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Respective contributions of MIAME, GeneOntology and UMLS for transcriptome analysis

Respective contributions of MIAME, GeneOntology and UMLS for transcriptome analysis. Fouzia Moussouni, Anita Burgun, Franck Le Duff, Emilie Guérin, Olivier Loréal INSERM U522 and Medical Informatics Laboratory, CHU Pontchaillou Rennes, FRANCE. Transcriptome & DNA microarray

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Respective contributions of MIAME, GeneOntology and UMLS for transcriptome analysis

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  1. Respective contributions of MIAME, GeneOntology and UMLS for transcriptome analysis Fouzia Moussouni, Anita Burgun, Franck Le Duff, Emilie Guérin, Olivier Loréal INSERM U522 and Medical Informatics Laboratory, CHU Pontchaillou Rennes, FRANCE

  2. Transcriptome & DNA microarray study of transcriptionnal response of the cell Normal Pathologic Response to chemics or foods treatment Response to a growth factor Response to genetic disturbances

  3.  DNA mutation(s) Hemochromatosis…  Chronic liver diseases IRON overload ? Mechanisms  Fibrosis  Cirrhosis  Hepatocarcinoma Pathological situations studied at INSERM U522

  4. 1 measure 1 Expression Level 1 Spot intensity 1 gene but multiple facets ! Experimental Raw Data Available knowledge on the expressed genes, that need to be capturized and organized. One may deposit thousands of genes Intensive data generation

  5. One gene but multiple descriptions • Nucleic Sequence components - promoters, introns, exons, transcripts, regulators, … • Chromosomal localization, • Functional proteins and known genes products, • Tissue distribution, • Known gene interactions, • Expression level in physiologic and pathologic conditions, • Known gene variations, • Clinical Implications, • Literature and bibliographic data on a gene.

  6. ? ? ? Data cleaning ! Gene Expression warehouse Analysis Integration SAGE Micro-arrays Substractive banks Need of an integrated gene expression environment (for the liver!) External Sources Clinical Data experimental data

  7. BIO KNOWLEDGE Gene Expression Warehouse Standardization and controlled specification ONTOLOGY DESIGN Knowledge extraction and data exchange

  8. Respective contributions MIAME GO UMLS Standardization ONTOLOGY DESIGN

  9. MIAME MIAME will provide a standard framework to represent the minimum information that must be reported about microarray experiments : • Experience • Array • Samples • Hybridization • Measures • Normalisation and control Work in progress ... Minimum information about a microarray experiment (MIAME) toward standards for microarray data', A. Brazma, at al., Nature Genetics, vol 29 (December 2001), pp 365 - 371.

  10. GOA GeneOntology (GO) GO is an ontology for molecular biology and Genomics, But GO is not populated with : • gene sequences • gene products, ...

  11. UMLS • The Unified Medical Language System (UMLS) is intended to help health professionals and researchers to use biomedical information from different sources.

  12. Examples from iron metabolism are studied • How pathologic disease states related to iron metabolism alteration are described in GO and UMLS ?

  13. alteration PATHOLOGIC STATES Iron metabolism diseases Other diseases hyperferritinemia cataract Other diseases hyperferritinemia cataract Iron overload aceruloplasminemia Iron deficiency BIOLOGICAL MODEL FOR IRON METABOLISM IRON METABOLISM GENES

  14. Gene Ceruloplasmin mutation NO Feroxydase activity in plasma Fe2+ Fe3+ Iron binding with plasmatic transferrin THE IRON STAYS INSIDE THE CELL !! Iron overload due to a gene alteration Iron overload during Aceruloplasminemia NO

  15. alteration PATHOLOGIC STATES Iron metabolism diseases Other diseases hyperferritinemia cataract Iron overload aceruloplasminemia Iron deficiency BIOLOGICAL MODEL FOR IRON METABOLISM IRON METABOLISM GENES

  16. IRE gene Translation in excess L_Ferritin mutation IRP L_Ferritin protein in excess A second scenario related to iron metabolism genes alteration Cataract and hyperferritinemia mRNA L_Ferritin CATARACT and HYPERFERRITINEMIA !

  17. Iron compound Metalloprotein H_Ferritin L_Ferritin IRP Iron Sulfur Prot RNAbinding Protein Cataract UMLS view Cataract and hyperferritinemia AA, Peptide or Prorein Biologically Active Substance Ferritin AA, Peptide or Protein Co-occurs In Medline IRE Co-occurs In Medline (freq 26)

  18. Cell component Ligand binding Prot or carrier Ferritin Ferric iron binding Iron homeostasis Iron transport Ferritin Light Chain Metabolism IRE Hydro-lyase Cataract GO/ GOAnnotations view Cataract and hyperferritinemia Link in GO Annotations DB Ferritin Heavy Chain IRP

  19. Ligand binding Prot or carrier Ferritin Ferric iron binding Iron homeostasis Ferritin Heavy Chain Iron transport Ferritin Light Chain IRE Dynamic links Modeling of biological functions Target representation Cataract and hyperferritinemia Hyperferritinemia Genes Mutated genes IRP Cataract

  20. Information on disease states, clinical treatments and followups. Normal vs. pathologic DNA Chips Information on biological samples, experiments and results ? Information on Roles of the genes in Biological and metabolic states And more generally … Recapitulative UMLS MIAME We need precise and dynamic models to get the whole picture GOA

  21. Gene products for Iron metabolism, as they are actually described in GO and UMLS.

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