Guide to Choosing the Right NGS Assay

Advancements in next-generation sequencing (NGS) technologies have improved and broadened the scope of sequencing applications. While this progress has benefited researchers, it has also increased the complexity of choosing the most appropriate assay to fulfill the goals of the research. This article will share expert advice on the important factors to consider when selecting an NGS assay for your research.

Key considerations for assay selection

Research objective

The first step to selecting the most suitable assay is to focus on the specific experimental objectives. “This decision-making process typically begins with identifying the key questions driving the research study,” explained Jeff Smith, NGS Precision Medicine Director of Clinical Next-Generation Sequencing and Oncology at Thermo Fisher Scientific. Smith noted that by understanding the specific questions that need to be answered, researchers can determine whether a broad approach like whole genome sequencing or a more focused approach like targeted sequencing best suits the study’s objectives. Aligning the choice of the assay with the research questions ensures that scientists can optimize their experimental design for maximum efficiency and relevance.

Applications

The next step in assay selection is identifying the research application. Verity Johnson, VP of Archer Product at Integrated DNA Technologies, recommended that researchers begin their assay search by defining the application they wish to use (e.g., biomarker discovery, infectious disease, oncology research, etc.). After the application is identified, researchers can significantly pare down their options to find an appropriate assay. Moreover, Johnson encourages scientists to determine whether it would be best to use an existing assay or utilize a custom solution to answer their research question. This decision is also influenced by the nature of the application and the specific requirements of the study.

Sequencing platform

Craig Fishman, Market Development Associate Director at PacBio, emphasized the importance of the underlying technology in assay selection. “That technology not only needs to produce highly accurate data but also enable bold questions and discover new insights into biological mechanisms.” Additionally, the sequencing platform used for the experiment plays a key role in determining the compatibility of the assay, as some kits are designed to work exclusively with certain instruments. However, Johnson pointed out that some researchers might opt for sequencing-agnostic solutions. This approach allows researchers to mix and match library preparation reagents with adapters designed for specific sequencers and make them adaptable to various platforms.

Sample input

Sample input is another significant factor that impacts assay choice. Jason Liu, Senior Field Applications Scientist at Roche Diagnostics Corporation, shared that researchers should consider their need for DNA or RNA sequencing, the specificity of their target, sample type (e.g., cfDNA, FFPE, hgDNA, etc.), sample throughput, and preference for manual or automated solutions. Specific assays may be needed for samples with low input or those that are highly degraded.

While some assays have certain sample requirements, Liu encouraged selecting assays that are versatile and well-suited for analyzing a range of sample types and differing characteristics like quantity and quality. He noted that utilizing assays with adjustable parameters allows researchers to optimize their results and reduces the time spent on planning and troubleshooting.

Fishman expanded on this sentiment by explaining how different sample types and assays can also influence the sequencing platform. For example, high-quality, high molecular weight nucleic acids are ideal for long-read systems, which facilitate the examination of large genomic segments, novel RNA isoforms, and methylation data. Conversely, in experiments involving challenging samples such as formalin-fixed paraffin-embedded (FFPE) tissues, the use of short-read sequencing technologies provides the precision and sensitivity needed for detailed variant analysis.

Bioinformatics considerations

“Data analysis is integral to the selection of an NGS assay, as the analysis package and assay design must align to ensure accurate and reliable results,” stated Smith. He explained that the analysis tools must be purpose-built with the specific assay in mind, and similarly, the assay design must accommodate the requirements of the downstream analysis. “This interdependence ensures that variant calling and other analytical processes are tailored to the assay's capabilities, preventing mismatches between the intended targets and the analysis outcomes,” he added.

Fishman also highlighted how data analysis is a crucial aspect of selecting an NGS assay and that users should ensure the chosen technology aligns with the data analysis pipeline. He emphasized how compatibility and a wide range of applications are essential, as successful assays often require analyzing various data types to answer complex biological questions.

Additional considerations

Researchers may also need to narrow down their assays by considering several other factors. For example, Johnson highlighted the importance of workflow characteristics, such as turnaround time and hands-on time in selecting the right NGS assay. Assays offering faster turnaround times might be ideal for projects with tight timelines. Moreover, researchers may want to determine the throughput of the assay; high-throughput assays that can process numerous samples simultaneously are generally preferable for large-scale studies. Budget constraints are another important factor in assay selection. The cost of the assay includes the direct expenses of the kit, as well as the sequencing and data analysis costs. Researchers should evaluate the total costs for the reagents, labor, sequencing, and analysis.

Conclusion

Selecting the right NGS assay requires careful assessment of various considerations such as the research objectives, sample characteristics, desired sequencing depth, technological compatibility, throughput requirements, cost, and analysis requirements. Evaluating each of these factors allows researchers to choose the NGS assay that best aligns with their research goals and ensures the success of their research projects.

Assay selection overview

• Research objectives
  • Specificity of the target (broad or target focus)
• Application (e.g., biomarker discovery, infectious disease, oncology research, etc.)
  • Custom or pre-made assay
• Sequencing platform specifications and compatibility
• Sample input
  • DNA or RNA
  • Quality and quantity
  • Specific sample type
• Manual or automated solution
• Compatibility with the analysis pipeline
• Additional considerations
  • Turnaround time
  • Budget constraints
  • Total costs of the workflow