SIGMA

SIGMA performs strain-level identification and quantification of pathogens from metagenomic sequencing of clinical samples for biosurveillance and outbreak analysis.

Key Features:

• Strain-Level Identification and Quantification: Leverages sequence similarity to reference genomes derived from metagenomic data to identify and quantify pathogen strains and resolve serotypes.
• Statistical Uncertainty Quantification: Performs hypothesis testing on identified genomes and estimates confidence intervals for relative abundances to quantify statistical uncertainty.
• Strain Variant Calling: Assigns metagenomic reads to their most likely reference genomes to enable strain variant calling and distinguish closely related strains.
• Parallel Computing Support: Supports parallel computing for efficient analysis of large metagenomic datasets, optimizing performance and scalability.

Scientific Applications:

• Biosurveillance: Enables rapid and precise detection and quantification of pathogen strains to inform public health monitoring and interventions.
• Outbreak Tracking and Transmission Dynamics: Provides strain-resolved genetic data to track outbreaks and understand transmission dynamics.
• Source Attribution: Aids in identifying potential sources of infection by resolving serotypes and strain-level variation.

Methodology:

Leverages sequence similarity to reference genomes derived from metagenomic data; assigns reads to most likely reference genomes for strain variant calling; performs hypothesis testing and estimates confidence intervals for relative abundances; evaluated using simulated mock communities and fecal samples with spike-in pathogen strains; implemented in C++ and supports parallel computing.