Quality and Quantity of Nucleic Acids in NGS

To get the most from next-generation sequencing (NGS), scientists need to know what they’re working with and continue to assess samples and data throughout the process. To keep track of both the quality and quantity of nucleic acids, a variety of new tools and techniques simplify the processes.

For a useful overview on getting started with NGS, see “Fundamentals of NGS sample preparation” by Andrew Gane, strategy and technology manager at Cytiva (formerly GE Life Sciences). As he pointed out: “Getting your DNA ready for sequencing requires the preparation of a sequencing library as well as a few other steps that depend on the type of sample and the NGS platform.”

The size of the DNA sample for NGS must meet the requirements of the platform. To help scientists explore the details of this prerequisite, Gane wrote “Size selection brings better data to NGS workflows.” He noted that “library fragment size selection is a key step toward data quality.”

Much of the preparation of an NGS library can now be automated. Still, scientists must keep in mind many factors to get the best results from NGS runs.

Limitations in library preparation

Even with automated options for preparing an NGS library and many articles written about this process, there is more to learn about these key steps. As an example, Nathan Hillson—computational staff scientist at the Lawrence Berkeley National Laboratory—and his colleagues wrote: “As the throughput of sequencing experiments increases, the preparation of conventional multiplexed libraries becomes more labor intensive. Conventional library preparation typically requires quality control (QC) testing for individual libraries such as amplification success evaluation and quantification, none of which occur until the end of the library preparation process.”

So, Hillson and his colleagues developed an NGS workflow that incorporates quantitative PCR (qPCR) and allows QC in a single tube with a single reagent. As the team pointed out: “qPCR enabled individual library quantification for pooling in a single tube without the need for additional reagents” and “a melting curve analysis was implemented as an intermediate QC test to confirm successful amplification.” This technique allows pooled samples that use indexed libraries. As Hillson’ team reported: “Sequencing analysis showed comparable percent reads for each indexed library, demonstrating that pooling calculations based on qPCR allow for an even representation of sequencing reads.”

This streamlined method is easier, and the scientists noted that it could also reduce errors. Although the researchers only tested the method on plasmids and bacteria genomes, the team stated that the technique’s “versatility could lead to successful application across other library types.”

Keys to consistency

“One of the biggest challenges in NGS workflows is consistency,” said Ruediger Salowsky—marketing and support director, biomolecular analysis division, Agilent Technologies. “Consistency is key to reproducible results, and a major hurdle can be sample variability.”

Electrophoresis-based QC tools, such as the Agilent TapeStation systems, “give researchers the ability to objectively assess quality and quantity throughout the entire NGS workflow,” Salowsky said. “This includes QC steps for the incoming samples, intermediate products during library preparation and final libraries.”

Scientists apply NGS to a wide range of sample types—coming from different sources and tissue types, which “can exhibit large variations in nucleic-acid quality and quantity,” Salowsky said. “Without quality control to standardize inputs, variable sample quality and quantity limit the conclusions that can be drawn from NGS data, can necessitate costly resequencing runs, and make optimizing workflows more of a challenge.”

With so much written about QC in NGS, it tends to be a standard operating procedure, but even a normal process needs a review or update now and then. “While QC of the starting sample is common, comprehensive QC is not always prioritized throughout the NGS workflow,” Salowsky said. Monitoring the quality of a sample throughout the workflow “is the foundation of accurate and reliable results in NGS experiments,” he noted.

So, QC needs to be a consistent part of an NGS process. Plus, one QC step won’t be enough. “By integrating QC checkpoints at key steps throughout the NGS workflow, researchers can save significant time, money, and effort—by ensuring that every sample is viable before it’s sequenced,” Salowsky noted.

The data analysis of an NGS run must also be included in the QC process. “Due to the rapid explosion of data generated with NGS methods, one of the most significant challenges is bioinformatics,” Salowsky pointed out. “Constructing and implementing the bioinformatics analytical pipeline is one of the more difficult parts of an NGS experiment due to its complexity and sensitivity to perturbations in raw sequencing data.”

Exploring quality options

Scientists continue to explore other ways to apply QC in NGS, especially in specific applications. The particular task at hand plays a big part in determining the best ways to maintain sample quality throughout the workflow.

As an example, scientists in Korea developed procedures to maintain control of sample quantity and quality in studying somatic mutations with NGS. This team noted the importance of accurately detecting somatic mutations in clinical applications. They stated, however, that “specific applications or guidelines, especially for quantitative QC, are currently insufficient.” So, this group developed a method of quantitative and qualitative QC to detect specific kinds of somatic mutations called clonal hematopoiesis of indeterminate potential. With this method, these scientists reported: “Accuracy, analytical sensitivity, analytical specificity, qualitative precision (concordance), and limit of detection achieved were 99.87%, 98.53%, 100.00%, 100.00%, and 1.00%, respectively.” Such results make this method useful in clinical NGS assays, and the researchers added: “This approach could also be theoretically expanded to a general NGS assay for detecting somatic variants.”

In all NGS runs, the results depend on the quality and quantity of the sample. Those features must also complement the requirements of the sequencing platform being used. In basic research, NGS QC ensures accurate and repeatable results. In clinical applications, correct NGS results turn critical—perhaps ensuring that a patient receives the proper medication or even the correct dose—because an error could be life-threatening. Consequently, even the most advanced NGS platform only performs as accurately as a sample allows.