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Comment: removed freebayes as it is no longer being included in our standard set
Note

Our pipeline configuration is still in-development, and the contents of this document are subject to change.

Summary of Processing

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

v2

v3.

7.1Data Type

0

Datatype

Method

Output

Tried yet?

WES or WGS

DeepVariant

Germline SNV, INDEL

Yes

WES or WGS

Strelka, Mutect2

, Freebayes (question)

Somatic SNV, INDEL

Yes

WES or WGS

TBD

Germline and Somatic Structural Variants

No

WES or WGS

TBD

Germline and Somatic CNV

No

WES or WGS

TBD

Tumor MSI

No

SNV, INDEL variants

TBD

Annotated Variants

No

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

5

7

Data Type

Datatype

Method

Output

Tried yet?

RNA-Seq

STAR with Salmon in alignment-based mode

Gene expression counts

Yes

More information about the workflows is available here: /wiki/spaces/WF/pages/2363359776

bam/cram to fastq conversion

  • When fastq files are not available, cram/bam files are converted to fastq using this pipeline: https://github.com/qbic-pipelines/bamtofastq (v1.2.0).

  • If unaligned bam files are available instead of fastq files, we recommend providing u-bam files for direct input to sarek 3.0.

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 Homo_sapiens/GATK/GRCh38 (https://github.com/nf-core/sarek/blob/2.7.1/conf/igenomes.config#L38-L58)

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) :a comma-separated file (.csv) with at least 3 columns, and a header row as shown below. (More information here)

sample

subject

status

sex

file_1

file_2

lane

parentId

bed_file

output_parent_Id

Synapse specimenID

Synapse individualID

1 (Tumor = 1, Normal 0)

XX or XY

synID

synID

syn://synId

syn://synId

Lane information

SynapseID of parent folder

Synapse ID of BED file (if WES sata)

Synapse ID of folder where all processed files will be indexed

  • 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

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.

Params:

Code Block
input: s3://jhu-biobank-nf-project-tower-bucket/jobs/02-sage-sarek-2.7.1-deepvariant/inputs/sample-sheet.tsv
outdir: s3://jhu-biobank-nf-project-tower-bucket/jobs/02-sage-sarek-2.7.1-deepvariant/outputs/
genome: GRCh38
igenomes_base: s3://sage-igenomes/igenomes
model_type: WES
tools: "deepvariant"

...

Profiles:

Code Block
aws_tower

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.

...

...
 Somatic SNV + INDEL
TBD
Annotated Variants
Currently germ-line , germline 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). 
...
As of Jan 2022, the reference genome used is Homo_sapiens/NCBI/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. While the naming convention is a best practices recommendation and not a strict rule, the exclusion of whitespaces is required.
  • 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) (More information here) :
sample
single_end
fastq_1
fastq_2
strandedness
Synapse specimenID
0 (1 if paired-end)
synID
synID
reverse or forward
auto
  • 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:
 Code Block
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
...
Profile:
 Code Block
aws_tower 
Estimated costs for processing
...
Estimated Cost per sample = $0.20 ($51 for 261 samples)
...