In this tutorial, we demonstrate how to build an advanced yet accessible Bioinformatics AI Agent using Biopython and popular Python libraries, designed to run seamlessly in Google Colab. By combining sequence retrieval, molecular analysis, visualization, multiple sequence alignment, phylogenetic tree construction, and motif searches into a single streamlined class, the tutorial provides a hands-on approach to explore the full spectrum of biological sequence analysis. Users can start with built-in sample sequences such as the SARS-CoV-2 Spike protein, Human Insulin precursor, and E. coli 16S rRNA, or fetch custom sequences directly from NCBI. With built-in visualization tools powered by Plotly and Matplotlib, researchers and students alike can quickly perform comprehensive DNA and protein analyses without needing prior setup beyond a Colab notebook. Check out the FULL CODES here.
We begin by installing essential bioinformatics and data science libraries, along with ClustalW for sequence alignment. We then import Biopython modules, visualization tools, and supporting packages, while setting up Entrez with our email to fetch sequences from NCBI. This ensures our Colab environment is fully prepared for advanced sequence analysis. Check out the FULL CODES here.
We define a BioPython AIAgent that allows us to fetch or create sequences, run core analyses (composition, GC%, translation, and protein properties), and visualize results interactively. We also perform pairwise alignments, build phylogenetic trees, scan motifs, profile codon usage, analyze GC with sliding windows, and compare multiple sequences -- then bundle everything into one comprehensive pipeline. Check out the FULL CODES here.
We instantiate the BioPythonAIAgent, generate sample sequences (COVID Spike, Human Insulin, and E. coli 16S), and run a full analysis pipeline. The outputs confirm that our agent successfully performs nucleotide, codon, and GC-content analyses while also preparing comparative visualizations. Finally, we print the list of available sequences and supported methods, indicating that the agent's full analytical capabilities are now ready for use. Check out the FULL CODES here.
We visualize nucleotide composition, scan E. coli 16S GC% in sliding windows, and profile codon usage for the COVID Spike sequence. We then compare sequences side-by-side, search for the "ATG" motif, and build/plot a quick phylogenetic tree from the first 300 nt of each sequence.
In conclusion, we have a fully functional BioPython AI Agent capable of handling multiple layers of sequence analysis, from basic nucleotide composition to codon usage profiling, GC-content sliding windows, motif searches, and even comparative analyses across species. The integration of visualization and phylogenetic tree construction provides both intuitive and in-depth insights into genetic data. Whether for academic projects, bioinformatics education, or research prototyping, this Colab-friendly workflow showcases how open-source tools like Biopython can be harnessed with modern AI-inspired pipelines to simplify and accelerate biological data exploration.