Practical updates from the trenches: AI-aware bioinformatics, open infrastructure, pipeline validation, and tools built to hold up under real data.
Fulcrum Field Notes
Practical updates from the trenches: AI-aware bioinformatics, open infrastructure, pipeline validation, and tools built to hold up under real data.
This quarter, the loudest conversation in bioinformatics has been about AI. Some of it is useful. Some of it is hand-waving.
We ask practical questions to get to what is useful: Can the workflow explain what it did? Can a rewritten tool be tested against the old one? Are the intermediate metrics preserved, or thrown away? Does the pipeline produce enough structured evidence for a human to debug it and a model to learn from it?
Fulcrum has been working at that layer for years. In this issue: AI-ready tool design, sequencing QC, old plumbing, and how Fulcrum helps teams modernize pipelines without losing scientific rigor.
Sticking points
3
Rewrites, pipeline confidence, and data exhaust are where teams are stalling.
Riker goal
Every dataset
Deep sequencing QC should be routine, not only something teams reach for when a run fails.
minibwa speedup
4x
Reported improvement over BWA-MEM in the authors' evaluation.
What We Are Seeing
3 places teams are getting stuck
Fulcrum has been working on the pieces that decide whether bioinformatics systems can be trusted, maintained, and reused. This quarter, three themes keep surfacing in client work and open infrastructure conversations.
AI-assisted rewrites
LLMs can accelerate implementation, but a faster rewrite still needs equivalency testing, benchmarks, edge-case coverage, and a support plan.
Pipeline confidence
A workflow can run and still be fragile. Confidence comes from tests, releases, clear dependencies, structured logs, and comparable outputs.
Data exhaust
AI-ready bioinformatics requires richer metrics, structured outputs, and enough context for both humans and models to reason about results.
Fulcrum can help your team implement, test, and validate AI tools to make your team more efficient.
AI can help write the code. It cannot validate the science.
Nils Homer joined OMGenomics for a conversation with Robert Aboukhalil, Robert Patro, Phil Ewels, and others about AI-assisted bioinformatics rewrites.
LLMs can make it much easier to produce a first draft of a new tool, port old code into a modern language, or refactor software that has become hard to maintain. That does not make the new tool inherently trustworthy.
For bioinformatics teams, the deeper questions are whether the outputs match, whether edge cases were tested, whether the benchmarks are meaningful, and whether someone will be around to support the tool after the launch post.
"AI can speed up implementation. Validation is still the hard part."
Riker: Deep sequencing QC should run on every dataset
Sequencing QC should not be something teams only reach for when a run fails.
Riker is Tim Fennell's modern successor to Picard for sequencing QC metrics. It is designed to make rich QC fast enough and cheap enough to run on every dataset.
Routine QC does more than catch failed runs. It helps teams compare projects, troubleshoot instruments, monitor workflow changes, and preserve structured evidence that can support downstream modeling.
A lot of genomics cost lives in tools people stop noticing. It lives in the file parsing, sorting, trimming, compression, alignment, QC, reference checks, variant normalization, and UMI consensus.
When those tools are slow, brittle, or under-documented, every downstream workflow pays for it. That is why Fulcrum spends time on the infrastructure layer.
• ref-solver for identifying which human reference a file actually matches
• Riker for richer sequencing QC
• fastquorum for UMI consensus in an nf-core workflow
• fgumi for high-performance UMI processing
• MutSeqR for error-corrected mutation analysis
• ferro-hgvs for fast, offline HGVS parsing and normalization
• chelae for short-read trimming
• bwa-mem3 for faster short-read alignment
These are not always the flashiest tools. They are the tools that make the rest of the stack less painful.
A recent preprint from Heng Li and Nils Homer introduces minibwa, a new genomic read aligner designed as a replacement for BWA-MEM.
The reported speedups are substantial: about four times faster than BWA-MEM and more than twice as fast as BWA-MEM2, with comparable or slightly improved small variant calling results in the authors' evaluation.
For teams running large-scale WGS, bisulfite sequencing, Hi-C, or mixed read-length workflows, the practical question is simple: where is alignment driving cost, and what validation would be required before making a switch?
“Every hour and every dollar spent on alignment sits between a sample and an answer. Cutting both reaches the patient, the clinical report, and the researcher.”
Bioinformatics support for teams trying to modernize without breaking what already works
Many teams are under pressure to move faster with AI, automation, and modern workflow infrastructure. The weak point is often the same: the existing pipeline was built for an earlier stage of the program.
It may still run. It may still produce useful results. But confidence gets thinner when the workflow has to support more users, more samples, a clinical validation effort, a customer deployment, a platform migration, or an AI-assisted rewrite.
Fulcrum helps teams pressure-test and modernize bioinformatics systems before the weak points become expensive.
Pipeline Validation and Automation
Validate the workflow you have, then build the automation, tests, documentation, and release discipline needed to use it with confidence.
The starting point is usually a technical conversation: what is the workflow being asked to support, where is confidence thin, and what needs to be tested, automated, repaired, or left alone?
Stay Connected
If something here overlaps with the work your team is doing, we would be glad to talk through how we can help.
That may be an AI-assisted rewrite that needs validation, an inherited pipeline that needs to be made reproducible, an assay workflow moving toward clinical use, or a recurring bioinformatics bottleneck that your internal team does not have time to clear.
