🐶 🐶 🐶 🎶 WGS Leukemia Fluffy 🐍

Snakemake workflow to analyse hematological malignancies in whole genome data

💬 Introduction

This snakemake workflow uses modules from hydragenetics to process .fastq files and obtain different kind of variants (SNV, indels, CNV, SV). Alongside diagnosis-filtered .vcf files, the workflow produces a multiqc report .html file and some CNV plots and .tsv files with relevant information from mutect and Manta. One of the modules contains the commercial parabricks toolkit which can be replaced by sentieon or opensource GATK tools if required. The following modules are currently part of this pipeline:

• annotation
• cnv_sv
• compression
• filtering
• misc
• parabricks
• prealignment
• qc

❗ Dependencies

In order to use this module, the following dependencies are required:

🎒 Preparations

Sample and unit data

Input data should be added to samples.tsv and units.tsv. The following information need to be added to these files:

Column Id	Description
samples.tsv
sample	unique sample/patient id, one per row
tumor_content	ratio of tumor cells to total cells
units.tsv
sample	same sample/patient id as in samples.tsv
type	data type identifier (one letter), can be one of Tumor, Normal, RNA
platform	type of sequencing platform, e.g. NovaSeq
machine	specific machine id, e.g. NovaSeq instruments have @Axxxxx
flowcell	identifer of flowcell used
lane	flowcell lane number
barcode	sequence library barcode/index, connect forward and reverse indices by +, e.g. ATGC+ATGC
fastq1/2	absolute path to forward and reverse reads
adapter	adapter sequences to be trimmed, separated by comma

Reference data

Reference files should be specified in config.yaml

1. A .fasta reference file of the human genome is required as well as an .fai file and an bwa index of this file.
2. A .vcf file containing known indel sites. For GRCh38, this file is available as part of the Broad GATK resource bundle at google cloud.
3. An .interval_list file containing all whole genome calling regions. The GRCh38 version is also available at google cloud.
4. The trimmer_software should be specified by indicating a rule which should be used for trimming. This pipeline uses fastp_pe.
5. .bed files defining regions of interest for different diagnoses. This pipeline is assuming ALL and AML and different gene lists for SNVs and SVs.
6. For pindel, a .bed file containing the region that the analysis should be limited to.
7. simple_sv_annotation comes with panel and a fusion pair list which should also be included in the config.yaml.
8. Annotation with SnpEff a database is needed which can be downloaded through the cli.
9. For VEP, a cache resource should be downloaded prior to running the workflow.

🚀 Usage

To run the workflow, resources.yaml is needed which defines different resources as default and for different rules. For parabricks, the gres stanza is needed and should specify the number of GPUs available. You also need a config.yaml where all run-variables are defined.

snakemake --profile my-profile --configfile config/config.yaml

To run the integration test you only need to add lines in the tests/integration/config.yaml that differs from the original config.yaml. As of now it is only a dryrun test, no small dataset is available.

cd .tests/integration/
snakemake --snakefile ../../workflow/Snakefile --configfiles ../../config/config.yaml config.yaml -n

Output files

.fastq files are archived as compressed file pair as .spring: Archive/{project}/{sample}_{flowcell}_{lane}_{barcode}_{type}.spring

The MultiQC html report can be found here: Results/MultiQC_TN.html

All results (as described in table below) are located in: Results/{project}/{sample}/

File	Description
Cram/{sample}_{type}.crumble.cram	crumbled .cram file
Cram/{sample}_{type}.crumble.cram.crai	index for crumbled .cram file
SNV_indels/{sample}_T.vep.vcf.gz	.vcf output for SNV and small indels annotated with VEP for tumor_only
SNV_indels/{sample}_T.vep.vcf.gz.tbi	index for .vcf output from VEP for tumor_only
SNV_indels/{sample}_TN.vep.vcf.gz	.vcf output from VEP for tumor/normal
SNV_indels/{sample}_TN.vep.vcf.gz.tbi	index for .vcf output from VEP for tumor/normal
SNV_indels/{sample}_TN.vep.all.vcf.gz	.vcf output from VEP for tumor/normal, hard-filtered for ALL genes
SNV_indels/{sample}_TN.vep.all.vcf.gz.tbi	index for .vcf output from VEP for tumor/normal, hard-filtered for ALL genes
SNV_indels/{sample}_TN.vep.aml.vcf.gz	.vcf output from VEP for tumor/normal, hard-filtered for AML genes
SNV_indels/{sample}_TN.vep.aml.vcf.gz.tbi	index for .vcf output from VEP for tumor/normal, hard-filtered for AML genes
SNV_indels/{sample}_mutectcaller_TN.all.tsv	.tsv file for excel containing SNVs and small indels from mutect2 for ALL
SNV_indels/{sample}_mutectcaller_TN.aml.tsv	.tsv file for excel containing SNVs and small indels from mutect2 for AML
SNV_indels/{sample}.pindel.vcf.gz	.vcf output from pindel
SNV_indels/{sample}.pindel.vcf.gz.tbi	index for .vcf output from pindel
CNV/{sample}_T.vcf.gz	.vcf output from cnvkit
CNV/{sample}_T.vcf.gz.tbi	index for .vcf output from cnvkit
CNV/{sample}_{type}.CNV.xlsx	Excel file containing overview of CNVkit results
CNV/{sample}_T.png	scatter plot from cnvkit for entire genome
CNV/{sample}_T_chr{chr}.png	scatter plot per chromosome from cnvkit
SV/{sample}_manta_TN.ssa.vcf.gz	.vcf output from Manta
SV/{sample}_manta_TN.ssa.vcf.gz.tbi	index for .vcf output from Manta
SV/{sample}_manta_TN.ssa.all.vcf.gz	.vcf output from Manta filtered for ALL genes
SV/{sample}_manta_TN.ssa.all.vcf.gz.tbi	index for .vcf output from Manta filtered for ALL genes
SV/{sample}_manta_TN.ssa.aml.vcf.gz	.vcf output from Manta filtered for AML genes
SV/{sample}_manta_TN.ssa.aml.vcf.gz.tbi	index for .vcf output from Manta filtered for AML genes
SV/{sample}_manta_TN.del.tsv	.tsv file for excel containing deletions found by Manta (filtered)
SV/{sample}_manta_TN.ins.tsv	.tsv file for excel containing insertions found by Manta (filtered)
SV/{sample}_manta_TN.dup.tsv	.tsv file for excel containing duplications found by Manta (filtered)
SV/{sample}_manta_TN.bnd.tsv	.tsv file for excel containing breakends found by Manta (filtered)
SV/{sample}_manta_TN.bnd.all.tsv	.tsv file for excel containing breakends found by Manta (filtered), filtered for ALL genes
SV/{sample}_manta_TN.bnd.aml.tsv	.tsv file for excel containing breakends found by Manta (filtered), filtered for AML genes

General Statistics - DNA

The general statistics table are ordered based on the "s-index" in fastq-filename. This is done by renaming the samples in two steps using the script sample_order_multiqc.py. To toggle between "Sample Order" and "Sample Name" use the buttons just above General Stats header.

Column Name	Origin	Comment
M Reads	Picard HSMetrics	Total number of reads in inputfile (alignment/samtools_merge_bam/{sample}_{type}.bam)
% Mapped	Samtools stats	Only reads on target (config[reference][design_bed])
% Proper pairs	Samtools stats	Only reads on target (config[reference][design_bed])
Average Quality	Samtools stats	Ratio between sum of base quality over total length. Only reads on target (config[reference][design_bed])
Median	Mosdepth	Median Coverage over reference
>= 10X	Mosdepth	Fraction of reference with coverage over 10x
>= 30X	Mosdepth	Fraction of reference with coverage over 30x
>=50X	Mosdepth	Fraction of reference with coverage over 50x
Error sex check	Peddy	Result of sex check based on sex in units.tsv
Predicted sex sex check	Peddy
Bases on Target	Picard HSMetrics	Bases inside the capture design (config[reference][design_intervals])
Fold80	Picard HSMetrics	The fold over-coverage necessary to raise 80% of bases in "non-zero-cvg" targets to the mean coverage level in those targets (config[reference][design_intervals])
% Dups	Picard DuplicationMetrics
Mean Insert Size	Picard InsertSizeMetrics
Target Bases with zero coverage [%]	Picard HSMetrics	Percent target (config[reference][design_intervals]) bases with 0 coverage
% Adapter	fastp

Program versions

default container: docker://hydragenetics/common:0.1.9

Program	Version	Container
Arriba	2.3.0	docker://hydragenetics/arriba:2.3.0
CNVkit	0.9.9	docker://hydragenetics/cnvkit:0.9.9 docker://python:3.9.9-slim-buster
Crumble	0.8.3	docker://hydragenetics/crumble:0.8.3
fastp	0.20.1	docker://hydragenetics/fastp:0.20.1
FastQC	0.11.9	docker://hydragenetics/fastqc:0.11.9
FusionCatcher	1.33	docker://blcdsdockerregistry/fusioncatcher:1.33
GATK	4.2.2.0	docker://hydragenetics/gatk4:4.2.2.0
Manta	1.6.0	docker://hydragenetics/manta:1.6.0
Mosdepth	0.3.2	docker://hydragenetics/mosdepth:0.3.2
MultiQC	1.11	docker://hydragenetics/multiqc:1.11
Parabricks	4.0.0-1	docker://nvcr.io/nvidia/clara/clara-parabricks:4.0.0-1
Peddy	0.4.8	docker://hydragenetics/peddy:0.4.8
Picard	2.25.0	docker://hydragenetics/picard:2.25.0
Pindel	0.2.5b9	docker://hydragenetics/pindel:0.2.5b9
RSeQC	4.0.0	docker://hydragenetics/rseqc:4.0.0
simple_sv_annotation.py	2019.02.18	docker://hydragenetics/simple_sv_annotation:2019.02.18
snpEff	5.0	docker://hydragenetics/snpeff:5.0
SortMeRNA	4.3.4	docker://hydragenetics/sortmerna:4.3.4
SPRING	1.0.1	docker://hydragenetics/spring:1.0.1
STAR	2.7.10a	docker://hydragenetics/star:2.7.10a
STAR-Fusion	1.10.1	docker://trinityctat/starfusion:1.10.1
svdb	2.6.0	docker://hydragenetics/svdb:2.6.0
VEP	105	docker://hydragenetics/vep:105

🧑‍⚖️ Rule Graph Parabricks version

🧑‍⚖️ Rule Graph Sentieon version