Castanet: a pipeline for rapid analysis of targeted multi-pathogen genomic data

Area of Science:

• Genomics
• Bioinformatics
• Pathogen Surveillance

Background:

• Target enrichment strategies offer sensitive, cost-effective, high-throughput genomic data generation for multiple pathogens.
• Limited computational tools hinder the analysis of multi-pathogen enrichment sequencing data in research and clinical settings.

Purpose of the Study:

• To introduce Castanet, a novel computational pipeline designed for analyzing multi-pathogen enrichment sequencing data.
• To provide a flexible and efficient tool for genome reconstruction and quantification of multiple pathogens from enrichment experiments.

Main Methods:

• Castanet is a Python 3.10 pipeline compatible with short-read data from targeted enrichment strategies.
• It can process any BAM file and includes an optional graphical interface and installer.
• The pipeline is designed to process a flexible set of pathogens of interest.

Main Results:

• Castanet accurately reconstructs consensus sequences, even with multiple strains of the same pathogen.
• It provides metrics for capture efficiency, pathogen load, contamination differentiation, and sequencing quality.
• The pipeline efficiently processes large datasets (e.g., 96 samples, 50M reads) on standard computers in under 2 hours.

Conclusions:

• Castanet addresses the need for specialized computational tools in multi-pathogen enrichment sequencing.
• Its accuracy, efficiency, and comprehensive metrics support advanced pathogen surveillance and clinical applications.