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https://nf-co.re/sarek/usage v2.7.1

Data Type

Method

Output

Tried yet?

WES or WGS

DeepVariant

Germline SNV, INDEL

Yes

WES or WGS

Strelka, Mutect2, Freebayes (question)

Somatic SNV, INDEL

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

Annotated Variants

https://nf-co.re/rnaseq v3.5

Data Type

Method

Output

Tried yet?

RNA-Seq

Salmon

Gene expression counts

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).

As of Jan 2022, the reference genome used is GRCh38.

The processing steps include the following:

  • Raw fastq files uploaded to Synapse by researcher in a folder with name format experiment_name_rnaseq_fastq_date . No white space should be present in the filenames (all filenames should have _ for whitespaces.

  • All experiment and sample related annotations need to be added on Synapse before processing can start. This is a required step so that a sample sheet can be generated to trigger the processing workflow

  • The sample sheet should contain the following information in the following format (saved as a .txt file) :

sample

subject

status

sex

file_1

file_2

lane

parentId

Synapse specimenID

Synapse individualID

1 (Tumor = 1, Normal 0)

XX or XY

synID

synID

Lane information

Synapse folder information

  • The files are pulled into NextFlow workflow setup and processed using the following versions of software:

Code Block
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

Estimated costs for germline variant calling (per 50 samples)

  • 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).

  • 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.

  • More information here:

    Jira Legacy
    serverSystem JIRA
    serverIdba6fb084-9827-3160-8067-8ac7470f78b2
    keyWORKFLOWS-98

Somatic SNV + INDEL

TBD

Annotated Variants

Currently germ-line variant calls in VCF format are being processed manually using VEP and vcf2maf

Estimated costs for germ-line variant annotation (per 50 samples) using VEP

  • 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.

RNA SEQUENCING DATA QUANTIFICATION

Processing RNA-seq files involve transformation of raw data (fastq files) to transcript counts (quants.sf files).

The quantification software of choice is Salmon.

As of Jan 2022, the reference genome used is GRCh38.

Processing involves the following steps:

  • Raw fastq files uploaded to Synapse by researcher in a folder with name format experiment_name_rnaseq_fastq_date . No white space should be present in the filenames (all filenames should have _ for whitespaces.

  • All experiment and sample related annotations need to be added on Synapse before processing can start. This is a required step so that a sample sheet can be generated to trigger the processing workflow

  • The sample sheet should contain the following information in the following format (saved as a .csv file):

sample

single_end

fastq_1

fastq_2

strandedness

Synapse specimenID

0 (1 if paired-end)

synID

synID

reverse or forward

  • 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'

Estimated costs for processing

  • Estimated Cost per sample = $0.20 ($51 for 261 samples)

  • Estimated time per 100 samples = approx 6 hrs