Metadata-Version: 2.4
Name: protfetch
Version: 0.1.3
Summary: A tool to fetch and process protein FASTA sequences and metadata from NCBI based on a gene list.
Author-email: Ingmar Lesch <ingmar.lesch@tum.de>
License: MIT License
        
        Copyright (c) 2025 Ingmar Lesch
        
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Project-URL: Repository, https://github.com/aulyxair/protfetch
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: biopython>=1.79
Requires-Dist: rapidfuzz>=3.0.0
Requires-Dist: requests>=2.25.0
Requires-Dist: tqdm>=4.60.0
Provides-Extra: test
Requires-Dist: pytest>=7.0; extra == "test"
Requires-Dist: pytest-mock>=3.0; extra == "test"
Dynamic: license-file

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# protfetch

**protfetch** is a command-line tool designed to fetch and process protein FASTA sequences and associated metadata from NCBI's Entrez databases. Given a list of gene symbols (or protein names and gene symbols), it retrieves relevant protein sequences, filters them based on various criteria (identity, similarity, fragments), and outputs curated FASTA files and a metadata CSV.

## Features

- **Flexible Input**: Accepts gene lists in two formats:
  - `Protein Name | GENE_SYMBOL` (e.g., "Mitofusin 1 | MFN1")
  - `GENE_SYMBOL` (e.g., "MFN1")

- **NCBI Entrez Integration**: Uses Biopython to query NCBI Entrez for gene UIDs, linked protein UIDs, and FASTA sequences.

- **Keyword Filtering**: Filters fetched protein sequences based on keywords derived from the input (protein name or gene symbol) found in FASTA headers. This step can be skipped.

- **Sequence Processing & Filtering**:
  - Parses complex FASTA headers to extract accession and other identifiers
  - Removes duplicate entries by accession (first seen is kept)
  - Filters out identical sequences (keeps one representative, typically by lexicographical accession)
  - Filters near-identical sequences using Levenshtein distance (configurable threshold)
  - Removes fragment sequences (shorter sequences that are substrings of longer ones)

- **Concurrent Fetching**: Utilizes multiple workers to fetch data for different genes concurrently, speeding up processing for large lists.

- **Organized Output**:
  - Generates combined FASTA files (short headers and full headers) and a combined metadata CSV file
  - Optionally saves processed files for each gene individually
  - Combined files are deduplicated to ensure unique protein entries

## Installation

### Prerequisites
- Python 3.8 or higher
- pip (Python package installer)

### From PyPI (Recommended)
```bash
pip install protfetch
```
*(Note: This command will work once the package is published to PyPI.)*

### From Source (for development)
1. Clone the repository:
```bash
git clone https://github.com/yourusername/protfetch.git  # Update with your repo URL
cd protfetch
```

2. Install in editable mode:
```bash
pip install -e .
```

## Usage

```bash
protfetch <input_gene_list_file> [OPTIONS]
```

### Required Arguments:
- `input_gene_list_file`: Path to the input file containing the list of genes.
  - Each line should be either `Protein Name | GENE_SYMBOL` or just `GENE_SYMBOL`
  - Lines starting with `#` are treated as comments and ignored

### NCBI Configuration:
- `--entrez-email YOUR_EMAIL`: **Strongly recommended.** Your email address for NCBI Entrez. NCBI requires this for reliable access and to contact you if there are issues with your queries.
- `--entrez-api-key YOUR_API_KEY`: *(Optional)* Your NCBI API key for higher request rates.

### Key Options:
- `-o, --output-dir DIR`: Directory to save output files (default: `protfetch_results`)
- `--max-dist INT`: Max Levenshtein distance for filtering near-identical sequences (default: 4; 0 to disable)
- `--max-workers INT`: Maximum number of concurrent workers for fetching data (default: 5)
- `--save-individual-files`: Save processed FASTA and CSV for each gene individually
- `--skip-keyword-filter`: Skip filtering FASTA sequences by keyword
- `--timeout SECONDS`: Timeout for NCBI requests (default: 60)
- `--retries INT`: Number of retries for NCBI requests (default: 3)
- `--debug`: Enable detailed debug logging
- `-v, --version`: Show program's version number and exit
- `-h, --help`: Show help message and exit

### Example:

Create a sample gene list file (e.g., `genes.txt`):
```
# Mitofusin 1 | MFN1
# Calreticulin | CALR
# CANX
# PDIA3
```

Run protfetch:
```bash
protfetch genes.txt --entrez-email your.name@example.com -o my_protein_data --save-individual-files
```

This command will:
1. Read `genes.txt`
2. Fetch data for MFN1, CALR, CANX, and PDIA3 from NCBI
3. Filter and process the sequences
4. Save combined results (e.g., `genes_combo_short.fasta`, `genes_combo_full.fasta`, `genes_combo_meta.csv`) in the `my_protein_data` directory
5. Save individual files for each gene in `my_protein_data/individual_gene_files/`

## Output Files

In the specified output directory:

- `{input_file_stem}_combo_short.fasta`: Combined FASTA file with short headers (e.g., `>ACCESSION`), deduplicated by accession
- `{input_file_stem}_combo_full.fasta`: Combined FASTA file with full original headers, deduplicated by full header
- `{input_file_stem}_combo_meta.csv`: Combined metadata CSV file (accession, gene_input, identifier_from_header), deduplicated by accession

If `--save-individual-files` is used, a subdirectory (default: `individual_gene_files`) will contain:
- `{GENE_SYMBOL}_filtered_short.fasta`
- `{GENE_SYMBOL}_filtered_full.fasta`
- `{GENE_SYMBOL}_filtered_meta.csv`

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
