clonearmy 0.2.0

Analyze haplotypes from Illumina paired-end amplicon sequencing

CloneArmy

CloneArmy is a modern Python package for analyzing haplotypes from Illumina paired-end amplicon sequencing data. It provides a streamlined workflow for processing FASTQ files, aligning reads, identifying sequence variants, and performing comparative analyses between samples.

Features

• Fast paired-end read processing using BWA-MEM
• Quality-based filtering of bases and alignments
• Haplotype identification and frequency analysis
• Statistical comparison between samples with FDR correction
• Interactive visualization of mutation frequencies
• Rich command-line interface with progress tracking
• Comprehensive HTML reports
• Multi-threading support
• Support for full-length sequence analysis

Installation

pip install cloneArmy

Requirements

• Python ≥ 3.8
• BWA (must be installed and available in PATH)
• Samtools (must be installed and available in PATH)

Usage

Command Line Interface

Basic Analysis

# Basic usage
cloneArmy run /path/to/fastq/directory reference.fasta

# With all options
cloneArmy run /path/to/fastq/directory reference.fasta \
    --threads 8 \
    --output results \
    --min-base-quality 20 \
    --min-mapping-quality 30 \
    --no-report  # Skip HTML report generation

Comparative Analysis

# Compare two samples
cloneArmy compare \
    /path/to/sample1/fastq \
    /path/to/sample2/fastq \
    reference.fasta \
    --threads 8 \
    --output comparison_results \
    --min-base-quality 20 \
    --min-mapping-quality 30 \
    --full-length-only  # Only consider full-length sequences

Python API

from pathlib import Path
from clone_army.processor import AmpliconProcessor
from clone_army.comparison import run_comparative_analysis

# Initialize processor
processor = AmpliconProcessor(
    reference_path="reference.fasta",
    min_base_quality=20,
    min_mapping_quality=30
)

# Process samples
results1 = processor.process_sample(
    fastq_r1="sample1_R1.fastq.gz",
    fastq_r2="sample1_R2.fastq.gz",
    output_dir="results/sample1",
    threads=4
)

results2 = processor.process_sample(
    fastq_r1="sample2_R1.fastq.gz",
    fastq_r2="sample2_R2.fastq.gz",
    output_dir="results/sample2",
    threads=4
)

# Perform comparative analysis
comparison_results = run_comparative_analysis(
    results1={"sample1": results1},
    results2={"sample2": results2},
    reference_seq="ATCG...",  # Reference sequence string
    output_path="comparison_results.csv",
    full_length_only=False
)

# Results are returned as pandas DataFrames
print(results1)  # Sample 1 haplotypes
print(comparison_results)  # Statistical comparison

Output Files

Single Sample Analysis

• Sorted BAM file with alignments
• CSV file containing haplotype information:
  • Sequence
  • Read count
  • Frequency
  • Number of mutations
  • Full-length status
• Interactive HTML report (optional)
• Console output with summary statistics

Comparative Analysis

• CSV file with statistical comparisons:
  • Mutation positions and types
  • Frequencies in each sample
  • Statistical significance (p-values)
  • FDR-corrected p-values
• Interactive HTML plot showing mutation frequency differences
• Console output with significant mutations