RNA Sequencing and transcriptome reconstruction Manfred G. Grabherr

1. RNA Sequencing and transcriptome reconstruction Manfred G. Grabherr

2. A historic perspective • Traditional: sequence cDNA libraries by Sanger • Tens of thousands of pairs at most (20K genes in mammal) • Redundancy due to highly expressed genes • Not only coding genes are transcribed • Poor full-lengthness (read length about 800bp) • Indels are the dominant error mode in Sanger (frameshifts)

3. A historic perspective • Quantification: microarrays • Sequences have to be known • Annotations are often incomplete • No novel transcripts • Hybridization bias (SNPs) • Noise

4. Next-Gen Sequencing technologies • 1 Lane of HiSeq yields 30GB in sequence • Short reads (100nt), but: • Good depth, high dynamic range • Full-length transcripts • Novel transcripts • Allow for expression quantification • Error patterns are mostly substitutions • Strand-specific libraries

5. Strategy: read mapping vs. de novo assembly Haas and Zody, Nature Biotechnology 28, 421–423 (2010)

6. Strategy: read mapping vs. de novo assembly Good reference No genome Haas and Zody, Nature Biotechnology 28, 421–423 (2010)

7. Leveraging RNA-Seq for Genome-free Transcriptome Studies Brian Haas

8. A Paradigm for GenomicResearch WGS Sequencing Assemble Draft Genome Scaffolds Methylation Tx-factorbinding sites SNPs Proteins

9. A Paradigm for GenomicResearch RNA-Seq WGS Sequencing Assemble Align Draft Genome Scaffolds Transcripts Methylation Tx-factorbinding sites Expression SNPs Proteins

10. A Maturing Paradigm for TranscriptomeResearch RNA-Seq WGS Sequencing Assemble Align Assemble Draft Genome Scaffolds Transcripts Methylation Tx-factorbinding sites Expression SNPs Proteins

11. A Maturing Paradigm for TranscriptomeResearch RNA-Seq $$$$$ $$$$$ $$$$$ $$$$$ WGS Sequencing + $ Assemble Align Assemble Draft Genome Scaffolds $ Transcripts Methylation Tx-factorbinding sites Expression SNPs Proteins

12. A Maturing Paradigm for TranscriptomeResearch RNA-Seq $$$$$ $$$$$ $$$$$ $$$$$ WGS Sequencing + $ Assemble Align Assemble Draft Genome Scaffolds $ Transcripts Methylation Tx-factorbinding sites Expression SNPs Proteins

13. A Maturing Paradigm for TranscriptomeResearch RNA-Seq $$$$$ $$$$$ $$$$$ $$$$$ WGS Sequencing + $ Assemble Align Assemble Draft Genome Scaffolds $ Transcripts Methylation Tx-factorbinding sites Expression SNPs Proteins

14. A Maturing Paradigm for TranscriptomeResearch RNA-Seq $$$$$ $$$$$ $$$$$ $$$$$ WGS Sequencing + $ Assemble Align Assemble Draft Genome Scaffolds $ Transcripts Methylation Tx-factorbinding sites Expression SNPs Proteins

20. De-novo transcriptome assembly Brian Haas Moran Yassour Kerstin Lindblad-Toh Aviv Regev NirFriedman David Eccles AlexiePapanicolaou Michael Ott …

21. The problem Transcript

22. The problem Transcript Reads

23. The problem Transcript Reads Assembly Transcript

24. The problem Transcript Paralog A Paralog B Reads Assembly Transcript

25. The problem Transcript Isoform A Isoform B Reads Assembly Transcript

26. Transcriptome vs. Genome assembly • Genome: • Large • High coverage • Long mate pairs (hard to make) • Linear sequences • Even coverage • Transcriptome: • Smaller • Standard paired-end Illumina (1 lane) • Multiple solutions (alternative splicing) • Uneven coverage (expression)

27. Transcriptome vs. Genome assembly • Genome: • Large • High coverage • Long mate pairs (hard to make) • Linear sequences • Even coverage • Transcriptome: • Smaller • Standard paired-end Illumina (1 lane) • Multiple solutions (alternative splicing) • Uneven coverage (expression) • In common: k-mer based approach 

28. The k-mer • K consecutive nucleotides Reads K-mers Graph

29. The de Bruijn Graph • Graph of overlapping sequences • Intended for cryptology • Fixed length element: k • CTTGGAA • TTGGAAC • TGGAACA • GGAACAA • GAACAAT

30. The de Bruijn Graph • Graph has “nodes” and “edges” • G GGCAATTGACTTTT… • CTTGGAACAAT TGAATT • A GAAGGGAGTTCCACT…

31. Iyer MK, Chinnaiyan AM (2011) Nature Biotechnology29, 599–600

32. Iyer MK, Chinnaiyan AM (2011) Nature Biotechnology29, 599–600

33. Iyer MK, Chinnaiyan AM (2011) Nature Biotechnology29, 599–600

34. Iyer MK, Chinnaiyan AM (2011) Nature Biotechnology29, 599–600

35. Inchworm Algorithm Decompose all reads into overlapping Kmers (25-mers) Identify seed kmer as most abundant Kmer, ignoring low-complexity kmers. Extend kmer at 3’ end, guided by coverage. G A GATTACA 9 T C

36. Inchworm Algorithm G 4 A GATTACA 9 T C

37. Inchworm Algorithm G 4 A 1 GATTACA 9 T C

38. Inchworm Algorithm G 4 A 1 GATTACA 9 T 0 C

39. Inchworm Algorithm G 4 A 1 GATTACA 9 T 0 C 4

40. Inchworm Algorithm G 4 A 1 GATTACA 9 T 0 C 4

41. Inchworm Algorithm G A 0 5 T 1 G C 4 0 A 1 GATTACA 9 T 0 G C 1 4 A 1 T C 1 1

42. Inchworm Algorithm G A 0 5 T 1 G C 4 0 A 1 GATTACA 9 T 0 G C 1 4 A 1 T C 1 1

43. Inchworm Algorithm A 5 G 4 GATTACA 9

44. Inchworm Algorithm A 5 C G 0 4 T 0 GATTACA A 9 6 G 1

45. Inchworm Algorithm A 5 G 4 GATTACA A 9 6 A 7 Report contig: ….AAGATTACAGA…. Remove assembled kmers from catalog, then repeat the entire process.

46. Inchworm Contigs from Alt-Spliced Transcripts=> Minimal lossless representation of data +

47. Chrysalis Integrate isoforms via k-1 overlaps

48. Chrysalis Integrate isoforms via k-1 overlaps

49. Chrysalis Integrate isoforms via k-1 overlaps Verify via “welds”

50. Chrysalis Integrate isoforms via k-1 overlaps Verify via “welds” Build de Bruijn Graphs (ideally, one per gene) Build de Bruijn Graphs (ideally, one per gene)