DNA Sequencing

1. DNA Sequencing

2. DNA sequencing How we obtain the sequence of nucleotides of a species …ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

3. 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. Why humans are so similar Out of Africa N A small population that interbred reduced the genetic variation Out of Africa ~ 40,000 years ago Heterozygosity: H H = 4Nu/(1 + 4Nu) u ~ 10-8, N ~ 104  H ~ 410-4

5. 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. DNA Sequencing – Overview 1975 • 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 • Microbial and environmental sequencing • Cancer genome sequencing 2015

7. 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. DNA Sequencing – vectors DNA Shake DNA fragments Known location (restriction site) Vector Circular genome (bacterium, plasmid) + =

9. Different types of vectors

10. DNA Sequencing – gel electrophoresis • Start at primer (restriction site) • Grow DNA chain • Include dideoxynucleoside (modified a, c, g, t) • Stops reaction at all possible points • Separate products with length, using gel electrophoresis

11. Method to sequence longer regions genomic segment cut many times at random (Shotgun) Get one or two reads from each segment ~900 bp ~900 bp

12. Reconstructing the Sequence (Fragment Assembly) reads Cover region with high redundancy Overlap & extend reads to reconstruct the original genomic region

13. Definition of Coverage C 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

14. Repeats Bacterial genomes: 5% Mammals: 50% Repeat types: • Low-Complexity DNA (e.g. ATATATATACATA…) • Microsatellite repeats (a1…ak)N where k ~ 3-6 (e.g. CAGCAGTAGCAGCACCAG) • Transposons • SINE(Short Interspersed Nuclear Elements) e.g., ALU: ~300-long, 106 copies • LINE(Long Interspersed Nuclear Elements) ~4000-long, 200,000 copies • LTRretroposons(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. AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCTAGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT Sequencing and Fragment Assembly 3x109 nucleotides 50% of human DNA is composed of repeats Error! Glued together two distant regions

16. What can we do about repeats? Two main approaches: • Cluster the reads • Link the reads

17. What can we do about repeats? Two main approaches: • Cluster the reads • Link the reads

18. What can we do about repeats? Two main approaches: • Cluster the reads • Link the reads

19. AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCTAGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT A R B D R C Sequencing and Fragment Assembly 3x109 nucleotides ARB, CRD or ARD, CRB ?

20. AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCTAGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT Sequencing and Fragment Assembly 3x109 nucleotides

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

22. Hierarchical Sequencing

23. a BAC clone map Hierarchical Sequencing Strategy • Obtain a large collection of BAC clones • Map them onto the genome (Physical Mapping) • Select a minimum tiling path • Sequence each clone in the path with shotgun • Assemble • Put everything together genome

24. a BAC clone map Hierarchical Sequencing Strategy • Obtain a large collection of BAC clones • Map them onto the genome (Physical Mapping) • Select a minimum tiling path • Sequence each clone in the path with shotgun • Assemble • Put everything together genome

25. 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. 1. Hybridization Short words, the probes, attach to complementary words • Construct many probes • Treat each BAC with all probes • Record which ones attach to it • Same words attaching to BACS X, Y  overlap p1 pn

27. 2. Digestion Restriction enzymes cut DNA where specific words appear • Cut each clone separately with an enzyme • Run fragments on a gel and measure length • Clones Ca, Cb have fragments of length { li, lj, lk }  overlap Double digestion: Cut with enzyme A, enzyme B, then enzymes A + B

28. Online Clone-by-cloneThe Walking Method

29. The Walking Method • Build a very redundant library of BACs with sequenced clone-ends (cheap to build) • Sequence some “seed” clones • “Walk” from seeds using clone-ends to pick library clones that extend left & right

30. Walking: An Example

31. 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 BACBacterial 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 coveragethe 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. cut many times at random Whole Genome Shotgun Sequencing genome plasmids (2 – 10 Kbp) forward-reverse paired reads known dist cosmids (40 Kbp) ~800 bp ~800 bp

33. Fragment Assembly(in whole-genome shotgun sequencing)

34. Fragment Assembly Given N reads… Where N ~ 30 million… We need to use a linear-time algorithm

35. Steps to Assemble a Genome 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 1. Find overlapping reads 2. Merge some “good” pairs of reads into longer contigs 3. Link contigs to form supercontigs 4. Derive consensus sequence ..ACGATTACAATAGGTT..

36. 1. Find Overlapping Reads (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 actgcagta cccaaactg cggatctac ctactacac ctgcagtac ctgcagtac gcccaaact ggcccaaac gggcccaaa gtacggatc gtacggatc tacggatct tacggatct tactacaca aaactgcagtacggatct aaactgcag aactgcagt … gtacggatct tacggatct gggcccaaactgcagtac gggcccaaa ggcccaaac … actgcagta ctgcagtac gtacggatctactacaca gtacggatc tacggatct … ctactacac tactacaca

37. T GA TACA | || || TAGA TAGT 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 ||||||||||||||||| TAGATTACACAGATTAC • Caveat: repeats • A k-mer that occurs N times, causes O(N2) read/read comparisons • ALU k-mers could cause up to 1,000,0002 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. 1. Find Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

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

40. 2. Merge Reads into Contigs • Overlap graph: • Nodes: reads r1…..rn • Edges: overlaps (ri, rj, shift, orientation, score) Reads that come from two regions of the genome (blue and red) that contain the same repeat Note: of course, we don’t know the “color” of these nodes