Use case I: SNV and CNV calling from Whole exome sequencing data

1. Use case I: SNV and CNV calling from Whole exome sequencing data#

1.1. Background#

Whole-exome sequencing (WES) is widely used in clinical and translational genomics to interrogate coding regions of the genome and identify clinically relevant variants. Key applications include:

  1. ​​Rare Disease Diagnosis​​: Rapid detection of pathogenic variants in undiagnosed genetic disorders, reducing diagnostic odysseys.

  2. ​​Cancer Genomics​​: Profiling somatic and germline mutations to guide targeted therapy and risk assessment. ​​3. Carrier Screening​​: Identifying recessive mutations in prospective parents for reproductive planning. ​​Pharmacogenomics​​: Assessing drug-response variants to optimize treatment regimens.

  3. WES improves diagnostic yield while remaining more cost-efficient than whole-genome sequencing in many clinical settings.

The ClinDet WES workflow supports whole-exome, panel, and targeted DNA sequencing data. It performs somatic and germline variant calling, supports both paired tumor-normal and tumor-only analyses, and integrates multiple tools for detecting single nucleotide variants (SNVs), small insertions and deletions (INDELs), copy-number variants (CNVs), and structural variants (SVs), followed by quality control and downstream reporting.

In this example, we use ClinDet to analyze whole-exome sequencing samples from the publicly available Chinese Glioma Genome Atlas (CGGA) dataset. Reads are aligned to the human b37 reference genome, followed by somatic mutation and copy-number analysis.

glioma

1.2. Why this case matters#

This case serves as a practical paired-WES example for clinical cancer genomics. It demonstrates how ClinDet can combine multiple somatic callers, germline callers, and CNV tools in a single analysis, while still keeping the configuration compact enough for routine project-level use.

1.3. Setup a project folder#

Note

Before starting the analysis, please ensure that you have set up the analysis environment using the build_conda_envs.sh script.

Create a folder named project/CGGA_WES in your home directory and activate the Clindet conda environment.

mkdir -p ~/projects/CGGA_WES
cd ~/projects/CGGA_WES
conda activate clindet

1.4. Download data and prepare the sample sheet#

Download data from the GSA database using wget and prepare the sample information file.

cd ~/projects/CGGA_WES
mkdir -p data && cd data
## sample CGGA_D14 tumor-sample paired fqs
wget -c --no-check-certificate -O T_CGGA_D14_r1.fq.gz https://download.big.ac.cn/gsa-human/HRA000071/HRR025119/HRR025119_f1.fq.gz
wget -c --no-check-certificate -O T_CGGA_D14_r2.fq.gz https://download.big.ac.cn/gsa-human/HRA000071/HRR025119/HRR025119_r2.fq.gz
wget -c --no-check-certificate -O B_CGGA_D14_r1.fq.gz https://download.big.ac.cn/gsa-human/HRA000071/HRR024833/HRR024833_f1.fq.gz
wget -c --no-check-certificate -O B_CGGA_D14_r2.fq.gz https://download.big.ac.cn/gsa-human/HRA000071/HRR024833/HRR024833_r2.fq.gz

## sample CGGA_653 tumor-sample paired fqs
wget -c -O T_CGGA_653_r1.fq.gz	ftp://download.big.ac.cn/gsa-human/HRA000071/HRR025103/HRR025103_f1.fq.gz	wget -c -O T_CGGA_653_r2.fq.gz  ftp://download.big.ac.cn/gsa-human/HRA000071/HRR025103/HRR025103_r2.fq.gz
wget -c -O B_CGGA_653_r1.fq.gz	ftp://download.big.ac.cn/gsa-human/HRA000071/HRR024817/HRR024817_f1.fq.gz
wget -c -O B_CGGA_653_r2.fq.gz	ftp://download.big.ac.cn/gsa-human/HRA000071/HRR024817/HRR024817_r2.fq.gz

Next, create a CSV file named pipe_wes.csv in the ~/projects/CGGA_WES directory with the following content:

Tumor_R1_file_path,Tumor_R2_file_path,Normal_R1_file_path,Normal_R2_file_path,Sample_name,Target_file_bed,Project
/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_D14_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_D14_r2.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_D14_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_D14_r2.fq.gz,CGGA_D14,/AbsoPath/of/target.bed,CGGA_WES
/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_653_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/T_CGGA_653_r2.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_653_r1.fq.gz,/AbsoPath/of/projects/CGGA_WES/data/B_CGGA_653_r2.fq.gz,CGGA_653,/AbsoPath/of/target.bed,CGGA_WES

1.5. Prepare the YAML workflow config#

In the current workflow, you no longer need to create a project-specific snake_wes.smk file. Instead, prepare a YAML configuration file and pass it to Snakemake with --configfile.

For this example, create test/CGGA_config.yaml and update the following fields:

  1. project.output_dir: output directory for this analysis.

  2. project.genome_version: reference genome version, such as b37.

  3. project.recal_BQSR: whether to run BQSR. Set False to skip it.

  4. project.vcf2maf: VCF-to-MAF mode.

  5. project.sample_sheet: absolute path to the sample sheet CSV file.

  6. run_params.somatic_caller_list: somatic SNV/INDEL callers to run.

  7. run_params.stages: workflow stages to run, for example conpair, call_mut, and report.

  8. run_params.germ_caller_list: germline callers to run.

  9. run_params.somatic_cnv_list: somatic CNV callers to run.

  10. run_params.somatic_sv_list: somatic SV callers to run.

  11. run_params.tumor_only_caller and run_params.tumor_only_cnv_caller: callers used for tumor-only samples.

  12. run_params.purple_sv: SV caller used by PURPLE-related downstream steps.

Note

project:
  output_dir: 'test'
  genome_version: 'b37'
  recal_BQSR: False
  vcf2maf: 'raw'
  sample_sheet: '/public/ClinicalExam/lj_sih/projects/project_clindet/data/CGGA_primary_sample_info.csv'
run_params:
  somatic_caller_list:
    - HaplotypeCaller
    - strelkasomaticmanta
    - cgppindel
    - caveman
    - muse
    - Mutect2
  stages:
    - conpair
    - call_mut
  germ_caller_list:
    - sage
    - caveman
  somatic_cnv_list:
    - defulet
    - ASCAT
    - facets
  somatic_sv_list:
    - BRASS
    - delly
    - gridss
    - igcaller
    - linx
    - svaba
    - Manta
  tumor_only_caller:
    - sage
  tumor_only_cnv_caller:
    - freec
  purple_sv: "gridss"

1.6. Configuration rationale#

This YAML configuration is designed for a paired WES cancer cohort. We keep genome_version=b37, disable recal_BQSR to reduce runtime, and use call_mut together with conpair so that both somatic calling and sample-pair quality checks are performed. The selected somatic callers provide complementary support for SNV/INDEL discovery, while ASCAT and facets are retained as CNV methods commonly used in exome-based tumor-normal analyses.

1.7. Run clindet#

There are two ways to run ClinDet in this example:

  1. run on a local node

  2. submit to HPC through slurm

1.7.1. Run on local node#

After preparing the YAML config, you can run the analysis on a local node with:

snakemake -c 30 --config run_type=wes \
--configfile test/CGGA_config.yaml \
--rerun-triggers mtime --benchmark-extended \
--use-singularity --singularity-args "--bind /your/home/path:/your/home/path" \
--latency-wait 300 --use-conda --conda-frontend conda -k

1.7.2. Submit to HPC use slurm#

We provide a Slurm config.yaml file under the clindet/workflow/config_slurm folder. When submitting jobs, users can specify the partition parameter in the YAML file to the desired node name according to their needs.

executor: cluster-generic
cluster-generic-submit-cmd:
  mkdir -p logs/{wildcards.project}/slurm &&
  sbatch
    --partition=SVC
    --cpus-per-task={threads}
    --job-name={rule}
    --output=logs/{wildcards.project}/slurm/{wildcards.sample}_{rule}.%N.%j.out
default-resources:
  - partition=SVC
latency-wait: 60
jobs: 50
keep-going: True
rerun-incomplete: True
printshellcmds: True
scheduler: greedy
use-conda: True

After preparing the YAML config, run the analysis with the following command:

nohup snakemake --config run_type=wes \
--configfile test/CGGA_config.yaml \
--profile workflow/config_slurm \
-j 30 --printshellcmds --rerun-triggers mtime --benchmark-extended \
--use-singularity --singularity-args "--bind /your/home/path:/your/home/path" \
--latency-wait 300 --use-conda --conda-frontend conda -k >> CGGA.out &

1.7.3. Output#

1.7.4. Overview of output#

After success run, you will get the all the results under {project}/{genome_version}/results folder.

/WES/b37
├── logs
│   └── paired
└── results
    ├── cnv  **# Copy Number results**
    ├── dedup **# deduplication BAM files**
    ├── logs **# Task run logs**
    ├── maf  **# annotation somatic mutation MAF files**
    ├── maf_germline  **# annotation germline mutation MAF files**
    ├── mapped  **# annotation somatic mutation MAF files**
    ├── multiqc  **# annotation somatic mutation MAF files**
    ├── multiqc_data
    ├── multiqc_report_data
    ├── multiqc_report.html
    ├── qc  **# QC results for fastp conpair and so on**
    ├── recal **# BAM files after base recalibration**
    ├── report **# Case report files**
    ├── stats  **# BAM statistics info**
    ├── trimmed  **#temporary trimmed fastq and fastp output**
    ├── vcf **# RAW somatic mutation VCF files**
    └── vcf_germline **# RAW germline mutation VCF files**

1.7.5. What to expect#

For this case, the most informative outputs are the merged somatic MAF files, the CNV results from ASCAT and facets, and the QC outputs produced during paired-sample processing. Successful completion should allow readers to compare somatic calls across samples, inspect broad copy-number patterns, and confirm that the tumor-normal pairing behaves as expected.

1.8. Common pitfalls#

  1. sample_sheet should be an absolute path and must point to files visible inside the Singularity bind mount.

  2. genome_version in the YAML file must match the reference resources configured in the global config.yaml.

  3. Target_file_bed should be set correctly for each WES sample, otherwise coverage-aware downstream steps may not behave as expected.

  4. If conpair is enabled, tumor and matched normal samples must be correctly paired in the sample sheet.

1.8.1. case report#

There is a example case report of CGGA_P438 example report HTML