1. DNA Sequencing

2. Method to sequence longer regions
genomic segment
cut many times at random (Shotgun)
Get one or two reads from each segment
~500 bp
~500 bp

3. Reconstructing the Sequence (Fragment Assembly)
reads
Cover region with ~7-fold redundancy (7X)
Overlap reads and extend to reconstruct the original genomic region

4. Definition of Coverage
Length of genomic segment: L
Number of reads: n
Length of each read: l
Definition: Coverage C = n l / L
How much coverage is enough?
Lander-Waterman model:
Assuming uniform distribution of reads, C=10 results in 1 gapped region /1,000,000 nucleotides

5. 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
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

6. Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides
50% of human DNA is composed of repeats
Error!
Glued together two distant regions

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

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

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

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

11. Sequencing and Fragment Assembly
AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT
3x109 nucleotides

12. 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
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

13. Hierarchical Sequencing

14. Hierarchical Sequencing Strategy
a BAC clone
map
genome
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

15. 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

16. 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

17. 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

18. 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 kb
read a 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

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

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

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

22. Steps to Assemble a Genome
Some Terminology
read a 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..

23. 1. Find Overlapping Reads
(read, pos., word, orient.)
aaactgcag
aactgcagt
actgcagta …
gtacggatc
tacggatct
gggcccaaa
ggcccaaac
gcccaaact
ctgcagtac
acggatcta
ctactacac
tactacaca
(word, read, orient., pos.)
aaactgcag
aactgcagt
acggatcta
actgcagta
cccaaactg
cggatctac
ctactacac
ctgcagtac
gcccaaact
ggcccaaac
gggcccaaa
gtacggatc
tacggatct
tactacaca
aaactgcagtacggatct
aaactgcag
aactgcagt …
gtacggatct
tacggatct
gggcccaaactgcagtac
gggcccaaa
ggcccaaac
actgcagta
ctgcagtac
gtacggatctactacaca
gtacggatc
ctactacac
tactacaca

24. 1. Find Overlapping Reads
Find pairs of reads sharing a k-mer, k ~ 24
Extend to full alignment – throw away if not >98% similar
TACA
TAGT ||
TAGATTACACAGATTAC
T GA
TAGA
| ||
|||||||||||||||||
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

25. 1. Find Overlapping Reads
Create local multiple alignments from the overlapping reads
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAGATTACACAGATTACTGA
TAG TTACACAGATTATTGA
TAGATTACACAGATTACTGA

26. 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

27. 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

28. 2. Merge Reads into Contigs
repeat region
Unique Contig
Overcollapsed Contig
We want to merge reads up to potential repeat boundaries

29. 2. Merge Reads into Contigs
repeat region
Ignore non-maximal reads
Merge only maximal reads into contigs

30. 2. Merge Reads into Contigs
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)

31. 2. Merge Reads into Contigs

32. 2. Merge Reads into Contigs
repeat boundary???
sequencing error b a … b a
Ignore “hanging” reads, when detecting repeat boundaries

33. Overlap graph after forming contigs
Unitigs:
Gene Myers, 95

34. Repeats, errors, and contig lengths
Repeats shorter than read length are easily resolved
Read that spans across a repeat disambiguates order of flanking regions
Repeats with more base pair diffs than sequencing error rate are OK
We throw overlaps between two reads in different copies of the repeat
To make the genome appear less repetitive, try to:
Increase read length
Decrease sequencing error rate
Role of error correction:
Discards up to 98% of single-letter sequencing errors
decreases error rate
 decreases effective repeat content
 increases contig length

35. 2. Merge Reads into Contigs
Insert non-maximal reads whenever unambiguous

36. 3. Link Contigs into Supercontigs
Normal density
Too dense
 Overcollapsed
Inconsistent links
 Overcollapsed?

37. 3. Link Contigs into Supercontigs
Find all links between unique contigs
Connect contigs incrementally, if  2 links
supercontig
(aka scaffold)

38. 3. Link Contigs into Supercontigs
Fill gaps in supercontigs with paths of repeat contigs

39. 4. Derive Consensus Sequence
TAGATTACACAGATTACTGA TTGATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAAACTA
TAG TTACACAGATTATTGACTTCATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
TAGATTACACAGATTACTGACTTGATGGGGTAA CTA
TAGATTACACAGATTACTGACTTGATGGCGTAA CTA
Derive multiple alignment from pairwise read alignments
Derive each consensus base by weighted voting
(Alternative: take maximum-quality letter)

40. Some Assemblers PHRAP Celera Arachne Phusion Euler
Early assembler, widely used, good model of read errors
Overlap O(n2)  layout (no mate pairs)  consensus
Celera
First assembler to handle large genomes (fly, human, mouse)
Overlap  layout  consensus
Arachne
Public assembler (mouse, several fungi)
Phusion
Overlap  clustering  PHRAP  assemblage  consensus
Euler
Indexing  Euler graph  layout by picking paths  consensus

41. Quality of assemblies
Celera’s assemblies of human and mouse

42. Quality of assemblies—mouse

43. Quality of assemblies—mouse
Terminology: N50 contig length
If we sort contigs from largest to smallest, and start
Covering the genome in that order, N50 is the length
Of the contig that just covers the 50th percentile.

44. Quality of assemblies—rat

45. History of WGA 1982: -virus, 48,502 bp 1995: h-influenzae, 1 Mbp
1997
1982: -virus, 48,502 bp
1995: h-influenzae, 1 Mbp
2000: fly, 100 Mbp
2001 – present
human (3Gbp), mouse (2.5Gbp), rat*, chicken, dog, chimpanzee, several fungal genomes
Let’s sequence the human genome with the shotgun strategy
That is impossible, and a bad idea anyway
Phil Green
Gene Myers

46. Genomes Sequenced