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

2014; Oxford University Press; Volume: 31; Issue: 2 Linguagem: Inglês

10.1093/bioinformatics/btu641

1367-4811

Tae-Hyuk Ahn, Juanjuan Chai, Chongle Pan,

Gene expression and cancer classification

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.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.Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org.Supplementary data are available at Bioinformatics online.