1. GO-based tools for functional modeling TAMU GO Workshop 17 May 2010

2. Functional Modeling 1. Grouping by function  GO Slim sets  GO browser tools  GOSlimViewer 2. Expression analysis  DAVID  EasyGO/agriGO  Onto-Express  Funcassociate 2.0 3. Pathway & network analysis

3. Workshop Part 2 contains some functional modeling tutorials that use some of these tools.

4. 1. Grouping by function

5. 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

6. 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

7. 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

9. 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 can manually add to it from GOanna excel file allows you to include your additional GO annotations in the analysis

10. GOSlimViewer output

13. 2. Expression analysis

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

16. 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.

17. 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?

18. 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

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

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

22.  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/

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

24. Species represented in Onto-Express

25. Can upload your own annotations using OE2GO

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

27. 3. Pathway & network analysis

28. 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

29. 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

30. 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

32. 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

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

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

36. 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

37. Retrieval of interaction datasets  Evaluate PPI resources such as Predictome or Prolinks for existence of species of interest  If unavailable, find orthologous proteins in related species that have interactions!

38. 4. Hypothesis testing using GO

39.  eGOn v2.0 can test statistical hypotheses of association between gene reporter lists: Master-Target Mutually Exclusive Target-Target Intersecting Target-Target situation  statistical hypothesis testing using the GO  allows addition of extra GO annotation http://www.genetools.microarray.ntnu.no/common/intro.php

40.  visualization for mapping SAGE data onto GO  graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing http://gdm.fmrp.usp.br/cgi-bin/gc/upload/upload.pl

41.  takes a user generated of hypothesis/GO term statements and tests the quantitative effect of gene expression values on these statements http://www.agbase.msstate.edu/cgi-bin/tools/GOModeler.pl

42. 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