Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance

Abstract

Motivation: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis.

Results: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic reads to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. The algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains.

Availability and implementation: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Conceptual overview of the Sigma algorithm. The inputs are metagenomic reads and user-defined reference genomes (top panel). The alignment of reads to genomes is used to define a probabilistic model of metagenomic sequencing (middle panel). Genomes are detected with hypothesis testing, quantified with confidence interval estimation, and scanned for sequence variations (bottom panel).

Fig. 2.

Identification of a Salmonella enterica strain at a serial dilution of relative abundances in a human fecal microbiota background. (a) Likelihood ratios of all aligned Salmonella enterica strains. Only the correct strain (highlighted in red outline) has statistically significant identification with <0.01 p-value down to the 0.001% dataset. (b) Estimated and expected relative abundances (RA) of the spike-in Salmonella enterica strain. Point estimates (red dots) were bracketed by 95% confidence intervals (blue error bars) with small relative standard deviations (RSD) down to 0.001% (0.027X coverage depth).