1. (with many slides adapted from Jim Noonan)
Introduction to Short Read Sequencing Analysis
James Knight
(with many slides adapted from Jim Noonan)
GENE 760

2. Rule to remember when using bioinformatics tools
Computer scientists and mathematicians/statisticians make simplifications to speed up computations or to create a robust statistical model
So, every tool solves an approximation of the problem
The answers you get will largely be correct, but must be taken with a grain of salt
What might the tools be missing?
What quality/confidence score values can be believed?
Do the downstream steps of the pipeline adjust for the simplifications?

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

4. 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

5. Fundamental simplification
Determining the identity and location of short sequence reads in the genome/exome/transcriptome
Fundamental simplification
Represent DNA molecules as strings (or sequences)
4 character alphabet, plus N
Algorithmic simplification
Finding exact matches is quickest
Comparing strings is slower
“Gapped” alignment is very slow
TAGATTAC
||||||||
TAGATTACTCAGA
|||||||| ||||
TAGATTACACAGA
TAGATTACTCAGA-TAC
|||||||| |||| |||
TAGATTACACAGATTAC

6. First attempt: A “telephone directory”
Suppose input is 100 bp reads
Create “telephone directory” of 100 bp sections of genome
Sorted list of sequences, with locations
<Sequence, chromosome, start position>
For each read, lookup in directory to find genome location(s) for the read
This solution does not work
Lookup might be slow for hundreds of millions of reads
Sequencing errors and variation prevent correct lookup

7. Older (and Newer) Algorithms: Seed and Extend
Find shorter exact matches, called seeds
Long enough to be mostly unique (20-25 bp for human)
Short enough to find exact matches for “all” reads
Encode seeds as integer values
2-bit encoding, A=00 C=01 G=10 T=11
32 nt seed fits within 64 bit integer
Exact seed match when integer values the same
Create “telephone directory” for reference, called an index
Choose forward strand only or forward and reverse strands?
Store every seed, or spaced seeds (every X nts)?
Newer algorithms create Burrows-Wheeler Transform index

8. Reference genome indexing using Burrows-Wheeler transform
alignment
Reference genome indexing using
Burrows-Wheeler transform
Reversible encoding scheme
All locations of every suffix stored in a contiguous region of the index
For any suffix, transform table can find region when adding 1 nt prefix
Lookups become very fast
Independent of reference size
Trapnell and Salzberg, Nat Biotechnology 27:455 (2009)

9. Older (and Newer) Algorithms: Seed and Extend
Lookup the seeds occurring in each read
Forward strand only or forward and reverse?
Every seed or spaced seeds?
Extend seeds to calculate alignment
Ungapped alignment just comparing strings
Gapped alignment using dynamic programming
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
TGCACACTGAAGGTCCTGGAATATGGCGAGAAAACTGAAAATCATGGAAA--GAGAAATACACACTTTAGGACGTG
Ref
Read

10. Alignments in Bowtie 2 Multiseed alignment (ungapped)
@HWI-ST974:58:C059FACXX:2:1201:10589: :N:0:TGACCA
TGCACACTGAAGGACCTGGAATATGGCGAGAAAACTGAAAATCATGGAAAATGAGAAATACACACTTTAGGACGTG
Multiseed alignment (ungapped)
Ref index: BWT, every nt, fwd only
Read seeds: 16 nt, every 10 nt, fwd+rev
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

11. Scoring alignments Ungapped: Match (+1) Mismatch (-1, -2, etc.)|
Match (+1)
Mismatch (-1, -2, etc.) T
TAGATTACTCAGATTAC
|||||||| ||||||||
TAGATTACACAGATTAC
Gapped:
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
TAGATTACTCAGA-TAC
|||||||| |||| |||
TAGATTACACAGATTAC
Simpler algorithms allow a fixed number of mismatches
Complex algorithms pick best scoring alignment, stopping extension if score falls below threshold from best score so far
Adapted from Mark Gerstein

12. Common algorithms for mapping short reads to a
reference genome
Program
Website
ELAND (v2)
N/A – integrated into Illumina pipeline
Bowtie/Bowtie2
BWA
Novoalign
Considerations
Alignment scoring method
Speed
Quality aware
Seeding
Gapped alignment
Split read alignment

13. Split read alignments for transcriptomes and structural variations
Splice junctions
(contiguous)
Aligning to transcripts:
Splice junctions
(split)
Aligning to genome:

14. Simplifications Aligners Use
Assume the reference is complete
The best scoring match is considered location (or locations) of the read in the genome
Speed is the priority
Difficult to align ends are “soft clipped”
Reads are aligned individually
Indels may not be consistent across reads

15. Quality Scores

16. Scoring using Likelihood Measures
Likelihood-based scoring methods more robust than empirically derived scoring methods
Construct a model of the error modes
Usually, a training-based Bayesian or HMM model
Common NGS quality scores
Basecall quality scores
Mapping quality scores
Variant/Genotype quality scores

17. Basecall quality scores (“Phred” 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

18. Errors in lllumina sequencing reads
Reverse termination
Add next base, etc.
1 cycle
Scan flow cell
Add base
Sequencing
by synthesis
with reversible
dye terminators
Wrong “color” called when scanning image
Wrong nucleotide is added
Terminator not removed for a cycle
Multiple bases added (terminator doesn’t work for a base)

19. Errors in lllumina sequencing reads
Error are mainly mismatches (substitutions)
Error rates increase with increasing cycle number

20. Errors in single-molecule sequencing
PacBio:
TAGATTA-ACAG-TT-C
||||||| |||| || |
TAGATTACACAGATTAC
Florescent molecules, attached to each nucleotide, captured
by direct observation of “zero-mode waveguide” during incorporation by polymerase
Single molecule screening - gaps
Incorporation happens to quickly to be observed
Nucleotide sits in polymerase “pocket” (and is detected), but is not incorporated
Wrong nucleotide incorporated
Errors are mainly insertions and deletions

21. Quality Score Metholodogy
Training-based calibration of scores
Identify key “features” of a platform’s sequencing signals
Signal Intensity/frequency/duration, base position, ...
Phred method: must be continuous value range, “monotonic” to error rate
Train using set of runs from known samples and genomes
Correlate errors to feature value ranges
Phred method: bin combinations of value ranges, then use error counts to set score
Must recalibrate with changes in instrument or reagent kits
Simplifications
The training set characterizes the range of genomes/transcriptomes/...
Library/Sequencing prep PCR errors not captured by sequencing measurements

22. GATK Recalibration

23. Quality score encoding in FASTQ format

24. Mapping Quality

25. Mappability
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:

26. 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

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

28. Mapping quality score
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
Mapping quality 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
Mapping quality score = -10 log(0.25) = 6.

29. Counting and Frequencies

30. Counting Reads, Converting to Frequencies
NGS reads are all “clonally amplified”
Start with a single molecule
Either sequence it directly, or amplify it into a cluster/spot/well
Post-alignment analysis begins with read counting
Variant vs Non-variant
Transcript
ChIP-seq peaks
Read counts (depth) converted into frequencies
Normalizes across datapoints to permit comparisons
“Alt frequency”, FPKM/RPKM, fold change
Filtering applied to produce call set
Distinguish significant events from random chance or sequencing error

31. Counting Reads, Converting to Frequencies
Confounders
Sampling variation (across genome/transcriptome)
PCR duplicates
Non-random sequencing error
GC-bias
Strand bias
Amplification bias
Alignment bias
Sample purity
Transcript length

32. Variant Calling Quality Scores
At each location where reads differ from the reference, compute likelihood values
Difference from the reference
Each genotype (0/0, 0/1, 1/1) for diploid genomes
Models either pre-trained or trained on the reads using a “truth set” of known variants
Variant quality score
Convert likelihood into phred-scaled score
Genotype quality score
Most likely genotype minus second most likely genotype
Phred-scaled score

33. Conclusion First alignment, then counting, and then comes the analysis
Alignment and counting are computation-focused
Pipelines tradeoff speed vs. accuracy
Quality/confidence measures calculated and used in the analysis
Simple likelihoods or trained models?
Your choices depend on your objectives/budgets