Brendel Group Presentation: 15 Oct 2013

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A literature review of RSEM for our research group

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  • G_n = isoformS_n = start positionO_n = orientationR_n = read\theta = expression level
  • G_n = isoformS_n = start positionO_n = orientationR_n = read\theta = expression level
  • Brendel Group Presentation: 15 Oct 2013

    1. 1. RSEM: accurate transcript quantification from RNA-seq data Daniel S. Standage, Brendel Group Meeting, 15 Oct 2013 
    2. 2. Expression profiling  qPCR  Microarrays  SAGE  RNA-seq
    3. 3. RNA-seq
    4. 4. Benefits of RNA-seq  Large dynamic range  Low (relative) cost  High throughput  Ability to detect novel transcripts
    5. 5. Challenges with RNA-seq  Large dynamic range  Sequencing biases  Mapping uncertainty
    6. 6. Enter RSEM  RNA-Seq by Expectation Maximization  No reference genome required  Improved handling of multireads
    7. 7. Handling multireads  Discard  Rescue  Estimate ML expression levels
    8. 8. RSEM Model  Rn: reads (observed data)  Latent random variables  Gn: isoform  Sn: start position  On: orientation
    9. 9. Likelihood
    10. 10. Evaluation on simulated data  RSEM and IsoEM >> Cufflinks and rQuant  Cufflinks uses rescue strategy  rQuant multiread handling unclear  RSEM vs IsoEM  Similar accuracy for paired-end data  RSEM slightly more accurate for single-end data
    11. 11. Evaluation on simulated data  PE not necessarily better than paired end  Longer reads not necessarily better  Accuracy  Gene level: SE > PE  Isoform level: PE > SE  Global isoform level: ?  Claim: 25bp SE reads optimal for quantification

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