Automated Mutation Calling Case Study

Context

A life sciences team needed a reliable path from raw NGS reads to reviewed variant calls. Releases were slow and quality checks were mostly manual. The goal was to move to a predictable, testable flow without locking to a single vendor or instrument family.

Constraints

Vendor neutrality across common bench top NGS instruments
Readable failures with exact stage and reason
Automation friendly for CI and nightly runs
No disruption to current lab operations

Approach

1. Discovery

Mapped current paths, data shapes, and decision points. Wrote short design notes that the team could review in under ten minutes.

2. Architecture

Split the flow into small stages with explicit inputs and outputs. Used schemas to keep types honest. Added simple observability so stages report what they saw and produced.

3. Pipeline build

Ingest and QC with clear thresholds
Alignment to reference with versioning tracked
Variant calling with tuned parameters per assay
Post processing and annotation

4. Test strategy

Unit tests at each stage boundary
Golden sample end to end runs in CI
Schema validation gates for all outputs

Results

Release pace

Fewer blocked releases and simpler hotfixes due to smaller blast radius.

QA effort

Repeatable checks and golden runs reduced manual review time.

Confidence

Type safe stages and readable failures made issues quicker to locate.

Teams moved from long test passes to focused checks on the exact stage that changed.

Tech notes

Schema first design so data contracts are documented in code
Small modules that are easy to test and swap
Logs with enough context for quick triage

Where this helps

Useful for teams that want steady releases without trading off quality. Works well with instrument fleets that change over time.