273a Lecture 4, Autumn 08, Batzoglou Hierarchical Sequencing
Dec 21, 2015
CS273a Lecture 4, Autumn 08, Batzoglou
Hierarchical Sequencing
CS273a Lecture 4, Autumn 08, Batzoglou
Hierarchical Sequencing Strategy
1. Obtain a large collection of BAC clones2. Map them onto the genome (Physical Mapping)3. Select a minimum tiling path4. Sequence each clone in the path with shotgun5. Assemble6. Put everything together
a BAC clone
mapgenome
CS273a Lecture 4, Autumn 08, Batzoglou
Hierarchical Sequencing Strategy
1. Obtain a large collection of BAC clones2. Map them onto the genome (Physical Mapping)3. Select a minimum tiling path4. Sequence each clone in the path with shotgun5. Assemble6. Put everything together
a BAC clone
mapgenome
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
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
p1 pn
CS273a Lecture 4, Autumn 08, Batzoglou
2. Digestion
Restriction enzymes cut DNA where specific words appear
1. Cut each clone separately with an enzyme2. Run fragments on a gel and measure length3. Clones Ca, Cb have fragments of length { li, lj, lk }
overlap
Double digestion:Cut with enzyme A, enzyme B, then enzymes A + B
CS273a Lecture 4, Autumn 08, Batzoglou
Online Clone-by-cloneThe Walking Method
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
CS273a Lecture 4, Autumn 08, Batzoglou
Walking: An Example
CS273a Lecture 4, Autumn 08, Batzoglou
Some Terminologyinsert 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
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~800 bp
CS273a Lecture 4, Autumn 08, Batzoglou
Fragment Assembly(in whole-genome shotgun sequencing)
CS273a Lecture 4, Autumn 08, Batzoglou
Fragment Assembly
Given N reads…Given N reads…Where N ~ 30 Where N ~ 30
million…million…
We need to use a We need to use a linear-time linear-time algorithmalgorithm
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 endsof the same insert fragment
contig a contiguous sequence formed by several overlapping readswith no gaps
supercontig an ordered and oriented set(scaffold) of contigs, usually by mate
pairs
consensus sequence derived from thesequene multiple alignment of reads
in a contig
CS273a Lecture 4, Autumn 08, Batzoglou
1. Find Overlapping Reads
aaactgcagtacggatctaaactgcag aactgcagt… gtacggatct tacggatctgggcccaaactgcagtacgggcccaaa ggcccaaac… actgcagta ctgcagtacgtacggatctactacacagtacggatc tacggatct… ctactacac tactacaca
(read, pos., word, orient.)
aaactgcagaactgcagtactgcagta… gtacggatctacggatctgggcccaaaggcccaaacgcccaaact…actgcagtactgcagtacgtacggatctacggatctacggatcta…ctactacactactacaca
(word, read, orient., pos.)
aaactgcagaactgcagtacggatcta actgcagta actgcagtacccaaactgcggatctacctactacacctgcagtacctgcagtacgcccaaactggcccaaacgggcccaaagtacggatcgtacggatctacggatcttacggatcttactacaca
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
TAGATTACACAGATTAC|||||||||||||||||
T GA
TAGA| ||
TACA
TAGT||
• 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
CS273a Lecture 4, Autumn 08, Batzoglou
1. Find Overlapping Reads
Create local multiple alignments from the overlapping reads
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG TTACACAGATTATTGATAGATTACACAGATTACTGA
CS273a Lecture 4, Autumn 08, Batzoglou
1. Find Overlapping Reads
• Correct errors using multiple alignment
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAGATTACACAGATTATTGATAGATTACACAGATTACTGATAG-TTACACAGATTACTGA
TAGATTACACAGATTACTGATAGATTACACAGATTACTGATAG-TTACACAGATTATTGATAGATTACACAGATTACTGATAG-TTACACAGATTATTGA
insert A
replace T with Ccorrelated errors—probably caused by repeats disentangle overlaps
TAGATTACACAGATTACTGATAGATTACACAGATTACTGA
TAG-TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAG-TTACACAGATTATTGA
In practice, error correction removes up to 98% of the errors
CS273a Lecture 4, Autumn 08, Batzoglou
2. Merge Reads into Contigs
• Overlap graph: Nodes: reads r1…..rn
Edges: overlaps (ri, rj, shift, orientation, score)
Note:of course, we don’tknow the “color” ofthese nodes
Reads that comefrom two regions ofthe genome (blueand red) that containthe same repeat