1. Whole Genome Sequencing (Lecture for CS498-CXZ Algorithms in Bioinformatics) Sept. 13, 2005 ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign Most slides are taken/adapted from Serafim Batzoglou’s lectures

2. Outline Practical challenges in genome sequencing Whole genome sequencing strategies Sequencing coverage (Lander-Waterman model) Overlap-Layout-Consensus approach

3. Challenges with Fragment Assembly Sequencing errors ~1-2% of bases are wrong Repeats Computation: ~ O( N 2 ) where N = # reads false overlap due to repeat Bacterial genomes:5% Mammals:50%

4. Repeat Types Low-Complexity DNA (e.g. ATATATATACATA…) Microsatellite repeats (a 1 …a k ) N where k ~ 3-6 (e.g. CAGCAGTAGCAGCACCAG) Transposons/retrotransposons –SINE Short Interspersed Nuclear Elements (e.g., Alu: ~300 bp long, 10 6 copies) –LINE Long Interspersed Nuclear Elements ~500 - 5,000 bp long, 200,000 copies –LTR retroposonsLong Terminal Repeats (~700 bp) at each end Gene Families genes duplicate & then diverge Segmental duplications ~very long, very similar copies

5. The sequencing errors, repeats, and the complexity of genomes make it necessary to use many heuristics in practice… The Shortest Superstring formulation is an over-simplification of the problem

6. Strategies for whole-genome sequencing 1. Hierarchical – Clone-by-clone yeast, worm, human i.Break genome into many long fragments ii.Map each long fragment onto the genome iii.Sequence each fragment with shotgun 2. Online version of (1) – Walkingrice genome i.Break genome into many long fragments ii.Start sequencing each fragment with shotgun iii.Construct map as you go 3. Whole Genome Shotgunfly, human, mouse, rat, fugu One large shotgun pass on the whole genome

7. Hierarchical Sequencing vs. Whole Genome Shotgun Hierarchical Sequencing –Advantages: Easy assembly –Disadvantages: Build library & physical map; Redundant sequencing Whole Genome Shotgun (WGS) –Advantages: No mapping, no redundant sequencing –Disadvantages: Difficult to assemble and resolve repeats Whole Genome Shotgun appears to get more popular…

8. Whole Genome Shotgun Sequencing cut many times at random genome forward-reverse paired reads known dist ~500 bp

9. Fragment Assembly Cover region with ~7-fold redundancy Overlap reads and extend to reconstruct the original genomic region reads

10. Read Coverage Length of genomic segment:G Number of reads: N Length of each read:L Definition: Coverage C = NL/ G C

11. Enough Coverage How much coverage is enough? According to the Lander-Waterman model: Assuming uniform distribution of reads, C=7 results in 1 gap per 1,000 nucleotides

12. Lander-Waterman Model Major Assumptions –Reads are randomly distributed in the genome –The number of times a base is sequenced follows a Poisson distribution Implications –G= genome length, L=read length, N = # reads –Mean of Poisson: =LN/G (coverage) –% bases not sequenced: p(X=0) =0.0009 = 0.09% –Total gap length: p(X=0)*G –Total number of gaps: p(X=0)*N Average times This model was used to plan the Human Genome Project…

13. Repeats, Errors, and Read lengths Repeats shorter than read length are OK Repeats with more base pair diffs than sequencing error rate are OK To make a smaller portion of the genome appear repetitive, try to: –Increase read length –Decrease sequencing error rate Role of error correction: Discards ~90% of single-letter sequencing errors decreases error rate  decreases effective repeat content However, we have only limited read length. Many heuristics have been introduced to handle repeats…

14. Overlap-Layout-Consensus Assemblers:ARACHNE, PHRAP, CAP, TIGR, CELERA Overlap: find potentially overlapping reads Layout: merge reads into contigs and contigs into supercontigs Consensus: derive the DNA sequence and correct read errors..ACGATTACAATAGGTT..

15. Overlap Find the best match between the suffix of one read and the prefix of another Due to sequencing errors, need to use dynamic programming to find the optimal overlap alignment Apply a filtration method to filter out pairs of fragments that do not share a significantly long common substring

16. Overlapping Reads TAGATTACACAGATTAC ||||||||||||||||| Sort all k-mers in reads (k ~ 24) Find pairs of reads sharing a k-mer Extend to full alignment – throw away if not >95% similar T GA TAGA | || TACA TAGT ||

17. Overlapping Reads and Repeats A k-mer that appears N times, initiates N 2 comparisons For an Alu that appears 10 6 times  10 12 comparisons – too much Solution: Discard all k-mers that appear more than t  Coverage, (t ~ 10)

18. Finding Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

19. Finding Overlapping Reads (cont’d) Correct errors using multiple alignment TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA C: 20 C: 35 T: 30 C: 35 C: 40 C: 20 C: 35 C: 0 C: 35 C: 40 Score alignments Accept alignments with good scores A: 15 A: 25 A: 40 A: 25 - A: 15 A: 25 A: 40 A: 25 A: 0 Multiple alignments will be covered later in the course…

20. Layout Repeats are a major challenge Do two aligned fragments really overlap, or are they from two copies of a repeat?

21. Merge Reads into Contigs Merge reads up to potential repeat boundaries repeat region

22. Merge Reads into Contigs (cont’d) Ignore non-maximal reads Merge only maximal reads into contigs repeat region

23. Merge Reads into Contigs (cont’d) Ignore “hanging” reads, when detecting repeat boundaries sequencing error repeat boundary??? b a

24. Merge Reads into Contigs (cont’d) ????? Unambiguous Insert non-maximal reads whenever unambiguous

25. Link Contigs into Supercontigs Too dense: Overcollapsed? (Myers et al. 2000) Inconsistent links: Overcollapsed? Normal density

26. Link Contigs into Supercontigs (cont’d) Find all links between unique contigs Connect contigs incrementally, if  2 links

27. Link Contigs into Supercontigs (cont’d) Fill gaps in supercontigs with paths of overcollapsed contigs

28. Link Contigs into Supercontigs (cont’d) Define G = ( V, E ) V := contigs E := ( A, B ) such that d( A, B ) < C Reason to do so: Efficiency; full shortest paths cannot be computed d ( A, B ) Contig A Contig B

29. Link Contigs into Supercontigs (cont’d) Contig A Contig B Define T: contigs linked to either A or B Fill gap between A and B if there is a path in G passing only from contigs in T

30. Consensus A consensus sequence is derived from a profile of the assembled fragments A sufficient number of reads is required to ensure a statistically significant consensus Reading errors are corrected

31. 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

32. What You Should Know The challenges in assembling fragments for whole genome shotgun sequencing Lander-Waterman model Main heuristics used in Overlap-Layout- Consensus