1. GO-based tools for functional modeling GO Workshop 3-6 August 2010

2. Functional Modeling  Grouping by function  GO Slim sets  GO browser tools  GOSlimViewer  GO enrichment analysis  DAVID  EasyGO/agriGO  Onto-Express  Funcassociate 2.0  Pathway & network analysis  Hypothesis testing

3. Grouping by function

4. GO Slim Sets  slim sets are abbreviated versions of the GO  contain broader functional terms  made by different GO Consortium groups (for different purposes, eg. plant, yeast, etc)  need to cite which one you used! More information about GO terms for each slim set can be found at EBI QuickGO: http://www.ebi.ac.uk/QuickGO/ GO Slim and Subset Guide http://www.geneontology.org/GO.slims.shtml

5. QuickGO: Create your own subset/slim of GO terms  http://www.ebi.ac.uk/QuickGO/ http://www.ebi.ac.uk/QuickGO/  GO slims tutorial available  This tutorial will describe GO slims, what they are used for and how to use QuickGO for: * creating a custom GO slim * using a pre-defined GO slim * obtaining GO annotations to a GO slim * customising a set of slimmed annotations * using statistics calculated by QuickGO to generate graphical representations of the data

6. AmiGO: GO Slimmer  http://amigo.geneontology.org/cgi- bin/amigo/slimmer?session_id=4878amig o1273279396 http://amigo.geneontology.org/cgi- bin/amigo/slimmer?session_id=4878amig o1273279396

8. GOSlimViewer input file Input is a text file containing 3 tab separated columns: 1.accession 2.GO:ID 3.aspect (P,F or C) file provided by GORetriever and GOanna2ga can manually add to it from GOanna excel file allows you to include your additional GO annotations in the analysis

9. GOSlimViewer output

12. GO Enrichment analysis

13. http://www.geneontology.org/ Determining which classes of gene products are over-represented or under-represented.

15. However….  many of these tools do not support agricultural species  the tools have different computing requirements A list of these tools that can be used for agricultural species is available on the workshop website at the “Summary of Tools for gene expression analysis” link.

16. Evaluating GO tools Some criteria for evaluating GO Tools: 1. Does it include my species of interest (or do I have to “humanize” my list)? 2. What does it require to set up (computer usage/online) 3. What was the source for the GO (primary or secondary) and when was it last updated? 4. Does it report the GO evidence codes (and is IEA included)? 5. Does it report which of my gene products has no GO? 6. Does it report both over/under represented GO groups and how does it evaluate this? 7. Does it allow me to add my own GO annotations? 8. Does it represent my results in a way that facilitates discovery?

17. Some useful expression analysis tools:  Database for Annotation, Visualization and Integrated Discovery (DAVID) http://david.abcc.ncifcrf.gov/  AgriGO -- GO Analysis Toolkit and Database for Agricultural Community http://bioinfo.cau.edu.cn/agriGO/ used to be EasyGO chicken, cow, pig, mouse, cereals, dicots includes Plant Ontology (PO) analysis  Onto-Express http://vortex.cs.wayne.edu/projects.htm#Onto-Express can provide your own gene association file  Funcassociate 2.0: The Gene Set Functionator http://llama.med.harvard.edu/funcassociate/ can provide your own gene association file

19. http://david.abcc.ncifcrf.gov/  functional grouping – including GO, pathways, gene-disease association  ID Conversion  search functionally related genes  regular updates  online support & publications

20.  May 2010: EasyGO replaced by agriGO http://bioinformatics.cau.edu.cn/easygo/

21.  enrichment analysis using either GO or Plant Ontology (PO)  40 species: chicken, cow, pig, mouse, cereals, poplar, fruits  GenBank, EMBL, UniProt  Affymetrix, Operon, Agilent arrays http://bioinfo.cau.edu.cn/agriGO/

22. Onto-Express Onto-Express analysis instructions are Available in onto-express.ppt http://vortex.cs.wayne.edu/projects.htm

23. Species represented in Onto-Express

24. Can upload your own annotations using OE2GO

25. http://llama.med.harvard.edu/funcassociate/

26. Pathway & network analysis

27. GO, Pathway, Network Analysis  Many GO analysis tools also include pathway & network analysis  Ingenuity Pathways Analysis (IPA) and Pathway Studios – commercial software  DAVID – includes multiple functional categories  Onto-Tools – includes Pathways Express tool

28. Pathways & Networks  A network is a collection of interactions  Pathways are a subset of networks Network of interacting proteins that carry out biological functions such as metabolism and signal transduction  All pathways are networks of interactions  Not all networks are pathways

29. KEGG http://www.genome.jp/kegg/pathway.html/ BioCyc http://www.biocyc.org/ Reactome http://www.reactome.org/ GenMAPP http://www.genmapp.org/ BioCarta http://www.biocarta.com/ Pathguide – the pathway resource list http://www.pathguide.org/ Pathways Resources

31. Biological Networks  Networks often represented as graphs  Nodes represent proteins or genes that code for proteins  Edges represent the functional links between nodes (ex regulation)  Small changes in graph’s topology/architecture can result in the emergence of novel properties

32. Types of interactions  protein (enzyme) – metabolite (ligand) metabolic pathways  protein – protein cell signaling pathways, protein complexes  protein – gene genetic networks

33. Sod1 Mus musculus Network example: STRING Database http://string.embl.de/

34. Database/URL/FTP DIP http://dip.doe-mbi.ucla.edu BIND http://bind.ca MPact/MIPS http://mips.gsf.de/services/ppi STRING http://string.embl.de MINT http://mint.bio.uniroma2.it/mint IntAct http://www.ebi.ac.uk/intact BioGRID http://www.thebiogrid.org HPRD http://www.hprd.org ProtCom http://www.ces.clemson.edu/compbio/ProtCom 3did, Interprets http://gatealoy.pcb.ub.es/3did/ Pibase, Modbase http://alto.compbio.ucsf.edu/pibase CBM ftp://ftp.ncbi.nlm.nih.gov/pub/cbm SCOPPI http://www.scoppi.org/ iPfam http://www.sanger.ac.uk/Software/Pfam/iPfam InterDom http://interdom.lit.org.sg DIMA http://mips.gsf.de/genre/proj/dima/index.html Prolinks http://prolinks.doe- mbi.ucla.edu/cgibin/functionator/pronav/ Predictome http://predictome.bu.edu/ PLoS Computational Biology March 2007, Volume 3 e42

35. Some comments on analysis tools:  > 68 GO based analysis tools listed on the GO Consortium website (not a comprehensive list!)  several tools combine GO, pathway and network functional analysis  many different ways of visualizing the results  expanding the species supported by analysis tools – check with tool developers  check for last updates & user support information

36. Tutorial 5 In this tutorial we will use several GO modeling tools. We will use GOSlimViewer to summarize the GO function from the cassava data set. Next we will use two GO enrichment analysis tools, DAVID and AgriGO to do GO enrichment analysis of a maize data set and compare the results from the two tools.

37. http://www.agbase.msstate.edu/