Genomic Protocols

Genomic data includes whole-genome resequencing data from the HudsonAlpha Institute for Biotechnology, Alabama for 384 samples for accessions from the sorghum Bioenergy Association Panel (BAP) and genotyping-by-sequencing (GBS) data from Kansas State University for 768 samples from a population of sorghum recombinant inbred lines (RIL).

Danforth Center genomics pipeline

Outlined below are the steps taken to create a raw vcf file from paired end raw FASTQ files. This was done for each sequenced accession so a HTCondor DAG Workflow was written to streamline the processing of those ~200 accessions. While some cpu and memory parameters have been included within the example steps below those parameters varied from sample to sample and the workflow has been honed to accomodate that variation. This pipeline is subject to modification based on software updates and changes to software best practices.

Software versions:

• BBDuk2 version 36.67

• bwa v 0.7.12-r1039

• samtools v 1.3.1

• picard-tools-2.0.1

• GATK v3.5-0-g36282e4

• VCFtools (0.1.14)

• Tassel Version: 5.2.27

Preparing reference genome

Download Sorghum bicolor v3.1 from Phytozome

Generate:

BWA index:

bwa index –a bwtsw Sbicolor_313_v3.0.fa

fasta file index:

samtools faidx Sbicolor_313_v3.0.fa

Sequence dictionary:

java –jar picard.jar CreateSequenceDictionary R=Sbicolor_313_v3.0.fa O=Sbicolor_313_v3.0.dict

Quality trimming and filtering of paired end reads

bbduk2 in=SampleA_R1.fastq in2=SampleA_R2.fastq out=SampleA_R1.PE.fastq.gz \
  out2=SampleA_R2.PE.fastq.gz k=23 mink=11 hdist=1 tpe tbo qtrim=rl trimq=20 \
  minlen=20 rref=adapters_file.fa lref=adapters_file.fa

Aligning reads to the reference

bwa mem –M \
  –R “@RG\tIDSAMPLEA_RG1\tPL:illumina\tPU:FLOWCELL_BARCODE.LANE.SAMPLE_BARCODE_RG_UNIT\tLB:libraryprep-lib1\tSM:SAMPLEA” \
  Sbicolor_313_v3.0.fa SampleA_R1.PE.fastq.gz SampleA_R2.PE.fastq.gz > SAMPLEA.bwa.sam

Convert and Sort bam

Samtools view –bS SAMPLEA.bwa.sam | samtools sort - SAMPLEA.bwa.sorted

Mark Duplicates

java –Xmx8g –jar picard.jar MarkDuplicates MAX_FILE_HANDLES_FOR_READ_ENDS_MAP=1000 \
  REMOVE_DUPLICATES=true INPUT=SAMPLEA.bwa.sorted.bam OUTPUT=SAMPLEA.dedup.bam \
  METRICS_FILES=SAMPLEA.dedup.metrics

Index bam files

samtools index SAMPLEA.dedup.bam

Find intervals to analyze

java –Xmx8g –jar GenomeAnalysisTK.jar –T RealignerTargetCreator \
  –R Sbicolor_313_v3.0.fa –I SAMPLEA.dedup.bam –o SAMPLEA.realignment.intervals

Realign

java –Xmx8g –jar GenomeAnalysisTK.jar –T IndelRealigner –R Sbicolor_313_v3.0.fa \
  –I SAMPLEA.dedup.bam –targetIntervals SAMPLEA.realignment.intervals –o SAMPLEA.dedup.realigned.bam

Variant Calling with GATK HaplotypeCaller

java –Xmx8g –jar GenomeAnalysisTK.jar –T HaplotypeCaller –R Sbicolor_313_v3.0.fa \
  –I SAMPLEA.dedup.realigned.bam --emitRefConfidence GVCF --pcr_indel_model NONE \
  -o SAMPLEA.output.raw.snps.indels.g.vcf

Above this point is the workflow for the creation of the gVCF files for this project. The following additional steps were used to create the Hapmap file

Combining gVCFs with GATK CombineGVCFs

NOTE: This project has 363 gvcfs: multiple instances of CombineGVCFs, with unique subsets of gvcf files, were run in parallel to speed up this step below are examples

java –Xmx8g –jar GenomeAnalysisTK.jar –T CombineGVCFs –R Sbicolor_313_v3.0.fa \
  -V SAMPLEA.output.raw.snps.indels.g.vcf --variant SAMPLEB.output.raw.snps.indels.g.vcf\
  -V SAMPLEC.output.raw.snps.indels.g.vcf -o SamplesABC_combined_gvcfs.vcf

java –Xmx8g –jar GenomeAnalysisTK.jar –T CombineGVCFs –R Sbicolor_313_v3.0.fa \
  --variant SAMPLED.output.raw.snps.indels.g.vcf -V SAMPLEE.output.raw.snps.indels.g.vcf \
  -V SAMPLEF.output.raw.snps.indels.g.vcf -o SamplesDEF_combined_gvcfs.vcf

Joint genotyping on gVCF files with GATK GenotypeGVCFs

java –Xmx8g –jar GenomeAnalysisTK.jar –T GenotypeGVCFs –R Sbicolor_313_v3.0.fa \
  -V SamplesABC_combined_gvcfs.vcf -V SamplesDEF_combined_gvcfs.vcf -o all_combined_Genotyped_lines.vcf

Applying hard SNP filters with GATK VariantFiltration

java –Xmx8g –jar GenomeAnalysisTK.jar –T VariantFiltration –R Sbicolor_313_v3.0.fa \
  -V all_combined_Genotyped_lines.vcf -o all_combined_Genotyped_lines_filtered.vcf \
  --filterExpression "QD < 2.0" --filterName "QD" --filterExpression "FS > 60.0" \
  --filterName "FS" --filterExpression "MQ < 40.0" --filterName "MQ" --filterExpression "MQRankSum < -12.5" \
  --filterName "MQRankSum" --filterExpression "ReadPosRankSum < -8.0" --filterName "ReadPosRankSum"

Filter and recode VCF with VCFtools

vcftools --vcf all_combined_Genotyped_lines_filtered.vcf --min-alleles 2 --max-alleles 2 \
  --out all_combined_Genotyped_lines_vcftools.filtered.recode.vcf --max-missing 0.2 --recode

Adapt VCF for use with Tassel5

tassel-5-standalone/run_pipeline.pl -Xms75G -Xmx265G -SortGenotypeFilePlugin \
  -inputFile all_combined_Genotyped_lines_vcftools.filtered.recode.vcf \
  -outFile all_combined_Genotyped_lines_vcftools.filtered.recode.sorted.vcf -fileType VCF

Convert VCF to Hapmap with Tassel5

tassel-5-standalone/run_pipeline.pl -Xms75G -Xmx290G -fork1 -vcf \
  all_combined_Genotyped_lines_vcftools.filtered.recode.sorted.vcf -export -exportType Hapmap -runfork1

CoGe genomics pipeline

CoGe has integrated the tools that make up the Danforth Center’s variant calling pipeline into their easy point and click GUI, allowing users to reproduce a majority of the TERRA SNP analysis. Below, we detail how to run sequence data through CoGe’s system.

• Goto https://genomevolution.org/coge/ or click create an account to get started.

• If this is your initial attempt, you will need to create a Genome.

  1. Under Tools, click Load Genome or use this link.

• Under Tools, click Load Experiment or use this link.

• Select Data: to use the TERRA data click Community Data or choose from CoGe’s other data options.

• Select Options: This outlines CoGe’s choices for data processing and analysis. To reproduce pipeline used to create the TERRA SNPs, you can reference the exact tools and parameters used in the Danforth analysis above and enter the appropriate values into their equivalent drop downs or fields.

For the TERRA SNP the following were used:

FASTQ Format

• Read Type: Paired-end

• Encoding: phred33

Trimming

• Trimmer: BBDuk

• BBDuk parameters: k=23, mink=11, hdist=1, check mark both tpe and tbo, qtrim=rl, trimq=20, minlen=20, set trim adapters to both ends

Alignment

• Aligner: BWA-MEM

• BWA-MEM parameters: check mark -M, fill in Read Groups ID (identifier), PL (sequence platform), LB (library prep), SM (sample name)

SNP Analysis

• Check mark Enable which expands this section

• Method: GATK HaplotypeCaller (single-sample GVCF) using the default parameters but you can choose to use Realign reads around INDELS

General Options

• Checkmark both options to add results to your notebook and receive an email when pipeline has completed.

Describe Experiment: Enter an experiment name (required), your data processing version ie 1 for first time, Source if using TERRA Data, it’s TERRA (required), and Genome (required and if you start typing it will find your loaded genome but be sure to verify version and id .)