By Sophie Lindqvist

The lab cost of sequencing a genome broke the $100 barrier two years ago, and some laboratories now advertise 30x whole-genome sequencing with analysis for roughly $500 — price points that put NGS within reach of facilities that could not justify the expense a few years earlier.

Industry executives at Agilent, PacBio, Oxford Nanopore, New England Biolabs (NEB) and Illumina tell GEN the next phase of cost reduction will come not from sequencers alone but from the full workflow: library preparation, automation, multiplexing, compute and storage.

"There is potential to drive the cost of whole-genome sequencing down by another order of magnitude," said Mark Van Oene, president and CEO of PacBio. He pointed to throughput as a lever — spreading instrument amortization across many more genomes — alongside faster processing, data compression and cloud computing, which cuts hardware and storage requirements.

Where does the next performance gain come from?

Karen B. Chapman, PhD, associate VP of R&D Genomics at Agilent Technologies, framed the frontier: "The next major breakthrough in sequencing technology will likely come not from the sequencer alone, but from the ability to extract more meaningful biological insights from every sample." She cited converging advances in long-read sequencing, intelligent assay design, AI-enabled analysis and integrated automation across sample prep, target enrichment, QC and data interpretation.

Multiomics assays top most vendors' roadmaps. Van Oene noted that DNA molecules carry information beyond the four canonical bases — methylation, damage, secondary structure — that reveals different layers of biology. Lakmal Jayasinghe, PhD, CSO of Oxford Nanopore Technologies, went further: "The next major breakthrough will be the ability to analyze DNA, RNA, and proteins on a common sensing platform along with epigenetics, epitranscriptomics, and post-translational modifications of proteins." His company sees proteomics as a key part of that future.

Andrew Barry, associate director of product portfolio management at NEB, called multiomics "one of the next big opportunities in sequencing," provided different data types are delivered in a form that can be interpreted against the human genome.

Upstream bottlenecks remain. "Library prep may need a bit more work," said Keerthana "Kit" Krishnan, director of NGS applications and product development at NEB, who identified sample extraction and prep as the main upstream gaps and data analysis as the downstream one. She also argued that for NGS to become a routine clinical tool, results must link to follow-up actions, and she expects assay kits tailored to specific clinical applications.

What is AI already doing in the pipeline?

All five vendors deploy AI both upstream and downstream today:

• PacBio converts raw data into HiFi reads, detects DNA modifications, and applies AI in tertiary analysis to "identify and prioritize genetic variants associated with rare disease," per Van Oene.
• NEB uses AI for coding support, multi-instrument automation, anticipating customer pitfalls in the field, and enzyme discovery.
• Agilent learns from large datasets to improve probe placement and target-enrichment design for specificity, coverage and uniformity; Chapman listed base calling, variant detection, structural variant characterization, methylation assessment and clinical interpretation as further candidates.
• Oxford Nanopore builds more accurate models and directly detects DNA and RNA modifications.
• Illumina has embedded AI in its secondary analysis pipeline and across its software portfolio, and has published the peer-reviewed Primate, Splice and Promoter AI genomic models.

Looking forward, Chapman said AI-driven panel design systems could automatically optimize probe selection, predict assay performance and minimize sequencing costs. The more transformative role, she argued, may be "guiding researchers toward the right biological signals to measure, rather than merely processing larger volumes of data."

The executives agreed that explainable AI will become a requirement — essential for base calling on-instrument and critical for tertiary analysis, where the evidence behind a result must be auditable. "To use AI effectively, we have to know what's going on underneath the hood," Barry said.

How will costs keep falling?

Sequencing itself is shrinking as a share of total workflow cost. "The sequencing part of it is becoming a smaller part of the total cost of a workflow," said Fiona Kaper, PhD, VP and head of assay R&D at Illumina. She reported more customers layering in proteomics, transcriptomics, spatial and single-cell data — a shift pointing toward modular assay environments rather than closed systems. "Having a black box [solution] really doesn't work for everyone," Krishnan said.

Vendors are also cutting reagent volumes. "Reducing reagent volumes in library preparation and sequencing will lower consumable costs as well as shipping and storage requirements — which people don't usually think about when they think about the cost of a genome," Van Oene said.

Kaper added a budget dimension R&D managers know well: "Time is also a form of cost." She predicts that five to ten years from now, NGS may be the core technology for both research and clinical labs, with oncology among the beneficiaries of assays that interrogate the genome, epigenome and proteome together.

Chapman framed the industry's shifting metric plainly: the focus will move from the cost of sequencing a genome to "the cost of delivering a clinically meaningful answer." Van Oene's closing formulation set the same target — not simply a cheaper genome, but a more informative one, at a much lower cost.