dietmar rieder - i-med.ac.at

WM2 ‐ Bioinformatics

ExomeSeq data analysis

Dietmar Rieder

Ul%mate goal is to iden%fy soma%c varia%on in sequencing data

RAW READS SEQUENCE DATA

GENOMIC VARIATION

processing + analysis

Bedrock of soma%c variant discovery: T/N pair comparisons

TUMOR

NORM

AL

Types of variants

Meyerson, Gabriel and Getz. Nat. Rev. Genet. (2010)

Typical number of soma%c muta%ons

1000s to 10,000s soma%c single nucleo%de varia%ons (sSNVs) 100s to 1,000s soma%c small inser%ons and dele%ons (sINDELs) 100s to 1,000s soma%c structural varia%ons (sSVs) 100s to 1,000s soma%c copy number altera%ons (sCNAs)

Spectral karyotypes (SKY)

In a genome of 3 x 109 bases:

h[p://www.path.cam.ac.uk/~pawefish/BreastCellLineDescrip%ons/HCC1954.html

Cancer Cell Normal Cell

Types of variants

Meyerson, Gabriel and Getz. Nat. Rev. Genet. (2010)

Remember T/N pair comparisons

TUMOR

NORM

AL

Two main types of false posi%ves TU

MOR

NORM

AL

1.   NO EVENT, JUST NOISE

At risk: Every base Source: Misread bases Sequencing ar%facts Misaligned reads

At risk: ~1000 germline / Mb (known) Source: Low coverage in normal

TUMOR

NORM

AL

2.   GERMLINE EVENT (in T+N)

FPTotal = FPNoEvent + FPGermline

Addi%onal source of false nega%ves: “Tumor in Normal”

TUMOR

NORM

AL

•  Due to contamina%on of the normal sample by tumor cells •  Appears to be a germline event and leads to rejec%on of real variant

Now, throw in the variant allelic frac%on problem

N T

T

The (variant) allelic frac%on is the frac%on of alleles (DNA molecules) from a locus that carry the variant -‐> Also the expected frac%on of suppor%ng reads

33% N 67% T

(1) Purity = 67% (2) Local copy number in tumor = 4 (3) Number of mutated copies per cancer cell = 1 -‐> Allelic frac%on = 2/10 = 0.2

Carter et al. Nat. Biotechnol. (2012)

Which is even worse when the tumor involves subclones

N T

T

Carter et al. Nat. Biotechnol. (2012)

(1) Purity = 67% (2) Local copy number in tumor = 4 (3) Number of mutated copies per cancer cell = 1 (4) Cancer cell frac%on (CCF) = 0.5 -‐> Allelic frac%on = 1/10 = 0.1

Only this subclone has the variant!

Discovering low-‐frac%on variants requires deeper sequencing

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NATURE BIOTECHNOLOGY ADVANCE ONLINE PUBLICATION 3

A N A LY S I S

The LOD score is useful as a threshold for detection, as observed in the concordance of predicted sensitivity and measured sensitivity from the virtual-tumor approach (Fig. 2). Nonetheless, the LOD score cannot be immediately translated into the probability that a variant is due to true mutation rather than to sequencing error because the LOD score is calculated under an assumption of independent sequenc-ing errors and accurate read placement. As we discuss below, these assumptions are incorrect, and as a result, although direct applica-tion of the LOD score accurately estimates the sensitivity to detect a mutation, it substantially underestimates the false positive rate.

Variant filteringTo eliminate these additional false positives due to inaccurate read placement and non-independent sequencing errors, we developed six filters (Fig. 1 and Table 1). In addition, we used a panel of normal samples as controls to eliminate both germ-line events and artifacts (Online Methods). Subsets of these filters define several versions of the method (Fig. 1): (i) standard (STD), which applies no filters and thus includes all detected variants; (ii) high-confidence (HC), which applies the six filters and (iii) high-confidence plus panel of normal samples (HC + PON), which additionally applies the ‘panel of normal samples’ (PON) filter.

We tested the utility of these filters by applying them to the virtual-tumors benchmark and recomparing the results with the calculations (Fig. 2a). The sensitivity estimated for both with (HC) and with-out (STD) filters was similar, indicating that the model is accurate with respect to detection and that the filters do not adversely impact sensitivity. However, after applying the filters (HC), specificity increased and closely followed the calculations, suggesting that the filters largely eliminate systematic false positives (Fig. 2a and Supplementary Fig. 3).

Variant classificationFinally, each variant detected in the tumor sample is designated as somatic (not present in the matched normal sample), germ-line (present in the matched normal sample) or variant (present in the tumor sample but indeterminate status in the matched normal sample as a result of insufficient data). To perform this classification, we used a LOD score that compares the likelihood of the data under models in which the variant is present as a heterozygote or absent in the matched normal sample (Online Methods). We declare that there are

insufficient data for classification if the power to make a germ-line classification is less than 95%. We also used public germ-line variation databases41 as a prior probability of an event being germ-line.

SensitivityWe applied several benchmarking methods to evaluate the sensitiv-ity of our method to detect mutations as a function of sequencing depth and allelic fraction (Fig. 2b). First, we calculated the sensitivity under a model of independent sequencing errors and accurate read placement using our statistical test given an allelic fraction and tumor sample sequencing depth, and assuming that all bases have a fixed base quality score of Q35 (approximate mean base quality score in simulation data; Online Methods and Supplementary Fig. 4).

Next, to apply the downsampling benchmark, we used 3,753 validated somatic mutations, stratified by allele fraction (median = 0.28, range = 0.07–0.94), in colorectal cancer7 with deep-coverage ( 100×), exome-capture sequencing data downloaded from the data-base of Genotypes and Phenotypes (dbGAP; phs000178). Finally, to apply the virtual-tumor benchmark, we used deep-coverage data from two high-coverage, whole-genome samples (Coriell individ-uals NA12878 and NA12981) sequenced on Illumina HiSeq instru-ments as part of the 1000 Genomes Project42 and another previous study43, across 1 Gb of genomic territory. Note that we cannot use the PON filter (HC + PON) in the virtual-tumor sensitivity benchmark because it discards common germ-line sites.

Sensitivity estimates based on these three approaches were highly consistent with each other (median coefficients of variation for each depth of 3.1%). This suggests that the benchmarking approaches accu-rately estimate the sensitivity of mutation-calling methods and also that the calculated sensitivity is robust across a large range of para-meter values, enabling us to confidently extrapolate to higher sequenc-ing depths and lower allele fractions (Supplementary Table 1).

Based on this analysis, we observed that MuTect is a highly sensitive detection method. It detected mutations at a site with 30× depth in the tumor data (typical of whole-genome sequencing) and an allele frac-tion of 0.2 with 95.6% sensitivity. The sensitivity increased to 99.9% with deeper sequencing (50×) and dropped to 58.9% for detecting mutations with allelic fraction of 0.1 (at 30× sequencing; Fig. 2b and Supplementary Table 1). With 150× sequencing depth (typical of exome sequencing) we observed 66.4% sensitivity for 3% allele frac-tion events. It is this sensitivity to detect low-allele-fraction events

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MuTect STD (virtual tumors)

MuTect HC (virtual tumors)

MuTect HC + PON (downsampling)

MuTect HC (downsampling)

Figure 2 Sensitivity as a function of sequencing depth and allelic fraction. (a) Sensitivity and specificity of MuTect for mutations with an allele fraction of 0.2, tumor sample sequencing depth of 30× and normal sample sequencing depth of 30× using various values of the LOD threshold ( T) (0.1 T 100). Calculated sensitivity and false positive rate using a model of independent sequencing errors with uniform Q35 base quality scores and accurate read placement (Calculation) are shown as well as results from the virtual-tumor approach for the standard (MuTect STD) and high-confidence (MuTect HC) configurations. A typical setting of T = 6.3 is marked with black circles. (b) Sensitivity as a function of tumor sample sequencing depth and allele fraction (f ) using T = 6.3. Calculated sensitivity as in a is shown

as well as results from the virtual-tumor approach and the downsampling of validated colorectal mutations7. Error bars, 95% confidence intervals (typically smaller than marks).

Sensi%vity (recall) decreases with the variant allele frac%on

Cibulskis et al. Nat. Biotechnol (2013)

AF=0.4

AF=0.2

AF=0.1

AF=0.05

For MuTect

Soma%c Variant Discovery Workflow

Indels aǳ¢ŜŎǘн

+ some post-‐processing to rescue TiN variants and eliminate ar%facts

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Polygon

High-‐throughput sequencing yields a big pile of reads

Enormous pile of short reads from HTS

Library

Instruments generate short reads that must be mapped to the reference


Reference genome

Mapping and alignment algorithms

Reads mapped to reference

How we visualize aligned HTS reads (Integrated Genomics Viewer)

Depth of coverage

Individual reads aligned to the genome

Clean C/T heterozygote

First and second read from the same fragment

Reference genome

Non-‐reference bases are colored; reference bases are grey

Intergenic Exon I Exon IIIntron I IntergenicVariant site

Small targeted experiments, gene panels, RADseq •  Similar to exomes for most purposes

Whole genome

Exome

Whole genome (WGS) vs. Exome (WEx)

Key differences between whole genome and exome

Whole genome •  En)re genome is prepped •  Possible to do PCR-‐free •  Higher price •  Exons + introns •  Coverage everywhere

(well, almost – some missing)

Exome •  Capture of target regions •  PCR required •  Lower price •  Exons only •  Coverage only on targets

(well, almost – some spillover)

Key differences between whole genome and exome

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Base coverage depth

Typical Sample Metrics

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WGS

•  To achieve 80% at 20X target for exomes, many bases are covered up to 100X

•  WGS achieves mean coverage >20X with fewer high coverage bases

Drawbacks of PCR to keep in mind

•  Amplifica)on rate depends on sequence GC content –  Poor coverage of GC-‐rich

regions –  Skewed coverage profile

•  Systema)c PCR stuker in

repe))ve sequences –  Complicates indel calling

–  HaplotypeCaller includes -‐-‐pcr_indel_model seWng

Exome capture target regions

•  Involves baits (complementary sequences) )led to capture segments of target regions

Broad uses a custom-‐designed bait set; the target intervals list is available in our resource bundle

(Clark et al, Nat. Biotech., 2011)

•  Resul)ng covered intervals are specific to capture kit manufacturer

(Clark et al, Nat. Biotech., 2011)

Effect on ability to compare datasets

•  If samples were sequenced by different centers, with different capture or enrichment kits, covered (strictly usable) intervals are not equivalent.

•  Must decide whether to use union or intersec)on of interval lists (we use intersec)on)

common intervals

A B

What about gene panels?

•  Targeted gene panels –  Func)onally similar to exome

with very small interval list (e.g. 200)

o  Same drawbacks as exomes PLUS some new ones

o  Produces many independent fragments with iden)cal start and end, so MarkDuplicates should not be run

“stacks” of sequence

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From reads to variants : Major steps involve transforming data and storing results in specific formats

Mapping + cleanup

Variant Discovery

Variant EvaluaNon

FASTQ -‐> BAM BAM -‐> VCF

from raw reads to aligned reads

from aligned reads to genomic variaNon


Important file format #1: FASTQ (raw reads)

•  Simple extension from tradi)onal FASTA format. •  Each block has 4 elements ( in 4 lines):

•  Sequence Name (read name, group, etc.) •  Sequence •  + (op)onal: Sequence name again) •  Associated quality score.

•  Example record: @EAS54_6_R1_2_1_413_324 CCCTTCTTGTCTTCAGCGTTTCTCC +;;3;;;;;;;;;;;;7;;;;;;;88

Iden)fier Sequence

Base Quali)es (ASCII 33 + Phred scaled Q)

Official specifica)on in hkp://maq.sourceforge.net/fastq.shtml

RAW reads in fastq format: Q‐scorePhred systemSanger, Illumina 1.3+, Iontorrent/Proton

Q = -10 log10 (p)

Illumina < 1.3Q = -10 log10(p/1-p)

Where Q is the quality and p is the

probability of the base being incorrect.

Q values are not necessarily comparable between different platforms. The error rates are determined by different methods. („Good“ thumb rule: Illumina Q>20, IonT/P Q>17)

RAW reads in fastq format: Q‐scoreQuality scores (numbers) are stored ASCII encoded in the fastq

IonT/P

Illumina (now)

Visualizing the meaning of Phred Scores

Accuracy

Error

20 and higher is generally a good score

Quality assessment using FastQC• Free Download

– Download: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

• Samples reads (200K default): fast, low resource use

Quality assessment using FastQC

BAD base quality(run or library issue)

leading bases!?(library artifact)


Adapter dimer contamination


Good Illumina data

How to get from BAD to Good?

?

How to get from BAD to Good?

Produce high quality data to begin with!

Filter out artifacts using “cleaning” tools!

Mapping adds informa)on : codified in SAM/BAM format


Reference genome

Mapping and alignment algorithms

Reads mapped to reference

Mapping + cleanup

Variant Discovery

Variant EvaluaNo

n FASTQ -‐> BAM BAM -‐> VCF




Important file format #2: SAM/BAM (aligned reads)

SLX1:1:127:63:4 … 1 10052169 … 23M6N10M … GAAGATACTGGTTTTTTTCTTATGAGACGGAGT 768832'48::::::;;:/78$88818099897 NM:i:0 AS:i:30 XS:i:30 …

Read name

Locus

Alignment information

Read sequence

Quality scores

Meta data

Data processing

and analysis

BAM file allows us to represent the data of any sequencer. Analyses can then be conducted largely agnostic to the particular sequencer used.

A BAM file can contain data from a single or from several samples

-> technology-independent

Sequence Alignment Map / Binary Alignment Map (compressed)

BAM headers: an essen)al part of a BAM file

@HD VN:1.0 GO:none SO:coordinate @SQ SN:chrM LN:16571 @SQ SN:chr1 LN:247249719 @SQ SN:chr2 LN:242951149 [cut for clarity] @SQ SN:chr9 LN:140273252 @SQ SN:chr10 LN:135374737 @SQ SN:chr11 LN:134452384 [cut for clarity] @SQ SN:chr22 LN:49691432 @SQ SN:chrX LN:154913754 @SQ SN:chrY LN:57772954 @RG ID:20FUK.1 PL:illumina PU:20FUKAAXX100202.1 LB:Solexa-‐18483 SM:NA12878 CN:BI @RG ID:20FUK.2 PL:illumina PU:20FUKAAXX100202.2 LB:Solexa-‐18484 SM:NA12878 CN:BI @RG ID:20FUK.3 PL:illumina PU:20FUKAAXX100202.3 LB:Solexa-‐18483 SM:NA12878 CN:BI @RG ID:20FUK.4 PL:illumina PU:20FUKAAXX100202.4 LB:Solexa-‐18484 SM:NA12878 CN:BI @RG ID:20FUK.5 PL:illumina PU:20FUKAAXX100202.5 LB:Solexa-‐18483 SM:NA12878 CN:BI @RG ID:20FUK.6 PL:illumina PU:20FUKAAXX100202.6 LB:Solexa-‐18484 SM:NA12878 CN:BI @RG ID:20FUK.7 PL:illumina PU:20FUKAAXX100202.7 LB:Solexa-‐18483 SM:NA12878 CN:BI @RG ID:20FUK.8 PL:illumina PU:20FUKAAXX100202.8 LB:Solexa-‐18484 SM:NA12878 CN:BI @PG ID:BWA VN:0.5.7 CL:tk @PG ID:GATK PrintReads VN:1.0.2864 20FUKAAXX100202:1:1:12730:189900 163 chrM 1 60 101M = 282 381

GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTA…[more bases] ?BA@A>BBBBACBBAC@BBCBBCBC@BC@CAC@:BBCBBCACAACBABCBCCAB…[more quals] RG:Z:20FUK.1 NM:i:1 AM:i:37 MD:Z:72G28 MQ:i:60 PG:Z:BWA UQ:i:33

Required: Standard header

EssenNal: con)gs of aligned reference

sequence. Should be in karyotypic order.

EssenNal: read groups. Carries pla�orm (PL), library (LB), and sample (SM) informa)on. Each read is associated with a read

group

Useful: Data processing tools applied to the reads

Official specifica)on in hkp://samtools.sourceforge.net/SAM1.pdf

Variant calls are a summarized representa)on of the original sequence data

Mapping + cleanup

Variant Discovery

Variant EvaluaNo

n FASTQ -‐> BAM BAM -‐> VCF




Variant calling algorithms

site 1 descrip)on + sample genotypes



Important file format #3: VCF (genomic varia)on)

##fileformat=VCFv4.1 ##reference=1000GenomesPilot-NCBI36 ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership"> ##FILTER=<ID=s50,Description="Less than 50% of samples have data"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> #CHROM POS ID REF ALT QUAL FILTER INFO

FORMAT NA00001 NA00002 NA00003

20 14370 rs6054257 G A 29 PASS DP=14;AF=0.5;DB GT:GQ:DP 0/0:48:1 1/0:48:8 1/1:43:5

20 1110696 rs6040355 A G,T 67 PASS DP=10;AF=0.333,0.667;DB

GT:GQ:DP 1/2:21:6 2/1:2:0 2/2:35:4

20 1230237 . T . 47 PASS DP=13 GT:GQ:DP 0/0:54:7 0/0:48:4 0/0:61:2

20 1234567 microsat1 GTCT G,GTACT 50 PASS DP=9

GT:GQ:DP 0/1:35:4 0/2:17:2 1/1:40:3

Header

Variant records

Official specifica)on in www.1000genomes.org/wiki/Analysis/Variant Call Format/vcf-‐variant-‐call-‐format-‐version-‐42

And that’s all you need to know to get started

Mapping + cleanup

Variant Discovery

Variant EvaluaNon





•  Toolkit focused on variant discovery (SNP & indel)

•  Components:

-  Engine and infrastructure

-  Tools (walkers)

-  Also a programming framework for developing genome analysis soGware

GATK = Genome Analysis Toolkit

GATK reads-‐to-‐variants workflow

Mapping + cleanup

Variant Discovery

Variant EvaluaTon


+ other soUware (BWA, STAR, Picard, Samtools)


•  Java-‐based command line tool (see running requirements in FAQs)

•  Consult online documenta)on for details about each tool! -  Argument names and default values can change -  Exact arguments depend on the given tool

java –jar GenomeAnalysisTK.jar –T ToolName \

–R reference.fasta \ –I inputBAM.bam \ –V inputVCF.vcf \ –o outputs.someformat \ –L 20:1000000-‐2000000

GATK command syntax

Soma)c Variant Discovery

Mapping and pre-‐processing

BWA + Picard/samtools + GATK Done separately for each sample in a tumor/normal pair

Overview of mapping & processing

Enormous pile of short reads from NGS

Reference genome

Key processing steps: •  Map reads to the reference & clean up •  Mark PCR duplicates •  (RNAseq only) Process reads that span

splice juncMons

Ideally, we’d just align the sample genome to the reference genome

Region 1 Region 2 Region 3

truncated region duplicated region

= local variant (SNP/indel)

Reference

Sample

…But we don’t have the whole sample in one piece.

We have a pile of reads that need to be mapped individually

Enormous pile of short reads from

NGS

Region 1 Region 2 Region 3

Region 1 Region 2A Region 2B

Easy

Harder

Mapping is complicated by mismatches (true mutations or sequencing errors), indels, duplicated regions etc.

Mapping produces a SAM alignment summarizing position, quality, and structure for a given sequence

read1 99 ref 2 30 3M1D2M1I1M = 14 20 CATCTAG *

See also: •  SAM format spec: hap://samtools.github.io/hts-‐specs/SAMv1.pdf •  Explain SAM flags: hap://broadinsMtute.github.io/picard/explain-‐flags.html

POS (alignment start)

CIGAR (structure) SEQ (sequence) MAPQ (quality)

Mate informaDon

For DNAseq: map reads using BWA

The BWA soUware package by Heng Li & Richard Durbin hGp://bio-‐bwa.sourceforge.net/bwa.shtml

•  “Burrows-‐Wheeler Aligner” •  mem algorithm for 70bp or longer Illumina, 454, Ion Torrent and Sanger

reads, assembly conMgs and BAC sequences •  Use –M flag to flag extra alignment hits as secondary (for downstream

compaMbility)

Generic recommendaMon for mapping starMng from FASTQ:

Reference genome

FASTQ

Mapped, cleaned, sorted SAM

•  Use BWA MEM algorithm with –M flag and also –R to add read group info •  OpMonally run Picard CleanSam and FixMateInformaMon and set

SO=coordinate •  If you forgot to feed BWA the read group info, add it with Picard

AddOrReplaceReadGroups •  Will not clip overhanging bases for short inserts!

BWA MEM Raw mapped SAM

CleanSam + FixMate

We now have properly mapped and sorted reads

Why mark duplicates?

Reference

Mapped reads

= sequencing error propagated in duplicates

•  Duplicates are sets of reads pairs that have the same unclipped alignment start and unclipped alignment end

•  They’re suspected to be non-‐independent measurements of a sequence •  Sampled from the exact same template of DNA •  Violates assumpMons of variant calling

•  What’s more, errors in sample/library prep will get propagated to all the duplicates •  Just pick the “best” copy – miMgates the effects of errors

Mark duplicates

Ar7factual SNPs around an indel missed by the mapper

Several consecu2ve

“SNPs” only found on reads ending on the right of the homopolymer

Several consecu2ve

“SNPs” only found on reads ending on the le? of the homopolymer

7bp “T” homopolymer run

Why realign around indels?

•  InDels in reads (especially near the ends) can trick the mappers into mis-‐aligning with mismatches

•  These ar7factual mismatches can harm base quality recalibra7on and variant detec7on (unless a sophis7cated caller like MuTect2 is used)

þ Realignment around indels helps improve the accuracy of several of the downstream processing steps.

Local realignment uncovers the hidden indel in these reads and eliminates all the poten7al FP SNPs

Indel Realignment works in two phases

•  Iden7fy what regions need to be realigned

•  Perform the actual realignment

How do we find which regions need to be realigned?

•  Known sites (e.g. dbSNP, 1000 Genomes)

•  Indels seen in some of the read alignments (in CIGARs)

•  Sites where evidence suggests a hidden indel (presence of mismatches and soRclips)

à Entropy calcula7on: Compute “ac7vity score” If above threshold per window include as realignment target

If we know there is a known indel at this site, we usually prefer to use that for the consensus, especially if there are several alternate possibili7es.

Known indels can be used to determine consensus

AAGAGTAGRef:

AAG---AGTAG

AAGAGTAG

Read pile consistent with CTA inser7on

Read pile consistent with the reference sequence Realigning

determines which is be[er

Three adjacent

SNPs

GGCTA

dbSNP

Is realignment s7ll necessary with latest soRware?

•  Latest tools being implemented for variant discovery (HaplotypeCaller, MuTect 2, Platypus) all include some sort of assembly step (for which upstream realignment is not really helpful).

•  BUT poten7al improvement for Base Quality Score Recalibra7on when run on realigned BAM files (ar7factual SNPs are replaced with real indels).

•  Also s7ll useful for legacy tools, e.g. full realignment should be performed if using the GATK’s Unified Genotyper or MuTect for calling variants.

Real data is messy -‐> properly es2ma2ng the evidence is cri2cal

AA AG CA CG GA GG TA TG

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•  Quality scores are cri2cal for all downstream analysis •  Systema2c biases are a major contributor to bad calls

Example of bias: quali2es reported depending on nucleo2de context

original recalibrated

Quality scores emitted by sequencing machines are biased and inaccurate

BQSR method identifies bias and applies correction

Base Recalibra2on phases

•  Model the error modes

•  Apply recalibra2on and write to file

•  Make before/aHer plots

•  Systema2c errors correlate with basecall features

•  Several relevant features: –  Reported quality score –  Posi2on within the read

(machine cycle) –  Sequence context

(sequencing chemistry effects)

•  Calculate error empirically and find paNerns in how error varies with basecall features

•  Method is empowered by looking at en2re lane of data (works per read group)

How do we identify the error modes in the data?


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Covariation patterns allow us to calculate adjustment factors

For each base in each read:

-‐ -‐ is it in AA context? -‐> adjust by X points -‐ -‐ ... -‐ -‐ is it at 3rd posi2on? -‐> adjust by Y points -‐ -‐ ...

•  Any sequence mismatch = error except known variants*!

•  Keep track of number of observa2ons and number of errors as a func2on of various error covariates (lane, original quality score, machine cycle, and sequencing context)

# of reference mismatches+1# of observed bases+ 2

PHRED-‐scaled quality score

How do we derive the adjustment factors used for recalibration?

* If you don’t have known varia=on, bootstrap (see later on)

SLX GA 454 SOLiD HiSeq Complete Genomics

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first of pair reads second of pair reads first of pair reads second of pair reads first of pair reads second of pair reads

Base Qu Base Quality Score Recalibration provides a calibrated error model from which to make mutation calls

Highlighted as one of the major methodological advances of the 1000 Genomes Pilot Project!

Base Recalibra2on workflow: data processing path

Recalibra2on table

Recalibrated BAM file

BaseRecalibrator

PrintReads

Original BAM file + Known sites

BaseRecalibrator

•  Builds recalibra2on model

java –jar GenomeAnalysisTK.jar –T BaseRecalibrator \

–R human.fasta \ –I realigned.bam \ –knownSites dbsnp137.vcf \ –knownSites gold.standard.indels.vcf \

[ –L exome_targets.intervals \ ] –o recal.table

Why specify –L intervals when running BaseRecalibrator on WEx?

•  BQSR depends on key assump2on: every mismatch is an error, except sites in known variants

•  Off-‐target sequence likely to have higher error rates with different error modes

•  If off-‐target sequence is included in recalibra2on, may skew the model and mess up results

Ø Use –L argument with BaseRecalibrator to restrict recalibra=on to capture targets.

Print Reads

•  General-‐use tool co-‐opted with –BQSR flag and fed a recalibra2on report

•  Creates a new bam file using the input table generated previously which has exquisitely accurate base subs2tu2on, inser2on, and dele2on quality scores

•  Original quali2es can be retained with OQ tag (not default)

java –jar GenomeAnalysisTK.jar –T PrintReads \

–R human.fasta \ –I realigned.bam \ –BQSR recal.table \ –o recal.bam

… and subs2tute the ploing workflow

Recalibra2on table (1)

BaseRecalibrator (1)

Original BAM file + Known sites

Recalibra2on table (2)

BaseRecalibrator (2) Plots

AnalyzeCovariates

Base Recalibrator

•  Second pass evaluates what the data looks like aHer recalibra2on

java –jar GenomeAnalysisTK.jar –T BaseRecalibrator \

–R human.fasta \ –I realigned.bam \ –knownSites dbsnp137.vcf \ –knownSites gold.standard.indels.vcf \ –BQSR recal.table \ –o a[er_recal.table

AnalyzeCovariates

•  Makes plots based on before/aHer recalibra2on tables

•  There is an op2on to keep the intermediate .csv file used for ploing, if you want to play with the plot data.

java –jar GenomeAnalysisTK.jar –T AnalyzeCovariates \

–R human.fasta \ –before recal.table \ –a[er a[er_recal.table \ –plots recal_plots.pdf

42

Post-‐recalibra2on quality scores should fit the empirically-‐derived quality scores very well; no obvious systema2c biases should remain

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Recalibration produces a more accurate estimation of error (doesn’t fix it!)

Variant discovery

•  Call SNPs with MuTect2

Indels: M2

q025dr

Text Box

q025dr

Text Box

•  Ac<ve Regions are iden=fied using original MuTect soma=c sta=s=c, including indel events, with low threshold (LOD ≥ 4.0, similar to MuTect callstats threshold)

•  Reads are differen=ally filtered for tumor vs. normal –  Tumor is strict: MAPQ ≥ Q20, discarding discrepant overlapping fragments

–  Normal is permissive: MAPQ ≥ Q0, keep alternate read from discrepant overlapping fragments

This is how MuTect 2 works

•  Assembly + PairHMM are extremely similar to the Haplotype Caller

•  Only high quality reads are used in the assembly

•  Very minor technical changes which impact soma=c calling because our events are rare and at low allele frac=on (lower tolerance for losing reads)


•  Soma<c Genotyping Engine is very similar to the MuTect calcula=on, but rather than using a likelihood based on base quality scores, we use the PairHMM Likelihoods

•  New sta=s=cs available versus MuTect –  Now that we’re calling an en=re region at once, we can “see” what you typically see in an IGV screenshot


WM2 ‐ Bioinformatics

ExomeSeq data analysis

Dietmar Rieder

dietmar rieder - i-med.ac.at

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