[I0D51A] Bioinformatics: High-Throughput AnalysisNext-generation sequencing.

Part 3: Variation discovery

Prof Jan AertsFaculty of Engineering - ESAT/SCDjan.aerts@esat.kuleuven.be

TA: Alejandro Sifrim (alejandro.sifrim@esat.kuleuven.be)

1

Context

2

Types of genomic variation

SNPs vs structural variation

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A - Single nucleotide polymorphisms (SNPs)

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What are SNPs and why are they important?

• SNP = single nucleotide polymorphism

• It’s the differences that matter:

• Human vs chimp: 98% identical (<2 differences every 100bp)

• Between any 2 individuals: 1 difference every 1000bp

• Disease: A or G == life or death

• Mutations can result in:

• change in level of transcription or translation (loss/gain)

• change in protein structure

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SNP discovery - overview

generate sequence reads

➡ map reads to reference sequence

➡ convert from read-based to position-based (“pileup”)

➡ identify differences

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8

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Monet “Meule, Effet de Neige, le Matin”

Not a trivial problem...

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Many SNP callers:

• samtools

• GATK

• SOAPsnp

• ...

Read-based -> position-based

Here: (1) samtools -> pileup; (2) GATK -> VCF

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pileup

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pileup

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1 272 T 24 ,.$.....,,.,.,...,,,.,..^+. <<<+;<<<<<<<<<<<=<;<;7<&1 273 T 23 ,.....,,.,.,...,,,.,..A <<<;<<<<<<<<<3<=<<<;<<+1 274 T 23 ,.$....,,.,.,...,,,.,... 7<7;<;<<<<<<<<<=<;<;<<61 275 A 23 ,$....,,.,.,...,,,.,...^l. <+;9*<<<<<<<<<=<<:;<<<<1 276 G 22 ...T,,.,.,...,,,.,.... 33;+<<7=7<<7<&<<1;<<6<1 277 T 22 ..CCggC,C,.C.,,CC,..g. +7<;<<<<<<<&<=<<:;<<&<1 278 G 23 ....,,.,.,...,,,.,....^k. %38*<<;<7<<7<=<<<;<<<<<1 279 C 23 A..T,,.,.,...,,,.,..... ;75&<<<<<<<<<=<<<9<<:<<

alignment mapping quality

Intermezzo: quality scores

“Phred-score”: used for sequence quality as well as mapping quality

Chance of 1/1000 that read is mapped at wrong position = 10-3 => phred-score = 30Chance of 1/100 that read is mapped at wrong position = 10-2 => phred-score = 20

Sanger encoding: quality score 30 = “>”

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pileup

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1 272 T 24 ,.$.....,,.,.,...,,,.,..^+. <<<+;<<<<<<<<<<<=<;<;7<&1 273 T 23 ,.....,,.,.,...,,,.,..A <<<;<<<<<<<<<3<=<<<;<<+1 274 T 23 ,.$....,,.,.,...,,,.,... 7<7;<;<<<<<<<<<=<;<;<<61 275 A 23 ,$....,,.,.,...,,,.,...^l. <+;9*<<<<<<<<<=<<:;<<<<1 276 G 22 ...T,,.,.,...,,,.,.... 33;+<<7=7<<7<&<<1;<<6<1 277 T 22 ..CCggC,C,.C.,,CC,..g. +7<;<<<<<<<&<=<<:;<<&<1 278 G 23 ....,,.,.,...,,,.,....^k. %38*<<;<7<<7<=<<<;<<<<<1 279 C 23 A..T,,.,.,...,,,.,..... ;75&<<<<<<<<<=<<<9<<:<<

Heterozygous SNPs and the binomial distribution

SNPs are bi-allelic => allele combinations for heterozygous SNP follow binomial distribution

outcome = binary (red/white, head/tail, yes/no, A/G)probability p of the outcome of a single draw is the same for all draws

E.g. 8 A’s + 12 G’s = SNP?hypothesis: heterozygous => nr of draws = 20; nr of “successes” = 8; probability p of outcome in single draw = 0.5

table with cumulative bionomial probabilities: http://bit.ly/cumul_binom_prob

8 A’s given coverage of 20 => cumulative probability = 0.252 > 0.05=> heterozygote

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samtools pileup \ -vcs \ -r 0.001 \ -l CCDS.txt \ -f human_b36_plus.fasta \ input.bam \ output.pileup

samtools

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VCF file##fileformat=VCFv3.3##FILTER=DP,"DP < 3 || DP > 1200"##FILTER=QUAL,"QUAL < 25.0"##FILTER=SnpCluster,"SNPs found in clusters"##FORMAT=DP,1,Integer,"Read Depth"##FORMAT=GQ,1,Integer,"Genotype Quality"##FORMAT=GT,1,String,"Genotype"##INFO=AB,1,Float,"Allele Balance for hets (ref/(ref+alt))"##INFO=DB,0,Flag,"dbSNP Membership"##INFO=DP,1,Integer,"Total Depth"##INFO=HRun,1,Integer,"Largest Contiguous Homopolymer Run of Variant Allele In Either Direction"##INFO=HaplotypeScore,1,Float,"Consistency of the site with two (and only two) segregating haplotypes"##INFO=LowMQ,3,Integer,"3-tuple: <fraction of reads with MQ=0>,<fraction of reads with MQ<=10>,<total nubmer of

reads>"##INFO=MQ,1,Float,"RMS Mapping Quality"##INFO=MQ0,1,Integer,"Total Mapping Quality Zero Reads"##INFO=QD,1,Float,"Variant Confidence/Quality by Depth"##annotatorReference=human_b36_plus.fasta##reference=human_b36_plus.fasta##source=VariantAnnotator##source=VariantFiltration#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT a_a:bwa057_b:picard.bam1 856182 rs9988021 G A 36.00 0;TARGET DB;DP=3;HRun=0;MQ=60.00;MQ0=0;QD=12.00;OnTarget=FALSE

GT:DP:GQ 1/1:3:36.001 866362 rs4372192 A G 45.00 0;TARGET DB;DP=6;HRun=6;MQ=60.00;MQ0=0;QD=7.50;OnTarget=FALSE

GT:DP:GQ 1/1:6:45.00. . .

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VCF file

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##fileformat=VCFv3.3##FILTER=DP,"DP < 3 || DP > 1200"##FILTER=QUAL,"QUAL < 25.0"##FILTER=SnpCluster,"SNPs found in clusters"##FORMAT=DP,1,Integer,"Read Depth"##FORMAT=GQ,1,Integer,"Genotype Quality"##FORMAT=GT,1,String,"Genotype"##INFO=AB,1,Float,"Allele Balance for hets (ref/(ref+alt))"##INFO=DB,0,Flag,"dbSNP Membership"##INFO=DP,1,Integer,"Total Depth"##INFO=HRun,1,Integer,"Largest Contiguous Homopolymer Run of Variant Allele In Either Direction"##INFO=HaplotypeScore,1,Float,"Consistency of the site with two (and only two) segregating haplotypes"##INFO=LowMQ,3,Integer,"3-tuple: <fraction of reads with MQ=0>,<fraction of reads with MQ<=10>,<total nubmer of

reads>"##INFO=MQ,1,Float,"RMS Mapping Quality"##INFO=MQ0,1,Integer,"Total Mapping Quality Zero Reads"##INFO=QD,1,Float,"Variant Confidence/Quality by Depth"##annotatorReference=human_b36_plus.fasta##reference=human_b36_plus.fasta##source=VariantAnnotator##source=VariantFiltration#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT a_a:bwa057_b:picard.bam1 856182 rs9988021 G A 36.00 0;TARGET DB;DP=3;HRun=0;MQ=60.00;MQ0=0;QD=12.00;OnTarget=FALSE

GT:DP:GQ 1/1:3:36.001 866362 rs4372192 A G 45.00 0;TARGET DB;DP=6;HRun=6;MQ=60.00;MQ0=0;QD=7.50;OnTarget=FALSE

GT:DP:GQ 1/1:6:45.00. . .

file header

column header

actual data

VCF file

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INFODB;DP=3;HRun=0;MQ=60.00;MQ0=0;QD=12.00;OnTarget=FALSEDB;DP=6;HRun=6;MQ=60.00;MQ0=0;QD=7.50;OnTarget=FALSE

FORMAT a_a:bwa057_b:picard.bamGT:DP:GQ 1/1:3:36.00GT:DP:GQ 1/1:6:45.00

genotype

depthgenotype

quality

1/1 = homozygous non-reference0/1 = heterozygous

java \ -Xmx6g \ -jar /path_to/GenomeAnalysisTK.jar \ -l INFO \ -R human_b36_plus.fasta \ -I input.bam \ -T UnifiedGenotyper \ --heterozygosity 0.001 \ -pl Solexa \ -varout output.vcf \ -vf VCF \ -mbq 20 \ -mmq 10 \ -stand_call_conf 30.0 \ --DBSNP dbsnp_129_b36_plus.rod

GATK

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SNP annotation

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by piculak (Flickr)

We have: chromosome + position + alleles

We need:

• in gene?

• damaging?

will be basis for filtering

SIFT (http://sift.bii.a-star.edu/sg), annovar, PolyPhen, ...

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3,81780820,1,T/C 2,43881517,1,A/T2,43857514,1,T/C

#SNP codon substitution region type prediction gene OMIM3,81780820,1,T/C AGA-gGA R190G EXON CDS Nonsynonymous DAMAGING GBE1 POLYGLUCOSAN BODY DISEASE2,43881517,1,A/T ATA-tTA I230L EXON CDS Nonsynonymous TOLERATED DYNC2LI12,43857514,1,T/C TTT-TcT F33S EXON CDS Nonsynonymous TOLERATED DYNC2LI1

SIFT

input

output

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3,81780820,1,T/C 2,43881517,1,A/T2,43857514,1,T/C

#SNP codon substitution region type prediction gene OMIM3,81780820,1,T/C AGA-gGA R190G EXON CDS Nonsynonymous DAMAGING GBE1 POLYGLUCOSAN BODY DISEASE2,43881517,1,A/T ATA-tTA I230L EXON CDS Nonsynonymous TOLERATED DYNC2LI12,43857514,1,T/C TTT-TcT F33S EXON CDS Nonsynonymous TOLERATED DYNC2LI1

SIFT

input

output

SNP filtering

2 aspects:

• filtering to improve quality of SNP calls

• filtering to find likely candidates

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Reduce false positives without increasing false negatives:

• depth of coverage

• mapping quality

• SNP clusters

• allelic balance (diploid genome)

• number of reads with mq0

• consequence

Filtering to improve quality

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java \ -Xmx4g \ -jar GenomeAnalysisTK.jar \ -T VariantFiltration \ -R human_b36_plus.fasta \ -o output.vcf \ -B variant,VCF,input.vcf \ --clusterWindowSize 10 \ --filterExpression 'DP < 3 || DP > 1200' \ --filterName 'DP' \ --filterExpression 'QUAL < 20' \ --filterName 'QUAL' \ --filterExpression 'AB > 0.75 && DP > 40' \ --filterName 'AB'

GATK

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VCF file

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##fileformat=VCFv3.3##FILTER=DP,"DP < 3 || DP > 1200"##FILTER=QUAL,"QUAL < 25.0"##FILTER=SnpCluster,"SNPs found in clusters"##FORMAT=DP,1,Integer,"Read Depth"##FORMAT=GQ,1,Integer,"Genotype Quality"##FORMAT=GT,1,String,"Genotype"##INFO=AB,1,Float,"Allele Balance for hets (ref/(ref+alt))"##INFO=DB,0,Flag,"dbSNP Membership"##INFO=DP,1,Integer,"Total Depth"##INFO=HRun,1,Integer,"Largest Contiguous Homopolymer Run of Variant Allele In Either Direction"##INFO=HaplotypeScore,1,Float,"Consistency of the site with two (and only two) segregating haplotypes"##INFO=LowMQ,3,Integer,"3-tuple: <fraction of reads with MQ=0>,<fraction of reads with MQ<=10>,<total nubmer of

reads>"##INFO=MQ,1,Float,"RMS Mapping Quality"##INFO=MQ0,1,Integer,"Total Mapping Quality Zero Reads"##INFO=QD,1,Float,"Variant Confidence/Quality by Depth"##annotatorReference=human_b36_plus.fasta##reference=human_b36_plus.fasta##source=VariantAnnotator##source=VariantFiltration#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT a_a:bwa057_b:picard.bam1 856182 rs9988021 G A 36.00 DP DB;DP=2;HRun=0;MQ=60.00;MQ0=0;QD=12.00;OnTarget=FALSE

GT:DP:GQ 1/1:3:36.001 866362 rs4372192 A G 45.00 PASSED DB;DP=6;HRun=6;MQ=60.00;MQ0=0;QD=7.50;OnTarget=FALSE

GT:DP:GQ 1/1:6:45.00. . .

Transition/transversion ratio

Transition/transversion ratio Ti/Tv

random: Ti/Tv = 0.5

whole genome: Ti/Tv = 2.0-2.1

exome: Ti/Tv = 3-3.5

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Novel SNPs

Number of novel SNPs

exome:

total = 20k - 25k

novel = 1k - 3k

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Factors that influence SNP accuracy

• sequencing technology

• mapping algorithms and parameters

• post-mapping manipulation

duplicate removal, base quality recalibration, local realignment around indels, ...

• SNP calling algorithms and parameters

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Specificity vs sensitivity

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true

pos

itive

s

false positives

Filtering to find likely candidates

Which are the most interesting?

• only highqual: DP, QUAL, AB, but keep eye on Ti/Tv

• novel

• loss-of-function (stop gained, splice site, ...) or predicted to be damaging (non-synonymous)

• found in multiple individuals

• conserved

• homozygous non-reference or compound heterozygous

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Disease model

• dominant: a single heterozygous SNP is damaging

• recessive: either homozygous non-reference or compound heterozygous necessary to lead to disease phenotype

(e.g. phenylketonuria: cannot convert phenylalanine to tyrosine. Can lead to: mental retardation, microcephaly, ...)

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B - Structural variation

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Why bother?

Iafrate et al, Nat Genet 2004 & Sebat et al, Science 2004

Redon et al, Nature 2006: 12% of genome is covered by copy number variable regions (270 individuals) => more nucleotide content per genome than SNPs

• colour vision in primates

• CCL3L1 copy number -> susceptibility to HIV

• AMY1 copy number -> diet

=> “the dynamic genome”

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Case 1: Evolution - chromosome fusion

human chromosome 2

chimp chromosome 12

chimp chromosome 13

by Beth Kramer

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Molecular Biology of the Cell, 4th Edition

colorectal cancer karyotype

normal karyotype

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Case 2: Cancer - rearranged genome

Robberecht et al, 2010

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Case 3: Embryogenesis - “abnormal” cells

segmental chromosomal imbalancesmosaicism for whole chromosomesuniparental isodisomy

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Case 4: Down Syndrome = trisomy 21

Types of structural variation

Aerts & Tyler-Smith, In: Encyclopedia of Life Sciences, 2009

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Types of structural variation

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Aerts & Tyler-Smith, In: Encyclopedia of Life Sciences, 2009

CNV = Copy Number Variation

Copy number variation (CNV)

Not equally distributed over genome: more pericentromeric and subtelomeric (especially in primates)

Pericentromeric & subtelomeric regions: bias towards interchromosomal rearrangements; interstitial regions: bias towards intrachromosomal

Generation of duplications:

pericentromeric: 2-stage model (Sharp & Eichler, 2006)

1. series of seeding events: one of more progenitor loci transpose together to pericentromeric receptor => generates mosaic block of duplicated segments derived from different loci

2. inter- & intrachromosomal duplication => large blocks are duplicated near other centromeres

subtelomeric: due to normal recombination: cross-overs lead to translocation of distal sequences between chromosomes

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Copy number variation and segmental duplications

Close relationship between CNVs and segmental duplications (aka low-copy repeats aka LCRs; genomic regions with >1 copy that are at least 1kb long and have at least 90% sequence similarity):

• Copy number variation that is fixed in population = segmental duplication (in other words: segmental duplications started out themselves as copy number variations)

• Segmental duplications can stimulate formation of new CNVs due to NAHR (see later)

➡In human + chimp: 70-80% of inversions and 40% of insertions/deletions overlap with segmental duplications

➡80% of human segmental duplications arose after the divergence of Great Aples from the rest of the primates

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Effects of structural variation

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Feuk et al, 2006

Mechanisms of formation for structural variation

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Gu et al, 2008

Mechanisms: NAHR

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NAHR = non-allelic homologous recombination

often between segmental duplications

• can recur

• clustered breakpoints

• larger

Hastings et al, 2009

Mechanisms: NHEJ

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Gu et al, 2008

NHEJ = non-homologous end-joining

pathway to repair double-strand breaks, but may lead to translocations and telomere fusion

not associated with segmental duplications

• more scattered

• unique origins

• smaller

Mechanisms: FoSTeS

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Hastings et al, 2009

FoSTeS = DNA replication fork-stalling and template switching

can occur multiple times in series => can generate very complex rearrangements

Feuk et al, 2006

Discovery of structural variation

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Approaches for discovery

• karyotyping, fluorescent in situ hybridization (FISH)

• array comparative genomic hybridization (aCGH)

• next-generation sequencing: combination of:

• read pair information

• read depth information

• split read information

• for fine-mapping breakpoints: local assembly

=> identify signatures

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Feuk et al, 2006

Feuk et al, 2006Feuk et al, 2006

FISH = fluorescent in-silico hybridization

duplicationinversion

duplication

Structural variation discovery using FISH

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Structural variation discovery using aCGH

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Xie & Tammi, 2009

aCGH = array comparative genome hybridization

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http://www.breenlab.org/array.html

61van de Wiel et al, 2010

Structural variation discovery using next-generation sequencing

General approaches:

1.Read depth

2.Read pairs

3.Split reads

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Structural variation discovery: read depth

Xie & Tammi, 2009

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Workflow

1.Mapping

2.Read filtering

3.GC correction

4.Spike identification

5.Validation

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General principle

• Similar to aCGH: using reference RD file (e.g. from 1000Genomes Project)

• In theory: higher resolution, but noisier than aCGH

• Algorithms not mature yet

• More complex steps

➡Data binned

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67van de Wiel et al, 2010

Xie & Tammi, 2009

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CNV = copy number variation

Combining CNV data for >1 individuals/samples

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CNVR = copy number variation region

CNVR = any region covered by at least 1 CNV

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CNVE = copy number variation event

CNVE = subgroups of CNVR with >= 50% reciprocal overlap

Data normalization

• Mainly: GC

• Other: repeat-rich regions, mapping Q, ...

• Fit linear model GC-content and RD => noise decreases

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Segmentation

• Identify spikes

• Many segmentational algorithms, e.g. GADA

• Issues: setting parameters: when to cut off peaks?

• Combine outputs from different runs with different parameters

• Compare to known CNVs

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Xie & Tammi, 2009

7543

Xie & Tammi, 2009

peak

764443

Xie & Tammi, 2009

...but is this?

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Abysov et al

Drawbacks

• Can only find unbalanced structural variation (i.e. CNVs)

• How to assess specificity and sensitivity? => compare with known CNVs

• Database of Genomic Variants DGV (http://projects.tcag.ca/variation/)

• Decipher (http://decipher.sanger.ac.uk/)

• Breakpoints: unknown

• Different parameters for rare vs common CNVs => which?

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Structural variation discovery: read pairs

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Korbel et al, 2007

Discordant readpairs

• Orientation

• Distance

• Plot insert size distribution for chromosome

• Very long tail!! => difficult to set cutoff: 4 MAD or 0.01%?

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Read pair signatures

Medvedev et al, 2009

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Real data

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Read pair workflow

1. Map reads

2. Identify discordant pairs

3. Cluster on location

4. Filter on number of readpairs per cluster

5. Filter on read depth

6. Filter on mapping quality for read pairs

7. Identify signatures

8. (Optionally) create alternative reference

9. Validate

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figure by Klaudia Walter

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figure by Klaudia Walter

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figure by Klaudia Walter

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figure by Klaudia Walter

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figure by Klaudia Walter

Clustering

• “standard clustering strategy”

• only consider mate pairs that do not have concordant mappings

• ignore read pairs that have more than one good mapping

• clustering: use insert size distribution (e.g. 2x4 MAD)

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Clustering: issues

• Ignores pairs that have >1 good mapping => no detection within repetitive regions (segmental duplications)

• What cutoff for what is considered abnormal distance? (4 MAD? 0.01%? 2stdev?)

• Low library quality of mix of libraries => multiple peaks in size distribution

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Filtering

• On number of RPs per cluster

• normally: n = 2

• for high coverage (e.g. 1000Genomes pilot 2: 80X): n = 5

• On drop in read depth and split reads

• On (mappingQ x nrRP)

• if published data available: look at specificity and sensitivity for different cutoffs mQ x nrRP

• if not: very difficult

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Filtering: issues

• Large insert size: low resolution for detecting breakpoints

• Small insert size: low resolution for detecting complex regions

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Structural variation discovery: split reads

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Mapping

• short subsequences => many possible mappings

• solution: “anchored split mapping” (e.g. Pindel)

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Medvedev et al, 2009

Local reassembly

• Aim: to determine breakpoints

• Which reads?

• for deletions: local reads

• for insertions: hanging reads for read pairs with only one read mapped

• (rather not: unmapped reads)

• For large region: split up

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97Nielsen et al, 2009

sequence reads -> contigs (using sequence overlap)contigs -> scaffolds (using read-pair information)

1 scaffold contigs

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+ -

read depth

read pairs

split reads

conceptually simple only unbalanced (CNVs)low resolution

wide range of types of variation

complicated

basepair resolution very small reads

General conclusions NGS & structural variation (1)

General conclusions NGS & structural variation (2)

• Available algorithms: more to demonstrate technique than comprehensive solution

• Difficult => different software = different results => “consensus set”

• based on read pairs and split reads: many sets agree

• based on read depth: totally different

• sometimes drop in read depth, but no aberrant read pairs spanning the region => why???

• Mapper = critical; maq/bwa: only 1 mapping (=> many false negatives); mosaik, mrFAST: return more results

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Software for structural variation discovery

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Medvedev et al, 2009

Chris Yoon

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Chris Yoon

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Websites

http://www.broadinstitute.org/gatk

http://samtools.sourceforge.net

http://picard.sourceforge.net

http://www.annotate-it.org

http://bit.ly/siftsnp

References and software

• Medvedev P et al. Nat Methods 6(11):S13-S20 (2009)

• Lee S et al. Bioinformatics 24:i59-i67 (2008)

• Hormozdiari F et al. Genome Res 19:1270-1278 (2009)

• Campbell P et al. Nat Genet 40:722-729 (2008)

• Ye K et al. Bioinformatics 25(21):2865-2871 (2009)

• Chen K et al. Genome Res 19:1527-1741 (2009)

• Yoon S et al. Genome Res 19:1586-1592 (2009)

• Du J et al PLoS Comp Biol 5(7):e1000432 (2009)

• Aerts J & Tyler-Smith C. In: Encyclopedia of Life Sciences (2009)

• Hastings P et al Nat Rev Genet 10:551-564 (2009)

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Exercises

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Finding SNPs using Galaxy

Based on the SAM-file you created in Galaxy in the last lecture, create a list of SNPs. You’ll first have to convert the SAM file to BAM, then create a pileup and finally filter the pileup (using “Filter pileup on coverage and SNPs”). Let this filter only return variants where the coverage is larger than 3 and the base quality is larger than 20.

How many SNPs do you find?

Calculate a histogram of the coverage over all SNPs (= column 4 in the filtered file you just created)

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Finding SNPs using samtools

Using the SAM file you created in the last lecture on the linux command line: Generate a BAM file and sort it. Next, generate a pileup for that BAM file using ~jaerts/i0d51a/chr9.fa as the reference sequence. When doing this: only print the variant sites and also compute the reference sequence (run “samtools pileup” without arguments to get more info).

How many SNPs are identified? Is the SNP at position 139,391,636 heterozygous or homozygous-non-reference? And the one at 139,399,365? Do you trust the SNP at 139,401,304?

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Annotating and filtering SNPs

Download ~jaerts/i0d51a/sift.input to your own machine and then upload it to the SIFT website at http://bit.ly/siftsnp. Positions in this file are on Homo sapiens build NCBI36. Make sure to let SIFT send the results by email.

How many SNPs are in/near genes?

How many are in exons?

What percentage of the SNPs is predicted damaging?

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Structural variation

We’ll be looking at copy number variation using the cnv-seq package. This software is available from http://tiger.dbs.nus.edu.sg/cnv-seq/We’ll be running the example from the cnv-seq tutorial at http://tiger.dbs.nus.edu.sg/cnv-seq/doc/manual.pdf. (Read that!)• Log into the server mentioned on Toledo.•Calculate CNVs in the file ~jaerts/i0d51a/test_1.hits compared to ~jaerts/

i0d51a/ref_1.hits:

/mnt/apps/cnv-seq/current/cnv-seq.pl --test ~jaerts/i0d51a/test_1.hits --ref ~jaerts/i0d51a/ref_1.hits --genome chrom1 --log2 0.6 -p 0.001 --bigger-window 1.5 --annotate --minimum-windows 4

• Finally investigate in R. Start R by typing “R”. Then:library(cnv)data <- read.delim(’test_1.hits-vs-ref_1.hits.log2-0.6.pvalue-0.001.miw-4.cnv’)cnv.print(data)cnv.summary(data)plot.cnv(data, CNV=4, upstream=4e+6, downstream=4e+6)ggsave(’sample_1.pdf’)

• Describe the main features in the plot.

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