Transcriptome Assembly and Quantification from Ion Torrent RNA-Seq Data

1. Transcriptome Assembly and Quantification from Ion Torrent RNA-Seq Data Alex Zelikovsky Department of Computer Science Georgia State University Joint work with Serghei Mangul, Sahar Al Seesi, Adrian Caciula, Dumitru Brinza, Ion Mandoiu

2. Advances in Next Generation Sequencing Illumina HiSeq 2000 Up to 6 billion PE reads/run 35-100bp read length Roche/454 FLX Titanium 400-600 million reads/run 400bp avg. length http://www.economist.com/node/16349358 Ion Proton Sequencer SOLiD 4/5500 1.4-2.4 billion PE reads/run 35-50bp read length

3. RNA-Seq RNA-Seq Make cDNA & shatter into fragments Sequence fragment ends Map reads A B C D E Isoform Expression Gene Expression Transcriptome Reconstruction A B C A C D E

4. Transcriptome Assembly • Given partial or incomplete information about something, use that information to make an informed guess about the missing or unknown data.

5. Transcriptome Assembly Types • Genome-independent reconstruction (de novo) • de Brujin k-mer graph • Genome-guided reconstruction (ab initio) • Spliced read mapping • Exon identification • Splice graph • Annotation-guided reconstruction • Use existing annotation (known transcripts) • Focus on discovering novel transcripts

6. Previous approaches • Genome-independent reconstruction • Trinity(2011), Velvet(2008), TransABySS(2008) • Genome-guided reconstruction • Scripture(2010) • Reports “all” transcripts • Cufflinks(2010), IsoLasso(2011), SLIDE(2012), CLIIQ(2012), TRIP(2012), Traph (2013) • Minimizes set of transcripts explaining reads • Annotation-guided reconstruction • RABT(2011), DRUT(2011)

7. Gene representation • Pseudo-exons - regions of a gene between consecutive transcriptional or splicing events • Gene - set of non-overlapping pseudo-exons Tr1: e1 e5 Tr2: e1 e3 e5 Tr3: e2 e4 e6 Pseudo-exons: pse2 pse3 pse4 pse5 pse6 pse7 pse1 Epse1 Spse2 Epse3 Spse4 Epse4 Spse5 Epse6 Spse7 Spse1 Spse3 Epse2 Epse5 Spse6 Epse7

8. Splice Graph pseudo-exons TSS TES Genome 6 7 8 1 9 5 2 4 3

9. MaLTA Maximum Likelihood Transcriptome Assembly • Map the RNA-Seq reads to genome • Construct Splice Graph - G(V,E) • V : exons • E: splicing events • Candidate transcripts • depth-first-search (DFS) • Select candidate transcripts • IsoEM • greedy algorithm Genome

10. How to select? • Select the smallest set of candidate transcripts • covering all transcript variants Transcript : set of transcript variants exon skipping alternative last exon alternative first exon intron retention alternative 5' splice junction alternative 5' splice junction splice junction Sharmistha Pal, Ravi Gupta, Hyunsoo Kim, et al., Alternative transcription exceeds alternative splicing in generating the transcriptome diversity of cerebellar development, Genome Res. 2011 21: 1260-1272

11. IsoEM: Isoform Expression Level Estimation • Expectation-Maximization algorithm • Unified probabilistic model incorporating • Single and/or paired reads • Fragment length distribution • Strand information • Base quality scores • Repeat and hexamer bias correction

12. Read-isoform compatibility graph

13. Fragment length distribution A B C A C i j Fa(i) Fa (j) A B C A B C A C A C

14. Greedy algorithm • Sort transcripts by inferred IsoEM expression levels in decreasing order • Traverse transcripts • Select transcripts if it contains novel transcript variant • Continue traversing until all transcript variant are covered

15. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

16. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

17. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

18. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

19. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

20. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

21. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

22. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

23. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

24. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels

25. Greedy algorithm Transcript Variants: Transcripts sorted by expression levels • STOP. All transcript variant are covered.

26. MaLTA results on GOG-350 dataset • 4.5M single Ion reads with average read length 121 bp, aligned using TopHat2 • Number of assembled transcripts • MaLTA : 15385 • Cufflinks : 17378 • Number of transcripts matching annotations • MaLTA : 4555(26%) • Cufflinks : 2031(13%)

27. Expression Estimationon Ion Torrent reads • Squared correlation • IsoEM / Cufflinks FPKMs vsqPCR values for 800 genes • 2 MAQC samples : Human Brain and Universal

28. Conclusions • Novel method for transcriptome assembly • Validated on Ion Torrent RNA-SeqData • Comparing with Cufflinks: • similar number of assembled transcripts • 2x more previously annotated transcripts • Transcript quantification is useful for transcript assembly  better quantification?