1. CS273a Lecture 4, Autumn 08, Batzoglou DNA Sequencing

2. CS273a Lecture 4, Autumn 08, Batzoglou DNA sequencing How we obtain the sequence of nucleotides of a species …ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

3. CS273a Lecture 4, Autumn 08, Batzoglou Which representative of the species? Which human? Answer one: Answer two: it doesn’t matter Polymorphism rate: number of letter changes between two different members of a species Humans: ~1/1,000 Other organisms have much higher polymorphism rates  Population size!

4. CS273a Lecture 4, Autumn 08, Batzoglou Why humans are so similar A small population that interbred reduced the genetic variation Out of Africa ~ 40,000 years ago Out of Africa Heterozygosity: H H = 4Nu/(1 + 4Nu) u ~ 10 -8, N ~ 10 4  H ~ 4  10 -4 N

5. CS273a Lecture 4, Autumn 08, Batzoglou Human population migrations Out of Africa, Replacement  “Grandma” of all humans (Eve) ~150,000yr Ancestor of all mtDNA  “Grandpa” of all humans (Adam) ~100,000yr Ancestor of all Y-chromosomes Multiregional Evolution  Fossil records show a continuous change of morphological features  Proponents of the theory doubt mtDNA and other genetic evidence New fossil records bury “multirigionalists”  Nice article in Economist on that http://www.economist.com/science/displaystory.cfm?story_id=9507453

6. CS273a Lecture 4, Autumn 08, Batzoglou DNA Sequencing – Overview Gel electrophoresis  Predominant, old technology by F. Sanger Whole genome strategies  Physical mapping  Walking  Shotgun sequencing Computational fragment assembly The future—new sequencing technologies  Pyrosequencing, single molecule methods, …  Assembly techniques Future variants of sequencing  Resequencing of humans Resequencing of humans  Microbial and environmental sequencing Microbial and environmental sequencing  Cancer genome sequencing Cancer genome sequencing 1975 2015

7. CS273a Lecture 4, Autumn 08, Batzoglou DNA Sequencing Goal: Find the complete sequence of A, C, G, T’s in DNA Challenge: There is no machine that takes long DNA as an input, and gives the complete sequence as output Can only sequence ~900 letters at a time

8. CS273a Lecture 4, Autumn 08, Batzoglou DNA Sequencing – vectors + = DNA Shake DNA fragments Vector Circular genome (bacterium, plasmid) Known location (restriction site)

9. CS273a Lecture 4, Autumn 08, Batzoglou Different types of vectors VECTORSize of insert Plasmid 2,000-10,000 Can control the size Cosmid40,000 BAC (Bacterial Artificial Chromosome) 70,000-300,000 YAC (Yeast Artificial Chromosome) > 300,000 Not used much recently

10. CS273a Lecture 4, Autumn 08, Batzoglou DNA Sequencing – gel electrophoresis 1.Start at primer(restriction site) 2.Grow DNA chain 3.Include dideoxynucleoside (modified a, c, g, t) 4.Stops reaction at all possible points 5.Separate products with length, using gel electrophoresis

11. CS273a Lecture 4, Autumn 08, Batzoglou Method to sequence longer regions cut many times at random (Shotgun) genomic segment Get one or two reads from each segment ~900 bp

12. CS273a Lecture 4, Autumn 08, Batzoglou Reconstructing the Sequence (Fragment Assembly) Cover region with high redundancy Overlap & extend reads to reconstruct the original genomic region reads

13. CS273a Lecture 4, Autumn 08, Batzoglou Definition of Coverage Length of genomic segment:G Number of reads: N Length of each read:L Definition: Coverage C = N L / G How much coverage is enough? Lander-Waterman model:Prob[ not covered bp ] = e -C Assuming uniform distribution of reads, C=10 results in 1 gapped region /1,000,000 nucleotides C

14. CS273a Lecture 4, Autumn 08, Batzoglou Repeats Bacterial genomes:5% Mammals:50% Repeat types: Low-Complexity DNA (e.g. ATATATATACATA…) Microsatellite repeats (a 1 …a k ) N where k ~ 3-6 (e.g. CAGCAGTAGCAGCACCAG) Transposons  SINE (Short Interspersed Nuclear Elements) e.g., ALU: ~300-long, 10 6 copies  LINE (Long Interspersed Nuclear Elements) ~4000-long, 200,000 copies  LTR retroposons (Long Terminal Repeats (~700 bp) at each end) cousins of HIV Gene Families genes duplicate & then diverge (paralogs) Recent duplications ~100,000-long, very similar copies

15. CS273a Lecture 4, Autumn 08, Batzoglou Sequencing and Fragment Assembly AGTAGCACAGA CTACGACGAGA CGATCGTGCGA GCGACGGCGTA GTGTGCTGTAC TGTCGTGTGTG TGTACTCTCCT 3x10 9 nucleotides 50% of human DNA is composed of repeats Error! Glued together two distant regions

16. CS273a Lecture 4, Autumn 08, Batzoglou What can we do about repeats? Two main approaches: Cluster the reads Link the reads

17. CS273a Lecture 4, Autumn 08, Batzoglou What can we do about repeats? Two main approaches: Cluster the reads Link the reads

18. CS273a Lecture 4, Autumn 08, Batzoglou What can we do about repeats? Two main approaches: Cluster the reads Link the reads

19. CS273a Lecture 4, Autumn 08, Batzoglou Sequencing and Fragment Assembly AGTAGCACAGA CTACGACGAGA CGATCGTGCGA GCGACGGCGTA GTGTGCTGTAC TGTCGTGTGTG TGTACTCTCCT 3x10 9 nucleotides C R D ARB, CRD or ARD, CRB ? ARB

20. CS273a Lecture 4, Autumn 08, Batzoglou Sequencing and Fragment Assembly AGTAGCACAGA CTACGACGAGA CGATCGTGCGA GCGACGGCGTA GTGTGCTGTAC TGTCGTGTGTG TGTACTCTCCT 3x10 9 nucleotides

21. CS273a Lecture 4, Autumn 08, Batzoglou Strategies for whole-genome sequencing 1.Hierarchical – Clone-by-clone i.Break genome into many long pieces ii.Map each long piece onto the genome iii.Sequence each piece with shotgun Example: Yeast, Worm, Human, Rat 2.Online version of (1) – Walking i.Break genome into many long pieces ii.Start sequencing each piece with shotgun iii.Construct map as you go Example: Rice genome 3.Whole genome shotgun One large shotgun pass on the whole genome Example: Drosophila, Human (Celera), Neurospora, Mouse, Rat, Dog

22. CS273a Lecture 4, Autumn 08, Batzoglou Hierarchical Sequencing

23. CS273a Lecture 4, Autumn 08, Batzoglou Hierarchical Sequencing Strategy 1.Obtain a large collection of BAC clones 2.Map them onto the genome (Physical Mapping) 3.Select a minimum tiling path 4.Sequence each clone in the path with shotgun 5.Assemble 6.Put everything together a BAC clone map genome

24. CS273a Lecture 4, Autumn 08, Batzoglou Hierarchical Sequencing Strategy 1.Obtain a large collection of BAC clones 2.Map them onto the genome (Physical Mapping) 3.Select a minimum tiling path 4.Sequence each clone in the path with shotgun 5.Assemble 6.Put everything together a BAC clone map genome

25. CS273a Lecture 4, Autumn 08, Batzoglou Methods of physical mapping Goal: Make a map of the locations of each clone relative to one another Use the map to select a minimal set of clones to sequence Methods: Hybridization Digestion

26. CS273a Lecture 4, Autumn 08, Batzoglou 1. Hybridization Short words, the probes, attach to complementary words 1.Construct many probes 2.Treat each BAC with all probes 3.Record which ones attach to it 4.Same words attaching to BACS X, Y  overlap p1p1 pnpn

27. CS273a Lecture 4, Autumn 08, Batzoglou 2.Digestion Restriction enzymes cut DNA where specific words appear 1.Cut each clone separately with an enzyme 2.Run fragments on a gel and measure length 3.Clones C a, C b have fragments of length { l i, l j, l k }  overlap Double digestion: Cut with enzyme A, enzyme B, then enzymes A + B

28. CS273a Lecture 4, Autumn 08, Batzoglou Online Clone-by-clone The Walking Method

29. CS273a Lecture 4, Autumn 08, Batzoglou The Walking Method 1.Build a very redundant library of BACs with sequenced clone- ends (cheap to build) 2.Sequence some “seed” clones 3.“Walk” from seeds using clone-ends to pick library clones that extend left & right

30. CS273a Lecture 4, Autumn 08, Batzoglou Walking: An Example

31. CS273a Lecture 4, Autumn 08, Batzoglou Some Terminology insert a fragment that was incorporated in a circular genome, and can be copied (cloned) vector the circular genome (host) that incorporated the fragment BAC Bacterial Artificial Chromosome, a type of insert–vector combination, typically of length 100-200 kb read a 500-900 long word that comes out of a sequencing machine coverage the average number of reads (or inserts) that cover a position in the target DNA piece shotgun the process of obtaining many reads sequencing from random locations in DNA, to detect overlaps and assemble

32. CS273a Lecture 4, Autumn 08, Batzoglou Whole Genome Shotgun Sequencing cut many times at random genome forward-reverse paired reads plasmids (2 – 10 Kbp) cosmids (40 Kbp) known dist ~800 bp

33. CS273a Lecture 4, Autumn 08, Batzoglou Fragment Assembly (in whole-genome shotgun sequencing)

34. CS273a Lecture 4, Autumn 08, Batzoglou Fragment Assembly Given N reads… Where N ~ 30 million… We need to use a linear-time algorithm

35. CS273a Lecture 4, Autumn 08, 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

36. CS273a Lecture 4, Autumn 08, Batzoglou 1. Find Overlapping Reads aaactgcagtacggatct aaactgcag aactgcagt … gtacggatct tacggatct gggcccaaactgcagtac gggcccaaa ggcccaaac … actgcagta ctgcagtac gtacggatctactacaca gtacggatc tacggatct … ctactacac tactacaca (read, pos., word, orient.) aaactgcag aactgcagt actgcagta … gtacggatc tacggatct gggcccaaa ggcccaaac gcccaaact … actgcagta ctgcagtac gtacggatc tacggatct acggatcta … ctactacac tactacaca (word, read, orient., pos.) aaactgcag aactgcagt acggatcta actgcagta cccaaactg cggatctac ctactacac ctgcagtac gcccaaact ggcccaaac gggcccaaa gtacggatc tacggatct tactacaca

37. CS273a Lecture 4, Autumn 08, Batzoglou 1. Find Overlapping Reads Find pairs of reads sharing a k-mer, k ~ 24 Extend to full alignment – throw away if not >98% similar TAGATTACACAGATTAC ||||||||||||||||| T GA TAGA | || TACA TAGT || Caveat: repeats  A k-mer that occurs N times, causes O(N 2 ) read/read comparisons  ALU k-mers could cause up to 1,000,000 2 comparisons Solution:  Discard all k-mers that occur “ too often ” Set cutoff to balance sensitivity/speed tradeoff, according to genome at hand and computing resources available

38. CS273a Lecture 4, Autumn 08, Batzoglou 1. Find Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

39. CS273a Lecture 4, Autumn 08, Batzoglou 1. Find Overlapping Reads Correct errors using multiple alignment TAGATTACACAGATTACTGA TAGATTACACAGATTATTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTACTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA insert A replace T with C correlated errors— probably caused by repeats  disentangle overlaps TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA In practice, error correction removes up to 98% of the errors

40. CS273a Lecture 4, Autumn 08, 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