Bridge Informatics

Spatial biology isn’t about picking a winner, it’s about building a toolkit.

From Stereo-seq + MERFISH convergence to DISCO’s targeted isolation and Xenium customization, the field is maturing into something more practical: knowing which tool to use, and when.

That shift, from platform wars to methodological fluency, is what will actually move biology forward. Read more in our blog: https://bridgeinformatics.com/meet-the-researchers-building-canadas-spatial-biology-toolkit/

How do you detect DNA–RNA complementarity at genome scale without blowing up compute?

We tackled this by moving beyond assembly-based assumptions and rethinking the problem at the raw-read level, making a previously intractable analysis feasible.

Read the case study on our blog: https://bridgeinformatics.com/untangling-the-dna-rna-relationshipin-extrachromosomal-circular-dna-research/

As bioinformaticians, we’ve all used AI to *suggest* pipelines, debug code, or summarize papers, but what happens when it starts actually *executing* parts of our workflows?

Our blog dives into the shift from conversational AI to structured, skills-based agents that can trigger pipelines, validate inputs, and handle failures, while staying within the constraints we care about: reproducibility, auditability, and governance.

The interesting part isn’t just capability. It’s control. These systems don’t have “god mode”; they operate within the same permissioned environments we already trust.

Read more: https://bridgeinformatics.com/how-structured-ai-is-starting-to-execute-real-rd-workflows/

Part 2 is live. Last week, we talked about governed, “on-rails” agents.

This week: what happens when they go off-script.

Autonomous agents can compress entire bioinformatics workflows, but without constraints, optimization turns into risk.

Where should we draw the line? Read more: https://bridgeinformatics.com/when-agents-go-off-script-the-promise-and-risk-of-unconstrained-autonomy/

Everyone saw the headline: the FDA wants to reduce animal testing.

But if you’re in bioinformatics, you know the real story is what replaces it: organoids, real-world evidence, computational models… human data at scale.

That shift changes everything.

The bottleneck isn’t molecules anymore, it’s data: who can generate it, validate it, and actually learn from it?

Drug discovery is becoming a data infrastructure problem.

The edge will go to teams that can make data reusable, connected, and reproducible, not just run better pipelines, but build better data engines.

Read more in our blog: https://bridgeinformatics.com/the-fdas-quiet-signal/

If TNS2026 showed us anything, it’s this: spatial biology isn’t about picking a winner, it’s about building a toolkit.

From Stereo-seq + MERFISH convergence to DISCO’s targeted isolation and Xenium customization, the field is maturing into something more practical: knowing which tool to use, and when.

That shift, from platform wars to methodological fluency, is what will actually move biology forward. Read more in today's blog: https://bridgeinformatics.com/meet-the-researchers-building-canadas-spatial-biology-toolkit/

Clinical trial terminations are rising, even as AI, bioinformatics, and predictive tools become more advanced.

A recent Nature Reviews Drug Discovery analysis found that sponsor-terminated phase II and III trials more than doubled from 11% in 2013 to 23% in 2023.

Why?

We spoke with Rich Harrison, Bridge Informatics board member and co-author of the study, about how stronger bioinformatics and biomarker-driven validation earlier in development could help teams enter the clinic with stronger evidence and lower risk.

The takeaway:
Better upstream validation, target biology, and patient stratification could significantly reduce costly late-stage failures.

Read the full blog to learn how bioinformatics can help de-risk clinical development earlier: https://bridgeinformatics.com/why-are-clinical-trial-terminations-increasing/

TODAY: Dan is on site at the NextGen Omics Spatial & Data US 2026 Conference in Boston, meeting with teams who are trying to:

- Unify fragmented datasets
- Accelerate analysis timelines
- Reduce dependency on specialized informatics resources

If that sounds familiar, it’s worth a conversation.

We're all witnessing a growing gap in single-cell and spatial data.

Teams are generating more data than they can realistically interpret - especially across single-cell and spatial workflows.

In most teams, that bottleneck shows up as weeks of delay between data generation and usable results.

That’s exactly where we’re spending our time.

We’re building a platform (paired with embedded expertise) that lets bench scientists run pipelines and keep large datasets organized without needing to rely on a dedicated bioinformatics team, so teams can move from raw data to insight faster, without bottlenecks.

Want to learn more? Meet with Dan on site today and tomorrow, or email him at dan.ryder(at)bridgeinformatics.com to schedule a conversation after the conference.

As bioinformaticians, we’ve all used AI to *suggest* pipelines, debug code, or summarize papers, but what happens when it starts actually *executing* parts of our workflows?

Today's blog dives into the shift from conversational AI to structured, skills-based agents that can trigger pipelines, validate inputs, and handle failures, while staying within the constraints we care about: reproducibility, auditability, and governance.

The interesting part isn’t just capability. It’s control. These systems don’t have “god mode”; they operate within the same permissioned environments we already trust.

Read more: https://bridgeinformatics.com/how-structured-ai-is-starting-to-execute-real-rd-workflows/

There’s a growing gap in single-cell and spatial data.

Teams are generating more data than they can realistically interpret - especially across single-cell and spatial workflows.

In most teams, that bottleneck shows up as weeks of delay between data generation and usable results.

That’s exactly where we’re spending our time.

We’re building a platform (paired with embedded expertise) that lets bench scientists run pipelines and keep large datasets organized without needing to rely on a dedicated bioinformatics team, so teams can move from raw data to insight faster, without bottlenecks.

We’ll be at the NextGen Omics, Spatial & Data US 2026 Conference in Boston, meeting with teams who are trying to:

- Unify fragmented datasets
- Accelerate analysis timelines
- Reduce dependency on specialized informatics resources

If that sounds familiar, it’s worth a conversation.

Dan will be on-site March 31 - April 1. If you’re attending, DM him on LinkedIn, or reply to this post to lock in time.