visualizing metabolic networks in kegg - vizbi

Visualizing Metabolic Networks in KEGG

Susumu Goto Kyoto University

Bioinformatics Center

VIZBI2011: Workshop on Visualizing Biological Data 2011/3/17

Contents

•  How do we visualize the metabolic networks?

•  Why do we visualize them in that way?

•  What can we see by visualizing them in that way?

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Contents




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The metabolic network

• Flow of chemical compounds: • Biosynthesis and biodegradation

• Consecutive enzymatic reactions

• Layout for biochemists

Two concepts

1.  Hierarchical visualization ‒  From reaction to networks

2.  Two aspects of enzyme reactions ‒  Chemical reactions and gene products

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Hierarchical visualization

•  Overview •  Diagram (Map) •  Module •  Reaction •  Molecule (Chemical compound) •  Reaction Pattern

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• Whole view of the network • Network of chemical compounds and reaction sets

• Color coded based on functional classification

• Manually drawn by KegSketch



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• Functional subsets of the whole network • Network of chemical compounds and reactions • Subsets definition based on biochemical knowledge

• Manually drawn by KegSketch



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• Functional subsets of the diagram • Network of chemical compounds and reactions • Biochemical knowledge and genomic contexts

• Manually defined and automatically drawn



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• Conversion of chemical compounds • Main metabolites and cofactors

• Manually defined and automatically drawn


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• 2D chemical structures

• Manually drawn by KegDraw


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• Manually defined reactant pairs

• Automatically aligned and manually modified to draw reaction patterns indicating reaction centers

Two concepts

1.  Hierarchical visualization ‒  From reaction to networks

2.  Two aspects of enzyme reactions ‒  Chemical reactions and gene products

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Two aspects of enzyme reactions 1.  Chemical reaction

‒  EC number ‒  Reaction ID

2.  Gene product ‒  Gene ID ‒  Ortholog ID

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Two aspects of enzyme reactions 1.  Chemical reaction

‒  EC number ‒  Reaction ID

2.  Gene product ‒  Gene ID ‒  Ortholog ID

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Network of enzyme genes

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Various omics data are accumulating

Chemical information

Carbohydrates

Metabolome

Glycome

Lipidome Small molecules

Lipids

Genome

DNA RNA Protein

Coexpression

Protein-protein interaction

Proteome

Metagenome

Transcriptome

Biomolecular information

Current knowledge? •  Pathway •  Function •  Disease •  etc.

Systems information

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Rapid increase of genomic and metabolomic data

Why do we visualize this way?

•  Easily reconstructing metabolic (and other) networks from genomic information

•  Easily interpreting omics data

•  Possibly predicting new metabolic networks

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Integration of metabolic and genomic information is necessary

KEGG Kyoto Encyclopedia of Genes and Genomes

Knowledge on Chemicals KEGG LIGAND

Knowledge on Genome KEGG GENES

Knowledge on Systems KEGG PATHWAY KEGG BRITE

Higher Functions

Linking biological knowledge to omics data such as genomic data 20

Automatic reconstruction 1.  Chemical reaction

2.  Gene product

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•  Functional annotation of a genome

•  Gene -> Ortholog

•  Mapping annotation to networks by coloring

Interpretation of omics data

Knowledge on Chemicals KEGG LIGAND

Knowledge on Genome KEGG GENES

Knowledge on Systems KEGG PATHWAY KEGG BRITE

Higher Functions

Linking biological knowledge to omics data such as genomic data

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Transcriptome Proteome Metabolome

Needs for integrated analysis 　e.g. Kleemann, R., et al., Genome Biol. 8:R200 (2007)

Interpretation of omics data

23 Mapping both transcriptomic and metabolomic data

Pathway prediction

Moriya, Y., et al. Nucleic Acids Res, 38, W138-W143 (2010)

Pathway prediction

Moriya, Y., et al. Nucleic Acids Res, 38, W138-W143 (2010)

RP09390

Reaction score: •  Jaccard coefficients of substrates and products • Weighted to RDM atoms

Pathway score: •  Average of reaction scores

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Species specific metabolic networks

Functional interpretation of omics data

• Relationship between symbiont and host, pathogen and host, human and its gut metagenome, etc.

• Pea aphid ‒  Insect

• Buchnera ‒  Aphid symbiont bacteria

Multiple species at once

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Multiple species at once

28 Pea aphid　Buphnera　Common

Amino acid metabolism

Nucleotide metabolism

Summary

•  How do we visualize the metabolic networks? ‒  Hierarchy: Whole network to molecule ‒  Two aspects: chemical reactions and gene products

•  Why do we visualize them in that way? ‒  Genomic context ‒  Omics data analysis ‒  Layout of the objects and connections

•  What can we see by visualizing them in that way ‒  Multiple species at once ‒  Other networks than metabolism

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Metabolic networks in KEGG

Simple

Informative

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Acknowledgements KEGG Project Leader: Minoru Kanehisa

KEGG in Kyoto Tomomi Kamiya, Rumiko Yamamoto, Miho Furumichi, Tomoko Komeno, Miwako Karikomi, Shiho Ikeuchi, Mayo Ishii, Masami Hamajima, Kanae Morishima, Etsuko Sano, Mao Tanabe, Hiromi Kinoshita, Mika Hirakawa, Masaaki Kotera, Toshiaki Tokimatsu, Yuki Moriya, Zennichi Nakagawa, Junko Yabuzaki

KEGG in Tokyo Yuriko Matsuura, Atsuko Yano, Aya Itoh, Atsuko Yoda, Makiko Ogata, Mari Watanabe, Akiko Hashiguchi, Waka Masuyama, Fumi Kiuchi, Toshiaki Katayama, Shuichi Kawashima

Fujitsu Kyushu Systems: Yoko Sato, Masayuki Kawashima, Junya Ohori, 　 Fujitsu Nagano System Engineering: Satoshi Miyazaki SGI Japan: Koichi Okubo, Hideya Uehara, Kentaro Ozawa

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visualizing metabolic networks in kegg - vizbi

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