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Tag profiling is dead...

This article delves into the transition from tag profiling to mRNA-Seq as a method for gene expression studies, highlighting its significance in the context of New Zealand flora. It reviews challenges and advantages associated with both methodologies while showcasing specific case studies on species diversification and adaptation. The analysis emphasizes how mRNA-Seq can provide comprehensive coverage of the transcriptome, enabling more accurate assessments of gene expression and contributing to a deeper understanding of biological processes.

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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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