phytominer 0.1.3

A Python library to fetch and process homolog data from Phytozome.

PhytoMiner

This is a package for fetching Phytozome data

A Python library for efficiently fetching and processing gene homolog data from the Phytozome database via its InterMine API.

This library is designed to simplify complex, iterative bioinformatic queries, allowing researchers to trace gene homology across multiple species with ease.

Features

• Initial Fetch: Start a search with a list of genes from a source organism.
• Iterative Search: Perform chained or subsequent searches using homologs found in previous steps.
• Parallel Processing: Utilizes multithreading for efficient, parallel data fetching, significantly speeding up large queries.
• Data Processing: Includes functions to clean, de-duplicate, and enrich the fetched data by calculating occurrence counts and aggregating source information.
• Visualisation: Comes with a utility function to quickly generate a heatmap of homolog distribution across species and subunits.

Installation

You can install the latest PhytoMiner release directly from PyPI:

pip install phytominer

Usage

Here is a complete example of a common workflow:

1. Start with a set of known genes in a source organism (e.g. A. thaliana TAIR10).
2. Perform an initial_fetch to find homologs in other species.
3. Use the results to perform a subsequent_fetch for a specific target organism (S. bicolor v3.1.1).
4. Combine and process the data.
5. Visualize the homolog distribution with pivotmap.

import pandas as pd
from phytozome_fetcher import (
    run_homolog_pipeline,
    initial_fetch,
    subsequent_fetch,
    process_homolog_data,
    pivotmap,
    print_summary
)

# 1. Define initial query genes for Arabidopsis thaliana
# (Using a small subset for this example)
athaliana_genes = {
    'AT5G52100': 'CRR1',
    'AT3G46790': 'CRR2',
    'AT2G01590': 'CRR3',
}

# 2. Run the whole pipeline
results = run_homolog_pipeline(
    initial_organism="A. thaliana TAIR10",
    initial_genes_dict=athaliana_genes,
    subsequent_organisms=[
    "B. distachyon v3.2", "M. esculenta v7.1", 'M. truncatula Mt4.0v1'],
    max_workers=4
)

# The results variable contains the processed DataFrame.

# 3. Alternatively perform the initial fetch from Arabidopsis thaliana
print("--- Starting Initial Fetch ---")
initial_df = initial_fetch(
    source_organism_name="A. thaliana TAIR10",
    transcript_names=list(athaliana_genes.keys()),
    subunit_dict=athaliana_genes,
    max_workers=4
)
print_summary(initial_df, "Initial Fetch Results")

# 4. Perform a subsequent fetch using homologs found in Sorghum bicolor
print("\n--- Starting Subsequent Fetch for Sorghum bicolor ---")
sorghum_df = subsequent_fetch(
    current_master_df=initial_df,
    target_organism_name="S. bicolor v3.1.1",
    max_workers=4
)
print_summary(sorghum_df, "Subsequent Fetch Results for Sorghum")

# 5. Combine and process the data
print("\n--- Combining and Processing Data ---")
master_df = pd.concat([initial_df, sorghum_df], ignore_index=True)
processed_df = process_homolog_data(master_df)
print_summary(processed_df, "Final Processed DataFrame")

# 6. Visualize the results
print("\n--- Generating Heatmap ---")
# For a cleaner plot, let's display the top 15 organisms by homolog count
top_organisms = processed_df['organism.shortName'].value_counts().nlargest(15).index
filtered_df = processed_df[processed_df['organism.shortName'].isin(top_organisms)]

pivot_table = pivotmap(filtered_df)
print("\nPivot Table Head:")
print(pivot_table.head())

API Overview

Core Functions

• initial_fetch(source_organism_name, transcript_names, subunit_dict, max_workers): Kicks off the homolog search with a defined set of genes.
• subsequent_fetch(current_master_df, target_organism_name, max_workers): Expands the search by using the results from a previous fetch as input for a new target organism.
• process_homolog_data(df_combined): Takes a raw DataFrame of combined fetch results and performs cleaning, aggregation, and de-duplication. It adds homolog.occurrences and origin.source.organisms columns.

Utility Functions

• pivotmap(dataframe, index, columns, values): Generates a pivot table and a corresponding heatmap to visualize the count of homologs.
• print_summary(df, stage_message): Prints a quick summary of a DataFrame's shape and contents.

Continuous Integration & Deployment

This project uses GitHub Actions for automated testing and publishing.

• Automated Testing:
  Every push to the main branch triggers the test suite using Python 3.9.
• Automated Publishing:
  When a new release is published on GitHub, the package is automatically built and uploaded to PyPI.

You can find the workflow configuration in .github/workflows/python-publish.yml.

Contributing

Contributions are welcome! If you have a suggestion or find a bug, please open an issue. Pull requests are also encouraged.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

Running Tests Locally

To run the test suite locally:

pip install -e .[dev]
pytest

License

This project is licensed under the MIT License. See the LICENSE file for details.