Quality Control (QC) for Next-Generation Sequencing (NGS) is essential for ensuring reliable, accurate, and reproducible sequencing data. QC is applied at multiple stages: starting material, library preparation, and post-sequencing data analysis.
QC of Starting Material
At the starting nucleic acid stage, QC involves spectrophotometric, fluorimetric (e.g., Qubit, PicoGreen), and electrophoretic methods (e.g., Bioanalyzer) to assess sample quality, quantity, and integrity. The RNA Integrity Number (RIN) is commonly used for RNA samples; a RIN >7 is generally considered acceptable.
Library Preparation Quality Control
Library QC typically employs Bioanalyzer or similar platforms to check fragment size distribution and detect contaminants such as adapter dimers, which can undermine cluster generation, especially on Illumina platforms.
Post-Sequencing Data Quality Control
After sequencing, raw data QC relies on software tools like FastQC to evaluate base quality scores, sequence duplication levels, GC-content, presence of adapter contamination, and overrepresented sequences. Mapping statistics (percent reads mapped uniquely to a reference genome) supplement quality evaluation and reflect the utility of the data for downstream analysis.
Integrating Quality Control Across the Workflow
Integrating QC at all these checkpoints—sample quality, library metrics, and sequencing data quality—is critical to save time and costs associated with sequencing failures or poor data quality. Moreover, tailored QC thresholds may vary based on the experiment (e.g., DNA-seq, RNA-seq, ChIP-seq), assay, and biological context. Comprehensive bioinformatics pipelines that include QC filtering and trimming contribute to the ultimate quality and interpretability of NGS datasets.
In summary, NGS QC is a multi-step, multi-metric process encompassing assessment of nucleic acid integrity, library preparation quality, and raw sequence data metrics for reliable and accurate genomic analyses.
