Genomics in 2026
Trends and Predictions from the Fulcrum Genomics Team
As 2025 winds down, genomics is at an inflection point. Long-read platforms are entering clinical workflows, agentic AI tools are everywhere, and the field is moving beyond the genome to the molecules that actually do the work in cells.
To cut through the noise, we asked members of our own team at Fulcrum Genomics what trends they’re seeing now and what they expect to shape 2026. Their answers cluster around a common theme: making genomics work in the real world through better tools, better standards, and deeper biology.
1. AI in Genomics: Powerful, but Not a Magic Wand
AI is already changing how genomics professionals work, but our team is clear-eyed about both the upside and the mess it can create.
“I’m seeing some serious time-multiplying advantages to all genomics professionals using AI agents. But I’m also predicting there’s going to be a lot of human time needed to clean up some of the messes this creates in 2026 (ironically, with the aid of AI agents no doubt).”
-Clint Valentine | Vice President, Operations
“[I’m seeing] a shift from the previous cycle of ‘we’ll just throw all of our data in a pile, and armed with nothing but the power of friendship we’ll empower our clinicians to make deep insights’ to ‘we’ll just throw all of our data in a pile and AI will figure it out’. Curious to see how it goes.”
-Jeff Gentry | Distinguished Bioinformatics Engineer
Taken together, their perspectives suggest that in 2026:
AI agents will be indispensable force multipliers for genomics teams
Thoughtful data practices, validation, and oversight will matter more than ever
“Just add AI” will not rescue disorganized data or ad hoc infrastructure
2. Standards Cross the Academia–Industry Divide
While tools and platforms evolve, data standards are also becoming more central to how genomics gets done.
“I’m seeing GA4GH standards that have been pushed/originated in academic bioinformatics starting to be used more in industry, and not just file formats e.g. BAM/VCF, but things like Phenopackets.”
-Alison Meynert | Principal Bioinformatics Scientist
This matters because:
Standardized data models make it easier to share, integrate, and re-use data across projects and organizations
Clinical and phenotypic standards are essential for robust AI and for scaling precision medicine efforts
In 2026, expect interoperability and well-defined standards to move from “nice to have” to “table stakes” for serious genomics programs.
3. The Genomics Stack Grows Up: Tools, Workflows, and Rust
On the engineering side, the tooling that underpins genomics is rapidly maturing.
“We’re seeing a trend of clunky environment management tools (conda, poetry) being replaced with faster and improved versions written in Rust. Specifically, pixi is fantastic as a drop-in replacement for conda and uv replaces poetry. Going into the new year, we also have our eye on ty as a mypy replacement.”
-Zach Norgaard | Associate Director, Bioinformatics
“We’re seeing the continuing maturation of some popular workflow languages (snakemake & nextflow) and platform-as-a-service products (e.g., seqera and latch). I’m predicting that 2026 will be a year where genomics professionals take the time to upgrade their projects to exploit these offerings.”
-Jason Fan | Staff Bioinformatics Engineer
The takeaway: the genomics software stack is moving from “held together with scripts and hope” toward something more like a modern, robust engineering environment that’s faster, safer, and easier to maintain.
4. From Short Reads to Structural Truth: Long Reads, SVs, and Error-Corrected Sequencing
Several team members highlighted a shift toward technologies that capture more complex and clinically relevant variation.
“Error-corrected sequencing expanded to larger target panels this year, with new methods addressing the double-sampling challenge and library preps that preserve native DNA features for epigenetic analysis. In 2026, I expect these advances and more to bring ECS into routine clinical workflows.”
-Nils Homer | Founding Partner
“Long-read sequencing should keep gaining momentum, especially with PacBio HiFi moving further into clinical workflows. I wonder if we’ll see some tools for Roche SBX processing/analysis start to be developed out in the open.”
-Erin McAuley | Staff Bioinformatics Scientist
“I’m seeing an interest in structural variants that began in academia shifting into industry, and with it the incorporation of long read data into standard bioinformatics workflows.”
-Tim Dunn | Senior Bioinformatics Scientist
Together, these perspectives point to a 2026 where:
Long reads are no longer niche, but an expected part of serious clinical and translational genomics efforts
Error-corrected approaches and SV-aware pipelines play a larger role in diagnostics and disease characterization
The field moves beyond SNPs and small indels toward a more complete view of genomic architecture
5. Beyond the Genome: Proteins Take Center Stage
Genomics is only one layer of biology, and Yossi expects 2026 to bring much more practical attention to proteins.
“In 2026 I expect proteins to get a lot more practical attention. AI-based protein design will become a standard early step in enzyme and therapeutic work, rather than optional or experimental. Improvements in automated prep and quantification will make pilot-scale proteomics studies far more reliable than they are today. Altogether, this will let us go beyond the genome and work with the molecules that do things in cells.”
-Yossi Farjoun | Principal Bioinformatics Scientist
In other words, 2026 may be the year where genomics and proteomics feel less like parallel worlds and more like a connected continuum of molecular insight.
Looking Ahead: Making Genomics Work in the Real World
Across the Fulcrum Genomics team, a coherent story emerges.
AI and agents will be everywhere, but the organizations that win will be the ones that pair them with sound data strategy, validation, and governance.
Modern tools and workflows, from Rust-based environment managers to mature workflow engines and PaaS platforms, will give genomics teams a stack they can build on instead of battle.
Standards like GA4GH’s will make it easier to share, integrate, and interpret data across research, clinical, and commercial settings.
Advanced sequencing approaches, including long reads, ECS, and SV-aware analyses, will enable a more accurate and clinically meaningful view of the genome.
And protein-level technologies will push us beyond sequence toward function, mechanism, and ultimately, better interventions.
Across AI, infrastructure, standards, sequencing, and proteins, one message stands out: the building blocks for real-world genomics are finally in place. The work of 2026 is to turn those capabilities into systems that are dependable, scalable, and clinically meaningful. And to do it without losing scientific rigor along the way.
If you’re planning how to evolve your pipelines, platforms, or assays in 2026, we’d love to partner on it. Get in touch with the Fulcrum Genomics team to design, optimize, or troubleshoot the genomics and multi-omics workflows that will carry your organization into the next phase.