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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 study of transcriptionnal response of the cell Normal Pathologic Response to chemics or foods treatment Response to a growth factor Response to genetic disturbances Pathological situations studied at INSERM U522 DNA mutation(s) Hemochromatosis… Chronic liver diseases IRON overload Mechanisms Fibrosis Cirrhosis Hepatocarcinoma One may deposit thousands of genes 1 measure 1 Expression Level 1 Spot intensity Intensive data generation 1 gene but multiple facets ! Experimental Raw Data Available knowledge on the expressed genes, that need to be capturized and organized. 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. Need of an integrated gene expression environment (for the liver!) External Sources ? ? ? Data cleaning ! Clinical Data Integration experimental data SAGE Micro-arrays Substractive banks Gene Expression warehouse Analysis BIO KNOWLEDGE Gene Expression Warehouse Standardization and controlled specification ONTOLOGY DESIGN Knowledge extraction and data exchange Standardization ONTOLOGY DESIGN Respective contributions MIAME GO UMLS MIAME MIAME will provide a standard framework to represent the minimum information that must be reported about microarray experiments : • • • • • • Experience Work in progress ... Array Samples Hybridization Measures Normalisation and control 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. GeneOntology (GO) GO is an ontology for molecular biology and Genomics, But GO is not populated with : gene sequences gene products, ... GOA UMLS The Unified Medical Language System (UMLS) is intended to help health professionals and researchers to use biomedical information from different sources. Examples from iron metabolism are studied How pathologic disease states related to iron metabolism alteration are described in GO and UMLS ? BIOLOGICAL MODEL FOR IRON METABOLISM IRON METABOLISM GENES alteration PATHOLOGIC STATES Iron metabolism diseases Iron overload aceruloplasminemia Iron deficiency Other diseases hyperferritinemia cataract Iron overload due to a gene alteration Iron overload during Aceruloplasminemia Gene Ceruloplasmin mutation NO NO Feroxydase activity in plasma Fe2+ Fe3+ Iron binding with plasmatic transferrin THE IRON STAYS INSIDE THE CELL !! BIOLOGICAL MODEL FOR IRON METABOLISM IRON METABOLISM GENES alteration PATHOLOGIC STATES Iron metabolism diseases Iron overload aceruloplasminemia Iron deficiency Other diseases hyperferritinemia cataract A second scenario related to iron metabolism genes alteration Cataract and hyperferritinemia mRNA gene L_Ferritin L_Ferritin mutation IRE Translation in excess IRP L_Ferritin protein in excess CATARACT and HYPERFERRITINEMIA ! UMLS view Cataract and hyperferritinemia Iron compound AA, Peptide or Prorein Biologically Active Substance Metalloprotein Ferritin AA, Peptide or Protein H_Ferritin L_Ferritin Co-occurs In Medline IRE RNAbinding Protein Iron Sulfur Prot Co-occurs In Medline (freq 26) Cataract IRP GO/ GOAnnotations view Cataract and hyperferritinemia Cell component Ligand binding Prot or carrier Ferritin Ferric iron binding Link in GO Annotations DB Ferritin Heavy Chain Iron homeostasis Iron transport Ferritin Light Chain Metabolism IRE IRP Hydro-lyase Cataract Target representation Cataract and hyperferritinemia Ligand binding Prot or carrier Ferritin Ferric iron binding Iron homeostasis Ferritin Heavy Chain Iron transport Ferritin Light Chain Genes Mutated genes IRE IRP Hyperferritinemia Dynamic links Modeling of biological functions Cataract And more generally … Recapitulative Information on disease states, clinical treatments and followups. Normal vs. pathologic UMLS DNA Chips Information M on biological I samples, A experiments and results M E We need precise and dynamic models to get the whole picture ? GOA Information on Roles of the genes in Biological and metabolic states Gene products for Iron metabolism, as they are actually described in GO and UMLS.
