Gene Ontology Enrichment Network Analysis -Tutorial
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This document outlines a tutorial for conducting gene ontology network enrichment analysis using various bioinformatics resources, primarily focusing on the DAVID platform. Key tasks include conducting enrichment analysis, visualizing results with Revigo, and creating networks using Cytoscape. The tutorial also provides details on input requirements, statistical tests for enrichment, and links to relevant databases and tools.
Gene Ontology Enrichment Network Analysis -Tutorial
- 1. Dmitry Grapov, PhD Gene Ontology Network Enrichment Analysis
- 2. Download all material for the tutorial https://sourceforge.net/projects/teachingdemos/files/2014%2 https://sourceforge.net/projects/teachingdemos/files/ Choose 2014 UC Davis Proteomics Workshop or use the full URL below
- 3. • decrease • increase Use functional analysis to identify if the changes in variables are enriched (increased compared to random chance) for some biological pathway, domain or ontological category.
- 4. Enrichment or Overrepresentation analysis Biochemical Pathway Biochemical Ontology
- 5. Major Tasks Using the proteins listed in the excel workbook: ‘proteomic data for analysis.xlsx’ and worksheet: ‘protein IDs’ 1. Conduct Gene Ontology (GO) Enrichment Analysis using DAVID Bioinformatics Resources http://david.abcc.ncifcrf.gov/home.jsp 2. Investigate enriched terms using Quick GO http://www.ebi.ac.uk/QuickGO/ 3. Summaries and visualize the results using REVIGO http://revigo.irb.hr/ 4. Create and modify GO network using Cytoscape http://www.cytoscape.org/
- 6. Protein IDs Common protein identifier UniProt/SwissProt Accession (default in scaffold) http://www.uniprot.org/ Use Biomart to translate to other database IDS http://www.biomart.org/ e.g. gene symbols
- 8. David Bioinformatics Resources 1. Upload list 2. Choose ID type 3. Select list type 4. Submit
- 9. David Bioinformatics Resources organism Make sure all IDs were recognized List of biochemical databases tested for enrichment
- 10. David Bioinformatics Resources List of biochemical databases tested for enrichment 1. Choose GO
- 12. David Bioinformatics Resources List of biochemical databases tested for enrichment 1. Overview BP: Biological process 2. Select
- 14. David Bioinformatics Resources 1. Overview most enriched term
- 15. Quick GO http://www.ebi.ac.uk/QuickGO/ 1. View children (lower hierarchy subsets) of this term
- 16. David Bioinformatics Resources/Quick GO 1. Can you identify any enriched children of this term in our DAVID output? ? 2. Download results
- 17. Overview and Format Results in Excel 1. Save results 2. Open in MS Excel
- 18. Overview Results Modified Fisher’s Exact Test p-value optionally: Check in R x<-data.frame(user=c(1,47),genome=c(690,13528)) fisher.test(x) # p-value = 5.41e-06 (13/47) / (690/13528)
- 19. Alternative to Fisher Exact Test: Hypergeometric Test How to calculate statistics to determine enrichment? hit.num = 51 # number of significantly changed pathway variables set.num = 1455 # number of variables in pathway full = 3358 # all possible variables in organism q.size = 72 # number of significantly changed variables phyper(hit.num-1, set.num, full-set.num, q.size, lower.tail=F) enrichment p-value = 1.717553e-06
- 20. Visualization Options Challenges: •Removal of redundant information •Visualizing term relationships (term-term, term-protein)
- 21. Use REVIGO to filter redundant terms http://revigo.irb.hr/ prepare input (term, p-value) 1. Upload to REVIGO Supek F, Bošnjak M, Škunca N, Šmuc T. "REVIGO summarizes and visualizes long lists of Gene Ontology terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021800 2. Run
- 22. REVIGO: overview scatterplot Position defined on similarity (MDS)
- 23. REVIGO: overview table Cluster leaders prioritized based on enrichment p-value
- 24. REVIGO: network • Edges: 3% of the strongest GO term pairwise similarities • Node size: generality of term (small = specific) • Node color: p-value Download network
- 25. Cytoscape 1. Open Cytoscape Import REVIGO network into cytoscape 2 3 4
- 26. Cytoscape: set layout and defaults 1. Set layout 3. Set network defaults 2 4 5
- 27. Cytoscape: map data to network properties 1. Set Edge width and color 2. Set Node labels, size and color
- 28. Cytoscape: overview network components Download edge information 1 2 3. View in excel Download node information 1 2 3. View in excel
- 29. Bonus: Modify Edge and Node Attributes to show term to protein connections See file ‘test edge.xlsx’ and ‘test node.xslx, for examples of upload formats See detailed instructions at http://www.slideshare.net/dgrapov/demonstration-of-network-mapping
- 30. See more Statistical and Multivariate Analysis Examples at http://imdevsoftware.wordpress.com/tutorials/ Questions? [email protected] This research was supported in part by NIH 1 U24 DK097154