1. Marius Nicolae and Ion Măndoiu (University of Connecticut, USA)
Accurate Estimation of Gene Expression Levels from Digital Gene Expression Sequencing Data
Marius Nicolae and Ion Măndoiu (University of Connecticut, USA)

2. Outline DGE/SAGE-Seq protocol EM algorithm Experimental results
Conclusions

3. RNA-Seq Protocol Make cDNA & shatter into fragments
Sequence fragment ends A B C D E
Map reads A B C D E
Isoform Discovery (ID)
Isoform Expression (IE)
Gene Expression (GE)

4. DGE Protocol Cleave with anchoring enzyme (AE)
AAAAA
Cleave with anchoring enzyme (AE)
AAAAA
CATG AE
TCCRAC
AAAAA
CATG AE TE
Attach primer for tagging enzyme (TE)
Cleave with tagging enzyme
CATG
Map tags A B C D E
Gene Expression (GE)

5. Our Approach Previous methods New DGE-EM algorithm
Discard ambiguous tags [Asmann et al. 09, Zaretzki et al. 10]
Heuristics to rescue some ambiguous tags [Wu et al. 10]
New DGE-EM algorithm
Uses all tags, including all ambiguous ones
Uses quality scores
Takes into account partial digest and gene isoforms

6. Tag Formation Probability

7. Tag-Isoform Compatibility

8. DGE-EM Algorithm assign random values to all f(i) while not converged
E-step
init all n(i,j) to 0
for each tag t
for (i,j,w) in t
M-step
for each isoform i

9. MAQC Data (UHRR, HBRR) DGE RNA-Seq qPCR
9 Illumina libraries, 238M 20bp tags [Asmann et al. 09]
Anchoring enzyme DpnII (GATC)
RNA-Seq
6 libraries, 47-92M 35bp reads each [Bullard et al. 10]
qPCR
Quadruplicate measurements for 832 Ensembl genes [MAQC Consortium 06]

10. Compared Algorithms DGE RNA-Seq
Uniq [Asmann et al. 09, Zaretzki et al. 10]
DGE-EM
RNA-Seq
IsoEM [Nicolae et al. 10]
Cufflinks [Trapnell et al. 10]

11. DGE-EM vs. Uniq on HBRR Library 4

12. DGE vs. RNA-Seq

13. DGE vs. RNA-Seq

14. DGE vs. RNA-Seq

15. Synthetic Data 1-30M tags, lengths 14-26bp
UCSC hg19 genome and known isoforms
Simulated expression levels
Gene expression for 5 tissues from the GNFAtlas2
Geometric expression for the isoforms of each gene
Anchoring enzymes from REBASE
DpnII (GATC) [Asmann et al. 09]
NlaIII (CATG) [Wu et al. 10]
CviJI (RGCY, R=G or A, Y=C or T) 15

16. MPE for 30M 21bp tags
RNA-Seq: 8.3 MPE

17. Conclusions Introduced new DGE-EM algorithm
Improves accuracy over previous methods by using ambiguous tags and considering isoforms and partial digestion
Source code freely availabe at
First direct comparison of RNA-Seq and DGE protocols
Best inference algorithms yield comparable cost-normalized accuracy on MAQC data
Simulations suggest possible DGE protocol improvements
Enzymes with degenerate recognition sites (e.g. CviJI)
Optimizing cutting probability

18. Work supported in part by NSF awards IIS-0546457 and IIS-0916948
Questions?
ACKNOWLEDGEMENTS
Work supported in part by NSF awards IIS and IIS

19. Anchoring Enzyme Statistics

20. RNA-Seq

21. DGE enzyme GATC p=1.0

22. DGE enzyme CATG p=1.0

23. DGE enzyme RGCY p=1.0

24. DGE enzyme GATC p=.5

25. DGE enzyme CATG p=.5

26. DGE enzyme RGCY p=.5