1. Genome Assembly: a brief introduction
Slides courtesy of Mihai Pop, Art Delcher, and Steven Salzberg

3. Mate-Pair Shotgun DNA Sequencing
DNA target sample
SHEAR & SIZE (16 of these)
End Reads / Mate Pairs
e.g., 10Kbp
± 8% std.dev.
550bp
CLONE (16 of these)
& END SEQUENCE (automated)
10,000bp

4. Shotgun DNA Sequencing (Technology)
DNA target sample
SHEAR
SIZE SELECT
e.g., 10Kbp
± 8% std.dev.
Primer
End Reads (Mates)
SEQUENCE
550bp
Vector
LIGATE & CLONE

5. Whole Genome Shotgun Sequencing
Collect 10x sequence in a 1-to-1 ratio of two types of read pairs:
~ 35million reads for Human.
Short
Long
2Kbp
10Kbp
Collect another 20X in clone coverage of 50Kbp end sequence pairs:
~ 1.2million pairs for Human.
Early simulations showed that if repeats were considered black boxes, one could still cover 99.7% of the genome unambiguously.
BAC 3’
BAC 5’
+ single highly automated process
+ only three library constructions
– assembly is much more difficult

6. Sequencing Factory

7. Celera’s Sequencing Factory (circa 2001)
300 ABI 3700 DNA Sequencers
50 Production Staff
20,000 sq. ft. of wet lab
20,000 sq. ft. of sequencing space
800 tons of A/C (160,000 cfm)
$1 million / year for electrical service
$10 million / month for reagents

8. Human Data (April 2000) Collected 27.27 Million reads = 5.11X coverage
21.04 Million are paired (77%) = Million pairs
2Kbp 5.045M 98.6% true * <6% std.dev.
10Kbp 4.401M 98.6% true * <8% std.dev.
50Kbp 1.071M 90.0% true * <15% std.dev.
* validated against finished Chrom. 21 sequence
The clones cover the genome 38.7X times
Data is from 5 individuals (roughly 3X, 4 others at .5X)

9. ? Pairs Give Order & Orientation Contig
Assembly without pairs results in contigs whose order and orientation are not known.
Contig
Consensus (15- 30Kbp)
Reads ?
2-pair
Pairs, especially groups of corroborating ones, link the contigs into scaffolds where the size of gaps is well characterized.
Mean & Std.Dev.
is known
Scaffold

10. Anatomy of a WGS Assembly
Chromosome
STS
STS-mapped Scaffolds
Contig
Gap (mean & std. dev. Known)
Read pair (mates)
Consensus
Reads (of several haplotypes)
SNPs
External “Reads”

11. Assembly gaps Physical gaps Sequencing gaps
sequencing gap - we know the order and orientation of the contigs and have at least one clone spanning the gap
physical gap - no information known about the adjacent contigs, nor about the DNA spanning the gap
Sequencing gap is "easy"
Physical gap resolution takes more than 1/2 of closure effort
Multiplex PCR

12. Shotgun sequencing statistics

13. Typical contig coverage
Imagine raindrops on a sidewalk

14. Lander-Waterman statistics
L = read length
T = minimum detectable overlap
G = genome size
N = number of reads
c = coverage (NL / G)
σ = 1 – T/L
E(#islands) = Ne-cσ
E(island size) = L((ecσ – 1) / c + 1 – σ)
contig = island with 2 or more reads

15. Example c N #islands #contigs bases not in any read
Genome size: 1 Mbp Read Length: Detectable overlap: 40 c N
#islands
#contigs
bases not in any read
bases not in contigs 1
1,667
655
614
698
367,806 3
5,000
304
250
121
49,787 5
8,334 78 57 20
6,735 8
13,334 7
335

16. Experimental data X coverage # ctgs % > 2X avg ctg size (L-W)
max ctg size
# ORFs 1
284 54
1,234 (1,138)
3,337
526 3
597 67
1,794 (4,429)
9,589
1,092 5
548 79
2,495 (21,791)
17,977
1,398 8
495 85
3,294 (302,545)
64,307
1,762
complete
100
1.26 M
1,329
Caveat: numbers based on artificially chopping up
the genome of Wolbachia pipientis dMel

17. Assembly paradigms Overlap-layout-consensus
greedy (TIGR Assembler, phrap, CAP3...)
graph-based (Celera Assembler, Arachne)
Eulerian path (especially useful for short read sequencing)

18. TIGR Assembler/phrap Greedy Build a rough map of fragment overlaps
Pick the largest scoring overlap
Merge the two fragments
Repeat until no more merges can be done

19. Overlap-layout-consensus
Main entity: read
Relationship between reads: overlap 1 4 7 2 5 8 3 6 9 1 2 3 4 5 6 7 8 9
ACCTGA
AGCTGA
ACCAGA 1 1 2 3 1 2 3 2 3 2 3 1 3 1 1 3 2 2

20. Paths through graphs and assembly
Hamiltonian circuit: visit each node (city) exactly once, returning to the start
Genome

21. Implementation details

22. Overlap between two sequences
overlap (19 bases)
overhang (6 bases)
…AGCCTAGACCTACAGGATGCGCGGACACGTAGCCAGGAC
CAGTACTTGGATGCGCTGACACGTAGCTTATCCGGT…
overhang
% identity = 18/19 % = 94.7%
overlap - region of similarity between regions
overhang - un-aligned ends of the sequences
The assembler screens merges based on:
length of overlap
% identity in overlap region
maximum overhang size.
when a pair of sequences is considered,the two sequences are merged only if they match the criteria

23. All pairs alignment Needed by the assembler
Try all pairs – must consider ~ n2 pairs
Smarter solution: only n x coverage (e.g. 8) pairs are possible
Build a table of k-mers contained in sequences (single pass through the genome)
Generate the pairs from k-mer table (single pass through k-mer table)
k-mer

25. Assembly Pipeline Trim & Screen Overlapper Unitiger Scaffolder
Find all overlaps  40bp allowing 6% mismatch.
Overlapper A B
Unitiger
implies A B
TRUE
Scaffolder
Repeat Rez I, II OR A B
REPEAT-INDUCED

26. Assembly Pipeline Trim & Screen Overlapper Unitiger Scaffolder
Compute all overlap consistent sub-assemblies: Unitigs (Uniquely Assembled Contig)
Overlapper
Unitiger
Scaffolder
Repeat Rez I, II

27. OVERLAP GRAPH E.G.: Edges are annotated with deltas of overlaps
Edge Types:
Regular Dovetail A B A B B A
Prefix Dovetail
Suffix Dovetail
E.G.:
Edges are annotated with deltas of overlaps

28. The Unitig Reduction 1. Remove “Transitively Inferrable” Overlaps: A C

29. The Unitig Reduction 2. Collapse “Unique Connector” Overlaps: 412 35245
2. Collapse “Unique Connector” Overlaps: A B A B

30. Identifying Unique DNA Stretches
Unique DNA unitig
Repetitive DNA unitig
Arrival Intervals
Discriminator Statistic is log-odds ratio of probability unitig is unique DNA versus 2-copy DNA.
-10
+10
Dist. For Unique
Dist. For Repetitive
Definitely Repetitive
Don’t Know
Definitely Unique

31. Assembly Pipeline Trim & Screen Mated reads Overlapper Unitiger
Scaffold U-unitigs with confirmed pairs
Mated reads
Overlapper
Unitiger
Scaffolder
Repeat Rez I, II

32. Assembly Pipeline Trim & Screen Overlapper Unitiger Scaffolder
Fill repeat gaps with doubly anchored positive unitigs
Overlapper
Unitig>0
Unitiger
Scaffolder
Repeat Rez I, II

33. REPEATS

34. Handling repeats Repeat detection Repeat resolution
pre-assembly: find fragments that belong to repeats
statistically (most existing assemblers)
repeat database (RepeatMasker)
during assembly: detect "tangles" indicative of repeats (Pevzner, Tang, Waterman 2001)
post-assembly: find repetitive regions and potential mis-assemblies.
Reputer, RepeatMasker
"unhappy" mate-pairs (too close, too far, mis-oriented)
Repeat resolution
find DNA fragments belonging to the repeat
determine correct tiling across the repeat

35. Statistical repeat detection
Significant deviations from average coverage flagged as repeats.
- frequent k-mers are ignored
- “arrival” rate of reads in contigs compared with theoretical value
(e.g., 800 bp reads & 8x coverage - reads "arrive" every 100 bp)
Problem 1: assumption of uniform distribution of fragments - leads to false positives
non-random libraries
poor clonability regions
Problem 2: repeats with low copy number are missed - leads to false negatives

36. Mis-assembled repeats
excision
collapsed tandem
rearrangement