Stability Analysis

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Stability Analysis Service

Next-generation sequencing (NGS) technology has become a core tool in modern life science and medical research, widely used in fields such as genomics, transcriptomics, and epigenetics. However, the generation of NGS data is complex and multi-step, and the stability and reliability of its results directly impact the accuracy of scientific conclusions. Therefore, systematic stability analysis of NGS technology is crucial. This article will explore stability analysis of NGS technology from four perspectives: principle, workflow, analysis cycle, and application scenarios.

Service Principle

Stability analysis of NGS technology aims to assess the variability and consistency of each step in the sequencing process to ensure the reproducibility and accuracy of data output. Its core principle is to quantify technical errors and systematic biases through statistical quality control (QC) metrics and replicate experiments.

Key technical indicators include:

1. Sequencing depth uniformity: This assesses the uniformity of the distribution of sequencing reads across the genome or target region. Uneven coverage can lead to reduced detection sensitivity in specific regions.

2. Base quality score (Q-score): This reflects the accuracy of sequencing base calls. Q30 (0.1% error rate) is a commonly used standard.

3. GC content deviation: Abnormal GC content may lead to reduced amplification or sequencing efficiency in certain regions.

4. Repeatability rate: A high repeatability rate may indicate PCR amplification bias or sample degradation.

5. Batch effect: Systematic variation caused by time, reagents, or operator differences.

Stability analysis typically compares multiple experiments or technical replicates of the same sample to calculate statistics such as coefficient of variation (CV), correlation coefficients (such as Pearson R²), and principal component analysis (PCA) to distinguish between technical variation and true biological differences.

Workflow

NGS stability analysis is performed throughout the entire sequencing workflow, including both the wet lab and dry lab stages:

Sample preparation

* Sample quality assessment: DNA/RNA integrity is assessed using a fluorometer or electrophoresis assay (e.g., RIN value).

* Standardization: Using standardized processes for fragmentation, end-repair, adapter ligation, and PCR amplification reduces operator variability.

Sequencing Run Phase

* Internal Quality Control: The sequencer's built-in quality control module (such as Illumina's Sequence Control Software) monitors signal intensity, error rate, and cluster density in real time.

* Standard Insertion: Base call errors are corrected using standards with known sequences (such as the PhiX control).

Data Analysis Phase

* Raw Data QC: Read quality, GC content, and adapter contamination are assessed using tools such as FastQC.

* Post-alignment Analysis: Calculation of coverage uniformity (such as BedTools), duplication rate (such as Picard MarkDuplicates), and target region capture efficiency (for targeted sequencing).

* Statistical Consistency Test: Analyze the correlation and variability of replicate samples using tools such as R packages (DESeq2 for transcriptomes) or custom scripts.

Reporting and Improvement

* Generate quality control reports and address weaknesses (such as PCR bias) by optimizing the protocol or introducing technologies such as UMIs (Unique Molecular Identifiers).

Application Scenarios

Clinical Diagnosis:In tumor genetic testing or genetic disease screening, stability directly impacts the risk of misdiagnosis. For example, stability analysis is used to ensure the reproducibility of low-frequency mutations (e.g., <5% VAF).

Drug Development:Biomarker discovery requires highly reproducible data. Stability analysis helps distinguish drug response signals from technical noise.

Basic Research:For example, in single-cell sequencing, technical variation can mask cellular heterogeneity, necessitating stability control (e.g., UMI correction) to improve accuracy.

Regulatory Compliance:
IVD (in vitro diagnostic) product development must meet certification requirements such as CLIA and CAP, and stability data is a critical component of submission materials.

Stability analysis of NGS technology is the cornerstone of ensuring data reliability and involves multi-metric, multi-stage systematic validation. As sequencing technology evolves toward single-molecule and spatial omics, stability analysis methods also require continuous innovation (e.g., incorporating machine learning for anomaly detection). Establishing a standardized, periodic stability monitoring system will not only enhance the rigor of scientific research but also accelerate the application of NGS in precision medicine.

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