prot-pipeline-creator 1.0.0

A tool for creating protein analysis pipelines for UniProt.

PPC - Prot Pipeline Creator

PPC - Prot Pipeline Creator is a Python-based tool designed for the bioinformatics processing and analysis of protein data.

The system is engineered to ingest .tsv files from UniProt (UniProtKB), enabling the creation of automated analysis pipelines for filtering, clustering, and generating sub-datasets based on taxonomy (Super Kingdoms), enzymatic activity (EC numbers) and others.

🛠️ Prerequisites

To run this project, you will need:

• Python 3.8+

• Pandas

🚀 Installation

1. Clone the repository:

   Bash

   git clone https://github.com/emanuelumbelino/prot-pipeline-creator.git
   cd prot-pipeline-creator
   

2. Install dependencies (using a virtual environment is recommended):

   Bash

   pip install pandas
   

💻 Usage

The project uses the concept of "Pipelines" to execute analyses. You can create your own script or use the templates in the example/ folder.

Basic Example

To run the default example included in the project, ensure you have a UniProt data file in the correct format and run:

Bash

python main.py data/file/path.tsv

Creating a Custom Pipeline

You can use the PPC_Dataframe class directly in your Python scripts:

from ppc.protein_dataframe import PPC_Dataframe
from ppc.helpers.printer_helper import PPC_Printer

# 1. Load the DataFrame
file_path = 'path/to/your/uniprot_file.tsv'
df = PPC_Dataframe(file_path)

# 2. Use the filters
bacterias_with_pdb = df.bacteria.has_pdb

# 3. Save all proteins filtered in a .tsv file
bacterias_with_pdb.to_tsv('output/my_analysis.tsv')

# 4. Use the Printer helper to print by ec class
PPC_Printer.print_by_ec_class(bacterias_with_pdb)

For a comprehensive list of currently implemented filters, please consult the Wiki. It is also possible to create new filters by following the existing ones.

If you have improvements or suggestions, feel free to submit a Pull Request or open an Issue.

🔬 Available Pipelines

The project already includes three example pipelines in the example/ folder:

1. KingdomPipeline:

   • Splits the original dataset into 4 .tsv files based on Super Kingdoms (Archaea, Bacteria, Eukaryota, Viruses).

   • Generates statistics on sequence and enzyme counts per kingdom.

2. EnzymesPipeline:

   • Classifies and separates proteins into the 7 main enzyme classes (EC 1 to EC 7).

   • Generates individual files for each class (e.g., 1_oxidoreductases.tsv, 2_transferases.tsv).

3. AnalogousPipeline:

   • Focuses on the analysis of HISE (Homologous Enzymes) and NISE (Non-Homologous/Analogous Enzymes).

   • Clusters enzymes that share the same EC number, verifying if they belong to the same Superfamily or different ones.

   • Filters datasets to contain only enzymes with complete EC numbers and PDB annotation.

📂 Project Structure

The project is organized as follows:

prot-pipeline-creator/
├── ppc/                     # Core source code
│   ├── enums/               # Enumerations for Kingdoms, Columns, and Clusters
│   ├── helpers/             # Helpers for Pandas and Printing
│   ├── clusterized_dataframe.py
│   └── protein_dataframe.py # Main class (Pandas Wrapper)
├── main.py                  # Main entry point
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├── example/                 # Example pipelines and execution scripts
│   ├── output/              # Output directory for examples
│   ├── analogous_pipeline.py
│   ├── enzymes_pipeline.py
│   ├── kingdom_pipeline.py
│   └── example.py
└── LICENSE                  # Apache 2.0 License

📄 License

This project is licensed under the Apache License 2.0. See the LICENSE file for more details.

Copyright 2025 - Emanuel Umbelino