debase 0.1.19

Enzyme lineage analysis and sequence extraction package

DEBase

Enzyme lineage analysis and sequence extraction package with advanced parallel processing capabilities.

Installation

Quick Install (PyPI)

pip install debase

Development Setup with Conda (Recommended)

1. Clone the repository

git clone https://github.com/YuemingLong/DEBase.git
cd DEBase

2. Create conda environment from provided file

conda env create -f environment.yml
conda activate debase

3. Install DEBase in development mode

pip install -e .

Manual Setup

If you prefer to set up the environment manually:

# Create new conda environment
conda create -n debase python=3.9
conda activate debase

# Install conda packages
conda install -c conda-forge pandas numpy matplotlib seaborn jupyter jupyterlab openpyxl biopython requests tqdm

# Install RDKit (optional - used for SMILES canonicalization)
conda install -c conda-forge rdkit

# Install pip-only packages
pip install PyMuPDF google-generativeai debase

Note about RDKit: RDKit is optional and only used for canonicalizing SMILES strings in the output. If not installed, DEBase will still function normally but SMILES strings won't be standardized.

Requirements

• Python 3.8 or higher
• A Gemini API key (set as environment variable GEMINI_API_KEY)

Setting up Gemini API Key

# Option 1: Export in your shell
export GEMINI_API_KEY="your-api-key-here"

# Option 2: Add to ~/.bashrc or ~/.zshrc for persistence
echo 'export GEMINI_API_KEY="your-api-key-here"' >> ~/.bashrc
source ~/.bashrc

# Option 3: Create .env file in project directory
echo 'GEMINI_API_KEY=your-api-key-here' > .env

Recent Updates

• Campaign-Aware Extraction: Automatically detects and processes multiple directed evolution campaigns in a single paper
• Improved Model Support: Updated to use stable Gemini models for better reliability
• Enhanced PDB Integration: Intelligent AI-based matching of PDB structures to enzyme variants
• Better Filtering: Automatic removal of non-enzyme entries (buffers, controls, media)
• Optimized Performance: Removed unnecessary rate limiting for faster processing
• External Sequence Fetching: Automatic retrieval from PDB and UniProt databases when sequences aren't in papers
• Improved SI Processing: Structure-aware extraction of supplementary information
• Vision Support: Extracts data from figures and tables using multimodal AI capabilities

Quick Start

Basic Usage

# Run the full pipeline (sequential processing)
debase --manuscript manuscript.pdf --si supplementary.pdf --output output.csv

High-Performance Parallel Processing

# Use parallel individual processing for maximum speed + accuracy
debase --manuscript manuscript.pdf --si supplementary.pdf --output output.csv \
  --use-parallel-individual --max-workers 5

# Use batch processing for maximum speed (slight accuracy trade-off)
debase --manuscript manuscript.pdf --si supplementary.pdf --output output.csv \
  --use-optimized-reaction --reaction-batch-size 5

Processing Methods

DEBase offers three processing approaches optimized for different use cases:

1. Parallel Individual Processing (Recommended)

• 42 individual API calls (21 for reactions + 21 for substrate scope)
• 5 calls running simultaneously for 4-5x speedup
• Maximum accuracy - each enzyme gets dedicated attention
• Best for: Production use, important analyses

debase --manuscript paper.pdf --si si.pdf --use-parallel-individual --max-workers 5

2. Batch Processing (Fastest)

• ~8 total API calls (multiple enzymes per call)
• Fastest processing - up to 8x speedup
• Good accuracy - slight trade-off for complex chemical names
• Best for: Quick analyses, large-scale processing

debase --manuscript paper.pdf --si si.pdf --use-optimized-reaction --reaction-batch-size 5

3. Sequential Processing (Most Accurate)

• 42 sequential API calls (one at a time)
• Highest accuracy but slowest
• Best for: Critical analyses, small datasets

debase --manuscript paper.pdf --si si.pdf  # Default method

Advanced Usage

Skip Steps with Existing Data

# Skip lineage extraction if you already have it
debase --manuscript paper.pdf --si si.pdf --output output.csv \
  --skip-lineage --existing-lineage existing_lineage.csv \
  --use-parallel-individual

Direct Module Usage

# Run only reaction extraction with parallel processing
python -m debase.reaction_info_extractor_parallel \
  --manuscript paper.pdf --si si.pdf --lineage-csv lineage.csv \
  --max-workers 5 --output reactions.csv

# Run only substrate scope extraction with parallel processing  
python -m debase.substrate_scope_extractor_parallel \
  --manuscript paper.pdf --si si.pdf --lineage-csv lineage.csv \
  --max-workers 5 --output substrate_scope.csv

Pipeline Architecture

The DEBase pipeline consists of 5 main steps:

1. Lineage Extraction (Sequential) - Identifies all enzymes and their relationships
   • Extracts mutation information and evolutionary paths
   • Detects multiple directed evolution campaigns automatically
   • Fetches sequences from external databases (PDB, UniProt)
   • Filters out non-enzyme entries automatically
2. Sequence Cleanup (Local) - Generates protein sequences from mutations
   • Applies mutations to parent sequences
   • Handles complex mutations and domain modifications
   • Validates sequence integrity
3. Reaction Extraction (Parallel/Batch/Sequential) - Extracts reaction conditions and performance data
   • Campaign-aware extraction for multi-lineage papers
   • Vision-based extraction from figures and tables
   • Automatic IUPAC name resolution
4. Substrate Scope Extraction (Parallel/Sequential) - Finds additional substrates tested
5. Data Formatting (Local) - Combines all data into final output

Features

• Multi-processing modes: Sequential, parallel individual, and batch processing
• Campaign detection: Automatically identifies and separates multiple directed evolution campaigns
• Intelligent error handling: Automatic retries with exponential backoff
• External database integration: Automatic sequence fetching from PDB and UniProt
• AI-powered matching: Uses Gemini to intelligently match database entries to enzyme variants
• Smart filtering: Automatically excludes non-enzyme entries (buffers, controls, etc.)
• Vision capabilities: Extracts data from both text and images in PDFs

Complete Command Reference

Core Arguments

--manuscript PATH           # Required: Path to manuscript PDF
--si PATH                  # Optional: Path to supplementary information PDF
--output PATH              # Output file path (default: manuscript_name_debase.csv)

Performance Options

--use-parallel-individual  # Use parallel processing (recommended)
--max-workers N            # Number of parallel workers (default: 5)
--use-optimized-reaction   # Use batch processing for speed
--reaction-batch-size N    # Enzymes per batch (default: 5)
--no-parallel-queries      # Disable parallel processing

Pipeline Control

--skip-lineage             # Skip lineage extraction step
--skip-sequence            # Skip sequence cleanup step  
--skip-reaction            # Skip reaction extraction step
--skip-substrate-scope     # Skip substrate scope extraction step
--skip-lineage-format      # Skip final formatting step
--skip-validation          # Skip data validation step

Data Management

--existing-lineage PATH    # Use existing lineage data
--existing-sequence PATH   # Use existing sequence data
--existing-reaction PATH   # Use existing reaction data
--keep-intermediates       # Preserve intermediate files

Advanced Options

--model-name NAME          # Gemini model to use
--max-retries N            # Maximum retry attempts (default: 2)
--max-chars N              # Max characters from PDFs (default: 75000)
--debug-dir PATH           # Directory for debug output (prompts, API responses)

Tips for Best Performance

1. Use parallel individual processing for the best balance of speed and accuracy
2. Set max-workers to 5 to avoid API rate limits while maximizing throughput
3. Use batch processing only when speed is critical and some accuracy loss is acceptable
4. Skip validation (--skip-validation) for faster processing in production
5. Keep intermediates (--keep-intermediates) for debugging and incremental runs