Advances in DNA sequencing and synthesis have made reading and writing genetic code faster and cheaper than ever. Yet most labs run experiments at the same scale they did a decade ago, not because the biology is limiting, but because the software hasn't caught up.

The conventional digital representation of a genome is a string of nucleotides. This works well enough for simple projects, but the model breaks down as complexity grows. Sequences aren't constant: they evolve, mutate, and are iterated on. Unlike software, there's no instant feedback loop to tell you if an edit worked; wetlab experiments take time. You gain some of that time back by working with multiple sequences in parallel. But keeping track of thousands of sequences and coordinate frames is tricky at best when a researcher is working solo, and far harder when collaborating with other people or agents on the same genetic codebase.

Gen is a version control system built specifically for biological sequences (http://github.com/genhub-bio/gen). It models genomic data as a graph rather than flat text, preserving the full structure of variation, editing history, and experimental lineage. On top of this, projects are organized into repositories with branching, diffing, and merging, just like git. Git was first released 20 years ago and transformed how software teams collaborate on shared codebases. Gen brings that same workflow to biology.

This talk will introduce Gen's design philosophy and walk through a real-world use case. Gen is open source under the Apache 2.0 license, implemented in Rust with a terminal interface and Python bindings, and designed to integrate with existing bioinformatics pipelines.