Castanet

Castanet analyzes targeted multi-pathogen enrichment sequencing data for end-to-end processing and consensus sequence generation.

Key Features:

• Genome reconstruction: Generates consensus sequences that accurately reconstruct reference sequences, including instances where multiple strains of the same pathogen are present.
• Method-specific metrics reporting: Reports metrics enabling quantification of capture efficiency, estimation of pathogen load, differentiation of low-level positives from contamination, and assessment of sequencing quality.
• Flexible pathogen processing: Processes a pre-defined set of pathogens of interest directly from multi-pathogen enrichment experiments.
• BAM file compatibility: Works with short-read data produced by existing targeted enrichment strategies and can be deployed on any BAM file generated by another methodology.
• Batch processing: Processes the entire output of a 96-sample enrichment sequencing run (50M reads) using a single batch process command.

Scientific Applications:

• Multi-pathogen surveillance: Enables cost-effective, high-throughput genomic surveillance of multiple pathogens through targeted enrichment sequencing.
• Clinical diagnostics: Supports clinical applications requiring sensitive detection and differentiation of pathogens from contamination.
• Strain-level resolution: Distinguishes multiple strains of the same pathogen present in a single sample.

Methodology:

Castanet accepts BAM files as input and implements end-to-end processing steps for consensus sequence generation; it is implemented in Python 3.10.