1. CS 6293 Advanced Topics: Translational Bioinformatics
Next-generation sequencing -
Mapping short reads

2. Short read mapping Input: Output:
A reference genome
A collection of many bp tags (reads)
User-specified parameters
Output:
One or more genomic coordinates for each tag
In practice, only 70-75% of tags successfully map to the reference genome. Why?

3. Multiple mapping
A single tag may occur more than once in the reference genome.
The user may choose to ignore tags that appear more than n times.
As n gets large, you get more data, but also more noise in the data.

4. Inexact matching ?
An observed tag may not exactly match any position in the reference genome.
Sometimes, the tag almost matches one or more positions.
Such mismatches may represent a SNP (single-nucleotide polymorphism, see wikipedia) or a bad read-out.
The user can specify the maximum number of mismatches, or a phred-style quality score threshold.
As the number of allowed mismatches goes up, the number of mapped tags increases, but so does the number of incorrectly mapped tags.

5. Read Length is Not As Important For Resequencing
Jay Shendure

6. Mapping Reads Back Hash Table (Lookup table) Array Scanning
FAST, but requires perfect matches. [O(m n + N)]
Array Scanning
Can handle mismatches, but not gaps. [O(m N)]
Dynamic Programming (Smith Waterman)
Indels
Mathematically optimal solution
Slow (most programs use Hash Mapping as a prefilter) [O(mnN)]
Burrows-Wheeler Transform (BW Transform)
FAST. [O(m + N)] (without mismatch/gap)
Memory efficient.
But for gaps/mismatches, it lacks sensitivity

7. Spaced seed alignment
Tags and tag-sized pieces of reference are cut into small “seeds.”
Pairs of spaced seeds are stored in an index.
Look up spaced seeds for each tag.
For each “hit,” confirm the remaining positions.
Report results to the user.

8. Burrows-Wheeler Store entire reference genome.
Align tag base by base from the end.
When tag is traversed, all active locations are reported.
If no match is found, then back up and try a substitution.

9. Why Burrows-Wheeler? BWT very compact: Linear-time search algorithm
Approximately ½ byte per base
As large as the original text, plus a few “extras”
Can fit onto a standard computer with 2GB of memory
Linear-time search algorithm
proportional to length of query for exact matches

10. Burrows-Wheeler Transform (BWT)
$acaacg
aacg$ac
acaacg$
acg$aca
caacg$a
cg$acaa
g$acaac
acaacg$
gc$aaac
Burrows-Wheeler Matrix (BWM)

11. Burrows-Wheeler Matrix
$acaacg
aacg$ac
acaacg$
acg$aca
caacg$a
cg$acaa
g$acaac

12. Burrows-Wheeler Matrix3 1 4 2 5 6
$acaacg
aacg$ac
acaacg$
acg$aca
caacg$a
cg$acaa
g$acaac
See the suffix array?

13. Key observation 1$acaacg1 2aacg$ac1 1acaacg$1 3acg$aca2 1caacg$a1
2cg$acaa3
1g$acaac2
a1c1a2a3c2g1$1
“last first (LF) mapping”
The i-th occurrence of character X in the last column corresponds to
the same text character as the i-th occurrence of X in the first column.

14. Recover text 5 6 4 3 2 1 6

15. Exact match 3 1 4 2 5 6

16. Exact match (another example)
BWT(agcagcagact) = tgcc$ggaaaac
Search for pattern: gca
gca
gca
gca
gca
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac
Test with your own seq and pattern at:

17. Auxiliary data structures
Key for efficient pattern matching: how to find the corresponding chars in the first column efficiently, in terms of both time and space. a c g T
rank 1 5 8 11
BWT 1 2 3 4 5 6 7 8 9 10 11 SA
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac t g c $ a a c g t 1 2 3 4 9 7 4 1 6 3 10 8 5 2 11
FM indices

18. Auxiliary data structures
Key for efficient pattern matching: how to find the corresponding chars in the first column efficiently, in terms of both time and space. a c g t
rank 1 5 8 11
BWT
gca 1 2 3 4 5 6 7 8 9 10 11 SA
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac t g c $ a a c g t 1 2 3 4 9 7 4 1 6 3 10 8 5 2 11
FM indices
Next block:
From = 1
to 1 + (4-1) = 4

19. Auxiliary data structures
Key for efficient pattern matching: how to find the corresponding chars in the first column efficiently, in terms of both time and space. a c g T
rank 1 5 8 11
BWT
gca 1 2 3 4 5 6 7 8 9 10 11 SA
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac t g c $ a a c g t 1 2 3 4 9 7 4 1 6 3 10 8 5 2 11
FM indices
Next block:
From = 5
to 5 + (2-1) = 6

20. Auxiliary data structures
Key for efficient pattern matching: how to find the corresponding chars in the first column efficiently, in terms of both time and space. a c g T
rank 1 5 8 11
BWT
gca 1 2 3 4 5 6 7 8 9 10 11 SA
$agcagcagact
act$agcagcag
agact$agcagc
agcagact$agc
agcagcagact$
cagact$agcag
cagcagact$ag
ct$agcagcaga
gact$agcagca
gcagact$agca
gcagcagact$a
t$agcagcagac t g c $ a a c g t 1 2 3 4 9 7 4 1 6 3 10 8 5 2 11
FM indices
Next block:
From = 9
to 8 + (3-1) = 10

21. Inexact match

22. Main advantage of BWT against suffix array
BWT needs less memory than suffix array
For human genome m = 3 * 109 :
Suffix array: mlog2(m) bits = 4m bytes = 12GB
BWT: m/4 bytes plus extras = GB
m/4 bytes to store BWT (2 bits per char)
Suffix array and occurrence counts array take 5 m log2 m bits = 20 n bytes
In practice, SA and OCC only partially stored, most elements are computed on demand (takes time!)
Tradeoff between time and space

23. Comparison Spaced seeds Requires ~50Gb of memory. Runs 30-fold slower.
Is much simpler to program.
MAQ
Burrows-Wheeler
Requires <2Gb of memory.
Runs 30-fold faster.
Is much more complicated to program.
Bowtie

24. Short-read mapping software
Software
Technique
Developer
License
Eland
Hashing reads
Illumnia ?
SOAP
Hashing refs
BGI
Academic
Maq
Sanger (Li, Heng)
GNUPL
Bowtie
BWT
Salzberg/UMD
BWA
SOAP2
BWT & hashing

25. References
(Bowtie) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Langmead et al, Genome Biology 2009, 10:R25 
SOAP: short oligonucleotide alignment, Ruiqiang Li et al. Bioinformatics (2008) 24: 713-4
(BWA) Fast and Accurate Short Read Alignment with Burrows-Wheeler Transform, Li Heng and Richard Durbin, (2009) 25:1754–1760
SOAP2: an improved ultrafast tool for short read alignment, Ruiqiang Li, (2009) 25: 1966–1967
(MAQ) Mapping short DNA sequencing reads and calling variants using mapping quality scores. Li H, Ruan J, Durbin R. Genome Res. (2008) 18:
Sense from sequence reads: methods for alignment and assembly, Paul Flicek & Ewan Birney, Nature Methods 6, S6 - S12 (2009)