1. Presented by: Pham Kien Cuong NUS Graduate School for Integrative Sciences and Engineering

2. Outline Problem background FusionSeq: Method FusionSeq: Assessment Conclusion 2Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering

3. Problem background Fusion genes as key molecular events for cancers RNA-Seq reveals expressed fusion genes State-of-the-art: – Lack of flexibility in choosing mapping tools – Little analysis of artefacts Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering3

4. FusionSeq: Method 1 Fusion transcript detection 2 Filtration cascade 3 Junction sequence identifier Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering4

5. FusionSeq: Method Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering5 1 Fusion transcript detection 2 Filtration cascade 3 Junction sequence identifier

6. FusionSeq: Method Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering6 1 Fusion transcript detection 2 Filtration cascade 3 Junction sequence identifier

7. FusionSeq: Method Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering7 Misalignment filters (Computational errors) Large scale sequence similarity Small scale sequence similarity Repetitive region Random pairing filter (Experimental errors) Abnormal insert size Misalignment + Random pairing Scoring and ranking 1 Fusion transcript detection 2 Filtration cascade 3 Junction sequence identifier

8. Abnormal insert size filter Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering8

9. Scoring and Ranking of candidates Supportive PE reads (SPER): number of inter- transcript PE reads (m i ) normalized by the total number of mapped PE reads (N mapped ) Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering9 DASPER (the difference between the observed and analytically calculated expected SPER) RESPER (the ratio of empirically computed SPERs)

10. FusionSeq: Method Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering10 1 Fusion transcript detection 2 Filtration cascade 3 Junction sequence identifier

11. FusionSeq: Assessment Apply on prostate cancer samples harbouring: – Common TMPRSS2-ERG fusion – Less common fusions (SLC45A3-ERG, NDRG1- ERG) – No evidence of known fusions – No fusions expected Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering11

12. FusionSeq: Assessment The most common fusion, TMPRSS2-ERG, is ranked at the top of the list The other known fusions between ERG and other 5 ’ partners, namely SLC45A3 and NDRG1, are also included in the top candidates The remaining candidates appear to be read- through events, including ZNF649-ZNF577 Novel fusion transcripts identified and experimentally validated Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering12

13. FusionSeq: Assessment Filters remove 98% of candidates Real fusion stands out from artefacts with higher sequencing depth Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering13

14. FusionSeq: Assessment Exons included in fusions expressed more than those not included in fusion transcripts. Junction sequence identified and then validated by RT- PCR Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering14

15. FusionSeq: Assessment Simulation: introduce inter-transcripts reads in to experimental data of sample with no expected fusion – FusionSeq identified the introduced fusions Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering15

16. Conclusion FusionSeq demonstrates abilities: – Filter and identify fusion transcripts – Independent of mapping tool choice However: need reference genome sequence and genes annotation Aggressive filters may discard some real fusions Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering16

17. Pham Kien Cuong - NUS Graduate School for Integrative Sciences and Engineering17