What are the core workflows in a gene lab from sample receipt to result delivery

A gene lab operates through a series of coordinated workflows that encompass both wet-lab and dry-lab processes, starting from sample receipt and culminating in the delivery of results.

Why it matters

• Data Integrity: Proper workflows ensure the accuracy and reliability of genetic data, which is critical for downstream analysis.
• Efficiency: Streamlined processes reduce turnaround times, allowing labs to handle more samples and deliver results faster.
• Quality Control: Rigorous quality checks at each stage minimize the risk of errors, ensuring high-quality outputs.
• Regulatory Compliance: Adhering to standardized workflows helps labs meet regulatory requirements and maintain accreditation.
• Traceability: Comprehensive documentation and barcoding facilitate sample tracking, enhancing accountability and reproducibility.

How to apply

1. Sample Receipt:

   • Log incoming samples into the laboratory information management system (LIMS).
   • Verify sample integrity and condition upon arrival.

2. Sample Verification:

   • Confirm sample identity against accompanying documentation.
   • Assess sample quality (e.g., DNA concentration, purity, RNA Integrity Number (RIN) for RNA).

3. Barcoding:

   • Assign unique barcodes to each sample for tracking throughout the workflow.
   • Ensure that barcodes are linked to the corresponding data in the LIMS.

4. Library Preparation:

   • Perform fragmentation of nucleic acids.
   • Carry out end repair, adapter ligation, and amplification via PCR.
   • Include positive and negative controls to validate the library preparation process.

5. Sequencing:

   • Plan sequencing runs based on desired depth and breadth of coverage.
   • Choose between short-read or long-read sequencing technologies based on project requirements.

6. Post-Run Quality Control:

   • Evaluate primary data quality metrics such as Q30 scores, cluster density, and yield.
   • Determine whether the data meets quality standards to proceed with analysis or requires re-sequencing.

7. Data Analysis and Result Delivery:

   • Analyze sequencing data using bioinformatics tools.
   • Generate reports and deliver results to stakeholders, ensuring clear communication of findings.

Metrics to track

• Sample Quality Metrics: DNA/RNA concentration, purity ratios, and RIN scores.
• Library Preparation Efficiency: Yield of libraries and success rates of control samples.
• Sequencing Quality Metrics: Q30 scores, cluster density, and overall yield from sequencing runs.
• Turnaround Time: Time taken from sample receipt to result delivery.
• Error Rates: Frequency of re-runs or failed samples to identify potential workflow issues.

Pitfalls

• Inadequate Sample Verification: Failing to properly verify sample identity can lead to erroneous results and compromised data integrity.
• Poor Quality Control: Skipping quality checks can result in low-quality data that may necessitate rework or invalidate results.
• Inefficient Workflow Coordination: Lack of communication between wet-lab and dry-lab teams can cause delays and errors in sample processing.
• Inconsistent Documentation: Incomplete or inaccurate records can hinder traceability and compliance with regulatory standards.
• Neglecting Controls: Omitting positive and negative controls during library preparation can mask issues that affect data quality.

Key takeaway: Establishing a robust and systematic workflow in a gene lab is essential for ensuring data quality, efficiency, and compliance.