Introduction
After the success of our “Battle of the Single-Cell Platforms” and “Battle of the Workflow Managers” articles, we wanted to take on another hot topic: how generative AI is quietly reshaping bioinformatics.
At Bridge Informatics, we see clients at all stages of their AI journey. Some aren’t sure what AI could even do for their analysis. Others have experimented with ChatGPT or Copilot and wonder how far it can really go. Some come to us with AI-assisted code that sort of works, but needs guardrails. And some are experts who know the tools inside out, but simply don’t have the bandwidth to keep up with the latest AI integrations, so they rely on us to help productionize them.
No matter your level of experience, the same question is popping up more and more: what role will generative AI actually play in bioinformatics pipelines?
If you are just here for the highlights, check out the quick comparison table below.
If you want the details, keep reading for a walkthrough of the AI tools making their way into everyday workflows.
Quick comparison
| AI Tool/Approach | Best for |
| Snakemaker | Turning ad hoc scripts into structured Snakemake workflows |
| Code Assistants (e.g. Copilot, ChatGPT) | Writing boilerplate, debugging, and documentation |
| Automated Annotation | Speeding up gene and variant annotation tasks |
| AI in QC and Reports | Generating summaries, dashboards, and figures automatically |
A closer look at each area
Snakemaker: From messy code to reproducible workflows
Snakemaker is a new AI-powered tool that takes exploratory terminal commands or notebook code and rewrites them into proper Snakemake pipelines. It even sets up Conda environments automatically. For researchers with “half-baked” code, this is a game-changer.
When to use it: If you want to level up your scripts into robust, shareable pipelines.
Code assistants: Pair programming for bioinformatics
Tools like GitHub Copilot or ChatGPT can autocomplete code, suggest improvements, and even generate documentation. In bioinformatics, this means fewer hours spent writing boilerplate or debugging regex patterns, and more time focusing on science.
When to use them: For routine coding tasks, especially when learning a new library or language.
Automated annotation: AI in genomics workflows
Generative AI is also being applied to annotation, helping speed up the process of mapping genes, variants, or regulatory regions. While human oversight is still critical, these tools can reduce time to insight.
When to use them: In large-scale genomics projects where manual annotation is too slow.
AI for QC and reporting: Instant summaries
Quality control is an essential but time-consuming part of any analysis. AI can generate narrative reports, highlight anomalies, and even propose next steps. Instead of combing through logs, you get an executive summary of what went right or wrong.
When to use it: To save time and improve communication with collaborators who may not be technical experts.
Wrapping up
Generative AI isn’t replacing bioinformaticians, but it is quietly becoming a powerful assistant. From cleaning up messy scripts, to writing boilerplate, to annotating genomes and summarizing results, AI tools are already helping researchers move faster.
At Bridge Informatics, we meet clients wherever they are on this journey. Sometimes that means introducing AI-assisted tools to someone for the first time. Sometimes it means refining or rescuing half-working AI code. And sometimes it means supporting experts who know the tools but don’t have the time to implement them at scale.
No matter where you stand, we’re here to help ensure that AI makes your workflows more reproducible, efficient, and impactful.
Click here to schedule a free introductory call with a member of our team.