Read mapping and variant calling in human short-read DNA sequencesGabor T. MarthBoston College Biology Department

1000 Genomes MeetingCold Spring Harbor LaboratoryMay 5-6. 2008

1000 Genomes related workSoftwareRead 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 studiesRead accuracy / quality value analysisRead simulationsVariant calling based studySNP discovery: sample size / read coverage (Aaron Quinlan)

MOSAIK sequence aligner/assembler maps reads to reference: short-hash based scan + Smith-Waterman alignmentMichael Strmberg(see poster at Genome Meeting)Posterthumb

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 PerformanceRun dissection (timeline figure from Michael)Uses a lot of RAM for mammalian alignments precomputed file based versions are available for RAM-limited users

MOSAIK Accuracy

Erroricity read accuracy / quality valuesMotivation: why does read accuracy matter?we are using quality values to distinguish between sequencing error and true allelic differenceQ-values should correspond well with actual sequencing error rateWhy does quality value accuracy matter?

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-valuesDerek 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 systemWeichun 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)aacgtCaggctaacgtCaggctaacgtCaggctaacgtCaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtCaggctaacgtCaggctaacgtTaggctreadsindividualsfragmentspopulationpriorssampling likelihoodquality valuesaacgtCaggctaacgtCaggctaacgtCaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtCaggctaacgtCaggctaacgtTaggctaacgtCaggctaacgtTaggctaacgtCaggctaacgtCaggctaacgtCaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtTaggctaacgtCaggctaacgtCaggctaacgtCaggctaacgtTaggctaacgtCaggctaacgtCaggctaacgtTaggctaacgtTaggctaacgtCaggctaacgtTaggctaacgtCaggctaacgtCaggctG1G2G3

Bayesian variant detection mathPriors: (1) based on all the individuals from which reads are aligned. (2) Theta, P(AF=i), specific diploid genotype layout given AF=IWhich 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 genotypingP(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 dataAaron Quinlan(see poster at the Genome Meeting)

Variant calling Estimated vs. population AF

Variant calling AF (contd)

Variant calling SNP discovery sensitivity

Variant calling Genotype completeness

Variant calling Genotype completeness

Summary / Conclusions

ThanksDerekAaronWeichunMichaelChip

MOSAIK (Michael Stromberg) MOSAIK is a reference-sequence guided aligner / assemblerreplace 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

Read length and throughputread lengthbases per machine run10 bp1,000 bp100 bp100 Mb10 Mb1Mb1GbIllumina/Solexa, AB/SOLiD short-read sequencersABI capillary sequencer454 pyrosequencer(20-100 Mb in 100-250 bp reads)(1-4 Gb in 25-50 bp reads)

Current and future application areasGenome re-sequencing: somatic mutation detection, organismal SNP discovery, mutational profiling, structural variation discoveryDe novo genome sequencingShort-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)DELSNPreference genome

Fundamental informatics challenges1. Interpreting machine readouts base calling, base error estimation2. Dealing with non-uniqueness in the genome: resequenceability

Informatics challenges (contd)5. Data visualization4. SNP and short INDEL, and structural variation discovery6. 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 numberdata likelihoodspriorsposterior base number probabilityNew 454 base caller:

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 (contd) 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

Repeats at the fragment levelbase maskingfragment masking

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

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

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 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

Challenge 6. Massive data volumes Short-read format working groupssrformat@ubc.ca(Asim Siddiqui, UBC)Assembly format working groupBoston Collegehttp://assembly.bc.edu two connected working groups to define standard data formats

Next-generation sequencing softwarehttp://bioinformatics.bc.edu/marthlab/Mosaikhttp://sourceforge.net/projects/maq/Machine manufacturers sites plus third-party developers sites, e.g.:

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) 3. SNP and INDEL discovery in deep Illumina / Solexa short-read coverage(Caenorhabditis elegans) (image from Nature Biotech.)

SNP calling in single-read 454 coverage 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?DNA courtesy of Chuck Langley, UC Davis 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 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 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 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)Pichia stipitis reference sequence 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)Image from JGI web site

SNP calling in short-read coverageC. elegans reference genome (Bristol, N2 strain)Pasadena, CB4858(1 machine runs)Bristol, N2 strain(3 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 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)SNPINS INDEL candidates validate and convert at similar rates to SNPs:

Validation rate = 89.3% (193/216) Conversion rate = 87.3% (193/221) 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)

Informatics of transcriptome sequencing measuring gene expression levels by sequence tag counting requires SAGE informatics-like approaches novel transcript discoveryInferred Exon 1Inferred Exon 2Inferred Exon 1Inferred Exon 2new genes & exonsnovel transcripts in known genes

Chart1

11318

7154

334

113

67

84

34

10

11

13

Count Of Sage Tag

Frequency

Counts Per Transcript Based On SAGE Data Of C. elegans Adult Worm (Jones et al. 2001, GEO 24438)

SageAdultWormVals

cts6898974reads

101BinFrequencyBinFrequency0.99700723616878327appear 1 gene

112.52511131800111318184341113180.79596589295491348appear 1 transcript

137.5502571541847212.5178182-256500184342-2571540.61684030188902382total cdses

162.5755033437.58622525-5086225-503340.99286049176927788

187.51007511362.52015050-7520150-75113

1112.52001006787.5747575-1007475-10067

1137.530020084112.547100100-12547101-20084

1162.540030034137.532125125-15032201-30034

1187.550040010162.521150150-17521301-40010

1212.550011187.513175175-20013401-50011

1237.5More13212.58200200-2258>5001313

1262.5237.58225225-2508

1287.5262.513250250-27513

1312.5287.56275275-3006

1337.5312.52300300-3252

1362.5337.54325325-3504

1387.5362.53350350-3753

1412.5387.51375375-4001

1437.5412.53400400-4253

1462.5437.53425425-4503

1487.5462.52450450-4752

1512.5487.53475475-5003

1537.5512.52500500-5252

1562.5537.52525525-5502

1587.5562.51550550-5751

1612.5587.50575575-6000

1637.5612.52600600-6252

1662.5637.51625625-6501

1687.5662.50650650-6750

1712.5687.50675675-7000

1737.5712.50700700-7250

1762.5737.50725725-7500

1787.5762.51750750-7751

1812.5787.50775775-8000

1837.5812.51800800-8251

1862.5837.50825825-8500

1887.5862.50850850-8750

1912.5887.51875875-9001

1937.5912.50900900-9250

1962.5937.50925925-9500

1987.5962.50950950-9750

11012.5987.50975975-10000

11037.51012.501000>10003

11062.51037.50

11087.51062.50

11112.51087.53

11112.50

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