to leukemic cells, erythrocytes, plasma and serum
Predicted possible glycan markers
Correlated well with experimental data

8. Glyco-Databases & Resources：the Vision
XML
XML
XML
XML
XML
Bacterial
Carbohydrate
Structure DataBase

9. Resource for Glycome Informatics at Soka (RINGS)
Goal: a publicly available web resource of tools for glycome analysis
Started April, 2006
Currently based on KEGG GLYCAN, REACTION, ENZYME data
Glycan structures
Glycan interaction information
Links proteins to related glycans
3D protein data from PDB
Currently available tools:
BLAST Server for nucleotide and protein sequences
2D Drawing Tool written in Java for queries
Glycan structure estimation from microarray expression data

10. DrawRINGS 2D glycan structure drawing tool
Can also query the RINGS database and retrieve similar structures
Resulting Glycan IDs are linked to corresponding entry pages
Each Interaction that the resulting glycans are involved in are also listed

12. BLAST Search
Glycan-related proteins can be searched for by sequence using BLAST

13. 3D Protein Structures

14. RINGS Microarray Tool
Based on method of S. Kawano et al., Bioinformatics, 2005
Input:
Glycosidic bonds and values
Corresponding to glycosyltransferase expression data
Output:
Glycan structures and related interaction information

15. RINGS Microarray Tool

16. Ongoing Work Implementation of more free web-based tools for analysis
Careful incorporation of other data is planned
Glycosciences, CFG, BCSDB, etc.
MSn data...
But...

17. For the Community What tools are currently lacking? Web Portal
Glycosciences.de has links to many resources
Search of multiple resources from a single query interface
By structure
By protein
By disease
Or combo of the above
Web-based MS analysis tool
Open to suggestions/requests

18. Acknowledgements
Masao Ichikawa, Shuichi Ikeda, Kouichi Yamada, Takako Yamaguchi
NIH