1. 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

2. 1000 Genomes – related work Software
Read alignment / assembly (Michael Stromberg)
SNP / short-INDEL calling (Gabor Marth)
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)

3. MOSAIK – sequence aligner/assembler
Poster
thumb
Michael Strömberg
(see poster at Genome Meeting)
maps reads to reference: short-hash based scan + Smith-Waterman alignment

4. 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)

5. MOSAIK – Resolving multiple map locations

6. MOSAIK – Performance
Uses a lot of RAM for mammalian alignments – precomputed file based versions are available for RAM-limited users
Run dissection (timeline figure from Michael)

7. MOSAIK – Accuracy

8. 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

9. 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

10. 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

11. 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)

12. Variant calling (SNPs and short-INDELs)
population
individuals
fragments
reads G1
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priors
sampling likelihood
quality values

13. Bayesian variant detection math
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

14. 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.

15. Variant calling tests in simulated data
Aaron Quinlan
(see poster at the Genome Meeting)

16. Variant calling – Estimated vs. population AF

17. Variant calling – AF (cont’d)

18. Variant calling – SNP discovery sensitivity

19. Variant calling – Genotype completeness
16x: /
8x: /
4x: /
2x: /

20. Variant calling – Genotype completeness

21. Summary / Conclusions

22. Thanks
Chip
Michael
Derek
Aaron
Weichun

23. 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

24. 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

25. 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
Ask Chip to provide images for this one slide

26. Software evaluation suite

27. GigaBayes

28. Read length and throughput
Illumina/Solexa, AB/SOLiD short-read sequencers
1Gb
(1-4 Gb in bp reads)
bases per machine run
100 Mb
454 pyrosequencer
( Mb in bp reads)
10 Mb
ABI capillary sequencer
1Mb
read length
10 bp
100 bp
1,000 bp

29. Current and future application areas
Genome re-sequencing: somatic mutation detection, organismal SNP discovery, mutational profiling, structural variation discovery
reference genome
SNP
DEL
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)

30. Fundamental informatics challenges
1. Interpreting machine readouts – base calling, base error estimation
2. Dealing with non-uniqueness in the genome: resequenceability
3. Alignment of billions of reads

31. Informatics challenges (cont’d)
4. SNP and short INDEL, and structural variation discovery
5. Data visualization
6. Data storage & management

32. 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?

33. 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

34. 454 base quality values
the native 454 base caller assigns too low base quality values

35. PYROBAYES: determine base number
New 454 base caller:
data likelihoods
priors
posterior base number probability

36. PYROBAYES: base calls and quality values
call the most likely number of nucleotides
produce three base quality values:
QS (substitution)
QI (insertion)
QD (deletion)

37. PYROBAYES: Performance
better correlation between assigned and measured quality values
higher fraction of high-quality bases

38. 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

39. 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

40. 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
Near-perfect micro-repeats can be also a problem because we want to align reads even with a few sequencing errors and / or SNPs

41. Repeats at the fragment level
“base masking”
“fragment masking”

42. Fragment level repeat annotation
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

43. 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)

44. 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)

45. MOSAIK: co-assembling different read types
ABI/cap.
454/FLX
Illumina
454/GS20

46. 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

47. Challenge 5. Data visualization
aid software development: integration of trace data viewing, fast navigation, zooming/panning
facilitate data validation (e.g. SNP validation): co-viewing of multiple read types, quality value displays
promote hypothesis generation: integration of annotation tracks

48. Challenge 6. Massive data volumes
two connected working groups to define standard data formats
Short-read format working group
(Asim Siddiqui, UBC)
Assembly format working group
Boston College

49. Next-generation sequencing software
Machine manufacturers’ sites plus third-party developers’ sites, e.g.:

50. 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)

51. 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?
For Aaron’s poster:
add consed picture with SNP example + validation trace
validation rate: put it on a 0-100% scale (not %)
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

52. SNP calling success rates
iso-1 reference
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)
Aaron: either co-assemble the 454 and ABI validation reads together or make a more concise, cropped version of these plots.
Aaron: Example of missed SNP (maybe a single validation trace with a TP and a FN SNP candidate?
Aaron: heterozygous FN examples!!!

53. 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

54. 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)

55. 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

56. 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:
Validation rate = 97.8% (224/229)
Conversion rate = 92.6% (224/242)
Missed SNP rate = 3.75% (26/693)
SNP
INDEL candidates validate and convert at similar rates to SNPs:
Validation rate = 89.3% (193/216)
Conversion rate = 87.3% (193/221)
INS

57. 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

58. Protein-DNA interactions: CHiP-Seq
Protein-bound DNA fragments are isolated with chromatin immunoprecipitation (ChIP) and then sequenced (Seq) on a high-throughput 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

59. Protein-DNA interactions: CHIP-SEQ
ChIP-Seq scales well for simultaneous analysis of binding sites in the entire genome.
Mikkelsen et al. Nature 2007.

60. 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