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Bioinformatics and Computational Biosciences Branch
Bioinformatics and Computational Biosciences Branch

Variant Analysis

Sciences
1  sur  84
Exome Sequencing & Variant Analysis
Exome Sequencing
Sequencers Ion torrent proton Ilumina MiSeq Ilumina HiSeq 2500 Ilumina XTen PacBio
Sequencers Machine Cost methods Throughput per run Read length Error rate Illumina MiSeq $128K Small genomes, targeted gene 1.5-2Gb 2X300 0.8% Ion Torrent $80K Small genomes, targeted gene 1Gb 400 1.71% Illumina NextSeq $250K Exomes/transcript ome 120Gb 2x150 0.8% Illumina HiSeq $654K Genomes/exomes/ transcriptomics 600Gb 2X150 0.76% Illumina X Ten $10 Mil. Genomes 1.6Tb 2x150 0.5% PacBio $695K Genomes 100Mb 15K 12.86%
Next-generation sequencing overview Exomes (ES/WES) Genomes (GS/WGS) Cost ~$1000 ~$2000 (~$1000 with HiSeq X Ten) Size of bam files ~10 Gb ~200 Gb DNA Targeted and captured Sheared DNA What you can get Most coding regions (+UTR) coding and non- coding Variants that can be examined SNVs, indels (CNVs) SNVs, indels, CNVs, structural variations
• Genome has even coverage • Even a deletion is observed by eye How exomes and genomes look! WT Deletion ES GS
All about exomes • What is exome? – Sequencing targeted exonic regions – ~2% of genome • Important to know – You will NOT get a whole exome! – Not all exons in all genes are captured! – Important to know the negative results vs no data • Coverage will vary in targets
What to know about your data • What is the sequence depth? – The depth your sequence – 10x, 30x, 50x, 100x • Read length – How long is your read length? • What software was used to align? • What variant calling was used to call variants? • Which reference was used? • Which capture kit was used? What is covered?
Partial list of capture enrichment kits Manufacturer Kits Regions targeted Bases covered Illumina Nextera Rapid capture Exons + UTRs+ miRNA 62 Mb Nimblegen SeqCap EZ Exome Exons + UTR 96 Mb SeqCap EZMedExome Disease-associated regions 47 Mb Agilent SureSelect Human All Exon V6 Exons+UTRs 60Mb Clinical Research Exome Disease-relevant targets 51Mb
Overview of next-generation sequencing processing Sequence Align reads/mapping Variant calling Annotate Downstream analysis
Popular tools Task Popular tools Align reads/mapping BWA-mem Novoalign Isaac Variant calling GATK (Broad) Platypus (Wellcome Trust) Starling (Illumina) Annotating variants AnnoVar VEP snpEff
Popular source Task Popular source Control population frequency ExAC 1000GP ESP Annotation RefSeq Ensemble UCSC genes GENCODE Visualization UCSC genome browser IGV Clinical relevance HGMD OMIM CGD ClinVar
Exome sequencing overview Ann https://en.wikipedia.org/wiki/Exome_sequencing#/media/File:E
GATK Best Practices Website https://www.broadinstitute.org/gatk/guide/best-practices
Introduction to exome sequencing analysis workflow
ES pipeline overview Sequence Alignment Quality control Variant discovery Quality control Annotate variants Analyze Pre-processing Variant discovery Analysis
Files and tools used File type Origin FASTQ Raw reads from sequencer SAM BAM Sequence Alignment/Map Binary version of SAM gVCF/VCF Variant call format Tool Purpose BWA mem Read alignment to reference Picard Mark duplicates GATK (Haplotype caller) QC/variant calling Samtools Sort sam/bam, convert sam<->bam
Reference genome • There are different versions of human reference genome Reference Name Chr notation Mitochondrial sequence Additional sequences included GRCh37 (Genome Reference Consortium) 1, 2… X,Y,MT Yes • Unlocalized • Unplaced • Alternate loci hg19 (UCSC genome browser) Chr1, Chr2…ChrX, ChrY, ChrM Copied from previous release • Unlocalized • Unplaced • Alternate loci b37/b37+decoy/hs37d5 (1000GP) 1, 2… X,Y,MT Yes • Unlocalized • Unplaced • “decoy” sequence • Human herpevirus 4 type 1 • Unlocalized: chromosome known, exact location unknown • Unplaced: known to originate from human genome, chromosome unknown • Alternate loci: alternate representation of specific human regions
Index files • Index files are needed for files in next-generation analysis, as file sizes are big! • Enables program to efficiently access the data, rather than having to read the whole file File type Index FASTA *fai BAM *bai VCF *vcf.idx
Reference FASTA index file
Why BWA+ GATK Haplotype caller? • Widely accepted as the “conventional” way of processing next-gen data • Well assessed • Well documented • Software is supported • Community support for troubleshooting or information
Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, VCF (GATK GenotypeGVCFs) Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis
FASTQ Sequence ID Sequence Sequence quality Sequence ID Bitwise flags chrosome position MappingQ CIGAR Paired-end chr Paired-end position Observed template length http://samtools.github.io/hts-specs/SAMv1.pdf BAM/SAM Align Pre-processing
Marking duplicates and why is it necessary? • It is assumed that each read corresponds to an independent DNA fragment from randomly sheared DNA • However, PCR amplification can cause duplicates – Identify based on start + stop of reads – Choose the best and ignore the rest Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator)
Indel realignment Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) • Misalignment around indels cause high number of SNPs • These regions are identified and locally realigned to minimize mismatches
Recalibrate Base Quality Scores Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) • Base scores are produced by sequencers • Quality scores are inaccurate and biased – Prone to various technical errors – QS are often over- or under estimated • To identify and correct non-random technical error – Physics or the chemistry of sequencing reactions – Manufacturing flaws in the equipment • Error covariates e.g. – Reported quality score – Position within the read (machine cycle) – Preceding and current nucleotide (sequencing chemistry)
Recalibrate Base Quality Scores Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Over-estimation Under-estimation • GATK BSQR builds model based on the known variants set • Adjusts the base quality scores in the data based on the model
Now we are ready to call variants!
Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis ✔
Generate genotype likelihoods in each sample (gVCF) • For a single sample, calculates normalized Phred-scaled likelihoods (PL) for genotypes: • “likelihood of the genotype”= “the probability that the genotype is not correct” • Normalized so that the most likely genotype is 0 Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples)
What is a joint genotyping? • If we analyze Sample 1 or Sample N alone, we are not confident that the variant is real • If we see both samples, we are more confident that there is real variation at this site in this cohort Broad Institute Variant Discovery
Generate new variant quality score using VQSR Broad Institute Variant Discovery-QC Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) • What is Variant Quality Score Recalibration – NOT adjusting scores!! – Generate new score VQSLOD (variant quality score log-odds) • Approach – Machine learning to profile good variants vs bad variants – Using multiple dimensions (5-8, typically) – Uses INFO annotations for each variant (eg. Allele count, allele frequency, etc)
Generate new variant quality score using VQSR Train a model using “truth” set of known variants Apply the model to your samples Toss Keep Variant Discovery-QC Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) http://gatkforums.broadinstitute.org/gatk/discussion/39/variant-quality-score-recalibration-vqsr
Alternatively! IF you have few samples Variant Discovery-QC Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) http://gatkforums.broadinstitute.org/gatk/discussion/39/variant-quality-score-recalibration-vqsr • Apply hard filter! • Define how to filter your variant or use default filter parameters – QualByDepth (QD) 2.0 – FisherStrand (FS) 60.0 – RMSMappingQuality (MQ) 40.0 – MappingQualityRankSumTest (MQRankSum) -12.5 – ReadPosRankSumTest (ReadPosRankSum) - 8.0 (only het calls)
Final VCF Variant Discovery-Final VCF Header Body
Annotating Variants using ANNOVAR Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Variant Annotation Gene Annotation refSeq Gene, mitochondrial variants, USC/EnSembl, GENCODE/CCDS Region-based Annotation Conserved genomic elements, transcription factor binding site, cytogenetic band, segmental duplications, GWAS.. Filter-based annotation 1000 GP, dbSNP, ESP, EXAC, non-synonymous variants annotation (SIFT/Polyphen2/MutationTast er/LRT/FATHMM/CADD..), ClinVar… For comprehensive list, see http://annovar.openbioinformatics.org/en/latest/
Shall we start!?!
Hands-on exercises
Question • We have fastq files from a “sample” • Are there any deleterious variants in this person? • We’ll only be looking at chromosome 16
Go to the class folder and make your folder Copy the commands to your folder and open it for your convenience TRICK TO A COPY/PASTE! Open 2 terminal windows: One to view commands One to RUN commands (will log in interactively) Terminal 1 (to view commands) Create your folder, copy commands.sh file to your folder, and open it cd /hpcdata/scratch/2016_Exome_Training mkdir directory_name cd directory_name cp ../commands.sh . vi commands.sh Terminal 2 (To run commands) Log in interactively and go to your folder qrsh -l h_vmem=10G,mem_free=5G #to log in to interactive node cd /hpcdata/scratch/2016_Exome_Training/directory_name
Load modules module load GATK module load FastQC module load BWA module load SAMtools module load VCFtools module load IGV module load annovar/1.0 module load BEDTools module load picard module load BCFtools
QC on fastq/bams (bad quality)
QC on fastq/bams (good quality)
Step 1: QC and align a fastq file 1) run fastqc on fastq files – generates .html file with qc statistics – Generates compressed folders with images fastqc ../sample.fastq -o ./ Pre-processing
Step 2: align fastq files • Align with BWA MEM using -M to mark secondary alignments and -R to annotate Read Groups (e.g., different samples) – ID, LB, SM, PU, and PL tags are required Pre-processing bwa mem -R "@RGtID:dadtLB:dadtSM:dadtPU:FCC1 89PACXXtPL:ILLUMINA" -M ../human_g1k_v37.fasta ../sample.fastq | gzip > ./sample.sam.gz
Step 3: sort sam, convert to bam • Aligned reads need to be sorted java -jar ${EBROOTPICARD}/picard.jar SortSam I=sample.sam.gz O=sample.bam SO=coordinate CREATE_INDEX=true Now we have mapped and sorted reads Pre-processing samtools index sample.bam
Step 4: Mark duplicates • Using picard to mark duplicates Pre-processing java -jar ${EBROOTPICARD}/picard.jar MarkDuplicates INPUT=sample.bam OUTPUT=sample.dedup.bam AS=true CREATE_INDEX=true M=sample.metrics.txt How many were marked as duplicates??
Step 5: Realign indels • Create target list for potential indel sites java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T RealignerTargetCreator -R ../human_g1k_v37.fasta -I sample.dedup.bam -o sample.intervals -L ../S03723314_Regions_chr16.fix.bed Pre-processing Let’s visualize a potential indel region!
Step 5: Realign indels Viewing potential indel site! • Use samtools tview to view bam file samtools tview sample.dedup.bam ../human_g1k_v37.fasta Pre-processing • #press "g" to prompt "Go to", type 16:46744672 and enter
Step 5: Realign indels • Realign target list java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T RealignerTargetCreator -R ../human_g1k_v37.fasta -I dad.chr16.dedup.bam -o dad.chr16.intervals - L ../S03723314_Regions_chr16.fix.bed Pre-processing
Step 6: Recalibrate base QS • Build a model java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T BaseRecalibrator -R ../human_g1k_v37.fasta - knownSites ../dbsnp_138.b37.vcf -knownSites ../Mills_and_1000G_gold_standard.indels.b37.vcf -I sample.realigned.bam -L ../S03723314_Regions_chr16.fix.bed -o sample.recal_report.grp Pre-processing • Recalibrate scores java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T PrintReads -R ../human_g1k_v37.fasta -I sample.realigned.bam -o sample.recal.bam - BQSR sample.recal_report.grp -L ../S03723314_Regions_chr16.fix.bed
Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis ✔
Step 1: Generate gVCFs Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T HaplotypeCaller -R ../human_g1k_v37.fasta -I sample.recal.bam -stand_call_conf 30.0 -stand_emit_conf 10.0 -o sample.g.vcf -ERC BP_RESOLUTION -L ../S03723314_Regions_chr16.fix.bed
Step 2: genotype gVCFs Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T GenotypeGVCFs -R ../human_g1k_v37.fasta --max_alternate_alleles 2 -stand_call_conf 30 -stand_emit_conf 10 --variant sample.g.vcf -o sample.vcf
Step 3: Recalibrate variants VQSR (>30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Build model Apply model Take the output and do indel recalibration (see commandline)
Step 3: Apply hard filter (<30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T SelectVariants -R ../human_g1k_v37.fasta -L ../S03723314_Regions_chr16.fix.bed -V sample.vcf -selectType SNP -o sample_SNPs.vcf java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T VariantFiltration -R ../human_g1k_v37.fasta -L ../S03723314_Regions_chr16.fix.bed -V sample_SNPs.vcf --filterExpression "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" --filterName "my_snp_filter" -o sample.filtered_SNPs.vcf • Extract SNPS • Apply filters on SNPS
Step 3: Apply hard filter (<30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T SelectVariants -R ../human_g1k_v37.fasta -V sample.vcf -selectType INDEL -o sample.indels.vcf • Extract INDELs • Apply filters in indels java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T VariantFiltration -R ../human_g1k_v37.fasta -V sample.indels.vcf --filterExpression "QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0" --filterName "my_indel_filter" -o sample.filtered_indels.vcf
Step 3: Apply hard filter (<30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) • Merge two vcf files java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T CombineVariants -L ../S03723314_Regions_chr16.fix.bed --variant:indel sample.filtered_indels.vcf --variant:snps sample.filtered_SNPs.vcf -R ../human_g1k_v37.fasta -o sample.filtered_SNP_indels.vcf -genotypeMergeOptions PRIORITIZE -priority snps,indel
Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis ✔ ✔
Step1: Annotate variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis • We will use annovar to annotate the VCF file • Left-align VCF file using bcftool bcftools norm -m-both -o trio_filtered_SNP_indels_step1.vcf trio_filtered_SNP_indels.vcf bcftools norm -f ../human_g1k_v37.fasta -o trio_filtered_SNP_indels_step2.vcf trio_filtered_SNP_indels_step1.vcf
Step1: Annotate variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis • Gene annotation • AA change, classification of mutation • Population frequency • ESP 6500 • 1000GP • Snp anntation (SNP138) • Deleteriousness of non-synonymous variants annotation • SIFT • Polyphen • LTR • MutationTaster • FATHMM • PPROVEAN • VEST3 • CADD • DANN • Fathmm-MKL • etc http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview
Step1: Annotate variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview table_annovar.pl sample.filtered_SNP_indels_step2.vcf -buildver hg19 -out sample.filtered_snps.annotated.vcf -remove -protocol refGene,esp6500siv2_all,ljb26_all -operation g,f,f -nastring . - vcfinput /sysapps/cluster/software/annovar/1.0/humandb/ http://annovar.openbioinformatics.org/en/latest/user- guide/download/
Now we have annotated VCF file!! Variant analysis
Now what? What do you do with all these variants?
Variant Analysis… like finding a needle in a ‘deep’ haystack 66
Look for evidence of variants of interest
Further filters needed • High number of variants • The goal is to narrow down your list of variants • Eliminate variants that are not interesting 68 2 novel in chr10 In house exome dbSNPs 1000 genomes 17,687 SNPs PLoS One. 2012;7(1):e29708
Things to consider Filter based on Population frequency/Novel- variants synonymous vs non- synonymous Exonic vs intronic Genes of interest/understand the gene Clinically- relevant genes (OMIM, HGMD) Predictions (quality of variants, deleteriousness) Study phenotype (literature search)
Step2: Filter variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview • Filter by nonsynonymous mutations cat sample.filtered_snps.annotated.vcf.hg19_multianno.txt | (head -1;grep nonsynonymous) >sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynony mous.txt • Filter by population frequency < 0.01 ESP cat sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynonym ous.txt | (head -1;awk -F"t" {'if ($11<0.01){print'}}) > sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynonym ous.0.01.txt
Step2: Filter variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview • Download annovar text file to your local computer • Open from your excel • Open terminal cd Desktop sftp username@ai-submit1.niaid.nih.gov input password when prompted Go to your working directory cd /hpcdata/scratch/2016_Exome_Training/XXXX • File should now be on your desktop, open with excel get sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynonymous.0.01.txt
Step2: Filter variants Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/
Open the file in excel • Examine variants that can be potentially disease causing • Examine for rarity of the variants • Look at the prediction scores • Candidate: – 16 23456431 23456431 G->C exonic in gene COG7 – Good quality variant – SIFT score- 0.04 (considered deleterious) – Polyphen2_HDIV-0.999 (probably damaging) – CADD-27.8 (top 1% of damaging variants) • Is this gene known to be disease-related? (OMIM, ClinVar, HGMD) • Check frequency in ExAC database • Probably a good candidate to follow-up! Variant analysis
Follow-up • Do literature search!!! – THIS IS A MUST! – Time consuming, but much needed to interpret variants accordingly
Beyond SNVs
Additional Analysis • Trio/Family Based Analysis – PhaseByTransmission (GATK) – GEMINI https://gemini.readthedocs.org/en/latest/ – pVAAST • Somatic Variation – Tumor/normal – MuTech http://archive.broadinstitute.org/cancer/cga/mutect • Association Studies • Copy number variation analysis
GWAS Association studies • A typical analysis: – Identify SNPs where one allele is significantly more common in cases than controls – Hardy- Weinberg Chi Square http://en.wikipedia.org/wiki/Genome-wide_association_study http://www.ebi.ac.uk/gwas/
GWAS manhattan plot • Only applicable for large cohort
Analysis Tools for genotype-phenotype association from sequencing data • Plink-seq - http://atgu.mgh.harvard.edu/plinkseq/ • EPACTS - http://genome.sph.umich.edu/wiki/EPACTS • SNPTestv2 / GRANVIL - http://www.well.ox.ac.uk/GRANVIL/ 79
CNVs from exome • High variability of read-depth in exomes • CNV prediction is very challenging problem – High false positives – Break-points limited due to targets – Long range CNVs have higher positive predictive value • Useful when other alternatives (SNPs or aCGH) are not available • Lots of prediction tools!! (some examples below) Population Caller Somatic Caller XHMM CoNIFER EXCAVATOR ExomeDepth CONTRA ADTEx ExomeCNV Varscan2 Control-FREEC
Popular utility of exome sequencing eXome Hidden Markov Model http://atgu.mgh.harvard.edu/xhmm/tutorial.shtml
Suggestions • Request for a helix/biowulf account – Programs are already installed – You can request programs to be installed
Credit: http://omogemura.com/thank-you/
Hands-on tutorials • https://github.com/niaid/ACE/tree/master/D NASeq • Gemini: https://gist.github.com/oleraj/cd33616a29bf5 6c62c63e68c788f3d72 • Old: /hpcdata/scratch/2016_Exome_Training/com mands.sh

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Hong_Celine_ES_workshop.pptx

  • 3. Sequencers Ion torrent proton Ilumina MiSeq Ilumina HiSeq 2500 Ilumina XTen PacBio
  • 4. Sequencers Machine Cost methods Throughput per run Read length Error rate Illumina MiSeq $128K Small genomes, targeted gene 1.5-2Gb 2X300 0.8% Ion Torrent $80K Small genomes, targeted gene 1Gb 400 1.71% Illumina NextSeq $250K Exomes/transcript ome 120Gb 2x150 0.8% Illumina HiSeq $654K Genomes/exomes/ transcriptomics 600Gb 2X150 0.76% Illumina X Ten $10 Mil. Genomes 1.6Tb 2x150 0.5% PacBio $695K Genomes 100Mb 15K 12.86%
  • 5. Next-generation sequencing overview Exomes (ES/WES) Genomes (GS/WGS) Cost ~$1000 ~$2000 (~$1000 with HiSeq X Ten) Size of bam files ~10 Gb ~200 Gb DNA Targeted and captured Sheared DNA What you can get Most coding regions (+UTR) coding and non- coding Variants that can be examined SNVs, indels (CNVs) SNVs, indels, CNVs, structural variations
  • 6. • Genome has even coverage • Even a deletion is observed by eye How exomes and genomes look! WT Deletion ES GS
  • 7. All about exomes • What is exome? – Sequencing targeted exonic regions – ~2% of genome • Important to know – You will NOT get a whole exome! – Not all exons in all genes are captured! – Important to know the negative results vs no data • Coverage will vary in targets
  • 8. What to know about your data • What is the sequence depth? – The depth your sequence – 10x, 30x, 50x, 100x • Read length – How long is your read length? • What software was used to align? • What variant calling was used to call variants? • Which reference was used? • Which capture kit was used? What is covered?
  • 9. Partial list of capture enrichment kits Manufacturer Kits Regions targeted Bases covered Illumina Nextera Rapid capture Exons + UTRs+ miRNA 62 Mb Nimblegen SeqCap EZ Exome Exons + UTR 96 Mb SeqCap EZMedExome Disease-associated regions 47 Mb Agilent SureSelect Human All Exon V6 Exons+UTRs 60Mb Clinical Research Exome Disease-relevant targets 51Mb
  • 10. Overview of next-generation sequencing processing Sequence Align reads/mapping Variant calling Annotate Downstream analysis
  • 11. Popular tools Task Popular tools Align reads/mapping BWA-mem Novoalign Isaac Variant calling GATK (Broad) Platypus (Wellcome Trust) Starling (Illumina) Annotating variants AnnoVar VEP snpEff
  • 12. Popular source Task Popular source Control population frequency ExAC 1000GP ESP Annotation RefSeq Ensemble UCSC genes GENCODE Visualization UCSC genome browser IGV Clinical relevance HGMD OMIM CGD ClinVar
  • 14. GATK Best Practices Website https://www.broadinstitute.org/gatk/guide/best-practices
  • 15. Introduction to exome sequencing analysis workflow
  • 16. ES pipeline overview Sequence Alignment Quality control Variant discovery Quality control Annotate variants Analyze Pre-processing Variant discovery Analysis
  • 17. Files and tools used File type Origin FASTQ Raw reads from sequencer SAM BAM Sequence Alignment/Map Binary version of SAM gVCF/VCF Variant call format Tool Purpose BWA mem Read alignment to reference Picard Mark duplicates GATK (Haplotype caller) QC/variant calling Samtools Sort sam/bam, convert sam<->bam
  • 18. Reference genome • There are different versions of human reference genome Reference Name Chr notation Mitochondrial sequence Additional sequences included GRCh37 (Genome Reference Consortium) 1, 2… X,Y,MT Yes • Unlocalized • Unplaced • Alternate loci hg19 (UCSC genome browser) Chr1, Chr2…ChrX, ChrY, ChrM Copied from previous release • Unlocalized • Unplaced • Alternate loci b37/b37+decoy/hs37d5 (1000GP) 1, 2… X,Y,MT Yes • Unlocalized • Unplaced • “decoy” sequence • Human herpevirus 4 type 1 • Unlocalized: chromosome known, exact location unknown • Unplaced: known to originate from human genome, chromosome unknown • Alternate loci: alternate representation of specific human regions
  • 19. Index files • Index files are needed for files in next-generation analysis, as file sizes are big! • Enables program to efficiently access the data, rather than having to read the whole file File type Index FASTA *fai BAM *bai VCF *vcf.idx
  • 21. Why BWA+ GATK Haplotype caller? • Widely accepted as the “conventional” way of processing next-gen data • Well assessed • Well documented • Software is supported • Community support for troubleshooting or information
  • 22. Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, VCF (GATK GenotypeGVCFs) Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis
  • 23. FASTQ Sequence ID Sequence Sequence quality Sequence ID Bitwise flags chrosome position MappingQ CIGAR Paired-end chr Paired-end position Observed template length http://samtools.github.io/hts-specs/SAMv1.pdf BAM/SAM Align Pre-processing
  • 24. Marking duplicates and why is it necessary? • It is assumed that each read corresponds to an independent DNA fragment from randomly sheared DNA • However, PCR amplification can cause duplicates – Identify based on start + stop of reads – Choose the best and ignore the rest Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator)
  • 25. Indel realignment Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) • Misalignment around indels cause high number of SNPs • These regions are identified and locally realigned to minimize mismatches
  • 26. Recalibrate Base Quality Scores Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) • Base scores are produced by sequencers • Quality scores are inaccurate and biased – Prone to various technical errors – QS are often over- or under estimated • To identify and correct non-random technical error – Physics or the chemistry of sequencing reactions – Manufacturing flaws in the equipment • Error covariates e.g. – Reported quality score – Position within the read (machine cycle) – Preceding and current nucleotide (sequencing chemistry)
  • 27. Recalibrate Base Quality Scores Broad Institute Pre-processing QC Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Over-estimation Under-estimation • GATK BSQR builds model based on the known variants set • Adjusts the base quality scores in the data based on the model
  • 28. Now we are ready to call variants!
  • 29. Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis ✔
  • 30. Generate genotype likelihoods in each sample (gVCF) • For a single sample, calculates normalized Phred-scaled likelihoods (PL) for genotypes: • “likelihood of the genotype”= “the probability that the genotype is not correct” • Normalized so that the most likely genotype is 0 Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples)
  • 31. What is a joint genotyping? • If we analyze Sample 1 or Sample N alone, we are not confident that the variant is real • If we see both samples, we are more confident that there is real variation at this site in this cohort Broad Institute Variant Discovery
  • 32. Generate new variant quality score using VQSR Broad Institute Variant Discovery-QC Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) • What is Variant Quality Score Recalibration – NOT adjusting scores!! – Generate new score VQSLOD (variant quality score log-odds) • Approach – Machine learning to profile good variants vs bad variants – Using multiple dimensions (5-8, typically) – Uses INFO annotations for each variant (eg. Allele count, allele frequency, etc)
  • 33. Generate new variant quality score using VQSR Train a model using “truth” set of known variants Apply the model to your samples Toss Keep Variant Discovery-QC Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) http://gatkforums.broadinstitute.org/gatk/discussion/39/variant-quality-score-recalibration-vqsr
  • 34. Alternatively! IF you have few samples Variant Discovery-QC Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) http://gatkforums.broadinstitute.org/gatk/discussion/39/variant-quality-score-recalibration-vqsr • Apply hard filter! • Define how to filter your variant or use default filter parameters – QualByDepth (QD) 2.0 – FisherStrand (FS) 60.0 – RMSMappingQuality (MQ) 40.0 – MappingQualityRankSumTest (MQRankSum) -12.5 – ReadPosRankSumTest (ReadPosRankSum) - 8.0 (only het calls)
  • 36. Annotating Variants using ANNOVAR Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Variant Annotation Gene Annotation refSeq Gene, mitochondrial variants, USC/EnSembl, GENCODE/CCDS Region-based Annotation Conserved genomic elements, transcription factor binding site, cytogenetic band, segmental duplications, GWAS.. Filter-based annotation 1000 GP, dbSNP, ESP, EXAC, non-synonymous variants annotation (SIFT/Polyphen2/MutationTast er/LRT/FATHMM/CADD..), ClinVar… For comprehensive list, see http://annovar.openbioinformatics.org/en/latest/
  • 39. Question • We have fastq files from a “sample” • Are there any deleterious variants in this person? • We’ll only be looking at chromosome 16
  • 40. Go to the class folder and make your folder Copy the commands to your folder and open it for your convenience TRICK TO A COPY/PASTE! Open 2 terminal windows: One to view commands One to RUN commands (will log in interactively) Terminal 1 (to view commands) Create your folder, copy commands.sh file to your folder, and open it cd /hpcdata/scratch/2016_Exome_Training mkdir directory_name cd directory_name cp ../commands.sh . vi commands.sh Terminal 2 (To run commands) Log in interactively and go to your folder qrsh -l h_vmem=10G,mem_free=5G #to log in to interactive node cd /hpcdata/scratch/2016_Exome_Training/directory_name
  • 41. Load modules module load GATK module load FastQC module load BWA module load SAMtools module load VCFtools module load IGV module load annovar/1.0 module load BEDTools module load picard module load BCFtools
  • 42. QC on fastq/bams (bad quality)
  • 43. QC on fastq/bams (good quality)
  • 44. Step 1: QC and align a fastq file 1) run fastqc on fastq files – generates .html file with qc statistics – Generates compressed folders with images fastqc ../sample.fastq -o ./ Pre-processing
  • 45. Step 2: align fastq files • Align with BWA MEM using -M to mark secondary alignments and -R to annotate Read Groups (e.g., different samples) – ID, LB, SM, PU, and PL tags are required Pre-processing bwa mem -R "@RGtID:dadtLB:dadtSM:dadtPU:FCC1 89PACXXtPL:ILLUMINA" -M ../human_g1k_v37.fasta ../sample.fastq | gzip > ./sample.sam.gz
  • 46. Step 3: sort sam, convert to bam • Aligned reads need to be sorted java -jar ${EBROOTPICARD}/picard.jar SortSam I=sample.sam.gz O=sample.bam SO=coordinate CREATE_INDEX=true Now we have mapped and sorted reads Pre-processing samtools index sample.bam
  • 47. Step 4: Mark duplicates • Using picard to mark duplicates Pre-processing java -jar ${EBROOTPICARD}/picard.jar MarkDuplicates INPUT=sample.bam OUTPUT=sample.dedup.bam AS=true CREATE_INDEX=true M=sample.metrics.txt How many were marked as duplicates??
  • 48. Step 5: Realign indels • Create target list for potential indel sites java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T RealignerTargetCreator -R ../human_g1k_v37.fasta -I sample.dedup.bam -o sample.intervals -L ../S03723314_Regions_chr16.fix.bed Pre-processing Let’s visualize a potential indel region!
  • 49. Step 5: Realign indels Viewing potential indel site! • Use samtools tview to view bam file samtools tview sample.dedup.bam ../human_g1k_v37.fasta Pre-processing • #press "g" to prompt "Go to", type 16:46744672 and enter
  • 50. Step 5: Realign indels • Realign target list java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T RealignerTargetCreator -R ../human_g1k_v37.fasta -I dad.chr16.dedup.bam -o dad.chr16.intervals - L ../S03723314_Regions_chr16.fix.bed Pre-processing
  • 51. Step 6: Recalibrate base QS • Build a model java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T BaseRecalibrator -R ../human_g1k_v37.fasta - knownSites ../dbsnp_138.b37.vcf -knownSites ../Mills_and_1000G_gold_standard.indels.b37.vcf -I sample.realigned.bam -L ../S03723314_Regions_chr16.fix.bed -o sample.recal_report.grp Pre-processing • Recalibrate scores java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T PrintReads -R ../human_g1k_v37.fasta -I sample.realigned.bam -o sample.recal.bam - BQSR sample.recal_report.grp -L ../S03723314_Regions_chr16.fix.bed
  • 52.
  • 53. Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis ✔
  • 54. Step 1: Generate gVCFs Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T HaplotypeCaller -R ../human_g1k_v37.fasta -I sample.recal.bam -stand_call_conf 30.0 -stand_emit_conf 10.0 -o sample.g.vcf -ERC BP_RESOLUTION -L ../S03723314_Regions_chr16.fix.bed
  • 55. Step 2: genotype gVCFs Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T GenotypeGVCFs -R ../human_g1k_v37.fasta --max_alternate_alleles 2 -stand_call_conf 30 -stand_emit_conf 10 --variant sample.g.vcf -o sample.vcf
  • 56. Step 3: Recalibrate variants VQSR (>30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Build model Apply model Take the output and do indel recalibration (see commandline)
  • 57. Step 3: Apply hard filter (<30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T SelectVariants -R ../human_g1k_v37.fasta -L ../S03723314_Regions_chr16.fix.bed -V sample.vcf -selectType SNP -o sample_SNPs.vcf java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T VariantFiltration -R ../human_g1k_v37.fasta -L ../S03723314_Regions_chr16.fix.bed -V sample_SNPs.vcf --filterExpression "QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" --filterName "my_snp_filter" -o sample.filtered_SNPs.vcf • Extract SNPS • Apply filters on SNPS
  • 58. Step 3: Apply hard filter (<30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T SelectVariants -R ../human_g1k_v37.fasta -V sample.vcf -selectType INDEL -o sample.indels.vcf • Extract INDELs • Apply filters in indels java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T VariantFiltration -R ../human_g1k_v37.fasta -V sample.indels.vcf --filterExpression "QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0" --filterName "my_indel_filter" -o sample.filtered_indels.vcf
  • 59. Step 3: Apply hard filter (<30 samples) Broad Institute Variant Discovery Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) • Merge two vcf files java -jar $EBROOTGATK/GenomeAnalysisTK.jar -T CombineVariants -L ../S03723314_Regions_chr16.fix.bed --variant:indel sample.filtered_indels.vcf --variant:snps sample.filtered_SNPs.vcf -R ../human_g1k_v37.fasta -o sample.filtered_SNP_indels.vcf -genotypeMergeOptions PRIORITIZE -priority snps,indel
  • 60. Data processing Align FASTQ to the reference genome (BWA-mem) SAM/BAM file Sort mapped reads Mark duplicates (PICARD) Realign indels (GATK IndelRealigner) Recalibrate base quality scores (GATK BaseRecalibrator) Generate genotype likelihoods for EVERY base, gVCFs (GATK HaplotypeCaller) Joint genotype, raw SNP & indel VCF (GATK GenotypeGVCFs Recalibrate variants (GATK VariantRecalibrator) OR apply hard filter (<30 samples) Variant classification annotation (ANNOVAR) Variant effect annotation (ANNOVAR) Filter variants to identify candidates of interest Pre-processing Variant discovery Analysis ✔ ✔
  • 61. Step1: Annotate variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis • We will use annovar to annotate the VCF file • Left-align VCF file using bcftool bcftools norm -m-both -o trio_filtered_SNP_indels_step1.vcf trio_filtered_SNP_indels.vcf bcftools norm -f ../human_g1k_v37.fasta -o trio_filtered_SNP_indels_step2.vcf trio_filtered_SNP_indels_step1.vcf
  • 62. Step1: Annotate variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis • Gene annotation • AA change, classification of mutation • Population frequency • ESP 6500 • 1000GP • Snp anntation (SNP138) • Deleteriousness of non-synonymous variants annotation • SIFT • Polyphen • LTR • MutationTaster • FATHMM • PPROVEAN • VEST3 • CADD • DANN • Fathmm-MKL • etc http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview
  • 63. Step1: Annotate variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview table_annovar.pl sample.filtered_SNP_indels_step2.vcf -buildver hg19 -out sample.filtered_snps.annotated.vcf -remove -protocol refGene,esp6500siv2_all,ljb26_all -operation g,f,f -nastring . - vcfinput /sysapps/cluster/software/annovar/1.0/humandb/ http://annovar.openbioinformatics.org/en/latest/user- guide/download/
  • 64. Now we have annotated VCF file!! Variant analysis
  • 65. Now what? What do you do with all these variants?
  • 66. Variant Analysis… like finding a needle in a ‘deep’ haystack 66
  • 67. Look for evidence of variants of interest
  • 68. Further filters needed • High number of variants • The goal is to narrow down your list of variants • Eliminate variants that are not interesting 68 2 novel in chr10 In house exome dbSNPs 1000 genomes 17,687 SNPs PLoS One. 2012;7(1):e29708
  • 69. Things to consider Filter based on Population frequency/Novel- variants synonymous vs non- synonymous Exonic vs intronic Genes of interest/understand the gene Clinically- relevant genes (OMIM, HGMD) Predictions (quality of variants, deleteriousness) Study phenotype (literature search)
  • 70. Step2: Filter variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview • Filter by nonsynonymous mutations cat sample.filtered_snps.annotated.vcf.hg19_multianno.txt | (head -1;grep nonsynonymous) >sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynony mous.txt • Filter by population frequency < 0.01 ESP cat sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynonym ous.txt | (head -1;awk -F"t" {'if ($11<0.01){print'}}) > sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynonym ous.0.01.txt
  • 71. Step2: Filter variants Variant classification annotation (Annovar) Variant effect annotation (Annovar) Filter variants to identify candidates of interest Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#overview • Download annovar text file to your local computer • Open from your excel • Open terminal cd Desktop sftp username@ai-submit1.niaid.nih.gov input password when prompted Go to your working directory cd /hpcdata/scratch/2016_Exome_Training/XXXX • File should now be on your desktop, open with excel get sample.filtered_snps.annotated.vcf.hg19_multianno.nonsynonymous.0.01.txt
  • 72. Step2: Filter variants Variant analysis http://annovar.openbioinformatics.org/en/latest/user-guide/filter/
  • 73. Open the file in excel • Examine variants that can be potentially disease causing • Examine for rarity of the variants • Look at the prediction scores • Candidate: – 16 23456431 23456431 G->C exonic in gene COG7 – Good quality variant – SIFT score- 0.04 (considered deleterious) – Polyphen2_HDIV-0.999 (probably damaging) – CADD-27.8 (top 1% of damaging variants) • Is this gene known to be disease-related? (OMIM, ClinVar, HGMD) • Check frequency in ExAC database • Probably a good candidate to follow-up! Variant analysis
  • 74. Follow-up • Do literature search!!! – THIS IS A MUST! – Time consuming, but much needed to interpret variants accordingly
  • 76. Additional Analysis • Trio/Family Based Analysis – PhaseByTransmission (GATK) – GEMINI https://gemini.readthedocs.org/en/latest/ – pVAAST • Somatic Variation – Tumor/normal – MuTech http://archive.broadinstitute.org/cancer/cga/mutect • Association Studies • Copy number variation analysis
  • 77. GWAS Association studies • A typical analysis: – Identify SNPs where one allele is significantly more common in cases than controls – Hardy- Weinberg Chi Square http://en.wikipedia.org/wiki/Genome-wide_association_study http://www.ebi.ac.uk/gwas/
  • 78. GWAS manhattan plot • Only applicable for large cohort
  • 79. Analysis Tools for genotype-phenotype association from sequencing data • Plink-seq - http://atgu.mgh.harvard.edu/plinkseq/ • EPACTS - http://genome.sph.umich.edu/wiki/EPACTS • SNPTestv2 / GRANVIL - http://www.well.ox.ac.uk/GRANVIL/ 79
  • 80. CNVs from exome • High variability of read-depth in exomes • CNV prediction is very challenging problem – High false positives – Break-points limited due to targets – Long range CNVs have higher positive predictive value • Useful when other alternatives (SNPs or aCGH) are not available • Lots of prediction tools!! (some examples below) Population Caller Somatic Caller XHMM CoNIFER EXCAVATOR ExomeDepth CONTRA ADTEx ExomeCNV Varscan2 Control-FREEC
  • 81. Popular utility of exome sequencing eXome Hidden Markov Model http://atgu.mgh.harvard.edu/xhmm/tutorial.shtml
  • 82. Suggestions • Request for a helix/biowulf account – Programs are already installed – You can request programs to be installed
  • 84. Hands-on tutorials • https://github.com/niaid/ACE/tree/master/D NASeq • Gemini: https://gist.github.com/oleraj/cd33616a29bf5 6c62c63e68c788f3d72 • Old: /hpcdata/scratch/2016_Exome_Training/com mands.sh

Notes de l'éditeur

  1. -finding CNVs from exome is an additional information,
  2. Part 1. Double-stranded genomic DNA is fragmented by sonication. Linkers are then attached to the DNA fragments, which are then hybridized to a capture microarray designed to target only the exons. Part 2. Target exons are enriched, eluted and then amplified by ligation-mediated PCR. Amplified target DNA is then ready for high-throughput sequencing.
  3. -done in 2 steps 1)Determining (small) suspicious intervals which are likely in need of realignment (see the RealignerTargetCreator tool) 2)Running the realigner over those intervals (IndelRealigner)
  4. Empirical quality based on known variants from publicly available databases
  5. This is especially useful for low-quality calls, Identify false negatives because: 1) present as known variant in population, and 2) present among other individuals in cohort. Would have missed them otherwise because not the best quality. Identify false positives because absent from population and cohort – remove because borderline quality. (If high quality, keep it – rare variants)
  6. QualByDepth (QD) 2.0: This is the variant confidence (from the QUAL field) divided by the unfiltered depth of non-reference samples. FisherStrand (FS) 60.0 :Phred-scaled p-value using Fisher’s Exact Test to detect strand bias (the variation being seen on only the forward or only the reverse strand) in the reads. More bias is indicative of false positive calls. RMSMappingQuality (MQ) 40.0 :This is the Root Mean Square of the mapping quality of the reads across all samples. MappingQualityRankSumTest (MQRankSum) -12.5 :This is the u-based z-approximation from the Mann-Whitney Rank Sum Test for mapping qualities (reads with ref bases vs. those with the alternate allele). Note that the mapping quality rank sum test can not be calculated for sites without a mixture of reads showing both the reference and alternate alleles, i.e. this will only be applied to heterozygous calls. ReadPosRankSumTest (ReadPosRankSum) -8.0: This is the u-based z-approximation from the Mann-Whitney Rank Sum Test for the distance from the end of the read for reads with the alternate allele. If the alternate allele is only seen near the ends of reads, this is indicative of error. Note that the read position rank sum test can not be calculated for sites without a mixture of reads showing both the reference and alternate alleles, i.e. this will only be applied to heterozygous calls.
  7. “Trick to copy/paste” In terminals, students can use their mouse to shortcut their copy/paste, highlight with their mouse and use the middle button to paste (without having to copy/paste). Very convenient to copy/paste
  8. -if you have family data or case/control, you can further eliminate variants
  9. Exclude SNPs in Hardy-Weinberg disequilibrium
  10. Also VarB - http://bioinformatics.oxfordjournals.org/content/early/2012/09/13/bioinformatics.bts557.full.pdf
  11. -top figure presents a clear CNV candidate that’s a good quality -bottom figure presents a noisy area, where CNV prediction can be challenging