Difference between revisions of "UGP Variant Pipeline 1.0.3"

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=== ReduceReads ===
 
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java -Xmx10g -XX:ParallelGCThreads=10 -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
 
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  -T ReduceReads
 
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  -I Sample1.recal.bam
 
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  -o Sample1.reduced.bam
 
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  -R human_g1k_v37_decoy.fasta
 
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  2> ReduceReads.report
 
  
 
== Variant Calling ==
 
== Variant Calling ==

Revision as of 22:07, 30 May 2014

Utah Genome Project

May. 2014 
Variant Calling Pipeline  Version 1.0.3

Software Versions

    • GenomeAnalysisTK-3.3-1
    • Picard : Version: 1.112
    • FastQC v0.10.1
    • Samtools Version: 0.1.19
    • BWA Version: 0.7.5
    • cApTUrE 1.0.3

Data Source

Data sets used for the variant calling pipeline come from the Broad GSA (GATK) group as the 'GATK resource bundle 2.5' version 2.8 

wget -r ftp://gsapubftp-anonymous@ftp.broadinstitute.org/bundle/2.5/b37
  • Reference Genome (GRCh37):
    • human_g1k_v37_decoy.fasta
  • VCF files for RealignerTargetCreator knowns and dbsnp for BaseRecalibrator.
    • known_indel: /data/GATK_Bundle/Mills_and_1000G_gold_standard.indels.b37.vcf
    • known_indel: /data/GATK_Bundle/1000G_phase1.indels.b37.vcf
    • known_dbsnp: /data/GATK_Bundle/dbsnp_137.b37.vcf
  • 1000Genomes Background BAMS ran concurrently with HaplotypeCaller
    • CEU
    • GBR
    • FIN
  • Resource files for VariantRecalibrator_SNP
    • hapmap_3.3.b37.vcf
    • 1000G_omni2.5.b37.vcf
    • 1000G_phase1.snps.high_confidence.b37.vcf
  • Resource files for VariantRecalibrator_INDEL
    • Mills_and_1000G_gold_standard.indels.b37.vcf
    • 1000G_phase1.indels.b37.vcf

Sequencing

This pipeline is designed for 100 bp (or greater) Illumina HiSeq PE exome or WGS sequence data with Sanger/Illumina 1.9 quality encoding, and uses Illumina naming convention found here [1]

Validate File Integrity with md5sum

An md5sum signature should be provided for each FastQ file by the sequencing center. After the file has been downloaded, locally check the md5sum to be sure that no data corruption occurred during the file transfer. 

md5sum file.fastq > file_local.md5
diff file_local.md5 file_provided.md5

If the md5sum signature differs from that provided for the file:

  • Check to be sure you have the correct file.
  • Check if the md5sum was calculated on that compressed or uncompressed file by the provider and be sure to do the same with the local copy.
  • Try the download again.
  • Contact the sequence provider.

FastQ File Analyses

fastqc Sample1_L1_R1.txt

From the fastqc_data.txt file we check the following values:

  • Encoding (must be Sanger / Illumina 1.9)
  • Total Sequences (Need to develop a acceptable range)
  • Filtered Sequences (Should be 0 or at least very low)
  • Sequence length (must be >= 100 bp)
  • %GC (should be 45 < x < 55)
  • Total Duplicate Percentage (This value may not be valuable - an acceptable range has not been determined).

Indexing

The following indexing is required using BWA, Picard and SamTools. GATK requires all three. However this step only needs to be done once "per-machine".

  • BWA
bwa index -a bwtsw human_g1k_v37_decoy.fasta
  • Picard
java -jar CreateSequenceDictionary.jar R=human_g1k_v37_decoy.fasta O=human_g1k_v37_decoy.dic
  • SamTools
samtools faidx human_g1k_v37_decoy.fasta

Alignment

Align reads to the genome with bwa.

The 'BWA-mem' program will find the reference coordinates of the input reads (independent of their mate-pair). The following parameters are those used by the 1KG project and GATK for aligning Illumina data. 

bwa mem -M -R "read group" human_g1k_v37_decoy.fasta Sample1_L1_R1.fq Sample1_L1_R2.fq | samtools view -bSho BAM_FILE -

BAM File Analyses

Alignment BAM files are improved in various ways to help increase the quality and speed of subsequent variant calling steps.

Merge lane level BAMs to individual

  • This step will only need to run if you have multiple lane per sample.
java -Xmx10g -XX:ParallelGCThreads=10 -Djava.io.tmpdir=/tmp MergeSamFiles.jar
    INPUT=[Lane 1 bam file]
    INPUT=[Lane 2 bam file]
    INPUT=[ ... ]
    OUTPUT=Sample1.bam                          
    VALIDATION_STRINGENCY: LENIENT
    MAX_RECORDS_IN_RAM: 5000000
    CREATE_INDEX: True
    SORT_ORDER: coordinate
    ASSUME_SORTED: True 
    USE_THREADING: True
    2> MergeSamFiles.report

Mark Duplicates

Remove PCR/Optical duplicate reads 

java -Xmx10g -XX:ParallelGCThreads=10 -Djava.io.tmpdir=/tmp MarkDuplicates.jar
   INPUT=[bam files]
   OUTPUT=[dedup bam files]
   METRICS_FILE=lane1_dup_metrics.txt  
   VALIDATION_STRINGENCY: LENIENT
   MAX_RECORDS_IN_RAM: 5000000
   CREATE_INDEX: True
   ASSUME_SORTED: True       
   2> MarkDuplicates.log

Develop range for duplicate levels here

Local Realignment of Indels

  • RealignerTargetCreator
java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar                            
   -T RealignerTargetCreator
   -I [bam files]
   -R human_g1k_v37_decoy.fasta                              
   -known /data/GATK_Bundle/Mills_and_1000G_gold_standard.indels.b37.vcf
   -known /data/GATK_Bundle/1000G_phase1.indels.b37.vcf
   -o realign.intervals
   -nt 24                                              
   &> RealignerTargetCreator.log
  • IndelRealigner
java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
   -T IndelRealigner 
   -R human_g1k_v37_decoy.fasta
   -I [bam files]
   -targetIntervals  realign.intervals
   -known Mills_and_1000G_gold_standard.indels.b37.vcf
   -known 1000G_phase1.indels.b37.vcf
  -o [dedup_realign bam files]

Base Quality Score Recalibration & PrintReads

  • BaseRecalibrator
java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T BaseRecalibrator
  -I [bam files]
  -R human_g1k_v37_decoy.fasta
  -knownSites /data/GATK_Bundle/dbsnp_137.b37.vcf
  -o [sorted_Dedup_realign_recal_data.table files]
  &> GATK_BaseRecalibrator.log
  • PrintReads
java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T PrintReads                       
  -I Sample1.realign.bam              
  -o Sample1.recal.bam                
  -R human_g1k_v37_decoy.fasta              
  -BQSR recalibration_report.grp

BAM Quality Control


CollectAlignmentSummaryMetrics 

java -Xmx10g -XX:ParallelGCThreads=10 -Djava.io.tmpdir=/tmp CollectAlignmentSummaryMetrics.jar
   I=[sorted_Dedup_realign_recal.bam files]
   O=[_sorted_Dedup_realign_recal.metrics files]
   PROGRAM=QualityScoreDistribution
   REFERENCE_SEQUENCE=human_g1k_v37_decoy.fasta
   &> Picard_CollectMultipleMetrics.log

Variant Calling

UnifiedGenotyper

SNP calling 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T UnifiedGenotyper                  
  -I reduced_bam.list
  -I 1000G background_bam.list
  -o Sample1.raw.vcf                  
  -R human_g1k_v37_decoy.fasta
  -nt 4
  -nct 6
  -stand_call_conf 30.0
  -stand_emit_conf 30.0
  -glm SNP   
  -out_mode EMIT_VARIANTS_ONLY
  -L NCBI Ref_GRCh37 region.bed 
  2> UnifiedGenotyper.report

INDEL calling 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T UnifiedGenotyper                  
  -I reduced_bam.list
  -I 1000G background_bam.list
  -o Sample1.raw.vcf                  
  -R human_g1k_v37_decoy.fasta
  -nt 4
  -nct 6
  -stand_call_conf 30.0
  -stand_emit_conf 30.0
  -glm INDEL   
  -out_mode EMIT_VARIANTS_ONLY
  -L NCBI Ref_GRCh37 region.bed 
  2> UnifiedGenotyper.report

VariantRecalibrator

SNP Recalibration 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T VariantRecalibrator
  -R human_g1k_v37_decoy.fasta
  -input Sample1.raw.vcf
  -nt 25
  -resource: hapmap,known=false,training=true,truth=true,prior=15.0
  -resource: omni,known=false,training=true,truth=true,prior=12.0
  -resource: 1000G,known=false,training=true,truth=false,prior=10.0
  -use_annotation: QD
  -use_annotation: HaplotypeScore
  -use_annotation: MQRankSum
  -use_annotation: ReadPosRankSum
  -use_annotation: FS
  -numBadVariants: 5000
  -mode SNP                           
  -recalFile Sample1_SNP.recal            
  -tranchesFile Sample1_SNP.tranches      
  -rscriptFile Sample1_SNP.plots.R       
  2> VariantRecalibrator_SNP.report

INDEL Recalibration 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T VariantRecalibrator
  -R human_g1k_v37_decoy.fasta
  -input Sample1.raw.vcf
  -nt 25
  -resource: mills,known=false,training=true,truth=true,prior=12.0
  -resource: 1000G,known=false,training=true,truth=true,prior=10.0
  -use_annotation: MQRankSum
  -use_annotation: ReadPosRankSum
  -use_annotation: FS
  -numBadVariants: 5000
  -mode INDEL
  -recalFile Sample1_INDEL.recal            
  -tranchesFile Sample1_INDEL.tranches      
  -rscriptFile Sample1_INDEL.plots.R       
  2> VariantRecalibrator_INDEL.report

ApplyRecalibration

SNP Apply 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T ApplyRecalibration
  -input Sample1_raw_SNP.vcf
  -o Sample1_vqsr_SNP.vcf
  -R human_g1k_v37_decoy.fasta
  -nt 25
  -ts_filter_level: 99.0
  -excludeFiltered : TRUE
  -tranchesFile Sample1.tranches
  -recalFile Sample1.recal
  -mode SNP 
  2> ApplyRecalibration_SNP.report

SNP INDEL 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T ApplyRecalibration
  -input Sample1_raw_INDEL.vcf
  -o Sample1_vqsr_INDEL.vcf
  -R human_g1k_v37_decoy.fasta
  -nt 25
  -ts_filter_level: 99.0
  -excludeFiltered : TRUE
  -tranchesFile Sample1.tranches
  -recalFile Sample1.recal
  -mode INDEL 
  2> ApplyRecalibration_INDEL.report

SelectVariants

These commands will remove the background files and output SNP and INDEL files, then combine them into a single VCF file.

Select SNP 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T SelectVariants
  -R human_g1k_v37_decoy.fasta
  --variant SNP_variant 
  -o output_SNP.file 
  2> SelectVariants_SNP.report

Select INDEL 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T SelectVariants
  -R human_g1k_v37_decoy.fasta
  --variant INDEL_variant 
  -o output_INDEL.file 
  2> SelectVariants_INDEL.report

CombineVarients 

java -Xmx10g -Djava.io.tmpdir=/tmp GenomeAnalysisTK.jar
  -T CombineVariants
  -R human_g1k_v37_decoy.fasta
  --variant INDEL_variant 
  --variant  output_INDEL.file
  --variant output_SNP.file
  -o Final.vcf 
  2> CombineVarients.report

Variant File QC

Quality Metrics on variants

  • Ti/Tv Ratio (2.1 for WGS ~2.8 for exome)
  • HapMap concordance
  • SNV/Indel Counts
  • Rare variant enrichment
  • DP
  • Q
  • GQ
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