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DNA Sequencing Lecture 9, Tuesday April 29, 2003.

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1 DNA Sequencing Lecture 9, Tuesday April 29, 2003

2 Reading Basic: ARACHNE: A Whole-Genome Shotgun Assembler Euler: A shotgun assembler based on finding Eulerian paths Optional: Transposons; Genome Sizes; ARACHNE 2: Assembly of the mouse genome Skim through following 2 free Nature issues: Mouse (December 2002); 50 year anniversary (last week!)

3 Lecture 9, Tuesday April 29, 2003 DNA sequencing – vectors + = DNA Shake DNA fragments Vector Circular genome (bacterium, plasmid) Known location (restriction site)

4 Lecture 9, Tuesday April 29, 2003 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

5 Lecture 9, Tuesday April 29, 2003 Output of PHRAP: a read A read: 500-700 nucleotides A C G A A T C A G …. A 16 18 21 23 25 15 28 30 32 21 Quality scores: -10  log 10 Prob(Error) Reads can be obtained from leftmost, rightmost ends of the insert Double-barreled sequencing: Both leftmost & rightmost ends are sequenced

6 Lecture 9, Tuesday April 29, 2003 Method to sequence segments longer than 500 cut many times at random (Shotgun) genomic segment Get one or two reads from each segment ~500 bp

7 Lecture 9, Tuesday April 29, 2003 Reconstructing the Sequence (Fragment Assembly) Cover region with ~7-fold redundancy (7X) Overlap reads and extend to reconstruct the original genomic region reads

8 Lecture 9, Tuesday April 29, 2003 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

9 Lecture 9, Tuesday April 29, 2003 Strategies for sequencing a whole genome 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, Fugu

10 Lecture 9, Tuesday April 29, 2003 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

11 Lecture 9, Tuesday April 29, 2003 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

12 Online Clone-by-clone The Walking Method Lecture 9, Tuesday April 29, 2003

13 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

14 Lecture 9, Tuesday April 29, 2003 Walking: An Example

15 Lecture 9, Tuesday April 29, 2003 Advantages & Disadvantages of Hierarchical Sequencing Hierarchical Sequencing –ADV. Easy assembly –DIS. Build library & physical map; redundant sequencing Whole Genome Shotgun (WGS) –ADV. No mapping, no redundant sequencing –DIS. Difficult to assemble and resolve repeats The Walking method – motivation Sequence the genome clone-by-clone without a physical map The only costs involved are: –Library of end-sequenced clones (CHEAP) –Sequencing

16 Lecture 9, Tuesday April 29, 2003 Walking off a Single Seed Low redundant sequencing Many sequential steps

17 Lecture 9, Tuesday April 29, 2003 Walking off a single clone is impractical Cycle time to process one clone: 1-2 months 1.Grow clone 2.Prepare & Shear DNA 3.Prepare shotgun library & perform shotgun 4.Assemble in a computer 5.Close remaining gaps A mammalian genome would need 15,000 walking steps !

18 Lecture 9, Tuesday April 29, 2003 Walking off Several Seeds in Parallel Few sequential steps Additional redundant sequencing In general, can sequence a genome in ~5 walking steps, with <20% redundant sequencing EfficientInefficient

19 Lecture 9, Tuesday April 29, 2003 Using Two Libraries Solution: Use a second library of small clones Most inefficiency comes from closing a small ocean with a much larger clone

20 Whole-Genome Shotgun Sequencing Lecture 9, Tuesday April 29, 2003

21 Whole Genome Shotgun Sequencing cut many times at random genome forward-reverse linked reads plasmids (2 – 10 Kbp) cosmids (40 Kbp) known dist ~500 bp

22 Lecture 9, Tuesday April 29, 2003 ARACHNE: Steps to Assemble a Genome 1. Find overlapping reads 4. Derive consensus sequence..ACGATTACAATAGGTT.. 2. Merge good pairs of reads into longer contigs 3. Link contigs to form supercontigs

23 Lecture 9, Tuesday April 29, 2003 1. Find Overlapping Reads Sort all k-mers in reads (k ~ 24) TAGATTACACAGATTAC ||||||||||||||||| Find pairs of reads sharing a k-mer Extend to full alignment – throw away if not >95% similar T GA TAGA | || TACA TAGT ||

24 Lecture 9, Tuesday April 29, 2003 1. Find Overlapping Reads One caveat: repeats A k-mer that appears N times, initiates N 2 comparisons ALU: 1,000,000 times Solution: Discard all k-mers that appear more than c  Coverage, (c ~ 10)

25 Lecture 9, Tuesday April 29, 2003 1. Find Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

26 Lecture 9, Tuesday April 29, 2003 1. Find 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

27 Lecture 9, Tuesday April 29, 2003 Basic principle of assembly Repeats confuse us Ability to merge two reads is related to our ability to detect repeats We can dismiss as repeat any overlap of < t% similarity Role of error correction: Discards ~90% of single-letter sequencing errors  Threshold t% increases

28 Lecture 9, Tuesday April 29, 2003 2. Merge Reads into Contigs (cont’d) Merge reads up to potential repeat boundaries (Myers, 1995) repeat region

29 Lecture 9, Tuesday April 29, 2003 2. Merge Reads into Contigs (cont’d) Ignore non-maximal reads Merge only maximal reads into contigs repeat region

30 Lecture 9, Tuesday April 29, 2003 2. Merge Reads into Contigs (cont’d) Ignore “hanging” reads, when detecting repeat boundaries sequencing error repeat boundary??? b a

31 Lecture 9, Tuesday April 29, 2003 2. Merge Reads into Contigs (cont’d) ????? Unambiguous Insert non-maximal reads whenever unambiguous

32 Lecture 9, Tuesday April 29, 2003 3. Link Contigs into Supercontigs Too dense: Overcollapsed? (Myers et al. 2000) Inconsistent links: Overcollapsed? Normal density

33 Lecture 9, Tuesday April 29, 2003 Find all links between unique contigs 3. Link Contigs into Supercontigs (cont’d) Connect contigs incrementally, if  2 links

34 Lecture 9, Tuesday April 29, 2003 Fill gaps in supercontigs with paths of overcollapsed contigs 3. Link Contigs into Supercontigs (cont’d)

35 Lecture 9, Tuesday April 29, 2003 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 3. Link Contigs into Supercontigs (cont’d) d ( A, B ) Contig A Contig B

36 Lecture 9, Tuesday April 29, 2003 3. 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

37 Lecture 9, Tuesday April 29, 2003 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

38 Lecture 9, Tuesday April 29, 2003 Simulated Whole Genome Shotgun Known genomes Flu, yeast, fly, Human chromosomes 21, 22 Make “realistic” shotgun reads Run ARACHNE Align output with genome and compare

39 Lecture 9, Tuesday April 29, 2003 Making a Simulated Read Simulated reads have error patterns taken from random real reads ERRORIZER Simulated read artificial shotgun read real read

40 Lecture 9, Tuesday April 29, 2003 Human 22, Results of Simulations Plasmid/ Cosmid cov 10 X / 0.5 X 5 X / 0.5 X 3 X/ 0 X N50 contig353 Kb15 Kb2.7 Kb Mean contig142 Kb10.6 Kb2.0 Kb N50 scaffold3 Mb 4.1 Kb Avg base qual413226 % > 2 kb97.391.167

41 Lecture 9, Tuesday April 29, 2003 Neurospora crassa Genome (Real Data) 40 Mb genome, shotgun sequencing complete (WI-CGR) Coverage: 1705 contigs 368 supercontigs 1% uncovered (of finished BACs) Evaluated assembly using 1.5Mb of finished BACs Efficiency: Time: 20 hr Memory: 9 Gb Accuracy: < 3 misassemblies compared with 1 Gb of finished sequence Errors/10 6 letters: Subst. 260 Indel: 164

42 Lecture 9, Tuesday April 29, 2003 Mouse Genome Improved version of ARACHNE assembled the mouse genome Several heuristics of iteratively: Breaking supercontigs that are suspicious Rejoining supercontigs Size of problem: 32,000,000 reads Time: 15 days, 1 processor Memory: 28 Gb N50 Contig size: 16.3 Kb  24.8 Kb N50 Supercontig size:.265 Mb  16.9 Mb

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