Trends in NGS

Next-generation sequencing (NGS) has become a crucial component of modern drug development and diagnostic laboratories. As costs continue to drop, many facilities previously unable to justify the expense can now afford it. Looking forward, the industry aims to extract more diverse types of data from samples, harness AI capabilities both before and after sequencing, and streamline workflows.

Industry experts believe that the next major breakthrough in sequencing technology will not be limited to the sequencer itself but will instead come from the ability to derive more meaningful biological insights from each sample.

Karen B. Chapman, PhD, associate VP of R&D Genomics at Agilent Technologies, envisions a convergence of long-read sequencing, intelligent assay design, AI-enabled analysis, and integrated automation. This includes sample preparation, target enrichment, quality control, sample tracking, and data interpretation. Mark Van Oene, president and CEO of PacBio, emphasizes that while DNA typically contains four canonical bases, it also holds additional information such as methylation, damage, and secondary structure.

He believes sequencing will evolve into a rapid test, delivering comprehensive, highly accurate results within hours, thus enabling faster decision-making for scientists and clinicians.

New England Biolabs (NEB) sees multiomics assays as the next big opportunity in sequencing. Andrew Barry, associate director of product portfolio management, states that these assays will provide richer, more comprehensive biological insights by allowing the simultaneous analysis of DNA, RNA, and proteins on a common sensing platform.

Lakmal Jayasinghe, CSO of Oxford Nanopore Technologies, adds that the future will involve the ability to analyze DNA, RNA, and proteins along with epigenetics, epitranscriptomics, and post-translational modifications of proteins. Jayasinghe sees proteomics as a key part of this future.

To improve sequencing performance, the industry is focusing on parallel workflows and automation to accommodate a broad range of samples. Keerthana "Kit" Krishnan, director of NGS applications and product development at NEB, highlights the need for advancements in library preparation, particularly in sample extraction and preparation.

Downstream, there are ample opportunities for improving data analysis. For NGS to become a routine clinical tool, analyses must be clinically meaningful and linked to follow-up actions. NEB envisions NGS assay kits tailored to specific clinical applications.

AI plays a critical role in making this vision a reality, according to Karen Chapman from Agilent Technologies. AI will help deliver deeper, more actionable biological insights while lowering computational and labor costs, enhancing accuracy, and increasing operational speed. Instrument developers across the board are leveraging AI both upstream and downstream.

PacBio uses AI to convert raw sequencing data into HiFi reads and detect DNA modifications. Downstream applications include identifying and prioritizing genetic variants associated with rare diseases. NEB employs AI for coding support, while its automation team explores its application in streamlining multi-instrument processes.

AI is also becoming crucial in enzyme discovery, helping optimize probe placement, target-enrichment design, and improving base calling, variant detection, structural variant characterization, methylation assessment, and clinical interpretations. Illumina has embedded AI tools in its secondary analysis pipeline and created AI genomic models for priming, splicing, and promoter analysis.

Looking ahead, AI will play an increasingly significant role in helping scientists, clinicians, and biopharma companies interpret complex multiomics datasets.