1. DRASTIC Database Resource for Analysis of Signal Transduction in Cells www.drastic.org.uk Gary Lyon Interrogating the DRASTIC Gene Expression Database 30 April 2004

2. Aim of DRASTIC To understand signal transduction in response to plant pathogens and other environmental stresses. To assist with putting into context the results of our own gene discovery work within the PPI Programme and Publicity !

3. Why do we need ‘DRASTIC’? Published gene expression data is not searchable. Too much data to remember e.g. microarray data. Cannot match ‘unknown’ genes with prior expression data (14.2% of entries in the database are ‘unknown’). Gene names associated with certain accession numbers change with time. Cell biology is complex. [Simple answers to complex problems are always wrong]

4. For example One gene can have a variety of names : HBZip homeobox domain HD-zip homeobox protein homeobox domain zipper protein transcription factor, homeobox protein Names can be wrong: ‘HB AtHB-14 like’ should be ‘AtHB-9’ ‘Htf9C’ should be ‘RNA methyltransferase-related’ ‘endo 1,4-beta-mannosidase like’ should be ‘protein kinase family’ Names can be confusing: ‘HSR201 like’ ‘RSH2 :Rel-SpoT homology’

5. www.drastic.org.uk

6. Access database Incorporates published data from microarrays and Northerns of ESTs regulated by various treatments (i) Environmental stress e.g. drought, NaCl, high and low temperatures (ii) Pathogens and elicitors (salicylic acid, ethylene, jasmonates) 424 references 266 treatments 67 plant species 10,193 gene accessions

9. Selection by Gene name

14. treatment 1 treatment 2 treatment 3 1 2 3 4 5 6 7 Potential signalling networks

17. Funded by a 1 year PGRA grant from Carnegie Trust awarded to: University of Abertay –Dr Les Ball, Dr Louis Natanson (Computing) –Prof Kevan Gartland, Dr Jill Gartland (Biotech.) –Davina Button (RA) University of Edinburgh –Prof Peter Ghazal (GTI; Scottish Centre for Genomic Technology and Informatics) University of St Andrews –Dr Ishbel Duncan (Computer Science) Aim: –To build an intelligent and generic system for new hypothesis formulation from complex biochemical pathway databases. Davina Button

18. ‘Road Map’

19. Options with the new database

20. Genes induced by BTH

21. pathogen induced – incompatible (Arabidopsis)

25. Pathways e.g glycolysis enzymes Conversion of glucose to pyruvate Wrong pathway Insufficient data Some errors (different time points? low homology!) Evidence of another pathway Possible interpretations:-

29. 1.Les Ball (Abertay), 2.Prof Bonnie Webber (School of Informatics, Edinburgh University), 3.CABI. Data input and Data analysis Could be used to provide a putative relationship between genes/proteins based on existing knowledge in the literature. This model could be combined with information in the gene expression database to provide a draft version of a regulatory gene network. Text mining

30. Web stats - Location of users Impact factors ?!

32. DRASTIC Database Resource for Analysis of Signal Transduction in Cells SCRI Gary Lyon Adrian Newton Bruce Marshall University of Abertay Les Ball Louis Natason Alasdair Houston www.drastic.org.uk

33. Can we group treatments?

36. Genes up-regulated by Sulphur depletion

37. Another example The same gene can have different accession numbers – a big problem with genes of unknown function. However, by converting accession numbers into AGI numbers we have shown that for the following ESTs down-regulated by :- chitin (viz H37231, R90140, T41806), drought (viz AV823744), ethylene (viz R90140), low oxygen (At2g10940) or sodium chloride (AV823744), or up-regulated by salicylic acid (R90140, H37231) are all the same gene viz At2g10940

38. up-regulated down-regulated Arabidopsis5052 1246 potato1688 tomato393213 Nicotiana tabacum25887 pepper1130 rice23443 ethylene10520 salicylic acid330146 jasmonates (methyl)344135 jasmonic acid782 Ecc350 Eca30 P. infestans (incompatible)151 P. infestans (compatible)513 cold436187 drought690263 sodium chloride546248 wounding51063 Abscisic acid35946 Total in database71271828 Plants Treatments Pathogens Environmental stresses Number of entries in the Gene expression Database - examples

42. What else could we do with the data? Identify potato and barley orthologs of stress induced genes Map the position of the stress inducible genes Statistical analysis of signal transduction genes What are the differences between different plant tissues e.g. roots v. leaves.

43. Information from Maleck et al., Nature Genetics (Dec 2000) 26, 403-410 Out of 50 accession numbers checked (March 2004):- 26 (52%) were correctly identified 3 (6%) were wrongly identified (though 2 of these could be classed as ‘additional information being made available’ with only 1 really wrong. 13 (26%) are newly identified with a gene name (these were originally described (‘no homology’) 8 (16%) remain unknown but have an AGI number (these were originally described as ‘no homology’)