Omic space: coordinate-based integration and analysis of genomic phenomic interactions

MOTIVATION With the recent progress in genomics, various data sets of omic interactions describing networks of omic elements have become available. In order to obtain reliable hypotheses from the data, it is effective to integrate interactions from different sorts of data sets. In order to facilitate a coordinate-based integration and analysis of omic interactions, we introduce the concept of an omic space comprising a comprehensive set of omic planes. Genomic, transcriptomic, proteomic, metabolomic, phenomic and other omic planes are defined by two orthogonal genomic-coordinate axes. RESULTS We show that the omic space concept helps us to assimilate biological findings comprehensively into hypotheses or models combining higher-order phenomena and lower-order mechanisms by demonstrating that a comprehensive ranking of correspondences among interactions in the space can be used effectively for estimating candidates of responsible gene pairs for epistatic interacting loci of tumors in mice. We also show that the omic space offers a convenient framework for database integration, by presenting a system named the 'Genome <==> Phenome Superhighway' (GPS) that serves as a framework for integration and visualization of omic interactions based on omic spaces of some model species including Homo sapiens, Mus musculus, Caenorhabditis elegans and Arabidopsis thaliana. AVAILABILITY For the GPS web site, see http://omicspace.riken.jp/gps/.

[1]  J. Nap,et al.  Genetical genomics : the added value from segregation , 2001 .

[2]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[3]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[4]  B. Reinhart,et al.  Prediction of Plant MicroRNA Targets , 2002, Cell.

[5]  Gary D Bader,et al.  Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry , 2002, Nature.

[6]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Yoshiyuki Sakaki,et al.  Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries. , 2003, DNA research : an international journal for rapid publication of reports on genes and genomes.

[8]  Albert-László Barabási,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002 .

[9]  Mark Gerstein,et al.  Bridging structural biology and genomics: assessing protein interaction data with known complexes. , 2002, Trends in genetics : TIG.

[10]  G. Church,et al.  Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae , 2001, Nature Genetics.

[11]  D. Eisenberg,et al.  Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  M. Hattori,et al.  The DNA sequence of human chromosome 21 , 2000, Nature.

[13]  D. Beach,et al.  Cdc25 cell-cycle phosphatase as a target of c-myc , 1996, Nature.

[14]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Takeo Yoshikawa,et al.  Identification of multiple genetic loci linked to the propensity for "behavioral despair" in mice. , 2002, Genome research.

[16]  L. Kruglyak,et al.  Genetic Dissection of Transcriptional Regulation in Budding Yeast , 2002, Science.

[17]  Akihiko Konagaya,et al.  KnowledgeEditor: a new tool for interactive modeling and analyzing biological pathways based on microarray data , 2003, Bioinform..

[18]  Gary D Bader,et al.  Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants , 2001, Science.

[19]  R. Stoughton,et al.  Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.

[20]  Philip Lijnzaad,et al.  The Ensembl genome database project , 2002, Nucleic Acids Res..

[21]  Akihiko Konagaya,et al.  GSCope: a clipped fisheye viewer effective for highly complicated biomolecular network graphs , 2003, Bioinform..

[22]  K. Hunter,et al.  An epistatic interaction controls the latency of a transgene-induced mammary tumor , 2000, Mammalian Genome.

[23]  Ritsert C. Jansen,et al.  Studying complex biological systems using multifactorial perturbation , 2003, Nature Reviews Genetics.

[24]  Edison T Liu,et al.  A bioinformatics-based strategy identifies c-Myc and Cdc25A as candidates for the Apmt mammary tumor latency modifiers. , 2002, Genome research.

[25]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[26]  M. Kanehisa.  A database for post-genome analysis. , 1997, Trends in genetics : TIG.

[27]  D. Eisenberg,et al.  Detecting protein function and protein-protein interactions from genome sequences. , 1999, Science.

[28]  M. Vidal.  A Biological Atlas of Functional Maps , 2001, Cell.

[29]  Peter D. Karp,et al.  The MetaCyc Database , 2002, Nucleic Acids Res..

[30]  Yoshihide Hayashizaki,et al.  The mammalian protein-protein interaction database and its viewing system that is linked to the main FANTOM2 viewer. , 2003, Genome research.

[31]  Tetsuro Toyoda,et al.  Selection of Candidate Genes for Polygenic Diseases Utilizing Protein-Protein Interaction Networks , 2000 .

[32]  G A Churchill,et al.  Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. , 2001, Genome research.

[33]  A G Murzin,et al.  SCOP: a structural classification of proteins database for the investigation of sequences and structures. , 1995, Journal of molecular biology.

[34]  Tim J. P. Hubbard,et al.  SCOP: a Structural Classification of Proteins database , 1999, Nucleic Acids Res..