Babelomics Functional interpretation of genome-scale experiments Valencia, March 2008

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Babelomics Functional interpretation of genome-scale experiments Valencia, March 2008. David Montaner dmontaner@cipf.es http://bioinfo.cipf.es Bioinformatics Department Centro de Investigacion Principe Felipe (Valencia).
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BabelomicsFunctional interpretation of genome-scale experimentsValencia, March 2008David Montanerdmontaner@cipf.eshttp://bioinfo.cipf.esBioinformatics DepartmentCentro de Investigacion Principe Felipe(Valencia)Babelomics: A systems biology web resource for the functional interpretation of genome-scale experiments.http://www.babelomics.orgGenome-scale experiment outputFunctionalInterpretationFunctional interpretationTo “interpret” experimental results is to use current knowledge to rearrange them in a meaningful way.Experimental results observed in the lab (not always a wet-lab).
  • Recorded to:
  • Test a hypothesis.
  • Get a first insight of a biological process.
  • DBinformation.Already tested and storedBabelomics imported databasesENSEMBLwww.ensembl.orgGOKEGGInterproTranscription FactorsCisredBioentitiesLiteratureGene expressionHomo sapiensHGNC symbol EMBL accUniProt/Swiss-ProtUniProtKB/TrEMBLEnsembl IDs RefSeq EntrezGene Affymetrix Agilent PDB Protein Id IPI….Mus musculusRattus norvegicusEnsembl IDGallus gallusDrosophila melanogasterCaenorhabditis elegansSaccharmoyces cerevisaeArabidopsis thaliana Babelomics toolsFatiGO: Finds differential distributions of Gene Ontology terms between two groups of genes.FatiGOplus: an extension of FatiGO for InterPro motifs, pathways and SwissProt KW , transcription factors (TF), gene expression in tissues, bioentities from scientific literature, cis-regulatory elements CisRed.Tissues Mining Tool: compares reference values of gene expression in tissues to your results. MARMITE Finds differential distributions of bioentities extracted from PubMed between two groups of genes. FatiScan: detect significant functions with Gene Ontology, InterPromotifs, Swissprot KW and KEGG pathways in lists of genes ordered according to differents characteristics.MarmiteScan: Use chemical and disease-related information to detect related blocks of genes in a gene list with associated values. GSEA: Detects blocks of functionally related genes with significant coordinate over- or under-expression using the Gene Set Enrichment Analysis. Babelomics toolsInformation about genes which can be coded in binary variables, tags, labels.Information about genes which has to be coded into continuous numerical values.FatiGO
  • Compare two lists of genes.
  • Compare one list of genes against the rest of the genome.
  • One statistical test (Fisher’s exact) for each Block of annotation.
  • Multiple testing context.
  • Filtering of annotation is convenient. We test les terms; more interesting ones.
  • ABBiosynthesis62No biosynthesis48Testing the distribution of functional terms among two groups of genes(remember, we have to test hundreds of Blocks)One Gene List (A)The other list (B)Are this two groups of genes carrying out different biological roles?Biosynthesis 60%Biosynthesis 20%Sporulation 20%Sporulation 20%Genes in group A have significantly to do with biosynthesis, but not with sporulation.ABBiosynthesis62No biosynthesis48000Testing the distribution of functional terms among two groups of genes(remember, we have to test hundreds of Blocks)One Gene List (A)The other list (B)Are this two groups of genes carrying out different biological roles?Biosynthesis 60%Biosynthesis 20%Sporulation 20%Sporulation 20%Genes in group A have significantly to do with biosynthesis, but not with sporulation.FatiGO - Babelomics
  • Deal with duplicated genes
  • Exclude them
  • Include them
  • Try to reduce the background space of genes of interest
  • Using just genes with some annotation.
  • Use just genes annotated at certain level of the GO ontology.
  • Functional interpretationTo “interpret” experimental results is to use current knowledge to rearrange them in a meaningful way.Experimental results observed in the lab (not always a wet-lab).
  • Recorded to:
  • Test a hypothesis.
  • Get a first insight of a biological process.
  • DBinformation.Already tested and storedOrganismFatiGO – selecting the databaseDatabaseFatiGO – selecting the databaseYour own DatabaseFatiGO – your own databaseGene List2Rest of genomeGene List1FatiGO – introducing your dataFatiGO ResultsGene group1 is enriched in this functional blockGene group2 is enriched in this functional blockpercentagesp-valuescorrected p-valuesVery few genes selected to arrive to a significant conclusion on GO1 and GO2Functional Classes expressed as blocks in A and BFatiGO approach may not be very powerfullA B GO1 GO2 -Significantly over-expressed in BIf a threshold basedon the experimental values is applied, and the resulting selection of genes compared for enrichment of a functional term, this might not be foundt-test with two tails.p<0.05statisticSignificantly over-expressed in A+FatiScan
  • Interpret a ranked list of genes.
  • There is not need for choosing a cut-off. All information is included.
  • One statistical test for each Block of annotation.
  • Multiple testing context.
  • Filtering of annotation is convenient. We test les terms; more interesting ones.
  • OrganismDataBasesGene Listordered according the experimentalvalueFatiScanTesting along an ordered listAnnotation label AAnnotation label BAnnotation label CBCAList of genes+
  • Index ranking genes according to some biological aspect under study.
  • Database that stores gene class membership information.
  • FatiScan searches over the whole ordered list, trying to find runs of functionally related genes.
  • Block of genes enriched in the annotation AAnnotation C is homogeneously distributed along the listBlock of genes enriched in the annotation B-FatiScan resultsBCAList of genes+-Functional Blocks over-represented among genes over-expressed in AFunctional Blocks over-represented among genes over-expressed in BFatiScan results A B+Gene ranking index-FatiScanList of genes ranked by biological criteria+Fisher´s testSignificant Functional terms-Al-Shahrour et al., 2005 Bioinformatics; 2007 BMC BioinformaticsFatiScan Example – two classesTumor Controlt ~Tumor mean expression – Control mean expression+ tProliferationIs more associated with the genes on the top of the listAll genes in the arrayIs more associated with the genes that show higher expression in Tumors- tFatiScan Example - Survival Analysis
  • Cromer et all. Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis. Oncogene 2004, 23(14) : 2484-2498.
  • 34 hypopharyngeal cancer samples taken from patients undergoing surgery.
  • Analyzed using Affymetrix HG-U95A microarrays (~12650 distinct transcription features ).
  • Disease free survival time after intervention was recorded
  • Cox proportional hazards modelh(t) = h0 (t) * exp (b * gene expression)Gene Ontology: biological processHazard increased with expression+ blowest p-value = 0.96Hazard decreased with expression- b
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