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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?
2
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?
3
4
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
5
Hierarchical visualization
• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
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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
• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
Hierarchical visualization
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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
• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
Hierarchical visualization
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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
• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
Hierarchical visualization
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• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
Hierarchical visualization
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• Conversion of chemical compounds • Main metabolites and cofactors
• Manually defined and automatically drawn
Hierarchical visualization
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• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
• 2D chemical structures
• Manually drawn by KegDraw
Hierarchical visualization
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• Overview • Diagram (Map) • Module • Reaction • Molecule (Chemical compound) • Reaction Pattern
• 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
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?
17
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
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?
26
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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