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Tag profiling is dead...

Tag profiling is dead... . ...long live mRNA- Seq !. Claudia Voelckel Patrick Biggs. October 2009. Expression Studies in the New Zealand Flora. Hybridization & polyploidy. Ourisia. Ranunculus. Hebe. Species diversification & local adaptation. Pachycladon. Nothofagus. Totara.

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Tag profiling is dead...

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  1. Tag profiling is dead... • ...long live mRNA-Seq! Claudia Voelckel Patrick Biggs October 2009

  2. Expression Studies in the New Zealand Flora Hybridization & polyploidy Ourisia Ranunculus Hebe Species diversification & local adaptation Pachycladon Nothofagus Totara We are interested in: Biological processes that differ between species and populations Adaptive gene sets

  3. Expression Studies the Familiar Way: Microarrays DNA chip Sample 1 Sample 2 mRNA mRNA AAAAAA3’ AAAAAA3’ with gene probes AAAAAA3’ AAAAAA3’ TTTTTT5’ TTTTTT5’ red-labeled cDNA TTTTTT5’ green-labeled cDNA TTTTTT5’ DATA ANALYSIS intensity 1 intensity 2 Expression ratio: log

  4. Expression Studies Revolutionized: Tag Profiling AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ Sample 1 mRNA mRNA Sample 2 Solexa Genome Analyzer AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ 18 bp tag library 18 bp tag library TAG MAPPING Reference Sample 1 Sample 2 STATISTICAL ANALYSIS 1 2 2 1 1 1 count 1 count 2 log If needed – build reference transcriptome through RNA seq

  5. Advantages & Challenges of Tag Profiling Advantages • open to any organism (with a reference transcriptome) • any expressed transcript detectable (1 copy/cell) • less RNA needed (tag profiling = 1µg, microarrays = 100 µg) • minor data normalization, cross-species comparisons easier Challenges • mapping 18 bp tags (sequence differences Pachycladon/Arabidopsis) • counting tags per gene (noise, location, abundance) • statistical analysis of differential expression (proportion data)

  6. Tag Profiling Guinea Pig: Previous Microarray Study Pachycladonfastigiata vs. Pachycladonenysii Habitat Rosette Habitat Rosette Fruiting Fruiting Flowering Flowering Comparative gene expression study using Arabidopsis microarrays Voelckel et al. 2008, Molecular Ecology, 17: 4740–4753

  7. Microarray Study Results • Arabidopsis microarray • (20,468 genes) • 310 genes (1.5%) up in P. fastigiata • 324 genes (1.6%) up in P. enysii • up-regulation of ESP and ESM1 • predict P. fastigiatato produce • isothiocyanates and P. enysiito • producenitriles • prediction confirmed by HPLC • role for herbivory in species • diversification? P. fastigiata P. enysii Probability of differential expression ( log odds ratio) ESM1 ESP Magnitude of differential expression (log fold change)

  8. Tag Profiling Results • 8 data sets from different mapping strategies (ELAND, MySQL) • each analyzed with different normalization parameters (R, edgeR) • results vary! Example: P. fastigiata P. enysii • data set 2: • 17423 A. thaliana loci • noise filter 10 • count most abundant tag per gene • analyzed with tagwise normalization • -log2(1.5) < log fold ratio < log2 (1.5) • 2654 genes (15.2%) up in P. fastigiata • 1857 genes (10.7%) up in P. enysii

  9. Microarrays (MA) vs. Tag Profiling (TP) PF 269 41 2613 MA TP MA: 20,468 genes TP: 17,423 genes PE 274 50 1807 MA TP 2654 up in PF 1857 up in PE 310 up in PF 324 up inPE • more differentially expressed genes in TP (10.7-15.2% ) than with MA (1.5-1.6% ) • 13.2% (PF) and 15.4 % (PE) of MA results confirmed by TP results • biological inferences from both studies identical

  10. Tag Profiling is dead, long live mRNA-Seq! •  • One year later: Tag profiling works for a non-model plant with a distant reference transcriptome! Let’s do more experiments! •  2 Oct 09: “Illumina is discontinuing the support of Tag Profiling and will no longer be manufacturing the reagent kits for this application.” “...not a popular product, too expensive, tricky chemistry.. instead use: mRNA-Seq!”

  11. Expression Studies Revisited: mRNA-Seq AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ AAA3’ Sample 1 mRNA mRNA Sample 2 Solexa Genome Analyzer cDNA library cDNA library READ MAPPING Reference Sample 1 Sample 2 STATISTICAL ANALYSIS 1 2 2 1 1 1 count 1 count 2 gene length log If needed – build reference transcriptome through RNA seq

  12. Advantages & Challenges of mRNA-Seq Advantages • whole transcriptome coverage • longer reads reduce mapping noise and unmapped reads • multiplex-compatible • adequate coverage (too high with tag profiling) • additional benefits: EST libraries, SNPs • disentangling expression of allopolyloid copies may be easier Challenges • new mapping strategies needed • different statistical treatment required • hardly any R packages available yet

  13. Experiences with mRNA-Seq mRNA-Seq runs (75bp paired end) so far: Tuatara Ourisia Pachycladon Ranunculus await assembly and analysis EST data base built for P. fastigiata analysis in progress

  14. Straight from the Pachycladon EST library: Evidence for allopolyploid copies (e.g. glucosinolate hydrolysis gene)

  15. THANKS TO: Genome Service Patrick Biggs Lorraine Berry Lesley Collins, Maurice Collins, Pete Lockhart Helene Kretzmer Marsden Alexander von Humboldt Foundation

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