WGS VARIANT CALLING (SNV):
TBD
WES VARIANT CALLING (SNV):
...
Note |
---|
Our pipeline configuration is still in-development, and the contents of this document are subject to change. |
Summary of Processing
Datatype | Method | Output | Tried yet? |
---|---|---|---|
WES or WGS | DeepVariant | Yes | |
WES or WGS | Strelka, Mutect2 | 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 | No |
Datatype | Method | Output | Tried yet? |
---|---|---|---|
RNA-Seq | STAR with Salmon in alignment-based mode | 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
|
| 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 <span style="color:#999999;">N/A</span>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 germ-line 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 server System JIRA serverId ba6fb084-9827-3160-8067-8ac7470f78b2 key WORKFLOWS-98
B. Somatic Variant Calling using Strelka+Mutect2+Freebayes on Nextflow:
TBD
C. Variant Annotation:
...
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" |
Config
:
Code Block |
---|
process {
errorStrategy = 'retry'
maxRetries = 3
withLabel:deepvariant {
container = "google/deepvariant:1.1.0"
cpus = 24
}
} |
Pre-run Script
:
Code Block |
---|
export NXF_VER=21.10.5 |
Profiles
:
Code Block |
---|
aws_tower |
Somatic SNV + INDEL
TBD
Annotated Variants
Currently, 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
files 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'
Estimated costs for processing:
...
Estimated Cost per sample = $0.20 ($51 for 261 samples)
...
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 |
Config
:
Code Block |
---|
process {
errorStrategy = 'retry'
maxRetries = 3
} |
Pre-run script
:
Code Block |
---|
export NXF_VER=21.10.5 |
Profile
:
Code Block |
---|
aws_tower |