Quality Control & Verification
This section should outline the quality control (QC) measures and verification steps implemented to ensure the accuracy, reliability, and reproducibility of the procedure described in the SOP. Clear documentation of QC practices helps maintain confidence in results and supports compliance with regulatory and accreditation standards.
This section may include:
Control Samples: Use positive and negative controls, as well as samples of known characteristics (e.g. variant types, resistance markers, reference strains) to verify that the procedure consistently produces expected results.
Internal Quality Assurance (IQA) and External Quality Assurance (EQA): Document participation in internal QC programs (e.g. repeatability checks, peer review of results) and external proficiency testing schemes to benchmark performance against other laboratories or organisations.
Software and Code Verification: For computational workflows, include measures such as code review, unit testing, and continuous integration (CI) testing to ensure reproducibility, detect errors early, and maintain compatibility across versions.
Verification Procedures: Outline the steps taken to verify the pipeline’s performance.
Example content:
🧬 Bioinformatics QC Procedure
Example content from a bioinformatics SOP outlining the procedure for assessing the quality of Illumina sequencing data prior to downstream bioinformatics analyses.
Verification & Quality Control
The bioinformatics QC pipeline has been developed to ensure that data passed to downstream analyses is of sufficient quality to produce reliable results.
The pipeline was validated against a multi‑project dataset consisting of paired‑end sequencing reads generated from all microorganisms supported by the laboratory (see validation document VA043). To ensure continued performance, this dataset must be periodically processed through the pipeline (every 6-months). If the pipeline is updated, the validation dataset must be rerun before deploying the updated version into production.
👩🔬 Staff Training Procedure
This section is not relevant for this example.
🌌 Galaxy Training Procedure
This section is not relevant for this example.
🧪 Laboratory Procedure
Example content from a wet-lab SOP outlining the procedure for preparing sequencing libraries using the Illumina DNA Prep kit.
Quality Control & Verification
Control Samples
Each library preparation run must include a no-template control (NTC) to monitor for contamination. Positive controls or samples with known characteristics (e.g., reference strains, known variants, resistance markers) should also be included as specified in workstream-specific SOPs to verify that the procedure consistently produces expected results.
Verification
The pipeline was validated using DNA extracted from all microorganisms supported by the laboratory (see validation document VA021). To ensure continued performance, this procedure must undergo verification whenever new batches of reagents, consumables, or kits are introduced, significant changes are made to equipment, or other factors arise that could affect performance.
💻 Code Update & Review Procedure
Example content from an SOP outlining the procedure for updating and reviewing code within the Luma Genomics Unit (LGU).
Verification & Quality Control
To ensure accuracy, reproducibility, and compliance with coding standards, the following quality control measures are implemented during the code review process:
Peer Review: All merge requests must be reviewed by at least one qualified team member who is not the original developer. Reviewers assess correctness, adherence to coding standards, and potential security vulnerabilities.
Testing Verification: Developers are required to run unit tests and integration tests prior to submitting a merge request. Reviewers confirm that appropriate tests have been executed and may request additional tests if gaps are identified.
Version Control & Audit Trail: All changes are tracked in GitLab. Branch protection rules enforce that code cannot be merged without passing review and automated tests. Version tags are assigned to ensure reproducibility.
Documentation Check: Reviewers verify that any new or modified functionality is documented in accordance with LGU guidelines, ensuring clarity for future users.
Continuous Improvement: Required improvements (R) must be implemented before merging. Suggested improvements (S) are logged for future updates, supporting ongoing enhancement of code quality.