Five Genomics Watchpoints for 2026
Industrial functional genomics, modular gene editing, embryo ranking, falling sequencing costs, and scaled DNA synthesis start to connect into one end-to-end pipeline
The beginning of this year is already offering a couple of data points that pick up last year’s momentum and hint at where genomics might be moving next. On January 13, during JPM week, Illumina announced the Billion Cell Atlas — a genome-wide perturbation dataset built from 1B cells meant as the foundation for large-scale target validation and AI model training. With AstraZeneca, Eli Lilly, and MSD involved, the initiative was framed as an attempt to create a standardized map of gene function that could be reused across different drug discovery programs.
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.
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.
In short: as datasets expand from millions to billions of cells, the emphasis appears to be moving beyond sheer scale toward genome-wide coverage, reproducibility, and interoperability. In parallel, functional genomics is increasingly being positioned as an integral data layer for drug discovery, with value tied to the ability to build comprehensive gene-function maps and translate them into predictive models.
2️⃣ Personalized Intervention
The second characteristic shift entering 2026 is the rise of personalized gene-editing therapies built on programmable platforms.
In May 2025, NEJM reported the first administration of a personalized CRISPR-based therapy to an infant with severe carbamoyl phosphate synthetase 1 (CPS1) deficiency, a rare and often fatal metabolic disorder. The treatment was developed and delivered at Children’s Hospital of Philadelphia (CHOP) in collaboration with Penn Medicine. The infant, known as KJ, received his bespoke therapy in February 2025 at six-to-seven months of age after spending his early life on a highly restrictive diet. The therapy was administered safely, and follow-up reporting indicated that he was growing and developing well.
The key point is how the therapy was built. In a December 2025 AJHG article, Dr. Ahrens-Niklas and Dr. Musunuru said they worked with the FDA on a modular gene-editing approach. It reuses core components and adjusts each therapy mainly by changing the guide RNA or the editor.
Informed by baby KJ’s success, FDA officials outlined a novel regulatory pathway to enhance the development and approval of custom gene therapies.
By now, this has begun to spill into the startup ecosystem. In January 2026, Fyodor Urnov (involved in the KJ effort) and Nobel laureate Jennifer Doudna launched Aurora Therapeutics, a personalized gene-editing company backed by $16M in seed financing. It starts with phenylketonuria for the same platform reasons: many mutations, one mechanism, and adaptable editing components.
Scribe Therapeutics, another startup linked to Jennifer Doudna, said in January that it plans to start a Phase 1 trial of STX-1150, a one-time cardiovascular treatment based on a modified, catalytically disabled CasX. The company also said it reached a milestone in its collaboration with Eli Lilly on in vivo therapies using Scribe’s X-Editor.
In short: gene editing is moving from a “one therapy, many patients” model toward platform-based customization for rare and ultra-rare diseases. As regulatory and manufacturing frameworks adapt, competition increasingly centers on speed, modularity, and the ability to scale individualized treatments across mutation-defined patient groups.
3️⃣ Reproductive Genomics
One of the most visible and ethically contested genomics trends is the transition from classical embryo screening toward embryo ranking based on probabilistic genetic scores.
Preimplantation genetic testing (PGT) has been used since the 1990s to screen embryos for chromosomal abnormalities and single-gene disorders, helping parents avoid serious inherited diseases.