Read mapping and variant calling in
human short-read DNA sequences
Gabor T. Marth
Boston College Biology Department
1000 Genomes Meeting
Cold Spring Harbor Laboratory
May 5-6. 2008
1000 Genomes – related work
• Software
Read alignment / assembly (Michael Stromberg)
SNP / short-INDEL calling (Gabor Marth)
GigaBayes
Structural Variation calling (Chip Stewart)
Read simulator (Weichun Huang)
Benchmarking suite (Weichun Huang)
• Read mapping based studies
Read accuracy / quality value analysis
Read simulations
• Variant calling based study
SNP discovery: sample size / read coverage (Aaron Quinlan)
MOSAIK – sequence aligner/assembler
Poster
thumb
Michael Strömberg
(see poster at Genome Meeting)
• maps reads to reference: short-hash based scan + SmithWaterman alignment
MOSAIK – Features
• produces gapped alignments
essential for tolerating reads with
insertion-deletion read errors and
short insertion-deletion alleles
• adapted to all available NGS platforms
• can create “mixed” alignments of
reads from different platforms (except
SOLiD color-space reads)
MOSAIK – Resolving multiple map locations
MOSAIK – Performance
Illumina 35 bp (X Chromosome)
program
aligned reads/s
MOSAIK
180 - 16,658
ELAND
7,716
SOAP
1,637
MAQ
1,376
SHRIMP
39
Uses a lot of RAM for mammalian alignments –
precomputed file based versions are available for
RAM-limited users
Run dissection (timeline figure from Michael)
MOSAIK – Accuracy
Erroricity – read accuracy / quality values
Motivation: why does read accuracy matter?
Why does quality value accuracy matter?
we are using quality values to
distinguish between sequencing
error and true allelic difference
Q-values should correspond
well with actual sequencing
error rate
Erroricity – study design & pipeline
• Sampled 3 lanes each from 3 runs
• Aligned reads with MosaikPE (up to 4 mismatches),
keeping only consistently mapping pairs
• Looked at read-specific, position-specific error rates
• Compared SUB, IN, and DEL error rates
• Looked at overall quality value vs. measured error rate,
and position specific quality value vs. measured error rate
• Compared the first and the second end-reads of read pairs
• Compared RAW vs. CALIBRATED Q-values
Derek Barnett
Read simulations (Weichun Huang, Aaron)
• Input
• Conceptual schema of read simulation
• Representational biases (GC-driven and others…) [Chip]
• Error and Q-value generation: 2D tables of read position,
assigned Q-value  true Q-value, frequency
• Speed / RAM / space
• Data output
• Software benchmarking system
Weichun Huang, see poster at Genome Meeting
GigaBayes: SNP and short-INDEL calling
• The new GigaBayes program: pop-gen and diploid
priors, trio priors
• Speed
• Input / output behavior
• Bayesian math focused on the individual genotype
• How to deal with multiple reads from a single individual
• Diploid individual
• Multiple diploid individuals
• Trio members
• Prior frequency of an allele
• Taking into account Q-value for allele
• What is needed to call an allele? (# reads, Q, # people)
Variant calling (SNPs and short-INDELs)
population
aacgtCaggct
aacgtCaggct
individuals
G1
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aacgtCaggct
aacgtCaggct
aacgtCaggct
G2
aacgtCaggct
aacgtTaggct
aacgtCaggct
aacgtTaggct
fragments
reads
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aacgtCaggct
aacgtCaggct
aacgtCaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
G3
aacgtCaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
aacgtCaggct
aacgtCaggct
priors
aacgtTaggct
aacgtTaggct
aacgtTaggct
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aacgtTaggct
sampling
likelihood
aacgtCaggct
aacgtCaggct
aacgtCaggct
aacgtCaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
aacgtTaggct
quality values
Bayesian variant detection math


k
k
 Pr  Bi | Ti  Pr Ti | Gi   Pr  G1 , G2 , , Gn 

T k
i 1 


, Gn | B  
 n 

l
l
l
l
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 Pr  Bi | Ti  Pr Ti | Gi  Pr G1 , G2 , , Gn
l 
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Pr  G1 , G2 ,

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
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Priors: (1) based on all the individuals from which reads are
aligned. (2) Theta, P(AF=i), specific diploid genotype layout
given AF=I
Which of the two chromosomes a read represents?
Calculated from multinomial (or binomial distribution)
P(base call is an B | read template is
T) – comes from quality values
SNP calling and genotyping
P(SNP) = total probability of all non-monomorphic
genotype combinations
P(Gi) = marginal probability
consequence: data from other individuals influence the
genotype call of a given individual: include illustration
using testProb program in GigaBayes package.
Variant calling tests in simulated data
Aaron Quinlan
(see poster at the Genome Meeting)
Variant calling – Estimated vs. population AF
0.5
0.5
Observed minor allele frequency
Observed minor allele frequency
0.45
100 Inds @ 16X
0.45
0.4
corr = 0.89
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.4
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
corr = 0.92
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
200 Inds @ 8X
0.5
0
0.05
Expected minor allele frequency
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.45
0.5
0.5
0.45
400 Inds @ 4X
Observed minor allele frequency
Observed minor allele frequency
0.15
Expected minor allele frequency
0.5
0.4
corr = 0.93
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Expected minor allele frequency
0.45
0.5
0.45
800 Inds @ 4X
0.4
corr = 0.91
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Expected minor allele frequency
Variant calling – AF (cont’d)
Observed minor allele frequency
0.5
0.45
800 Inds @ 4X
0.4
corr = 0.91
0.35
0.05
0.3
0.045
0.25
0.2
0.04
0.15
0.035
0.1
0.05
0
0.03
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Expected minor allele
frequency
0.025
0.02
0.015
0.01
0.005
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
Variant calling – SNP discovery sensitivity
Variant calling – Genotype completeness
1600
1400
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1000
800
600
400
200
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
4
x 10
Fraction of confident genotypes
1
0.9
0.8
0.7
0.6
100 @ 16x: 0.975 +/- 0.121
0.5
200 @ 8x: 0.968 +/- 0.129
0.4
0.3
400 @ 4x: 0.924 +/- 0.151
0.2
800 @ 2x: 0.769 +/- 0.154
0.1
0
0
5000
10000
15000
20000
25000
30000
SNP Position
35000
40000
45000
50000
Variant calling – Genotype completeness
4000
4000
200 Inds @ 8X
3000
Number of SNPs
Number of SNPs
100 Inds @ 16X
2000
1000
0
2000
1000
0
0
0.2
0.4
0.6
0.8
1
Fraction of samples w/ confident genotype
0
0.2
0.4
0.6
0.8
1
Fraction of samples w/ confident genotype
3000
1500
400 Inds @ 4X
2500
800 Inds @ 2X
2000
Number of SNPs
Number of SNPs
3000
1500
1000
1000
500
500
0
0
0.2
0.4
0.6
0.8
1
Fraction of samples w/ confident genotype
0
0
0.2
0.4
0.6
0.8
Fraction of samples w/ confident genotype
1
Summary / Conclusions
Thanks
Michael
Derek
Aaron
Chip
Weichun
MOSAIK (Michael Stromberg)
• MOSAIK is a reference-sequence guided aligner / assembler
replace this with an animated figure illustrating mapping
against reference and padding, by moving / stretching
bases in the reads and in the reference sequence
MOSAIK – Features and characteristics
• aligns reads to genome (higher RAM usage but also many
desirable consequences)
• offers several algorithmic levels that trade off between
speed and accuracy
• able to report “every” decent alternative map location for
sequence reads, and distinguishes between uniquely and
non-uniquely mapped reads
• designed to work with all currently available technologies
(Illumina, 454, AB, Helicos) and to include mixed read sets
into a single anchored “assembly”
• PE-aware
• recently scaled up to mammalian alignments
Structural variation discovery
• copy number variations (deletions &
amplifications) can be detected from
variations in the depth of read coverage
• structural rearrangements (inversions and
translocations) require paired-end read data
Software evaluation suite
GigaBayes
bases per machine run
Read length and throughput
Illumina/Solexa, AB/SOLiD
short-read sequencers
1Gb
(1-4 Gb in 25-50 bp reads)
100 Mb
454 pyrosequencer
(20-100 Mb in 100-250 bp reads)
10 Mb
ABI capillary sequencer
1Mb
10 bp
100 bp
1,000 bp
read length
Current and future application areas
Genome re-sequencing: somatic mutation detection, organismal SNP discovery,
mutational profiling, structural variation discovery
reference genome
SNP
De novo genome sequencing
Short-read sequencing will be (at least) an
alternative to micro-arrays for:
• DNA-protein interaction analysis (CHiP-Seq)
• novel transcript discovery
• quantification of gene expression
• epigenetic analysis (methylation profiling)
DEL
Fundamental informatics challenges
1. Interpreting machine readouts – base calling, base error estimation
2. Dealing with nonuniqueness in the genome:
resequenceability
3. Alignment of billions
of reads
Informatics challenges (cont’d)
4. SNP and short INDEL, and
structural variation discovery
5. Data visualization
6. Data storage & management
Challenge 1. Base accuracy and base calling
• machine read-outs are
quite different
• read length, read accuracy, and sequencing error profiles are variable (and change
rapidly as machine hardware, chemistry, optics, and noise filtering improves)
• what is the instrument-specific error profile?
• are the base quality values satisfactory?
(1) are base quality values accurate?
(2) are most called bases high-quality?
454 pyrosequencer error profile
• multiple bases in a homo-polymeric run are incorporated in a single
incorporation test  the number of bases must be determined from a single
scalar signal  the majority of errors are INDELs
• error rates are nucleotide-dependent
454 base quality values
• the native 454 base caller assigns too low base quality values
PYROBAYES: determine base number
New 454 base caller:
data
likelihoods
priors
posterior base
number probability
PYROBAYES: base calls and quality values
• call the most likely number of nucleotides
• produce three base quality values:
QS (substitution)
QI (insertion)
QD (deletion)
PYROBAYES: Performance
• better correlation between assigned
and measured quality values
• higher fraction of high-quality bases
Illumina/Solexa base accuracy
• error rate grows as a
function of base position
within the read
• a large fraction of the reads
contains 1 or 2 errors
Illumina/Solexa base accuracy (cont’d)
• Actual base accuracy for a fixed base quality value is a function of base position
within the read (i.e. there is need for quality value calibration)
• Most errors are substitutions
 PHRED quality values work
Challenge 2. Resequenceability
• Reads from repeats cannot be
uniquely mapped back to their
true region of origin
• RepeatMasker does not capture
all micro-repeats, i.e. repeats at the
scale of the read length
100%
• Near-perfect micro-repeats can be
also a problem because we want to
align reads even with a few
sequencing errors and / or SNPs
Reads
80%
60%
40%
20%
0%
0
1
2
3
4
Mismatches
5
6
7
8
Repeats at the fragment level
“base masking”
“fragment masking”
Fragment level repeat annotation
0.40
0.35
Fraction of genome
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0
1
2
Number of mismatches allowed
• bases in repetitive fragments may be resequenced with reads representing
other, unique fragments  fragment-level repeat annotations spare a higher
fraction of the genome than base-level repeat masking
Find perfect and near-perfect micro-repeats
• Hash based methods (fast but only work out to a couple of mismatches)
• Exact methods (very slow but find every repeat copy)
• Heuristic methods (fast but miss a fraction of the repeats)
Challenge 3. Read alignment and assembly
• resequencing requires reference
sequence-guided read alignment
• to align billions of reads the aligner has to be fast and efficient
• INDEL errors require gapped alignment
• individually aligned reads must be “assembled” together
• has to work for every read type (short, medium-length, and long reads)
• must tolerate sequencing errors and SNPs
• must work with both base-level and fragment-level repeat annotations
• transcribed sequences require additional features e.g. splice-site aware
alignment capability
• most frequently used tools: BLAT (only pair-wise), SSAHA (pair-wise), MAQ
(pair-wise and assembly), ELAND (pair-wise), MOSAIK (pair-wise and
assembly, gapped)
MOSAIK: co-assembling different read types
ABI/cap.
454/FLX
454/GS20
Illumina
Challenge 4. Polymorphism discovery
• shallow and deep
read coverage
• most candidates will never be “checked”  only very low error rates are
acceptable
• we updated PolyBayes to deal with
new read types
• made the new software (PBSHORT)
much more efficient
Challenge 5. Data visualization
1. aid software development: integration of trace data viewing, fast navigation,
zooming/panning
2. facilitate data validation (e.g. SNP validation): co-viewing of multiple read
types, quality value displays
3. promote hypothesis generation: integration of annotation tracks
Challenge 6. Massive data volumes
• two connected working groups to define standard data formats
Short-read format working group
ssrformat@ubc.ca
(Asim Siddiqui, UBC)
Assembly format working group
Boston College
http://assembly.bc.edu
Next-generation sequencing software
Machine manufacturers’ sites plus thirdparty developers’ sites, e.g.:
http://sourceforge.net/projects/maq/
http://bioinformatics.bc.edu/marthlab/Mosaik
Applications in various discovery projects
1. SNP discovery in shallow,
single-read 454 coverage
(Drosophila melanogaster)
2. Mutational profiling in deep 454 data
(Pichia stipitis)
(image from Nature Biotech.)
3. SNP and INDEL discovery in deep
Illumina / Solexa short-read coverage
(Caenorhabditis elegans)
SNP calling in single-read 454 coverage
DNA courtesy of Chuck Langley, UC Davis
• collaborative project with Andy Clark (Cornell) and Elaine Mardis (Wash. U.)
• goal was to assess polymorphism rates between 10 different African and American
melanogaster isolates
• 10 runs of 454 reads (~300,000 reads per isolate) were collected
• key informatics question: can we detect SNPs with high accuracy in low-coverage,
survey-style 454 reads aligned to finished reference genome sequence?
• reads were base-called with PyroBayes and aligned to the 180Mb reference melanogaster
genome sequence with Mosaik  0.16 x nominal read coverage  most reads are singletons
• SNP detection with PolyBayes
SNP calling success rates
iso-1 reference
46-2 454 read
46-2 ABI reads
(2 fwd + 2 rev)
• 92.9 % validation rate (1,342 / 1,443)
single-read coverage: 92.9% (1,275 / 1,372 )
double-read coverage: 94.3% (67 / 71)
• 2.0% missed SNP rate (25 / 1247)
single-read coverage: 2.12% (25 / 1176)
double-read coverage: 0% (0 / 59)
Genome variation in melanogaster isolates
• 658,280 SNPs discovered among all 10 lines.
• Nucleotide diversity Ѳ ≈ 5x10-3 (1 SNP / 200 bp) between each line and reference (in
line with expectations).
• 20.2% (133,264 sites) polymorphic among two or more lines. The 1 SNP / 900 bp
nominal density is sufficient for high-resolution marker mapping
Mutational profiling in deep 454 data
Pichia stipitis reference sequence
Image from JGI web site
• collaboration with Doug Smith at Agencourt
• Pichia stipitis is a yeast that efficiently converts xylose to ethanol (bio-fuel production)
• one specific mutagenized strain had especially high conversion efficiency
• goal was to determine where the mutations were that caused this phenotype
• we analyzed 10 runs (~3 million reads) of 454 reads (~20x coverage of the 15MB genome)
• processed the sequences with our 454 pipeline
• found 39 mutations (in as many reads in which we found 650K SNP in melanogaster)
• informatics analysis in < 24 hours (including manual checking of all candidates)
SNP calling in short-read coverage
C. elegans reference genome (Bristol, N2 strain)
Bristol, N2 strain
(3 ½ machine runs)
Pasadena, CB4858
(1 ½ machine runs)
• goal was to evaluate the Solexa/Illumina technology for the complete resequencing of
large model-organism genomes
• 5 runs (~120 million) Illumina reads from the Wash. U. Genome Center, as part of a
collaborative project lead by Elaine Mardis, at Washington University
• primary aim was to detect polymorphisms between the Pasadena and the Bristol strain
Polymorphism discovery in C. elegans
• MOSAIK aligned / assembled the reads (< 4 hours on 1 CPU)
• PBSHORT found 44,642 SNP candidates (2 hours on our 160-CPU cluster)
• SNP density: 1 in 1,630 bp (of non-repeat genome sequence)
• SNP calling error rate very low:
SNP
Validation rate = 97.8% (224/229)
Conversion rate = 92.6% (224/242)
Missed SNP rate = 3.75% (26/693)
• INDEL candidates validate and convert
at similar rates to SNPs:
Validation rate = 89.3% (193/216)
Conversion rate = 87.3% (193/221)
INS
Informatics of transcriptome sequencing
• novel transcript discovery
Inferred Exon 1
Inferred Exon 2
new genes & exons
novel transcripts in
known genes
Inferred Exon 1
Inferred Exon 2
• measuring gene expression levels by sequence tag counting requires
SAGE informatics-like approaches
Counts Per Transcript Based On SAGE Data Of C. elegans Adult Worm
(Jones et al. 2001, GEO 24438)
12000
10000
Frequency
8000
6000
4000
2000
0
1
2-25
25-50
50-75
75-100
101-200
201-300
Count Of Sage Tag
301-400
401-500
>500
Protein-DNA interactions: CHiP-Seq
Protein-bound DNA fragments are isolated
with chromatin immunoprecipitation
(ChIP) and then sequenced (Seq) on a highthroughput sequencing platform.
Sequences are mapped to the genome
sequence with a read alignment program.
Regions over-represented in the sequences
are identified.
Johnson et al. Science, 2007
Protein-DNA interactions: CHIP-SEQ
ChIP-Seq scales well for simultaneous analysis of binding sites in the
entire genome.
Mikkelsen et al. Nature 2007.
1000 Genomes – related work
1. Read mapping
Aligner / assembler – MOSAIK
Read accuracy / quality value analysis – ERRORICITY
Read simulations – ART
Software evaluation suite – BTA
2. Variant calling
SNP discovery program – GIGABAYES
SNP calling: # individuals vs. individual coverage
Structural Variation calling – SPANNER