RNAseq SNV Calling Workflow (beta)

This repo contains snv calling methods using Broad GATK best practices. It also contains a very basic VEP annotation tool and fusion calling tools used by the Kids First Data Resource Center. The results are available on Cavatica from the Data Delivery Project. Please contact Deanne Taylor or Chris Mason for access. Additional unused, but possibly useful RNAseq SNV workflows are appended at the end

For all workflows, input bams should be indexed beforehand. This tool is provided in tools/samtools_index.cwl

GATK4 v4.1.7.0

The overall workflow picks up from post-STAR alignment, starting at mark duplicates. For the most part, tool parameters follow defaults from the GATK Best Practices WDL, written in cwl with added optimization for use on the Cavatica platform. A mild warning, sambamba is used in this workflow to mark dulicates for speed and efficiency instead of picard. Behavior should be the same except for markig optical duplicates. workflows/d3b_gatk_rnaseq_snv_wf.cwl is the wrapper cwl used to run all tools for GATK4. Run time (n=732) COV-IRT dataset, ~3 hours, cost on cavatica ~$1.15 per sample

Inputs

inputs:
  output_basename: string
  scatter_ct: {type: int?, doc: "Number of interval lists to split into", default: 50}
  STAR_sorted_genomic_bam: {type: File, doc: "STAR sorted alignment bam"}
  reference_fasta: {type: File, secondaryFiles: ['^.dict', '.fai'], doc: "Reference genome used"}
  reference_dict: File
  call_bed_file: {type: File, doc: "BED or GTF intervals to make calls"}
  exome_flag: {type: string?, default: "Y", doc: "Whether to run in exome mode for callers. Should be Y or leave blank as default is Y. Only make N if you are certain"}
  knownsites: {type: 'File[]', doc: "Population vcfs, based on Broad best practices"}
  dbsnp_vcf: {type: File, secondaryFiles: ['.idx']}
  tool_name: {type: string, doc: "description of tool that generated data, i.e. gatk_haplotypecaller"}
  mode: {type: ['null', {type: enum, name: select_vars_mode, symbols: ["gatk", "grep"]}], doc: "Choose 'gatk' for SelectVariants tool, or 'grep' for grep expression", default: "gatk"}

Outputs

outputs:
  filtered_hc_vcf: {type: File, outputSource: gatk_filter_vcf/filtered_vcf, doc: "Haplotype called vcf with Broad-recommended FILTER values added"}
  pass_vcf: {type: File, outputSource: gatk_pass_vcf/pass_vcf, doc: "Filtered vcf selected for PASS variants"}
  anaylsis_ready_bam: {type: File, outputSource: gatk_applybqsr/recalibrated_bam, doc: "Duplicate marked, Split N trimmed CIGAR BAM, BQSR recalibratede, ready for RNAseq calling"}
  bqsr_table: {type: File, outputSource: gatk_baserecalibrator/output, doc: "BQSR table"}

Docker Pulls

kfdrc/sambamba:0.7.1
kfdrc/gatk:4.1.7.0R

GATK4 RNAseq SNV Workflow Diagram

GATK4 simulated bash calls

Step	Type	Num scatter	Command
bedtools_gtf_to_bed	run step	NA	/bin/bash -c set -eo pipefail
bedtools_gtf_to_bed	run step	NA
bedtools_gtf_to_bed	run step	NA	cat /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.gtf
preprocess_rnaseq_bam_sambamba_md_sorted	run step	NA	/bin/bash -c set -eo pipefail
preprocess_rnaseq_bam_sambamba_md_sorted	run step	NA	mkdir TMP
preprocess_rnaseq_bam_sambamba_md_sorted	run step	NA	sambamba markdup --tmpdir TMP -t 4 /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/02-AlignedData/COVSUBJ_0048_1_N_HA_all-reads/COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.bam COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.bam
preprocess_rnaseq_bam_sambamba_md_sorted	run step	NA	mv COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.bam.bai COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.bai
gatk_intervallisttools	run step	NA	/bin/bash -c set -eo pipefail
gatk_intervallisttools	run step	NA	/gatk BedToIntervalList -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/bedtools_gtf_to_bed/ENS100_MN908947.3.dna.primary_assembly.gtf.bed -O ENS100_MN908947.3.dna.primary_assembly.gtf.interval_list -SD /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.dict; LIST=ENS100_MN908947.3.dna.primary_assembly.gtf.interval_list;BANDS=0;
gatk_intervallisttools	run step	NA	/gatk IntervalListTools --java-options "-Xmx2000m" --SCATTER_COUNT=50 --SUBDIVISION_MODE=BALANCING_WITHOUT_INTERVAL_SUBDIVISION_WITH_OVERFLOW --UNIQUE=true --SORT=true --BREAK_BANDS_AT_MULTIPLES_OF=$BANDS --INPUT=$LIST --OUTPUT=.;CT=`find . -name 'temp_0*'
preprocess_rnaseq_bam_gatk_splitntrim	run step	NA	/gatk SplitNCigarReads --java-options "-Xmx30G -XX:+PrintFlagsFinal -Xloggc:gc_log.log -XX:GCTimeLimit=50 -XX:GCHeapFreeLimit=10" --seconds-between-progress-updates 30 -R /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.fa -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/preprocess_rnaseq_bam_sambamba_md_sorted/COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.bam -OBI -O COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.bam
gatk_baserecalibrator	run step	NA	/gatk BaseRecalibrator --java-options "-Xmx7500m -XX:GCTimeLimit=50 -XX:GCHeapFreeLimit=10 -XX:+PrintFlagsFinal -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps -XX:+PrintGCDetails -Xloggc:gc_log.log" -R /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.fa -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/preprocess_rnaseq_bam_gatk_splitntrim/COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.bam --use-original-qualities -O COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.recal_data.csv --known-sites /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/GATK4_VCF_REFS/Homo_sapiens_assembly38.ens100.known_indels.vcf.gz --known-sites /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/GATK4_VCF_REFS/Mills_and_1000G_gold_standard.indels.hg38.ens100.vcf.gz
gatk_applybqsr	run step	NA	/gatk ApplyBQSR --java-options "-Xms3000m -Xmx7500m -XX:+PrintFlagsFinal -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps -XX:+PrintGCDetails -Xloggc:gc_log.log -XX:GCTimeLimit=50 -XX:GCHeapFreeLimit=10" --create-output-bam-md5 --add-output-sam-program-record -R /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.fa -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/preprocess_rnaseq_bam_gatk_splitntrim/COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.bam --use-original-qualities -O COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.aligned.duplicates_marked.recalibrated.bam -bqsr /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_baserecalibrator/COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.recal_data.csv
gatk_haplotype_rnaseq	scatter	50	/gatk HaplotypeCaller -R /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.fa -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_applybqsr/COVSUBJ_0048_1_N_HA_all-reads_Aligned.sortedByCoord_sorted.out.md.splitn.aligned.duplicates_marked.recalibrated.bam --standard-min-confidence-threshold-for-calling 20 -dont-use-soft-clipped-bases -L /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_intervallisttools/scattered.interval_list.0039.bed -O 00210a5f-77ec-4d07-9b1d-c08e5497e24c.gatk.hc.called.vcf.gz --dbsnp /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/GATK4_VCF_REFS/dbSNP_v153_ens.vcf.gz
merge_hc_vcf	run step	NA	/gatk MergeVcfs --java-options "-Xmx2000m" --TMP_DIR=./TMP --CREATE_INDEX=true --SEQUENCE_DICTIONARY=/sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.dict --OUTPUT=00210a5f-77ec-4d07-9b1d-c08e5497e24c.STAR_GATK4.merged.vcf.gz -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_haplotype_rnaseq_1_s/00210a5f-77ec-4d07-9b1d-c08e5497e24c.gatk.hc.called.vcf.gz -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_haplotype_rnaseq_2_s/00210a5f-77ec-4d07-9b1d-c08e5497e24c.gatk.hc.called.vcf.gz -I /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_haplotype_rnaseq_3_s/00210a5f-77ec-4d07-9b1d-c08e5497e24c.gatk.hc.called.vcf.gz
gatk_filter_vcf	run step	NA	/gatk VariantFiltration -R /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.fa -V /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/merge_hc_vcf/00210a5f-77ec-4d07-9b1d-c08e5497e24c.STAR_GATK4.merged.vcf.gz --window 35 --cluster 3 --filter-name "FS" --filter "FS > 30.0" --filter-name "QD" --filter "QD < 2.0" -O 00210a5f-77ec-4d07-9b1d-c08e5497e24c.gatk.hc.filtered.vcf.gz
gatk_pass_vcf	run step	NA	/bin/bash -c set -eo pipefail
gatk_pass_vcf	run step	NA	/gatk SelectVariants --java-options "-Xmx7500m" -V /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/00210a5f-77ec-4d07-9b1d-c08e5497e24c/gatk_filter_vcf/00210a5f-77ec-4d07-9b1d-c08e5497e24c.gatk.hc.filtered.vcf.gz -O 00210a5f-77ec-4d07-9b1d-c08e5497e24c.STAR_GATK4.PASS.vcf.gz --exclude-filtered TRUE

Variant Effect Predictor

Variant Effect Predictor is an ENSEMBL tool for annotating variants. The tool built for this repo has very basic and rigid functionality, but can be run on any of the vcf outputs from the worfklows. tools/variant_effect_predictor.cwl. Run time (n=782) COV-IRT dataset, ~6 minutes, cost on cavatica ~$0.22 per sample

VEP simulated bash calls

Step	Type	Num scatter	Command
vep-1oo-annotate	run step	NA	/bin/bash -c set -eo pipefail
vep-1oo-annotate	run step	NA	tar -xzf /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/homo_sapiens_merged_vep_100_GRCh38.tar.gz
vep-1oo-annotate	run step	NA	perl /ensembl-vep/vep --cache --dir_cache $PWD --cache_version 100 --vcf --symbol --merged --canonical --variant_class --offline --ccds --uniprot --protein --numbers --hgvs --hgvsg --fork 14 --sift b --vcf_info_field ANN -i /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/3c3c570b-cd69-4106-b169-ffa65411ec5a.STAR_GATK4.PASS.vcf.gz -o STDOUT --stats_file 9aeaeaf3-0e59-4c92-a709-c6bd37431294_stats.txt --stats_text --warning_file 9aeaeaf3-0e59-4c92-a709-c6bd37431294_warnings.txt --allele_number --dont_skip --allow_non_variant --fasta /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/genome_files/Homo_sapiens_ensembl_release100/Homo_sapiens.GRCh38.dna.primary_assembly_mainchrs_and_SARS-CoV-2-NC_045512.2.fa

inputs:
  reference: {type: File,  secondaryFiles: [.fai], label: Fasta genome assembly with index}
  input_vcf:
    type: File
    secondaryFiles: [.tbi]
  output_basename: string
  merged_cache: {type: boolean, doc: "If merged cache being used", default: true}
  tool_name: {type: string, doc: "Name of tool used to generate calls"}
  cache: {type: File, label: tar gzipped cache from ensembl/local converted cache}

outputs:
  output_vcf:
    type: File
    outputBinding:
      glob: '*.vcf.gz'
    secondaryFiles: [.tbi]
  output_txt:
    type: File
    outputBinding:
      glob: '*_stats.txt'
  warn_txt:
    type: ["null", File]
    outputBinding:
      glob: '*_warnings.txt'

Fusion Calls

STAR-Fusion v1.9.0

Tool built to run the STAR-Fusion caller. tools/star_fusion_covirt.cwl is the tool that runs this software. Run time per sample (n=732) ~9 minutes, cost on cavatica ~$0.10

inputs:
  Chimeric_junction: {type: File, doc: "Output from STAR alignment run"}
  genome_tar: {type: File, doc: "STAR-Fusion reference"}
  genome_untar_path: {type: ['null', string], doc: "This is what the path will be when genome_tar is unpackaged", default: "ctat_genome_lib_build_dir"}
  SampleID: string

outputs:
  abridged_coding:
    type: File
    outputBinding:
      glob: '*.fusion_predictions.abridged.coding_effect.tsv'
  chimeric_junction_compressed:
    type: File
    outputBinding:
      glob: "$(inputs.Chimeric_junction.basename).gz"

Docker Pulls

trinityctat/starfusion:1.9.0

STAR-Fusion simulated bash calls

tar -zxf /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3_CUSTOM_STAR_FUSION.tgz;
/usr/local/src/STAR-Fusion/STAR-Fusion --genome_lib_dir ./ctat_genome_lib_build_dir -J /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/02-AlignedData/COVSUBJ_0166_1_N_HA_all-reads/COVSUBJ_0166_1_N_HA_all-reads_Chimeric.out.junction --output_dir STAR-Fusion_outdir --examine_coding_effect --CPU 16
mv STAR-Fusion_outdir/star-fusion.fusion_predictions.abridged.coding_effect.tsv 04e94955-c96f-4d78-b9e7-c032d3de7ace.STAR.fusion_predictions.abridged.coding_effect.tsv
gzip -c /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/02-AlignedData/COVSUBJ_0166_1_N_HA_all-reads/COVSUBJ_0166_1_N_HA_all-reads_Chimeric.out.junction > COVSUBJ_0166_1_N_HA_all-reads_Chimeric.out.junction.gz

Star Fusion reference genome genereate

This tool generates a custom reference for STAR-Fusion, especially useful when pre-existing genomes don't match alignment. tools/star_fusion_refgen.cwl is the tool used to generate SATR-Fusion references. This tool takes 1 day (not a typo) to run, and costs $8.61 on Cavatica

inputs:
  reference_genome: File
  gtf: File
  max_readlength: {type: ['null', int], doc: "Read length of library aligned", default: 150}
  new_reference_name: {type: string, doc: "Name to use for newly created star fusion archive"}

outputs:
  star_fusion_reference:
    type: File
    outputBinding:
      glob: '*.tgz'

Docker Pulls

trinityctat/starfusion:1.9.0

arriba v1.1.0

Tool built to run arriba fusion caller. Tool used to run software tools/arriba_fusion.cwl. Run time per sample (n=732) ~18 minutes, cost on cavatica ~$0.09

 inputs:
  genome_aligned_bam: File
  genome_aligned_bai: File
  chimeric_sam_out: {type: File, doc: "Chimeric reads from STAR aligner as sam output"}
  reference_fasta: File
  gtf_anno: File
  outFileNamePrefix: string
  arriba_strand_flag: ['null', string]

outputs:
  arriba_fusions:
    type: File
    outputBinding:
      glob: "$(inputs.outFileNamePrefix).arriba.fusions.tsv"
  arriba_pdf:
    type: File
    outputBinding:
      glob: "$(inputs.outFileNamePrefix).arriba.fusions.pdf"

Docker Pulls:

kfdrc/arriba:1.1.0

arriba simulated bash calls

/arriba_v1.1.0/arriba -c /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/02-AlignedData/COVSUBJ_0668_1_P_HA_all-reads/COVSUBJ_0668_1_P_HA_all-reads_Chimeric.out.sam -x /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/02-AlignedData/COVSUBJ_0668_1_P_HA_all-reads/COVSUBJ_0668_1_P_HA_all-reads_Aligned.sortedByCoord_sorted.out.bam -a /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.fa -g /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.gtf -o 22810c80-7ef5-4c6e-ab5d-4df0487b7eba.arriba.fusions.tsv -O 22810c80-7ef5-4c6e-ab5d-4df0487b7eba.arriba.discarded_fusions.tsv -b /arriba_v1.1.0/database/blacklist_hg38_GRCh38_2018-11-04.tsv.gz -T -P -s auto
/arriba_v1.1.0/draw_fusions.R --annotation=/sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3.dna.primary_assembly.gtf --fusions=22810c80-7ef5-4c6e-ab5d-4df0487b7eba.arriba.fusions.tsv --alignments=/sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/02-AlignedData/COVSUBJ_0668_1_P_HA_all-reads/COVSUBJ_0668_1_P_HA_all-reads_Aligned.sortedByCoord_sorted.out.bam --cytobands=/arriba_v1.1.0/database/cytobands_hg38_GRCh38_2018-02-23.tsv --proteinDomains=/arriba_v1.1.0/database/protein_domains_hg38_GRCh38_2018-03-06.gff3 --output=22810c80-7ef5-4c6e-ab5d-4df0487b7eba.arriba.fusions.pdf

annoFuse

annoFuse is an added workflow to run the artifact filtering portion of annoFuse. Developed collaboratively between the Center for Data Driven Discovery in Biomedicine (D3b) and the Alex's Lemonade Stand Childhood Cancer Data Lab, this package adds annotation to arriba results, artifact filtering, and removes low-confidence fusion calls, as described in the paper. Run time (n=732) COV-IRT dataset, ~16 minutes, cost on cavatica ~$0.08 per sample

inputs:
  sample_name: {type: string, doc: "Sample name used for file base name of all outputs"}
  FusionGenome: {type: File, doc: "GRCh38_v27_CTAT_lib_Feb092018.plug-n-play.tar.gz", sbg:suggestedValue: {class: 'File', path: '5d8bb21fe4b0950c4028f854', name: 'GRCh38_v27_CTAT_lib_Feb092018.plug-n-play.tar.gz'}} # Custom input was used for COV-IRT
  genome_untar_path: {type: ['null', string], doc: "This is what the path will be when genome_tar is unpackaged", default: "GRCh38_v27_CTAT_lib_Feb092018/ctat_genome_lib_build_dir"}
  rsem_expr_file: {type: File, doc: "gzipped rsem gene expression file"}
  arriba_output_file: {type: File, doc: "Output from arriba, usually extension arriba.fusions.tsv"}
  col_num: {type: ['null', int], doc: "column number in file of fusion name", default: 25}
  star_fusion_output_file: {type: File, doc: "Output from arriba, usually extension STAR.fusion_predictions.abridged.coding_effect.tsv"}
  output_basename: string

outputs:
  annofuse_filtered_fusions_tsv: {type: File?, outputSource: annoFuse_filter/filtered_fusions_tsv, doc: "Filtered output of formatted and annotated Star Fusion and arriba results"}

Docker Pulls

kfdrc/annofuse:0.1.8
gaonkark/fusionanno:latest
kfdrc/annofuse:0.1.8

annoFuse simulated bash calls

Step	Type	Num scatter	Command
format_arriba_output	run step	NA	Rscript /rocker-build/formatFusionCalls.R --fusionfile /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/3321a8b3-a1f9-46e9-988d-f2b4452b8633.arriba.fusions.tsv --tumorid COVSUBJ_0663_1_P --caller arriba --outputfile COVSUBJ_0663_1_P.arriba_formatted.tsv
annotate_arriba	run step	NA	tar -zxf /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/ENS100_MN908947.3_CUSTOM_STAR_FUSION.tgz && /opt/FusionAnnotator/FusionAnnotator --genome_lib_dir ./ctat_genome_lib_build_dir --annotate /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/c5bfb05c-6fce-4b0b-abb4-3626f71e254f/format_arriba_output/COVSUBJ_0663_1_P.arriba_formatted.tsv --fusion_name_col 25 > c5bfb05c-6fce-4b0b-abb4-3626f71e254f.annotated.tsv
format_starfusion_output	run step	NA	Rscript /rocker-build/formatFusionCalls.R --fusionfile /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/85a181d4-db70-4b7a-bd62-d70d59448826.STAR.fusion_predictions.abridged.coding_effect.tsv --tumorid COVSUBJ_0663_1_P --caller starfusion --outputfile COVSUBJ_0663_1_P.starfusion_formatted.tsv
annoFuse_filter	run step	NA	A_CT=`wc -l /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/c5bfb05c-6fce-4b0b-abb4-3626f71e254f/annotate_arriba/c5bfb05c-6fce-4b0b-abb4-3626f71e254f.annotated.tsv
annoFuse_filter	run step	NA	S_CT=`wc -l /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/c5bfb05c-6fce-4b0b-abb4-3626f71e254f/format_starfusion_output/COVSUBJ_0663_1_P.starfusion_formatted.tsv
annoFuse_filter	run step	NA	if [ $A_CT -eq 1 ] && [ $S_CT -eq 1 ]; then
annoFuse_filter	run step	NA	echo "Both inputs are empty, will skip processing as there no fusions." >&2;
annoFuse_filter	run step	NA	exit 0;
annoFuse_filter	run step	NA	fi
annoFuse_filter	run step	NA	Rscript /rocker-build/annoFusePerSample.R --fusionfileArriba /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/c5bfb05c-6fce-4b0b-abb4-3626f71e254f/annotate_arriba/c5bfb05c-6fce-4b0b-abb4-3626f71e254f.annotated.tsv --fusionfileStarFusion /sbgenomics/workspaces/7c22397d-ea11-4069-9687-3049119a2d37/tasks/c5bfb05c-6fce-4b0b-abb4-3626f71e254f/format_starfusion_output/COVSUBJ_0663_1_P.starfusion_formatted.tsv --expressionFile /sbgenomics/Projects/7c22397d-ea11-4069-9687-3049119a2d37/NASA_HEC_transferred_data/COVIRT_all_data_20200729/03-RSEMcountData/COVSUBJ_0663_1_P_HA_all-reads.genes.results --tumorID COVSUBJ_0663_1_P --outputfile c5bfb05c-6fce-4b0b-abb4-3626f71e254f.annoFuse_filter.tsv

annoFuse Workflow Diagram

Kraken2

This tool allows users to process fastq reads and tease out by species in order to remove sensitive data Kraken2 is available to run at tools/kraken2_classification.cwl.

inputs:
  input_db: { type: File, doc: "Input TGZ containing Kraken2 database" }
  input_reads: { type: File, doc: "FA or FQ file containing sequences to be classified" }
  input_mates: { type: 'File?', doc: "Paired mates for input_reads" }
  db_path: { type: string, default: "./covid", doc: "Relative path to the folder containing the db files from input_db" }
  threads: { type: int, default: 32, doc: "Number of threads to use in parallel" }
  ram: { type: int, default: 50000, doc: "Recommended MB of RAM needed to run the job" }
  output_basename: { type: string, doc: "String to be used as the base filename of the output" }

outputs:
  output: { type: File, outputBinding: { glob: "*.output" } }
  classified_reads: { type: 'File', outputBinding: { glob: "*_1.fq" } }
  classified_mates: { type: 'File?', outputBinding: { glob: "*_2.fq" } }

Other RNAseq SNV Workflows

Strelka2 v2.9.10

This workflow is pretty straight forward, with a PASS filter step added to get PASS calls. workflows/d3b_strelka2_rnaseq_snv_wf.cwl is the wrapper cwl that runs this workflow. Run time per sample (n=1) ~50 minutes, cost on cavatica ~$0.40

Inputs

inputs:
  reference: { type: File, secondaryFiles: [.fai] }
  input_rna_bam: {type: File, secondaryFiles: [^.bai]}
  strelka2_bed: {type: File?, secondaryFiles: [.tbi], label: gzipped bed file}
  cores: {type: ['null', int], default: 16, doc: "Num cores to use"}
  ram: {type: ['null', int], default: 30, doc: "Max mem to use in GB"}
  output_basename: string

Outputs

  strelka2_prepass_vcf: {type: File, outputSource: strelka2_rnaseq/output_vcf, doc: "Strelka2 SNV calls"}
  strelka2_pass_vcf: {type: File, outputSource: gatk_pass_vcf/pass_vcf, doc: "Strelka2 calls filtered on PASS"}

Docker Pulls

kfdrc/strelka2:2.9.10
kfdrc/gatk:4.1.1.0

Workflow Diagram

Strelka2 simulated bash calls

Step	Type	Num scatter	Command
strelka2_rnaseq	run step	NA	/strelka-2.9.10.centos6_x86_64/bin/configureStrelkaGermlineWorkflow.py --bam /sbgenomics/Projects/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/da63df67-62a4-487b-aa68-d7f139809160.Aligned.out.sorted.bam --reference /sbgenomics/Projects/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/GRCh38.primary_assembly.genome.fa --rna --runDir ./ && ./runWorkflow.py -m local -j 16 -g 30
strelka2_rnaseq	run step	NA	mv results/variants/variants.vcf.gz STRELKA2_TEST.strelka2.rnaseq.vcf.gz
strelka2_rnaseq	run step	NA	mv results/variants/variants.vcf.gz.tbi STRELKA2_TEST.strelka2.rnaseq.vcf.gz.tbi
gatk_pass_vcf	run step	NA	/bin/bash -c set -eo pipefail
gatk_pass_vcf	run step	NA	/gatk SelectVariants --java-options "-Xmx7500m" -V /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/5f77306d-9650-4b82-83e1-1623eb07e211/strelka2_rnaseq/STRELKA2_TEST.strelka2.rnaseq.vcf.gz -O STRELKA2_TEST.strelka2.PASS.vcf.gz --exclude-filtered TRUE

VardictJava v1.7.0

This workflow is based on the Vardict run style of BC Bio. workflows/d3b_vardict_rnaseq_snv_wf.cwl is the wrapper cwl that runs this workflow. Tweaking vardict_bp_target and vardict_intvl_target_size maybe be needed to improve run time in high coverage areas, by reducing their values from defaults. Run time (n=1) ~9.5 hours, cost on cavatica ~$5.50.

Inputs

inputs:
  output_basename: string
  STAR_sorted_genomic_bam: {type: File, doc: "STAR sorted alignment bam", secondaryFiles: ['^.bai']}
  sample_name: string
  reference_fasta: {type: File, secondaryFiles: ['.fai', '^.dict'], doc: "Reference genome used"}
  reference_dict: File
  vardict_min_vaf: {type: ['null', float], doc: "Min variant allele frequency for vardict to consider.  Recommend 0.2", default: 0.2}
  vardict_cpus: {type: ['null', int], default: 4}
  vardict_ram: {type: ['null', int], default: 8, doc: "In GB"}
  vardict_bp_target: {type: ['null', int], doc: "Intended max number of base pairs per file.  Existing intervals large than this will NOT be split into another file. Make this value smaller to break up the work into smaller chunks", default: 60000000}
  vardict_intvl_target_size: {type: ['null', int], doc: "For each file, split each interval into chuck of this size", default: 20000}
  call_bed_file: {type: File, doc: "BED or GTF intervals to make calls"}
  tool_name: {type: string, doc: "description of tool that generated data, i.e. gatk_haplotypecaller"}
  padding: {type: ['null', int], doc: "Padding to add to input intervals, recommened 0 if intervals already padded, 150 if not", default: 150}
  mode: {type: ['null', {type: enum, name: select_vars_mode, symbols: ["gatk", "grep"]}], doc: "Choose 'gatk' for SelectVariants tool, or 'grep' for grep expression", default: "gatk"}

Outputs

outputs:
  vardict_prepass_vcf: {type: File, outputSource: sort_merge_vardict_vcf/merged_vcf, doc: "VarDict SNV calls"}
  vardict_pass_vcf: {type: File, outputSource: gatk_pass_vcf/pass_vcf, doc: "VarDict calls filtered on PASS"}

Docker Pulls

kfdrc/vardict:1.7.0
kfdrc/gatk:4.1.1.0
kfdrc/python:2.7.13

Workflow Diagram

Vardict simulated bash calls

Step	Type	Num scatter	Command
bedtools_gtf_to_bed	run step	NA	/bin/bash -c set -eo pipefail
bedtools_gtf_to_bed	run step	NA	cat /sbgenomics/Projects/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/gencode.v33.primary_assembly.annotation.gtf
python_vardict_interval_split	run step	NA	python -c 'def main():
python_vardict_interval_split	run step	NA	import sys
python_vardict_interval_split	run step	NA	bp_target = 20000000
python_vardict_interval_split	run step	NA	intvl_target_size = 20000
python_vardict_interval_split	run step	NA	bed_file = open("/sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/bedtools_gtf_to_bed/gencode.v33.primary_assembly.annotation.gtf.bed")
python_vardict_interval_split	run step	NA
python_vardict_interval_split	run step	NA	i=0
python_vardict_interval_split	run step	NA	intvl_set = {}
python_vardict_interval_split	run step	NA	cur_size = 0
python_vardict_interval_split	run step	NA	for cur_intvl in bed_file:
python_vardict_interval_split	run step	NA	f = 0
python_vardict_interval_split	run step	NA	if i not in intvl_set:
python_vardict_interval_split	run step	NA	intvl_set[i] = []
python_vardict_interval_split	run step	NA	data = cur_intvl.rstrip("\n").split("\t")
python_vardict_interval_split	run step	NA	(chrom, start, end) = (data[0], data[1], data[2])
python_vardict_interval_split	run step	NA	intvl_size = int(end) - int(start)
python_vardict_interval_split	run step	NA	if intvl_size >= bp_target:
python_vardict_interval_split	run step	NA	if len(intvl_set[i]) != 0:
python_vardict_interval_split	run step	NA	i += 1
python_vardict_interval_split	run step	NA	intvl_set[i] = []
python_vardict_interval_split	run step	NA	f = 1
python_vardict_interval_split	run step	NA	elif cur_size + intvl_size > bp_target:
python_vardict_interval_split	run step	NA	if len(intvl_set[i]) != 0:
python_vardict_interval_split	run step	NA	i += 1
python_vardict_interval_split	run step	NA	intvl_set[i] = []
python_vardict_interval_split	run step	NA	cur_size = intvl_size
python_vardict_interval_split	run step	NA	else:
python_vardict_interval_split	run step	NA	cur_size += intvl_size
python_vardict_interval_split	run step	NA	intvl_set[i].append([chrom, start, end])
python_vardict_interval_split	run step	NA	if f == 1:
python_vardict_interval_split	run step	NA	i += 1
python_vardict_interval_split	run step	NA	cur_size = 0
python_vardict_interval_split	run step	NA	bed_file.close()
python_vardict_interval_split	run step	NA
python_vardict_interval_split	run step	NA	for set_i, invtl_list in sorted(intvl_set.items()):
python_vardict_interval_split	run step	NA	set_size = 0
python_vardict_interval_split	run step	NA	out = open("set_" + str(set_i) + ".bed", "w")
python_vardict_interval_split	run step	NA	for intervals in invtl_list:
python_vardict_interval_split	run step	NA	(chrom, start, end) = (intervals[0], intervals[1], intervals[2])
python_vardict_interval_split	run step	NA	intvl_size = int(end) - int(start)
python_vardict_interval_split	run step	NA	set_size += intvl_size
python_vardict_interval_split	run step	NA	for j in range(int(start), int(end), intvl_target_size):
python_vardict_interval_split	run step	NA	new_end = j + intvl_target_size
python_vardict_interval_split	run step	NA	if new_end > int(end):
python_vardict_interval_split	run step	NA	new_end = end
python_vardict_interval_split	run step	NA	out.write(chrom + "\t" + str(j) + "\t" + str(new_end) + "\n")
python_vardict_interval_split	run step	NA	sys.stderr.write("Set " + str(set_i) + " size:\t" + str(set_size) + "\n")
python_vardict_interval_split	run step	NA	out.close()
python_vardict_interval_split	run step	NA
python_vardict_interval_split	run step	NA	if name == "main":
python_vardict_interval_split	run step	NA	main()'
vardict	scatter	89	/bin/bash -c set -eo pipefail; export VAR_DICT_OPTS='"-Xms768m" "-Xmx6g"'; /VarDict-1.7.0/bin/VarDict -G /sbgenomics/Projects/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/GRCh38.primary_assembly.genome.fa -f 0.2 -th 4 --nosv --deldupvar -N VARDICT_NEW_SPLIT -b '/sbgenomics/Projects/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/da63df67-62a4-487b-aa68-d7f139809160.Aligned.out.sorted.bam' -z -c 1 -S 2 -E 3 -g 4 -F 0x700 -V 0.01 -x 150 /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/python_vardict_interval_split/set_82.bed > vardict_results.txt && cat vardict_results.txt
sort_merge_vardict_vcf	run step	NA	/gatk SortVcf --java-options "-Xmx6g" -O VARDICT_NEW_SPLIT.vardict.merged.vcf --SEQUENCE_DICTIONARY /sbgenomics/Projects/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/GRCh38.primary_assembly.genome.dict --CREATE_INDEX false -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_1_s/VARDICT_NEW_SPLIT.set_0.vcf.gz -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_2_s/VARDICT_NEW_SPLIT.set_1.vcf.gz -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_3_s/VARDICT_NEW_SPLIT.set_10.vcf.gz -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_4_s/VARDICT_NEW_SPLIT.set_11.vcf.gz -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_5_s/VARDICT_NEW_SPLIT.set_12.vcf.gz -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_6_s/VARDICT_NEW_SPLIT.set_13.vcf.gz -I /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/vardict_7_s/VARDICT_NEW_SPLIT.set_14.vcf.gz && cat VARDICT_NEW_SPLIT.vardict.merged.vcf
gatk_pass_vcf	run step	NA	/bin/bash -c set -eo pipefail
gatk_pass_vcf	run step	NA	/gatk SelectVariants --java-options "-Xmx7500m" -V /sbgenomics/workspaces/598f0ba4-d8a8-45e7-8bf2-1fe004e4979a/tasks/f85f4ba0-7927-435e-b39a-b8c6571baa4c/sort_merge_vardict_vcf/VARDICT_NEW_SPLIT.vardict.merged.vcf.gz -O VARDICT_NEW_SPLIT.vardict.PASS.vcf.gz --exclude-filtered TRUE

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misc		misc
notebooks		notebooks
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tools		tools
workflows		workflows
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
fq_split_rg_instructions.md		fq_split_rg_instructions.md

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RNAseq SNV Calling Workflow (beta)

GATK4 v4.1.7.0

Inputs

Outputs

Docker Pulls

GATK4 RNAseq SNV Workflow Diagram

GATK4 simulated bash calls

Variant Effect Predictor

VEP simulated bash calls

Fusion Calls

STAR-Fusion v1.9.0

Docker Pulls

STAR-Fusion simulated bash calls

Star Fusion reference genome genereate

Docker Pulls

arriba v1.1.0

Docker Pulls:

arriba simulated bash calls

annoFuse

Docker Pulls

annoFuse simulated bash calls

annoFuse Workflow Diagram

Kraken2

Other RNAseq SNV Workflows

Strelka2 v2.9.10

Inputs

Outputs

Docker Pulls

Workflow Diagram

Strelka2 simulated bash calls

VardictJava v1.7.0

Inputs

Outputs

Docker Pulls

Workflow Diagram

Vardict simulated bash calls

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