Five Genomics Watchpoints for 2026

Just a day earlier, MIT Technology Review published its annual 10 Breakthrough Technologies list. This year, three of the highlighted technologies were in genomics: personalized gene editing, embryo scoring, and gene resurrection. From there, it seems like genomic applications are moving more into the mainstream technology discourse.

Another just-in data point from a few days ago is a report out of San Diego, where Element Biosciences says its newly announced VITARI benchtop sequencer can deliver a whole genome for $100, positioning it as a lower-cost alternative to Illumina’s high-throughput systems.

Photo: Roche’s SBX setup

Looking at these and many of last year’s developments, genomics come into view as an integrated technology wave that extends from data generation to interpretation, intervention, and biological reconstruction.

With those early-2026 pings as a starting point, let’s do a selective pass through a few genomics patterns that seem to be carrying momentum into 2026.

1️⃣ Perturb-Seq Data Cornerstone

The first recent trend in genomics worth mentioning is the industrialization of functional genomics data. The industry does not strive for simply “more single-cell sequencing” but rather for standardized, genome-wide perturbation datasets built for reuse across pharma and AI systems, like in Illumina’s case above.

Single-cell perturbation sequencing (Perturb-seq) captures how individual cells respond to targeted genetic interventions. Each cell carries a defined guide RNA that is read out alongside its transcriptomic state, linking a specific perturbation to a high-dimensional molecular response. Unlike observational atlases (Human Cell Atlas, CZI Biohub’s Tabula Muris/Sapiens, Allen Institute’s Cell Types resources/Allen Brain Cell Atlas etc.), these datasets are interventional in that they enable causal mapping of regulatory networks rather than correlation alone.

In this causal nature lies the strategic value of perturbation data: changes in gene expression signatures can support target validation, mechanism-of-action studies, drug repurposing, and in silico modeling. As a result, larger, well-annotated perturbation signature collections may improve drug-discovery potential, depending on data quality and coverage.

Early Perturb-seq datasets demonstrated feasibility but were too small to enable predictive systems at scale. The first dataset released by the technology developers in 2016 comprised roughly 200,000 cells. Subsequent efforts expanded with genome-wide Perturb-seq by Replogle et al. reaching ~2.5M cells (2022), while the PerturBase aggregates ~5M cells (2024-2025). This still reflects the upper bound of most public perturbation datasets. By contrast, private efforts are already pushing well beyond that range:

• Xaira Therapeutics’ 2025 X-Atlas/Orion dataset marked a previous record with roughly 8M single cells which targeted all human protein-coding genes, and featured deep sequencing coverage of over 16,000 unique molecular identifiers (UMIs) per cell. To foster collaboration within the virtual cell community, the dataset was made publicly available under a non-commercial license.
• Illumina’s Billion Cell Atlas set a new benchmark, comprising 1B cells across 200 disease-relevant cell lines with the planned expansion to 5B cells.