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🏗️ Architecture

Overview

CBIcall is a thin orchestration layer around one or more concrete pipelines. Its main responsibilities are:

  • Reading a YAML configuration file
  • Validating required parameters and paths
  • Resolving the selected pipeline and workflow engine
  • Preparing the project directory structure
  • Calling the appropriate workflow scripts (Bash or Snakemake)
  • Managing logs and collecting results in a standard layout

The actual bioinformatics work (alignment, variant calling, QC) is implemented in modular pipelines that can be extended or replaced.

Main components

At a high level, CBIcall consists of:

  • Python execution driver
    Parses the YAML configuration, validates parameters, resolves paths and dispatches execution to the selected pipeline and workflow engine.

  • Pipelines
    Implement the variant-calling workflows for WES, WGS and mtDNA analyses. Each pipeline lives in its own directory and can provide Bash and/or Snakemake workflows.
    Common parameters are loaded via env.sh.

  • Workflow engines
    Control how the workflow is executed:

    • Bash scripts for simple, transparent runs
    • Snakemake workflows for dependency tracking and parallelization

  • Project layout and logs
    A standard output structure with separate 01_bam/, 02_varcall/, 03_stats/ and logs/ directories reused across all pipelines.

  • External data
    Executables and databases for third-party tools, reference genomes and accessory data.

Architecture diagram

Diagram

Directory structure

<project_dir>/
  01_bam/
  02_varcall/
  03_stats/
  logs/

Typical usage:

  • Intermediate alignment and BAM files are stored under 01_bam/.
  • Variant-calling outputs (gVCFs, VCFs and related files) are stored under 02_varcall/.
  • Summary statistics and QC metrics are collected under 03_stats/.
  • Log files for all steps are stored under logs/.

Execution model

CBIcall supports two execution modes:

  • Single mode
    Each sample is processed independently.

  • Cohort mode
    Joint analysis using per-sample gVCFs from previous single runs.

The workflow engine is selected in the YAML:

  • workflow_engine: bash
  • workflow_engine: snakemake

Supported pipelines

Pipeline Mode Genome GATK version Status / Notes
WES single b37 (default) gatk-3.5, gatk-4.6 ✓ Supported
WES cohort b37 (default) gatk-3.5, gatk-4.6 ✓ Supported
WGS single b37 (default), hg38 gatk-4.6 ✓ Supported
WGS cohort b37 (default), hg38 gatk-4.6 ✓ Supported
MIT (mtDNA) single rsrs (fixed) gatk-3.5 ⚠ Not supported on ARM / aarch64
MIT (mtDNA) cohort rsrs (fixed) gatk-3.5 ⚠ Not supported on ARM / aarch64

✓ Fully supported configuration ⚠ Platform limitation

Date: March-2026

Extensibility

New pipelines can be added without modifying the core system:

  • Each pipeline lives under workflows/<engine>/<name>
  • The execution driver resolves the pipeline implementation through the workflow registry
  • Pipelines reuse the same directory layout and logging conventions
  • Pipelines may support single and/or cohort mode

See:

➡️ Adding a pipeline