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Transcriptome Analysis : Case studies from Chlamydomonas

Transcriptome Analysis : Case studies from Chlamydomonas

JGI User Meeting 8 @ 2013: Genome Tech Workshop, RNA-Seq talk

Abhishek Pratap

March 26, 2013
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  1. Transcriptome analysis: Case studies from Chalmydomonas Abhishek Pratap (Abhi) Genomic

    Technologies DOE-Joint Genome Institute apratap@lbl.gov
  2. Transcriptome/genome change in environment Problem Definition

  3. deprivation excess Sensory genes Transcriptome Before external stimuli

  4. Cu Mn N P Zn Fe S Cu Mn P

    Zn Fe S Nutrient depletion Experiment: Chlamydomonas as a biofuel model Lack of Nutrients induces lipid accumulation.
  5. 6h 24h 48h 8h 2h 1h 30’ 10’ 0 ~1

    cell cycle Control Visible lipid accumulation by microscopy Log phase 3days N Nitrogen depletion Sulfur depletion S Design
  6. Examples of candidate TF genes Transcriptional co-activator Up-regulated Down-regulated Expression

    (FPKM) Expression (FPKM) Time Time Transcription factor regulating nitrogen metabolism
  7. Approach • Build a catalogue of transcripts for an organism

    – Decipher transcriptional structure of genes – New discoveries w.r.t to reference • Novel genes/isoforoms • Gene fusions • Gene boundary extensions • Study abundance of genes/transcripts between different conditions • Find Key Regulator/s responsible for change in transcriptome dynamics RNA-Seq
  8. Building the Chlamy’s transcriptome Differential Expression

  9. Strand Specificity: power to resolve strand specific expression Forward strand

    cov Reverse strand cov Non stranded cDNA stranded cDNA
  10. Data Summary & Quality Replicate Correlation Background noise in strand

    specific data 10 • Sequenced 9 time points • 2 biological replicates / cond • ~100 million raw reads / cond • Strand Specific library • with ERCC Spike-in : quality control
  11. Transcriptome Assembly Advancing RNA-Seq Analysis : Zody et al. cDNA

    reads Tophat Cufflinks A B
  12. Assembled Transcripts Inside ref transcript model same- strand opp- strand

    Outside ref model Ref transcripts not found in assembly Ref gene model Inside – Ref: same – strand Inside – Ref : Opposite – strand Outside Ref: New genes Analysis
  13. Reference RNA-Seq based #genes 18,773 16,467 # transcripts 19,529 32,395

    Uniq Transcriptome size 94.6 Mb 96.1 Mb Mean transcript length 5411 +/- 4283 bp 6203 +/- 4925 bp Bioinformatics Challenges • gene fusions • Antisense transcripts • Low abundant real signal v/s noise
  14. False Gene Fusions RNA-Seq Model

  15. Reference Orig Cufflinks Mod Cufflinks

  16. New Gene No gene in the Ref Gene Models RNA-Seq

    Reads
  17. New Gene Boundary Augustus Gene Models RNA-Seq transcripts RNA-Seq Reads

  18. RNA-Seq RNA-PET ChIP-Seq Gene fusion

  19. Measuring isoform expression A B A B FPKM Time Points

  20. Analysis and design of RNA sequencing experiments for identifying isoform

    regulation. Nature Methods(2010). Visualizing Alternative Splicing
  21. Chlamy’s Transcriptome Reference RNA-Seq #genes 18,773 16,467 # transcripts 19,529

    32,395 Uniq Transcriptome size 94.6 Mb 96.1 Mb Mean transcript length 5411 +/- 4283 6203 +/- 4925 Discoveries made #novel genes 184 #novel exons inside ref genes 544 #novel Antisense genes 1727 #mergers (ref genes) ~685 #extensions (ref genes) 6100
  22. Building the Chlamy’s transcriptome Differential Expression

  23. Transcriptome Expression Analysis 24h 48h Control 6h 8h 2h 1h

    30’ 10’ 0
  24. Expression Summary Log10 (FPKM)

  25. Comparison with known targets Reported down-regulated genes fatty acid synthesis

    Carboxytransferase : syn of fatty acid de-novo fatty acid synthesis
  26. N- S-

  27. 0 1 hr Time course

  28. Time Course Profiles

  29. D O W N U P

  30. Summary • data quality control for RNA-Seq • Building a

    transcriptome to capture – new genes/transcripts – gene fusions – Improve annotation (if available) • response of the genetic machinery to a stimulus: differential expression • Picking the possible key regulators
  31. D O W N U P Challenge

  32. The work conducted by the U.S. Department of Energy Joint

    Genome Institute is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 Chew Yee Ngan, Chee Wong, James Han, Chia-Lin Wei, Simon Prochnik