1. Introduction to Short Read Sequencing Analysis
Jim Noonan
GENE 760

2. Sequence read lengths remain limiting
Chr1: 249 Mb
249 Mb sequencing read
Current platforms:
A moderate number (~500,000) of long reads (~10 kb)
A very large number (>200 M) of short reads (100 bp)
For most applications reads are aligned to a reference genome
Short reads contain inherently limited information
De novo assembly of short reads is difficult

3. Determining the identity and location of short sequence reads
in the genome/exome/transcriptome
@HWI-ST974:58:C059FACXX:2:1201:10589: :N:0:TGACCA
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
Aligning short reads to much larger reference
Need a computationally efficient method to perform
accurate alignments of millions of reads

4. Read length requirements vary depending on the
feature being studied
Exome: bp
Splice junctions
(connectivity)
Transcriptome:
10,000 bp

5. Determining the identity and location of short sequence reads
in the genome/exome/transcriptome
@HWI-ST974:58:C059FACXX:2:1201:10589: :N:0:TGACCA
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
@HWI-ST974:58:C059FACXX:2:1201:10589: :N:0:TGACCA
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
Aligning short reads to much larger reference
Exome or Genome
Considerations
Alignment scoring
Source of the reads
Sequencing format (PE or SE)
Read length
Error rates
Transcriptome

6. Topics Scoring alignments
Error rates and quality scores for short read sequencing
Mapability
Common algorithms for short read sequence alignment
Scoring short read sequence aligments
Uniform data output formats

7. Scoring alignments Correct: Match (+1) Mismatch (-1, -2, etc.) Wrong:|
Match (+1)
Mismatch (-1, -2, etc.) T
TAGATTACACAGATTAC
|||||||||||||||||
Wrong:
TAGATTACTCAGA-TAC
|||||||| |||| |||
TAGATTACACAGATTAC
Gap penalty:
P = a +bN
a = cost of opening a gap
b = cost of extending gap by 1
N = length of gap
A-TAC
|||||
ATTAC
A--AC
Many short read alignment algorithms
allow a fixed number of mismatches
Adapted from Mark Gerstein

8. Scoring alignments Correct (polymorphism): Match (+1)|
Match (+1)
Mismatch (-1, -2, etc.) T
TAGATTACTCAGATTAC
|||||||| ||||||||
TAGATTACACAGATTAC
Wrong:
TAGATTACTCAGA-TAC
|||||||| |||| |||
TAGATTACACAGATTAC
Gap penalty:
P = a +bN
a = cost of opening a gap
b = cost of extending gap by 1
N = length of gap
A-TAC
|||||
ATTAC
A--AC
Many short read alignment algorithms
allow a fixed number of mismatches
Adapted from Mark Gerstein

9. Quality scores
A quality score (or Q-score) expresses the probability that a basecall is incorrect.
Given a basecall, A:
The estimated probability that A is not correct is P(~A);
The quality score for A is Q (A) = -10 log10 (P(~A))
A quality score of 10 means a probability of 0.1 that A is the wrong basecall.
P(~A) is platform-specific; Q-scores can be compared across platforms.
Quality scores are logarithmic:
Q-score
Error probability 10
0.1 20
0.01 40
0.0001

10. Error rates in lllumina sequencing reads
Reverse termination
Add next base, etc.
1 cycle
Scan flow cell
Add base
Sequencing
by synthesis
with reversible
dye terminators
Individual synthesis reactions
go out of phase

11. Error rates in lllumina sequencing reads
Error rates are mismatch rates relative to reference genome
Error rates increase with increasing cycle number
Contingent on reference genome quality
Reads may be trimmed to improve alignment quality

12. Illumina quality score encoding in FASTQ format
(CASAVA v1.8)
>90% Q30 bases in high quality run
>80% mappable reads

13. Sources of error in single-molecule sequencing
Illumina:
TAGATTACACAGATTAC
|||||||||||||||||
Consensus signal
PacBio:
Single molecule screening - gaps
TAGATTA-ACAG-TT-C
||||||| |||| || |
TAGATTACACAGATTAC
One molecule, one read
Sequence templates multiple times

14. Mapability
The genome contains non-unique sequences (repeats, segmental duplications)
Short reads derived from repetitive regions are difficult to map
Chr3
Chr7
repeat
Longer reads:
Paired reads:

15. Mapability scores at UCSC
The genome contains non-unique sequences (repeats, segmental duplications)
Short reads derived from repetitive regions are difficult to map
36mers, 2 mismatches
75mers, 2 mismatches
100mers, 2 mismatches

16. Poorly mappable regions of the genome
36mers, 2 mismatches
75mers, 2 mismatches
100mers, 2 mismatches

17. Common algorithms for mapping short reads to a
reference genome
Program
Website
ELAND (v2)
N/A – integrated into Illumina pipeline
Bowtie
BWA
Maq
Considerations
Alignment scoring method
Speed
Quality aware
Seeding
Gapped alignment

18. Seed-based alignment strategy
Single seed alignments
Reference
Seed
Critical values are seed length and number of mismatches allowed
In ELAND:
Seed length = 32
Number of mismatches = 2
Multiseed alignments
(ELAND v2, others)
Seed interval
contingent on read length

19. Implementation in ELAND v2
A read must have at least one seed with no more than 2 mismatches and no gaps
Gapped alignment: extend each alignment to full length of read, allowing gaps up to 10 bp

20. Resolving ambiguous read alignments with multiple seeds
Reference
Seed

21. Resolving ambiguous read alignments with multiple seeds

22. Utility of gapped alignments
RNA-seq
Insertions and deletion variants in
exome and whole genome sequencing

23. Mapping paired end reads
Insert size
Insert size
within specified range

25. ELAND alignment scoring
Base quality values and mismatch positions in a candidate alignment are
used to assign a p value
P values reflect probability that candidate position in genome would
give rise to the observed read if its bases were sequenced at error rates
corresponding to the read’s quality values
Alignment score for a read is computed from p values of all candidate
alignments
If there are two candidates for a read with p values 0.9 and 0.3:
0.9/( ) = 0.75, chance highest scoring alignment is correct
, chance highest scoring alignment is wrong
Alignment score = -10 log(0.25) = 6.

26. BaseSpace

27. Spaced-seed indexing of the reference genome
alignment
Spaced-seed indexing of the
reference genome
Need to break up the genome into
manageable segments
Create index of short sequences
Match seeds against genome index
Trapnell and Salzberg, Nat Biotechnology 27:455 (2009)

28. Reference genome indexing using Burrows-Wheeler transform
alignment
Reference genome indexing using
Burrows-Wheeler transform
Reversible encoding scheme
Simplifies genome sequence
Results in “indexed” genome
Very rapid alignments
Trapnell and Salzberg, Nat Biotechnology 27:455 (2009)

29. Bowtie 2 Pre-built Indexed genomes Bowtie 1 and Bowtie 2
indexes are not compatible

30. Alignments in Bowtie 2 Multiseed alignment (ungapped)
@HWI-ST974:58:C059FACXX:2:1201:10589: :N:0:TGACCA
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
Multiseed alignment (ungapped)
Seed length: 16 nt, every 10 nt
# mismatches: 0
Mismatch = -6
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
TGCACACTGAAGGTCCTGGAATATGGCGAGAAAACTGAAAATCATGGAAA--GAGAAATACACACTTTAGGACGTG
Ref
Read
Gap = -11
-5 to open
-3 to extend by 1 bp
Seeds are extended (gaps allowed) to generate alignment
Match = 2

31. Mapping in highly repetitive regions
ELAND is conservative
Non-unique alignments are flagged; only one is reported in export.txt
Post-alignment CASAVA analyses ignore these
Bowtie will report non-unique alignments
User-specified options determine how these are reported

32. Sequence Alignment/Map (SAM) format
Standard format for reporting short read alignment data
BAM is compressed version
Header
Alignment
info

33. Summary Read the material posted for this lecture on the class wiki
Next week: first Regulomics lecture