Bio AI Agent Example

A Python application for automated phylogenetic analysis using local bioinformatics tools. This workflow fetches protein sequences directly from NCBI, performs multiple sequence alignment, and builds phylogenetic trees.

Overview

This workflow performs comparative analysis of flagellin proteins across different bacterial species:

1. Sequence Retrieval: Fetches protein sequences from NCBI using Biopython
2. Multiple Sequence Alignment: Aligns sequences using MAFFT, ClustalO, or MUSCLE
3. Phylogenetic Analysis: Builds trees using FastTree, RAxML, and IQ-TREE
4. Output: Generates publication-ready phylogenetic trees in Newick format

Prerequisites

Python Requirements

• Python 3.8 or higher
• Internet connection for NCBI database queries

External Bioinformatics Tools

You'll need to install the following command-line tools (install instructions below):

Alignment Tools: - MAFFT - Multiple sequence alignment - Clustal Omega - Multiple sequence alignment - MUSCLE - Multiple sequence alignment

Phylogenetic Tools: - FastTree - Fast phylogenetic tree construction - RAxML - Maximum likelihood phylogenetic analysis - IQ-TREE - Efficient phylogenetic inference

Setup Instructions

1. Clone the Repository

bash git clone git@github.com:braceal/bio_ai_agent_example.git cd bio_ai_agent_example

2. Create a Virtual Environment

Create and activate a Python virtual environment:

```bash

Create virtual environment

python3 -m venv venv

Activate virtual environment

source venv/bin/activate ```

3. Install Python Dependencies

Install the required Python packages:

bash conda create -n bio_ai_agent_example python=3.12 conda activate bio_ai_agent_example pip install biopython conda install -c bioconda mafft clustalo muscle fasttree raxml iqtree -y

4. Configure Email for NCBI

Edit workflow.py and update the email address for NCBI Entrez queries:

python Entrez.email = "your.email@example.com" # Replace with your actual email

Note: The code runs without changing the email address.

Usage

Run the complete phylogenetic analysis workflow:

bash python workflow.py

What the workflow does:

1. Fetches protein sequences for flagellin genes from 10 bacterial species
2. Creates individual FASTA files in the fasta_seqs/ directory
3. Combines and aligns sequences using MAFFT
4. Builds three phylogenetic trees using different methods:
   • FastTree (fast approximate method)
   • RAxML (maximum likelihood with bootstrap)
   • IQ-TREE (model selection + ultrafast bootstrap)

Output Files

The workflow generates several output files:

• fasta_seqs/ - Directory containing individual FASTA files for each species
• combined.fasta - All sequences combined into one file
• alignment_mafft.fasta - Multiple sequence alignment
• alignment_mafft.fasttree.nwk - FastTree phylogeny
• RAxML_bestTree.raxml_tree - RAxML phylogeny
• iqtree_out.treefile - IQ-TREE phylogeny

To view the trees, you can use the browser tool: https://itol.embl.de/upload.cgi

Customization

Modify Species List

Edit the species list in workflow.py to analyze different genes/organisms:

python species = [ ("gene_name", "Organism name"), ("fliC", "Escherichia coli"), # Add more species... ]

Change Alignment Method

Modify the alignment method call:

python msa_file = run_alignment(fasta_files, method="clustalo") # or "muscle"

Select Different Tree Methods

Comment out tree-building methods you don't need:

```python

fasttreeout = runfasttree(msa_file) # Skip FastTree

raxmlout = runraxml(msafile) # Keep RAxML iqtreeout = runiqtree(msafile) # Keep IQ-TREE ```

Project Structure

• workflow.py - Main phylogenetic analysis workflow
• requirements.txt - Python dependencies
• README.md - This documentation
• fasta_seqs/ - Output directory for individual sequences (created during run)

Deactivating the Virtual Environment

When finished, deactivate the virtual environment:

bash deactivate