Coverage and Sample Quality Control in Next Generation Sequencing (NGS)

Estimated reading time: 3 minutes

• Understanding Coverage in NGS
• Sample Quality Control in NGS
• Methods of Sample Validation
• Conclusion

Next Generation Sequencing (NGS) is a revolutionary technology used to determine DNA sequences. It has numerous applications, including exome sequencing and methylation analysis, contributing significantly to our understanding of biological processes. While NGS offers unparalleled output and flexibility, ensuring the accuracy and quality of sequencing data requires careful attention to certain parameters before starting an experiment.

This guide explains two crucial aspects of NGS: coverage (depth) and sample quality control, both of which are vital to producing reliable and meaningful results.

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Understanding Coverage in NGS

What is Coverage?
Coverage, or depth, refers to the number of times a nucleotide in a DNA sequence is read during an NGS run. High coverage ensures that every nucleotide is sequenced multiple times, reducing the impact of errors and improving the accuracy of the data.

• Why is it important?
  Even with modern NGS platforms achieving accuracies above 99%, errors such as misreading a nucleotide can occur. For instance, at 99% accuracy, 1 base pair in every 100 may be read incorrectly. When generating large datasets, these small errors can accumulate, impacting results. Sequencing nucleotides multiple times addresses this limitation.

How is Coverage Calculated?
Coverage per genome is calculated as:

Coverage=Total Output Generated (base pairs)Genome Size (base pairs)\text{Coverage} = \frac{\text{Total Output Generated (base pairs)}}{\text{Genome Size (base pairs)}}Coverage=Genome Size (base pairs)Total Output Generated (base pairs)​

For example, the human genome (approximately 3 billion base pairs) sequenced on an Illumina HiSeq 2500 with a maximum output of 1,000 gigabase pairs would provide approximately 333X coverage.

Choosing the Right Coverage
The optimal coverage depends on the specific application. For instance:

• Whole genome sequencing: Requires high coverage to ensure accuracy across the genome.
• Exome sequencing: Lower coverage may suffice as it targets only coding regions.
• Methylation analysis: Requires sufficient depth to accurately detect methylation patterns.

For tailored recommendations, consulting NGS experts is advisable.

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Sample Quality Control in NGS

Before sequencing, it is crucial to validate the sample library both quantitatively and qualitatively. This ensures the sequencing reaction will yield high-quality data.

1. Quantitative Validation
The sample’s DNA quantity must meet the specifications of the library preparation protocol. Insufficient or excessive DNA can lead to:

• Flow cell saturation (too much DNA)
• Reduced coverage (too little DNA)

2. Qualitative Validation
A good-quality library contains a diverse set of DNA fragments with minimal duplicates. Duplicate fragments can bias the sequencing output, as PCR amplification may result in over-representation of certain fragments.

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Methods of Sample Validation

a. Quantification Techniques

• qPCR (Quantitative Polymerase Chain Reaction):
  This is the most sensitive method, ideal for quantifying adapter-ligated fragments, especially when PCR amplification has not been performed. While precise, it is time-consuming.
• Qubit (Fluorometric Method):
  This method is faster than qPCR and involves mixing the sample with a fluorescent dye that binds to DNA. However, it is less sensitive and not suitable for libraries without PCR amplification.

b. Qualitative Validation with the Bioanalyzer
The Bioanalyzer checks the size distribution of library fragments to ensure the appropriate sizes are present before sequencing. It uses gel chips loaded with the sample and visualizes the results as peaks on a graph.

• Peaks indicate:
  • Position: The size of the fragments.
  • Height: The quantity of fragments of a specific size.

The Bioanalyzer’s accurate assessment helps identify any issues with the library before sequencing begins.

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Conclusion

Achieving high-quality sequencing data in NGS requires attention to both coverage and sample quality control. By sequencing nucleotides multiple times (adequate coverage) and ensuring proper validation of the sample library, researchers can mitigate errors and produce reliable results.