| Note |
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Our pipeline configuration is still in-development, and the contents of this document are subject to change. |
https://nf-co.re/sarek/usage v2.7.1 | |||
Data Type | Method | Output | Tried yet? |
|---|---|---|---|
WES or WGS | DeepVariant | Yes | |
WES or WGS | Strelka, Mutect2, Freebayes | ||
WES or WGS | TBD | Germline and Somatic Structural Variants | |
WES or WGS | TBD | Germline and Somatic CNV | |
WES or WGS | TBD | Tumor MSI | |
SNV, INDEL variants | TBD | ||
Data Type | Method | Output | Tried yet? |
|---|---|---|---|
RNA-Seq | Salmon | Yes | |
WES and WGS Variant Calling (SNV & INDEL)
Germline SNV + INDEL
This involves transformation of WES fastq or cram files to variant call files in VCF format (.vcf files).
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nf-core/sarek v2.7.1 Nextflow v21.10.5 BWA 0.7.17 GATK v4.1.7.0 FreeBayes v1.3.2 samtools v1.9 Strelka v2.9.10 Manta v1.6.0 TIDDIT v2.7.1 AlleleCount v4.0.2 ASCAT v2.5.2 Control-FREEC vv11.6 msisensor v0.5 SnpEff v4.3t VEP v99.2 MultiQC v1.8 FastQC v0.11.9 bcftools v1.9 CNVkit v0.9.6 htslib v1.9 QualiMap v2.2.2-dev Trim Galore v0.6.4_dev vcftools v0.1.16 R v4.0.2 |
Commands used for running JHU samples on DeepVariant:
All files and sample sheets are first staged in S3 buckets linked to NFTower. then the following command are used to launch the processing pipeline.
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Profiles:
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aws_tower |
Estimated costs for germline variant calling (per 50 samples)
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According to the DeepVariant docs, it costs about $1 per WES sample and $12 per WGS sample on Google Cloud using a n1-standard-16 machine (16 vCPUs, 60 GB of memory, $0.76/hour).
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If we infer the run time from the costs and price per hour, it should be roughly 2 hours per WES sample and 16 hours per WGS sample.
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Somatic SNV + INDEL
TBD
Annotated Variants
Currently germ-line , germline variant calls in VCF format are being processed manually using VEP and vcf2maf
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The compute cost should range from $50 to $2,500 depending on how many of the 50 samples are WGS and how many mutations they have.
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RNA SEQUENCING DATA QUANTIFICATION
Processing RNA-seq files involve transformation of raw data (fastq files) to transcript counts (quants.sf files).
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The files are pulled into NextFlow workflow setup and processed using the following versions of software:
Code Block BEDTOOLS_GENOMECOV: bedtools: 2.30.0 CAT_FASTQ: cat: 8.3 CUSTOM_DUMPSOFTWAREVERSIONS: python: 3.9.5 yaml: 5.4.1 DESEQ2_QC_STAR_SALMON: bioconductor-deseq2: 1.28.0 r-base: 4.0.3 DUPRADAR: bioconductor-dupradar: 1.18.0 r-base: 4.0.2 FASTQC: fastqc: 0.11.9 GET_CHROM_SIZES: samtools: 1.1 GTF_GENE_FILTER: python: 3.8.3 PICARD_MARKDUPLICATES: picard: 2.25.7 PRESEQ_LCEXTRAP: preseq: 3.1.1 QUALIMAP_RNASEQ: qualimap: 2.2.2-dev RSEM_PREPAREREFERENCE_TRANSCRIPTS: rsem: 1.3.1 star: 2.7.6a RSEQC_BAMSTAT: rseqc: 3.0.1 RSEQC_INFEREXPERIMENT: rseqc: 3.0.1 RSEQC_INNERDISTANCE: rseqc: 3.0.1 RSEQC_JUNCTIONANNOTATION: rseqc: 3.0.1 RSEQC_JUNCTIONSATURATION: rseqc: 3.0.1 RSEQC_READDISTRIBUTION: rseqc: 3.0.1 RSEQC_READDUPLICATION: rseqc: 3.0.1 SALMON_QUANT: salmon: 1.5.2 SALMON_SE_GENE: bioconductor-summarizedexperiment: 1.20.0 r-base: 4.0.3 SALMON_TX2GENE: python: 3.8.3 SALMON_TXIMPORT: bioconductor-tximeta: 1.8.0 r-base: 4.0.3 SAMPLESHEET_CHECK: python: 3.8.3 SAMTOOLS_FLAGSTAT: samtools: 1.13 SAMTOOLS_IDXSTATS: samtools: 1.13 SAMTOOLS_INDEX: samtools: 1.13 SAMTOOLS_SORT: samtools: 1.13 SAMTOOLS_STATS: samtools: 1.13 STAR_ALIGN: star: 2.6.1d STRINGTIE: stringtie: 2.1.7 TRIMGALORE: cutadapt: 3.4 trimgalore: 0.6.7 UCSC_BEDCLIP: ucsc: 377 UCSC_BEDGRAPHTOBIGWIG: ucsc: 377 Workflow: Nextflow: 21.10.5 nf-core/rnaseq: '3.4'
Command used to process JHU Biobank samples:
Params:
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input: s3://jhu-biobank-nf-project-tower-bucket/jobs/01-nfcore-rnaseq-3.4/inputs/sample-sheet.csv outdir: s3://jhu-biobank-nf-project-tower-bucket/jobs/01-nfcore-rnaseq-3.4/outputs/ genome: GRCh38 igenomes_base: s3://sage-igenomes/igenomes |
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Profile:
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aws_tower |
Estimated costs for processing
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Estimated Cost per sample = $0.20 ($51 for 261 samples)
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