A literature review of RSEM for our research group

1. RSEM: accurate transcript
quantification from RNA-seq data
Daniel S. Standage, Brendel Group Meeting, 15 Oct 2013
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2. Expression profiling
 qPCR
 Microarrays
 SAGE
 RNA-seq

3. RNA-seq

4. Benefits of RNA-seq
 Large dynamic range
 Low (relative) cost
 High throughput
 Ability to detect novel transcripts

5. Challenges with RNA-seq
 Large dynamic range
 Sequencing biases
 Mapping uncertainty

6. Enter RSEM
 RNA-Seq by Expectation Maximization
 No reference genome required
 Improved handling of multireads

7. Handling multireads
 Discard
 Rescue
 Estimate ML expression levels

8. RSEM Model
 Rn: reads (observed data)
 Latent random variables
 Gn: isoform
 Sn: start position
 On: orientation

9. Likelihood

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. 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