In Part 4.3, we introduce Genome Toolkit’s first validated biological sequence models. We build a reusable Sequence base model and a DNA-specific model that normalizes sequences and rejects invalid nucleotide symbols. We then connect validated DNA to our existing application.py while keeping the k-mer algorithms and scientific calculations unchanged. This gives Genome Toolkit its first real biological validation layer without disrupting the working project we already built.
In Part 4.2, we modernize Genome Toolkit and turn our original project into a proper installable Python package. We move from Pipenv to uv, organize the code under src/, move our existing k-mer algorithms into the new package, and update application.py to use them. Most importantly, we keep the scientific calculations unchanged and verify that Genome Toolkit still produces exactly the same results.
In Part 4.1, we take a step back and look at where Genome Toolkit is going next. We introduce refactoring, explain why our small project is ready to grow into a proper scientific Python package, and show how this gives us a cleaner foundation for future biological tools and experiments. We also look at something pretty exciting: by building Genome Toolkit properly now, we are gradually making it AI-ready for future APIs, MCP tools, and AI agents.
A k-mer is a contiguous sequence of k nucleotides (the building blocks of DNA) in a genome. Biologists often use k-mers to identify patterns or motifs in genomic sequences, such as repeated sequences or conserved regions. Let’s build an algorithm to do this.
In this article, we are going to take a look at one of the algorithms we wrote in Genome Toolkit series, Part 2, and attempt to optimize it.
In the previous article (Part 2 here), we wrote our first Genome Toolkit algorithm. Even though, it was a very simple algorithm to help us search for repeating patterns (k-mers) in a DNA/Genome sequences, and it seemed to worked correctly, we actually had a bug in it. Let’s take a look at what it is, and how we can fix it.
Bioinformatics with Python Cookbook: Use modern Python libraries and applications to solve real-world computational biology problems, 3rd Edition.
First function – counting patterns in a sequence.
Welcome to the new series, called “Genome Toolkit”. In this series, we will write a set of tools, that will help us find and build statistical data around any DNA, RNA and Protein sequences.
A guide and advice on how to get started, or how to transition into Bioinformatics for people with biology or programming backgrounds.