1. Sequencing and Assembly Cont’d

2. CS273a Lecture 5, Aut08, Batzoglou Steps to Assemble a Genome 1. Find overlapping reads 4. Derive consensus sequence..ACGATTACAATAGGTT.. 2. Merge some “good” pairs of reads into longer contigs 3. Link contigs to form supercontigs Some Terminology read a 500-900 long word that comes out of sequencer mate pair a pair of reads from two ends of the same insert fragment contig a contiguous sequence formed by several overlapping reads with no gaps supercontig an ordered and oriented set (scaffold) of contigs, usually by mate pairs consensus sequence derived from the sequene multiple alignment of reads in a contig

3. CS273a Lecture 5, Aut08, Batzoglou 2. Merge Reads into Contigs Overlap graph:  Nodes: reads r 1 …..r n  Edges: overlaps (r i, r j, shift, orientation, score) Note: of course, we don’t know the “color” of these nodes Reads that come from two regions of the genome (blue and red) that contain the same repeat

4. CS273a Lecture 5, Aut08, Batzoglou 2. Merge Reads into Contigs We want to merge reads up to potential repeat boundaries repeat region Unique Contig Overcollapsed Contig

5. CS273a Lecture 5, Aut08, Batzoglou 2. Merge Reads into Contigs Remove transitively inferable overlaps  If read r overlaps to the right reads r 1, r 2, and r 1 overlaps r 2, then (r, r 2 ) can be inferred by (r, r 1 ) and (r 1, r 2 ) rr1r1 r2r2 r3r3

6. CS273a Lecture 5, Aut08, Batzoglou 2. Merge Reads into Contigs

7. CS273a Lecture 5, Aut08, Batzoglou 2. Merge Reads into Contigs Ignore “hanging” reads, when detecting repeat boundaries sequencing error repeat boundary??? b a a b …

8. CS273a Lecture 5, Aut08, Batzoglou Overlap graph after forming contigs Unitigs: Gene Myers, 95

9. CS273a Lecture 5, Aut08, Batzoglou Repeats, errors, and contig lengths Repeats shorter than read length are easily resolved  Read that spans across a repeat disambiguates order of flanking regions Repeats with more base pair diffs than sequencing error rate are OK  We throw overlaps between two reads in different copies of the repeat To make the genome appear less repetitive, try to:  Increase read length  Decrease sequencing error rate Role of error correction: Discards up to 98% of single-letter sequencing errors decreases error rate  decreases effective repeat content  increases contig length

10. CS273a Lecture 5, Aut08, Batzoglou 3. Link Contigs into Supercontigs Too dense  Overcollapsed Inconsistent links  Overcollapsed? Normal density

11. CS273a Lecture 5, Aut08, Batzoglou Find all links between unique contigs 3. Link Contigs into Supercontigs Connect contigs incrementally, if  2 forward-reverse links supercontig (aka scaffold)

12. CS273a Lecture 5, Aut08, Batzoglou Fill gaps in supercontigs with paths of repeat contigs Complex algorithmic step Exponential number of paths Forward-reverse links 3. Link Contigs into Supercontigs

13. CS273a Lecture 5, Aut08, Batzoglou 4. Derive Consensus Sequence Derive multiple alignment from pairwise read alignments TAGATTACACAGATTACTGA TTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAAACTA TAG TTACACAGATTATTGACTTCATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGGGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA Derive each consensus base by weighted voting (Alternative: take maximum-quality letter)

14. CS273a Lecture 5, Aut08, Batzoglou Some Assemblers PHRAP Early assembler, widely used, good model of read errors Overlap O(n 2 )  layout (no mate pairs)  consensus Celera First assembler to handle large genomes (fly, human, mouse) Overlap  layout  consensus Arachne Public assembler (mouse, several fungi) Overlap  layout  consensus Phusion Overlap  clustering  PHRAP  assemblage  consensus Euler Indexing  Euler graph  layout by picking paths  consensus

15. CS273a Lecture 5, Aut08, Batzoglou Quality of assemblies—mouse N50 contig length Terminology: N50 contig length If we sort contigs from largest to smallest, and start Covering the genome in that order, N50 is the length Of the contig that just covers the 50 th percentile. 7.7X sequence coverage

16. CS273a Lecture 5, Aut08, Batzoglou Quality of assemblies—dog 7.5X sequence coverage

17. CS273a Lecture 5, Aut08, Batzoglou Quality of assemblies—chimp 3.6X sequence Coverage Assisted Assembly

18. CS273a Lecture 5, Aut08, Batzoglou History of WGA 1982: -virus, 48,502 bp 1995: h-influenzae, 1 Mbp 2000: fly, 100 Mbp 2001 – present  human (3Gbp), mouse (2.5Gbp), rat *, chicken, dog, chimpanzee, several fungal genomes Gene Myers Let’s sequence the human genome with the shotgun strategy That is impossible, and a bad idea anyway Phil Green 1997

19. CS273a Lecture 5, Aut08, Batzoglou $399 Personal Genome Service $2,500 Health Compass service $985 deCODEme (November 2007) (April 2008) $350,000 Whole-genome sequencing (November 2007) Genetic Information Nondiscrimination Act (May 2008)

20. CS273a Lecture 5, Aut08, Batzoglou Whole-genome sequencing Comparative genomics Genome resequencing Structural variation analysis Polymorphism discovery Metagenomics Environmental sequencing Gene expression profiling Applications Genotyping Population genetics Migration studies Ancestry inference Relationship inference Genetic screening Drug targeting Forensics

21. CS273a Lecture 5, Aut08, Batzoglou Sequencing applications Demand for more sequencing Sequencing technology improvement Increase in sequencing data output New sequencing applications

22. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology Sanger sequencing 19751980200819902000 $10.00 $1.00 $0.10 $0.01 Cost per finished bp: Read length:15 – 200 bp500 – 1,000 bp Throughput: “grad-student years”2 ∙ 10 6 bp/day Fred Sanger

23. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology Sanger sequencing 3 ∙ 10 9 bp 1x coverage 10x coverage 2 ∙ 10 6 bp/day = 40 years × 3 ∙ 10 9 bp 10x coverage × 3 ∙ 10 9 bp × $0.001/bp = $30 million

24. CS273a Lecture 5, Aut08, Batzoglou Pyrosequencing on a chip Mostafa Ronaghi, Stanford Genome Technologies Center 454 Life Sciences

25. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology Next-generation sequencing Read length:250 bp Throughput:300 Mb/day Cost: ~ 10,000 bp/$ De novo:yes Genome Sequencer / FLX “short reads”

26. CS273a Lecture 5, Aut08, Batzoglou Single Molecule Array for Genotyping—Solexa

27. CS273a Lecture 5, Aut08, Batzoglou Polony Sequencing

28. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology Next-generation sequencing Read length: ~ 35 bp Throughput:300 – 500 Mb/day Cost: ~ 100,000 bp/$ De novo:yes Genome AnalyzerSOLiD Analyzer “microreads”

29. CS273a Lecture 5, Aut08, Batzoglou Molecular Inversion Probes

30. CS273a Lecture 5, Aut08, Batzoglou Illumina Genotype Arrays

31. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology Next-generation sequencing Read length:1 bp Throughput:1 – 2 Mb/day Cost:5,000 bp/$ De novo:no Infinium AssayGeneChip Array genotypes “SNP chips”

32. CS273a Lecture 5, Aut08, Batzoglou Nanopore Sequencing http://www.mcb.harvard.edu/branton/index.htm

33. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology Next-generation sequencing

34. CS273a Lecture 5, Aut08, Batzoglou Sequencing technology TechnologyRead length (bp) Throughput (Mb/day) Cost (bp/$) De novo Sanger1,0002 45425030010,000 Solexa / ABI35500100,000 SNP chip125,000 ApplicationSanger454Solexa/ABI SNP chip Bacterial sequencing$ sometimes Mammalian sequencing$$$ not likely today Mammalian resequencing$$$$sort of Metagenomics$ ? Genotyping$$$ ?