Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework
Abstract
:1. Introduction
2. Quality Control Management with Laboratory Information Systems
2.1. Pre-Analytical
2.2. Analytical
2.2.1. Reagent Tracking and Inventory
2.2.2. Instrument Integration and Automation
2.2.3. Quality Control Checks and Tractability
QC Checkpoint 1: Post-PCR Amplification
QC Checkpoint 2: Library Preparation
QC Checkpoint 3: Post-Sequencing Run
QC Checkpoint 4: Pre-Processing
QC Checkpoint 5: Post-Reference Mapping
QC Checkpoint 6: Sample Mislabeling and Contamination
QC Checkpoint 7: “Bad” Mutations
Clonal and Repeated Sample Check
Turn-Around Time Check
2.2.4. Data Review, Results Authorization, and Release
2.3. Post-Analytical
- Performance comparison of different reagent lot numbers, equipment, operators, and test controls run in different batches. A useful control to monitor is a mixture of clonal samples with known nucleotide mixtures and the comparison of the frequency of those mixtures. A display of histograms of mutations in test controls or repeated samples with definable flags of signification deviation from historic mutation frequencies;
- Automated scheduling of equipment maintenance and alerts staff of appropriate QC tasks;
- Automated tracking and stock management of reagent and consumables;
- Automated notification to lab manager of specimens with increased turnaround time;
- Automated notification of low specimen volumes and identified bottle necks;
- The system should allow monitoring of equipment performance, such temperature logs, frequency of failed runs, environmental conditions, and any documentations required by accrediting bodies;
- The system should provide summaries of QC reports to supervisors for review, corrective and preventive actions;
- Trend interesting results that are of interest to public health, such as the identification of genetic or transmission clusters, or changes in the prevalence of certain drug-resistant mutations [54].
- Frequently update or investigate new bioinformatics software which cannot be locked down to traditional Information Technology (IT) change control processes often associated with universal software applications used in office settings;
- Have IT security experts imbedded within scientific computers to ensure the hardware and software are secure, protected and monitored against threats that could compromise the security of the data they hold;
- Facilitate evolution of laboratory test for HIV drug resistance. Changes in the status quo often require a business analyst, programmer and infrastructure personnel to analyze the requirement, develop/modify the application and maintain the infrastructure without impacting business continuity;
- Reduce licensing costs by eliminating redundant LIS in an organization.
3. Discussion
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Metric/Threshold | Sample Expected Value | Sample QC Tool |
---|---|---|
QC1: Post-PCR | ||
Amplicon | Negative control: no bandPositive control: band at correct size | Gel/Capillary electrophoresis |
QC2: Library Preparation | ||
Library size | Normal distribution around 300–500 bp | Bioanalyzer/Tapestation 1 |
Library concentration | 0.2 ng/μL | Bioanalyzer/Tapestation |
QC3: Post-Sequencing Run | See Hutchins et al. [8] | SAV 2 |
QC4: Pre-processing | See Hutchins et al. [8] | FastQC 3 |
QC5: Post-Reference Mapping (performed after final remapping) | ||
Sequence Coverage | PR: codon 10–93 RT: codon 41–238 IN: codon 51–263 | HIVDR Pipeline, Tablet 4, UGENE 5 |
Mean read depth | ≥1000 | HIVDR Pipeline, Tablet, UGENE |
QC6: Mislabeling/Contamination (Check for genetic relatedness) | ||
Nucleotide mixture | <3.5% nucleotide positions | MEGA 6 |
Sequences from same patient | <2.5% genetic dissimilarity | WHO BCCFE HIVDR QC 7 |
Intra-batch sample vs other sample | ≥0.5% genetic dissimilarity | WHO BCCFE HIVDR QC |
Sample vs control strain | ≥0.5% genetic dissimilarity | WHO BCCFE HIVDR QC |
Across-batch sample vs other sample | ≥0.5% genetic dissimilarity | WHO BCCFE HIVDR QC |
QC7: “Bad” Mutations/Variant Calls | ||
“Unusual” mutations | <1.0% | HIVdb-NGS 8 |
Signature APOBEC hypermutations | <3 | HIVdb-NGS |
APOBEC-context DRMs | <2 | HIVdb-NGS |
Stop codons | 0 | HIVdb-NGS |
Codon insertion/deletion | 0 | HIVdb-NGS |
Frameshift insertion/deletion | 0 | HIVdb-NGS |
Variant Calling | ||
Position depth | ≥100 reads | HIVDR Pipeline |
Q score | Q≥30 | HIVDR Pipeline |
Variant count | ≥5 reads | HIVDR Pipeline |
Turnaround Time | 5–6 Days | N/A |
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Capina, R.; Li, K.; Kearney, L.; Vandamme, A.-M.; Harrigan, P.R.; Van Laethem, K. Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework. Viruses 2020, 12, 645. https://doi.org/10.3390/v12060645
Capina R, Li K, Kearney L, Vandamme A-M, Harrigan PR, Van Laethem K. Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework. Viruses. 2020; 12(6):645. https://doi.org/10.3390/v12060645
Chicago/Turabian StyleCapina, Rupert, Katherine Li, Levon Kearney, Anne-Mieke Vandamme, P. Richard Harrigan, and Kristel Van Laethem. 2020. "Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework" Viruses 12, no. 6: 645. https://doi.org/10.3390/v12060645
APA StyleCapina, R., Li, K., Kearney, L., Vandamme, A. -M., Harrigan, P. R., & Van Laethem, K. (2020). Quality Control of Next-Generation Sequencing-Based HIV-1 Drug Resistance Data in Clinical Laboratory Information Systems Framework. Viruses, 12(6), 645. https://doi.org/10.3390/v12060645