* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Apoptosis-associtated pathways are induced vy Phytophthora
Genetic engineering wikipedia , lookup
Secreted frizzled-related protein 1 wikipedia , lookup
Vectors in gene therapy wikipedia , lookup
Point mutation wikipedia , lookup
Ridge (biology) wikipedia , lookup
Signal transduction wikipedia , lookup
Genomic imprinting wikipedia , lookup
Gene therapy wikipedia , lookup
Gene therapy of the human retina wikipedia , lookup
Promoter (genetics) wikipedia , lookup
Gene desert wikipedia , lookup
Expression vector wikipedia , lookup
Gene expression wikipedia , lookup
Endogenous retrovirus wikipedia , lookup
Community fingerprinting wikipedia , lookup
Biochemical cascade wikipedia , lookup
Gene nomenclature wikipedia , lookup
Silencer (genetics) wikipedia , lookup
Interrogating the DRASTIC Gene Expression Database Gary Lyon DRASTIC Database Resource for Analysis of Signal Transduction in Cells www.drastic.org.uk 30 April 2004 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 ! 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] 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’ www.drastic.org.uk 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 Selection by Gene name treatment 1 Potential signalling networks 1 treatment 2 treatment 3 4 7 2 5 3 6 Davina Button 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. ‘Road Map’ Options with the new database Genes induced by BTH pathogen induced – incompatible (Arabidopsis) Pathways e.g glycolysis enzymes Conversion of glucose to pyruvate • Wrong pathway • Insufficient data Possible interpretations:- • Some errors (different time points? low homology!) • Evidence of another pathway Text mining 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. Web stats - Location of users Impact factors ?! DRASTIC Database Resource for Analysis of Signal Transduction in Cells SCRI University of Abertay Gary Lyon Les Ball Adrian Newton Louis Natason Bruce Marshall Alasdair Houston www.drastic.org.uk Can we group treatments? Genes up-regulated by Sulphur depletion 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 Number of entries in the Gene expression Database - examples up-regulated down-regulated Arabidopsis potato tomato Nicotiana tabacum pepper rice 5052 168 393 258 113 234 1246 8 213 87 0 43 Treatments ethylene salicylic acid jasmonates (methyl) jasmonic acid 105 330 344 78 20 146 135 2 Pathogens Ecc Eca P. infestans (incompatible) P. infestans (compatible) 35 3 15 51 0 0 1 3 wounding 436 690 546 510 187 263 248 63 Abscisic acid 359 46 Total in database 7127 1828 Plants Environmental cold drought stresses sodium chloride 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. 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’)