DNA Sequencing

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DNA Sequencing
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Hierarchical Sequencing Strategy
genome

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

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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

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1. Hybridization
p1
pn

• 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

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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

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Online Clone-by-cloneThe Walking Method
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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

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Walking An Example
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Walking off a Single Seed

• Low redundant sequencing
• Many sequential steps

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Walking off a single clone is impractical

• Cycle time to process one clone 1-2 months
• Grow clone
• Prepare Shear DNA
• Prepare shotgun library perform shotgun
• Assemble in a computer
• Close remaining gaps
• A mammalian genome would need 15,000 walking
  steps !

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Walking off several seeds in parallel
Efficient
Inefficient

• Few sequential steps
• Additional redundant sequencing
• In general, can sequence a genome in 5 walking
  steps,
• with lt20 redundant sequencing

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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
insertvector 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
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Whole Genome Shotgun Sequencing
genome
plasmids (2 10 Kbp)
forward-reverse paired reads
known dist
cosmids (40 Kbp)
800 bp
800 bp
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Fragment Assembly(in whole-genome shotgun
sequencing)
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Fragment Assembly
Given N reads Where N 30 million We need to
use a linear-time algorithm
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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..
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1. Find Overlapping Reads
(read, pos., word, orient.) aaactgcag aactgcagt ac
tgcagta gtacggatc tacggatct gggcccaaa g
gcccaaac gcccaaact actgcagta ctgcagtac gtacggatc
tacggatct acggatcta ctactacac tactacaca
(word, read, orient., pos.) aaactgcag aactgcagt ac
ggatcta actgcagta actgcagta cccaaactg cgg
atctac ctactacac ctgcagtac ctgcagtac gcccaaact ggc
ccaaac gggcccaaa gtacggatc gtacggatc tacggatct tac
ggatct tactacaca
aaactgcagtacggatct aaactgcag aactgcagt
gtacggatct tacggatct gggcccaaactgcagtac g
ggcccaaa ggcccaaac actgcagta
ctgcagtac gtacggatctactacaca gtacggatc
tacggatct ctactacac tactacaca
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1. Find Overlapping Reads

• Find pairs of reads sharing a k-mer, k 24
• Extend to full alignment throw away if not gt98
  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

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1. Find Overlapping Reads

• Create local multiple alignments from the
  overlapping reads

TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
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1. Find Overlapping Reads

• Correct errors 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
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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 dont know the color
of these nodes
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2. Merge Reads into Contigs
Unique Contig
Overcollapsed Contig

• We want to merge reads up to potential repeat
  boundaries

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2. Merge Reads into Contigs

• Ignore non-maximal reads
• Merge only maximal reads into contigs

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2. Merge Reads into Contigs
r
r1
r2
r3

• Remove transitively inferable overlaps
• If read r overlaps to the right reads r1, r2, and
  r1 overlaps r2, then (r, r2) can be inferred by
  (r, r1) and (r1, r2)

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2. Merge Reads into Contigs