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Course on Functional Analysis ::: Daniel Rico, PhD. [email protected] Introduction to Functional Analysis Bioinformatics Unit CNIO ::: Schedule. 1. 2. 3. 4. Biological (Functional) Databases Threshold-based and threshold free methods Threshold-based example: FatiGO. Threshold free example 1: FatisScan. ACKNOWLEDGEMENTS Many of these slides have been taken and adapted from original slides by Fatima Al-Shahrour from Joaquin Dopazo’s group (Babelomics team). We are grateful for the material and for the great tools they have developed!!!! Homo sapiens Mus Rattus musculus norvegicus Gallus gallus UniProt/Swiss-Prot EntrezGene UniProtKB/TrEMBL Affymetrix Ensembl IDs Agilent Danio rerio Drosophila Caenorhabditis melanogaster elegans Genes IDs Saccharmoyces Arabidopsis thaliana cerevisae HGNC symbol PDB EMBL acc Protein Id RefSeq IPI…. Biological databases KEGG pathways Gene Ontology Biological Process Molecular Function Cellular Component Biocarta pathways Regulatory elements miRNA CisRed Transcription Factor Binding Sites Keywords Swissprot InterPro Motifs Gene Expression in tissues Bioentities from literature: Diseases terms Chemical terms Gene Ontology CONSORTIUM http://www.geneontology.org • The objective of GO is to provide controlled vocabularies for the description of the molecular function, biological process and cellular component of gene products. • These terms are to be used as attributes of gene products by collaborating databases, facilitating uniform queries across them. • The controlled vocabularies of terms are structured GO structure The three categories of GO Molecular Function GO tree structure the tasks performed by individual gene products; examples are transcription factor and DNA helicase Biological Process broad biological goals, such as mitosis or purine metabolism, that are accomplished by ordered assemblies of molecular functions Cellular Component subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and origin recognition complex IS_A relation PART_OF relation http://www.genome.ad.jp/kegg/pathway.html http://www.biocarta.com/genes/index.asp http://www.reactome.org/ http://www.pathwaycommons.org http://www.whichgenes.org/ http://www.cisred.org/ ::: Schedule. 1. 2. 3. 4. Biological (Functional) Databases Threshold-based and threshold free methods Threshold-based example: FatiGO. Threshold free example 1: FatisScan. Threshold-based functional analysis Threshold-free functional analysis Study the enrichment in functional terms in groups of genes defined by the experimental value. Select genes taking into account their functional properties. FatiGO FatiScan GOminer DAVID GSEA Marmite MarmiteScan The two-steps approach • • Genes of interest are selected using the experimental value. Selected genes are compared to the background. • Under a systems biology perspective. • Detect blocks of functionally related genes. Threshold-based functional analysis Class1 Class2 FDR<0.05 ttest cut-off FDR<0.05 Biological meaning? Threshold-free functional analysis - Gene Set 1 Class1 Class2 Gene Set 2 Gene Set 3 Gene set 3 enriched in Class 2 ES/NES statistic ttest cut-off Gene set 2 enriched in Class 1 + ::: Schedule. 1. 2. 3. 4. Biological (Functional) Databases Threshold-based and threshold free methods Threshold-based example: FatiGO. Threshold free example 1: FatisScan. http://babelomics.bioinfo.cipf.es/ ::: How the functional profiling should never be done It is not uncommon to find the following assertion in papers and talks: “then we examined our set of genes selected in this way (whatever) and we discover that 65% of them were related to metabolism, so we can conclude that our experiment activates metabolism genes”. Annotation is not a functional result!!! ::: Exercise 1: FatiGO SEARCH 1. Select “FatiGO Search” ” and “H. sapiens”. 2. Upload FatiGO_example.txt file 3. Select “KEGG pathways” and click “Run” ::: Exercise 1: FatiGO SEARCH 1. Select “FatiGO Search” ” and “H. sapiens”. 2. Upload FatiGO_example.txt file 3. Select “KEGG pathways” and click “Run” FatiGO-Search annotations Testing the distribution of GO terms among two groups of genes (remember, we have to test hundreds of GOs) Group A Group B Are this two groups of genes carrying out different biological roles? Biosynthesis 60% Biosynthesis 20% Sporulation Sporulation 20% Genes in group A have significantly to do with biosynthesis, but not with sporulation. Biosynthesis No biosynthesis A 6 4 20% B 2 8 Using FatiGO Comparing groups of genes List1: genes of interest (they are significantly over- or underexpressed when two classes of experiments are compared, colocated in the chromosomes, etc.) List2:the background (typically the rest of genes). Select suitable database, Run... Remove genes repeated in list1 “clean” List1 List1 Remove genes repeated between both lists Extract functional terms “clean” List2 List2 Remove genes repeated in list2 BABELOMICS GO KEGG Interpro KW Bioentities Gene Expression TF Cisred Matrix of functional terms 011000101010101 001 ...... 11001010 ........... 010001010 ........... 0110001010 ........... 1111001111......... ...... Fisher´s test Adjust p-value by FDR Significant functional terms List 1b / List 2b Class1 Class2 FDR<0.05 ttest cut-off FDR<0.05 List 1 List 2 (background) ::: Exercise 2: FatiGO COMPARE 1. Select “FatiGO Compare” and “H. sapiens”. 2. Upload FatiGO_example.txt file 3. Select “Rest of Genome” as background. 4. Select “KEGG pathways” and click “Run” ::: Exercise 2: FatiGO COMPARE 1. Select “FatiGO Compare” and “H. sapiens”. 2. Upload FatiGO_example.txt file 3. Select “Rest of Genome” as background. 4. Select “KEGG pathways” and click “Run” Only “Apoptosis” is significant