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.Contact: panc@ornl.govSupplementary information: Supplementary data are available at Bioinformatics online.

High-resolution characterization of the human microbiome

“…Indeed, by identifying within-species genetic variation directly from metagenomic samples, a more comprehensive set of strains can be characterized in a high-throughput manner from a single sequencing experiment. This approach has been successfully applied, for example, to detect pathogenic strains of E. coli in clinical samples or for biosurveillance 62,63 , to identify novel strain-level dynamics in the infant gut 64 , to confirm the retention of personal strains over time 65 , and to demonstrate extensive, widespread, and clinically-relevant strain-level variation in the gut microbiome 66 .…”

“…Most recently developed metagenomics-based SNP analysis methods take advantage of reference genome collections to estimate community diversity, detect strains of interest, or find shared strains between different metagenomic samples. These methods may use full genomes 62,63,65,67 or marker genes known to contain loci with strain-identifying SNPs 64 . Since some reference genomes may be extremely similar to each other, the first step in many of these methods is to cluster genomes by similarity and select a representative genome for each cluster to which metagenomic reads can be aligned.…”

Towards global interoperability for supporting biodiversity research on essential biodiversity variables (EBVs)

“…This needs to be carefully addressed, especially in light of the currently increasing contribution of metagenomics research to biodiversity discovery (Gilbert and Dupont 2011;Simon and Daniel 2011). For instance, the species concept is very elusive for the microbial world, where the strain/isolate definition might be more practically helpful to allow exhaustive description of phenotypic features (Ahn, Chai, and Pan 2014;Caro-Quintero and Konstantinidis 2012). This can be potentially addressed through specific and progressively advanced molecular methods such as DNA barcoding and metagenomics (Gilbert and Dupont 2011;Kress et al 2015;Shokralla et al 2014;Simon and Daniel 2011).…”

“…Out of the many meanings of biodiversity -including biotic variation from the level of genes to ecosystems (Purvis and Hector 2000) as well as taxonomic, functional and phylogenetic aspects (Naeem, Duffy, and Zavaleta 2012) -the GLOBIS-B project will mainly focus on the species level (Figure 1). This includes taking into account the above mentioned specificities in the case of microorganisms for which the species concept is not always tractable (Ahn, Chai, and Pan 2014;Caro-Quintero and Konstantinidis 2012). In particular, focus is put on data related to species distributions and abundances, species traits, and species interactions (Figure 1).…”

Snowball: strain aware gene assembly of metagenomes

“…They often rely on the availability of closely related reference genomes of the studied species (Ahn et al, 2015;Tö pfer et al, 2014;Zagordi et al, 2011), where reads are first mapped onto a reference genome, using a read mapping tool, e.g. BWA (Li and Durbin, 2009), strain variants are then identified through a reference guided strain aware assembly.…”