End-to-end WES and WGS examples

These examples use the bundled cbicall-core GATK 4.6 workflows. Complete the installation, install the resource bundle, and run cbicall doctor before starting.

Single sample

1. Prepare paired FASTQ files

Place the read pairs in one sample directory. Their names must share a prefix and distinguish R1 from R2:

CNAG999_exome/CNAG99901P_ex/
  CNAG99901P_ex_S1_L001_R1_001.fastq.gz
  CNAG99901P_ex_S1_L001_R2_001.fastq.gz

See Naming Conventions for supported file names and multi-lane samples.

2. Create the parameters YAML

Create wes_single.yaml:

mode:             single
pipeline:         wes
workflow_backend: bash
software_stack:   gatk-4.6
input_dir:        CNAG999_exome/CNAG99901P_ex
genome:           b37
cleanup_bam:      false

workflow_backend can be bash, snakemake, nextflow, or cromwell for the bundled GATK 4.6 WES/WGS workflows. The selected backend must be installed and available to CBIcall.

Running WGS

Use a directory containing WGS FASTQ files and change pipeline to wgs. Select b37 or hg38 to match the intended WGS reference resources:

mode:             single
pipeline:         wgs
workflow_backend: bash
software_stack:   gatk-4.6
input_dir:        CNAG999_genome/CNAG99901P_wg
genome:           hg38
cleanup_bam:      false

3. Run CBIcall

cbicall run -p wes_single.yaml -t 4

CBIcall prints the resolved workflow and run directory before launch. A successful run ends with the workflow log and the paths to run-report.json and run-report.html.

4. Inspect the outputs

The generated run directory contains:

cbicall_bash_gatk-4.6_wes_single_b37_*/
  01_bam/
  02_varcall/
  03_stats/
  logs/
  log.json
  run-report.json
  run-report.html

The principal files are:

File	Use
02_varcall/<id>.hc.QC.vcf.gz	Final filtered VCF.
02_varcall/<id>.hc.g.vcf.gz	Per-sample gVCF for later cohort joint genotyping.
03_stats/<id>.coverage.txt	Lightweight coverage summary.
03_stats/<id>.sex.txt	VCF-derived sex inference for QC.
run-report.html	Human-readable run and provenance report.

Cohort

1. Prepare per-sample gVCFs

Cohort mode starts from gVCFs produced by WES or WGS single-sample runs. Create a two-column, tab-separated sample map with one absolute gVCF path per sample:

SAMPLE_001	/absolute/path/SAMPLE_001.hc.g.vcf.gz
SAMPLE_002	/absolute/path/SAMPLE_002.hc.g.vcf.gz

Each gVCF must have its corresponding index.

2. Create the parameters YAML

Create wes_cohort.yaml:

mode:             cohort
pipeline:         wes
workflow_backend: bash
software_stack:   gatk-4.6
genome:           b37
sample_map:       ./sample_map.tsv

Relative paths such as sample_map are resolved from the YAML file location.

3. Run joint genotyping

cbicall run -p wes_cohort.yaml -t 4

For large cohorts, use the documented shard and finalize stages instead of constructing independent filtered chromosome VCFs. See WES/WGS Cohort Joint Genotyping.

4. Inspect the outputs

cbicall_bash_gatk-4.6_wes_cohort_b37_*/
  01_genomicsdb/
  02_varcall/
  logs/
  log.json
  run-report.json
  run-report.html

The final joint callset is 02_varcall/cohort.gv.QC.vcf.gz. The raw joint-genotyped VCF and conditional VQSR outputs are also retained in 02_varcall/.

See Outputs for the complete file reference and Configuration Reference for all accepted YAML keys.