This document discusses RSEM, a tool for accurate transcript quantification from RNA-seq data. RSEM uses expectation maximization to estimate transcript expression levels without requiring a reference genome. It handles multireads better than other tools by either discarding, rescuing, or estimating maximum likelihood expression levels. Evaluation on simulated data showed RSEM had higher accuracy than other tools like Cufflinks and rQuant.

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