Strategies for functional modeling TAMU GO Workshop 17 May 2010

Types of data sets and modeling  Commercial array data – more likely to have ID mapping to support functional modeling.  Custom/USDA array data – may need to do your own ID mapping: see examples on workshop page.  Proteomics data  RNA-Seq data sets – computational pipelines to assign GO (GOanna is limited; contact AgBase).  Real-time data or quantitative proteomics data – hypothesis testing.

Protein/Gene identifiers GORetriever GO annotations Genes/Proteins with no GO annotations GOanna Pathways and network analysis GO Enrichment analysis ArrayIDer Microarray Ids GOSlimViewer Yellow boxes represent AgBase tools Green/Purple boxes are non-AgBase resources Ingenuity Pathways Analysis (IPA) Pathway Studio Cytoscape DAVID Ingenuity Pathways Analysis (IPA) Pathway Studio Cytoscape DAVID EasyGO/AgriGO Onto-Express Onto-Express-to-go (OE2GO) Overview of Functional Modeling Strategy

Functional Modeling Considerations  Should I add my own GO? use GOSlimViewer to see how much GO is available for your species use GORetriever to see how much GO is available for your dataset  Should I do GO analysis and pathway analysis and network analysis? different functional modeling methods show different aspects about your data (complementary) is this type of data available for your species (or a close ortholog)?  What tools should I use? which tools have data for your species of interest? what type of accessions are accepted? availability (commercial and freely available)

Converting accessions  Depending on your data set & the tools you use, you are likely to need to convert between database accessions to do your functional modeling.  UniProt database – ID mapping tab  Ensembl BioMart  Online analysis tools: DAVID g:profiler GORetriever  ArrayIDer – converts EST accessions

Converting accessions (cont’d)  Commercial arrays  Custom arrays  EST arrays  Proteomics  RNA-Seq data  Commercial ID mapping eg. NetAffy  Ensembl BioMart  Online tools (g:convert, DAVID)  ArrayIDer  UniProt ID Conversion

Working on your own data or examples: 1. Your own data set retrieve existing GO (accession conversion?) & group using slim sets try functional grouping (DAVID, AgriGO, etc) 2. New to GO GO browser tutorials to familiarize yourself with GO work on some example data sets 3. Example data sets

Your own data  Start by retrieving existing GO (GORetriver) may need to do accession conversion  GOanna – for sequence data sets If you haven’t had results returned from GOanna, sample results are available in the example data sets  Try functional analysis using DAVID, AgriGO or etc  For help with hypothesis modeling etc, see me.

GO Browsers  search for gene products  search for GO terms  retrieve batch GO  some analysis tools (slim sets, enrichment analysis, etc)  QuickGO at EBI  AmiGO at GO Consortium

Example Dataset 1 Chicken Affymetrix Array 1. Converting Accessions 2. Retrieving GO annotations 3. Grouping using GOSlimViewer 4. GO term enrichment analysis using DAVID 5. GO term enrichment analysis using AgriGO

Example Dataset 2 EST Array and adding your own GO 1. Converting Accessions 2. Retrieving GO annotations 3. Adding GO annotations 4. GO enrichment analysis using additional GO annotations

Example Dataset 3 Modeling quantitative data 1. GOModeler 2. agriGO

What other information should we add to your workshop website??