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 Haas and Zody, Nature Biotechnology 28, 421–423 (2010) Good reference No genome

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

8. WGS Sequencing Assemble Draft Genome Scaffolds SNPs Methylation Proteins Tx-factor binding sites A Paradigm for Genomic Research

9. WGS Sequencing Assemble Draft Genome Scaffolds Expression Transcripts SNPs Methylation Proteins Tx-factor binding sites Align

10. A Maturing Paradigm for Transcriptome Research WGS Sequencing Assemble Draft Genome Scaffolds Methylation Tx-factor binding sites Align

11. A Maturing Paradigm for Transcriptome Research WGS Sequencing Assemble Draft Genome Scaffolds Methylation Tx-factor binding sites Align $$$$$ $ $ +

12. A Maturing Paradigm for Transcriptome Research WGS Sequencing Assemble Draft Genome Scaffolds Methylation Tx-factor binding sites Align $$$$$ $ $ +

13. A Maturing Paradigm for Transcriptome Research WGS Sequencing Assemble Draft Genome Scaffolds Methylation Tx-factor binding sites Align $$$$$ $ $ +

14. A Maturing Paradigm for Transcriptome Research WGS Sequencing Assemble Draft Genome Scaffolds Methylation Tx-factor binding sites Align $$$$$ $ $ +

20. De-novo transcriptome assembly Brian Haas Moran Yassour Kerstin Lindblad-Toh Aviv Regev Nir Friedman David Eccles Alexie Papanicolaou Michael Ott …

21. The problem Transcript

22. The problem Transcript Reads

23. The problem Transcript Reads Transcript Assembly

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

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

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 Biotechnology 29, 599–600

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

36. Inchworm Algorithm G A T C 4 GATTACA 9

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

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

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

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

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

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

43. GATTACA G A 4 9 5 Inchworm Algorithm

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

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

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)

54. Result: linear sequences grouped in components, contigs and sequences >comp1017_c1_seq1_FPKM_all:30.089_FPKM_rel:30.089_len:403_path:[5739,5784,5857,5863,353] TTGGGAGCCTGCCCAGGTTTTTGCTGGTACCAGGCTAAGTAGCTGCTAACACTCTGACTGGCCCGGCAGGTGATGGTGAC TTTTTCCTCCTGAGACAAGGAGAGGGAGGCTGGAGACTGTGTCATCACGATTTCTCCGGTGATATCTGGGAGCCAGAGTA ACAGAAGGCAGAGAAGGCGAGCTGGGGCTTCCATGGCTCACTCTGTGTCCTAACTGAGGCAGATCTCCCCCAGAGCACTG ACCCAGCACTGATATGGGCTCTGGAGAGAAGAGTTTGCTAGGAGGAACATGCAAAGCAGCTGGGGAGGGGCATCTGGGCT TTCAGTTGCAGAGACCATTCACCTCCTCTTCTCTGCACTTGAGCAACCCATCCCCAGGTGGTCATGTCAGAAGACGCCTG GAG >comp1017_c1_seq2_FPKM_all:4.913_FPKM_rel:2.616_len:525_path:[2317,2791] CTGGAGATGGTTGGAACAAATAGCCGGCTGGCTGGGCATCATTCCCTGCAGAAGGAAGCACACAGAATGGTCGTTAAGTA ACAGGGAAGTTCTCCACTTGGGTGTACTGTTTGTGGGCAACCCCAGGGCCCGGAAAGGACAGACAGAGCAGCTTATTCTG TGTGGCAATGAGGGAGGCCAAGAAACAGATTTATAATCTCCACAATCTTGAGTTTCTCTCGAGTTCCCACGTCTTAACAA AGTTTTTGTTTCAATCTTTGCAGCCATTTAAAGGACTTTTTGCTCTTCTGACCTCACCTTACTGCCTCCTGCAGTAAACA CAAGTGTTTCAGGCAAAGAAACAAAGGCCATTTCATCTGACCGCCCTCAGGATTTAGAATTAAGACTAGGTCTTGGACCC CTTTACACAGATCATTTCCCCCATGCCTCTCCCAGAACTGTGCAGTGGTGGCAGGCCGCCTCTTCTTTCCTGGGGTTTCT TTGAATGTATCAGGGCCCGCCCCACCCCATAATGTGGTTCTAAAC >comp1017_c1_seq3_FPKM_all:3.322_FPKM_rel:2.91_len:2924_path:[2317,2842,2863,1856,1835] CTGGAGATGGTTGGAACAAATAGCCGGCTGGCTGGGCATCATTCCCTGCAGAAGGAAGCACACAGAATGGTCGTTAAGTA ACAGGGAAGTTCTCCACTTGGGTGTACTGTTTGTGGGCAACCCCAGGGCCCGGAAAGGACAGACAGAGCAGCTTATTCTG TGTGGCAATGAGGGAGGCCAAGAAACAGATTTATAATCTCCACAATCTTGAGTTTCTCTCGAGTTCCCACGTCTTAACAA AGTTTTTGTTTCAATCTTTGCAGCCATTTAAAGGACTTTTTGCTCTTCTGACCTCACCTTACTGCCTCCTGCAGTAAACA

55. Result: linear sequences grouped in components, contigs and sequences GTTCGAGGACCTGAATAAGCGCAAGGACACCAAGGAGATCTACACGCACTTCACGTGCGCCACCGACACCAAGAACGTGC AGTTTGTGTTTGATGCCGTCACCGACGTCATCATCAAGAACAACCTGAAGGACTGCGGCCTCTTCTGAGGGGCAGCGGGG CCTGGCAGGATGG------------------------------------------------------------------- CCTGGCAGGATGGTGAGCCCGGGGTGGAGCGGAGCAGAGCTGTGGAGCCCAGAGAAGGGAGCGGTGGGGGCTGGGGTGGG -------------------------------------------------------------------------------- CCGTGGTGGGGGTATGGTGGTAGAGTGGTAGGTCGGTAGGACGACCTGAGGGGCATGGGCACACGGATAGGCCGGGCCGG -------------------------------------------------------------------------------- GGCCCAGATGGCAGAAGCATCCGGCCGTGCGCCGGGAGACAACGGAATGGCTGTCCTGACCACCCTTGGAGAAAGCTTAC -------------------------------------------------------------------------------- CGGCTCTGTGCTCAGCCCTGCAGTCTTTCCCTCAGACCTATCTGAGGGTTCTGGGCTGACACTGGCCTCACTGGCCGTGG -------------------------------------------------------------------------------- GGGAGATGGGCACGGTTCTGCCAGTACTGTAGATCCCCCTCCCTCACGTAACCCAGCAACACACACACTGGCTCTGGGGC -------------------------------------------------------------------------------- AGCCACTGGGTCCCTCATAACAGGTGGAGGAGAAAAAGGAGAGAGTCCTTGTCTAGGGAGGGGGGAGGAGAGACACACCC --------------------------------------------GCCACCGCCGACTCTGCTTCCCCCAGTTCCTGAGGA TGGCCACCTCCCGACCCATGCCCTGACTGTCCCCCACCTCCAGGGCCACCGCCGACTCTGCTTCCCCCAGTTCCTGAGGA AGATGGGGGCAAGAGGACCACGCTCTCTGCCTGTCCGTACCCCCGCCCTGGCTGCTTTTCCCCTTTTCTTTGTTCTTGGC TCCCCTGTTCCCTCCCTCAGTTCCAGAGACTCGTGGGAGGAGCTGCCACAGGCCTCCCTGTTTGAAGCCGGCCCTTGTCC

56. Result: linear sequences grouped in components, contigs and sequences GTTCGAGGACCTGAATAAGCGCAAGGACACCAAGGAGATCTACACGCACTTCACGTGCGCCACCGACACCAAGAACGTGC AGTTTGTGTTTGATGCCGTCACCGACGTCATCATCAAGAACAACCTGAAGGACTGCGGCCTCTTCTGAGGGGCAGCGGGG CCTGGCAGGATGG------------------------------------------------------------------- CCTGGCAGGATGGTGAGCCCGGGGTGGAGCGGAGCAGAGCTGTGGAGCCCAGAGAAGGGAGCGGTGGGGGCTGGGGTGGG -------------------------------------------------------------------------------- CCGTGGTGGGGGTATGGTGGTAGAGTGGTAGGTCGGTAGGACGACCTGAGGGGCATGGGCACACGGATAGGCCGGGCCGG -------------------------------------------------------------------------------- GGCCCAGATGGCAGAAGCATCCGGCCGTGCGCCGGGAGACAACGGAATGGCTGTCCTGACCACCCTTGGAGAAAGCTTAC -------------------------------------------------------------------------------- CGGCTCTGTGCTCAGCCCTGCAGTCTTTCCCTCAGACCTATCTGAGGGTTCTGGGCTGACACTGGCCTCACTGGCCGTGG -------------------------------------------------------------------------------- GGGAGATGGGCACGGTTCTGCCAGTACTGTAGATCCCCCTCCCTCACGTAACCCAGCAACACACACACTGGCTCTGGGGC -------------------------------------------------------------------------------- AGCCACTGGGTCCCTCATAACAGGTGGAGGAGAAAAAGGAGAGAGTCCTTGTCTAGGGAGGGGGGAGGAGAGACACACCC --------------------------------------------GCCACCGCCGACTCTGCTTCCCCCAGTTCCTGAGGA TGGCCACCTCCCGACCCATGCCCTGACTGTCCCCCACCTCCAGGGCCACCGCCGACTCTGCTTCCCCCAGTTCCTGAGGA AGATGGGGGCAAGAGGACCACGCTCTCTGCCTGTCCGTACCCCCGCCCTGGCTGCTTTTCCCCTTTTCTTTGTTCTTGGC TCCCCTGTTCCCTCCCTCAGTTCCAGAGACTCGTGGGAGGAGCTGCCACAGGCCTCCCTGTTTGAAGCCGGCCCTTGTCC

57. Reference transcript log 2 (FPKM) Trinity Assembly *Abundance Estimation via RSEM. R 2 =0.95 Near-Full-Length Assembled Transcripts Are Suitable Substrates for Expression Measurements (80-100% Length Agreement) Expression Level Comparison 0 2 4 6 81012 14 0

58. *Abundance Estimation via RSEM. Reference transcript log 2 (FPKM) Trinity Assembly R 2 =0.95 R 2 =0.83R 2 =0.72 R 2 =0.58R 2 =0.40 Trinity Partially-reconstructed Transcripts Can Serve as a Proxy for Expression Measurements 60-80% Length 40--60% Length 20-40% Length 0-20% Length Only 13% of Trinity Assemblies (80-100% Length Agreement) Expression Level Comparison 14 0 2 4 6 81012 14 0

59. General design issues Q: How many reads do I need? A: Depends on your biological question (1 lane saturates the sample). Q: How many tissues do I need? A: Depends on your organism. Q: Do I want strand-specific libraries? A: Yes! Q: polyA+ or duplex-specific nuclease (DSN)? A: polyA+ specific to pol II transcripts, DSN also gets others. Q: Can I assemble a mix of species? A: With limited success, yes. More to come.

60. Questions?