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

Ahn, Tae-Hyuk; Chai, Juanjuan; Pan, Chongle

doi:10.1093/bioinformatics/btu641

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

Journal Article · Mon Sep 29 00:00:00 EDT 2014 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btu641· OSTI ID:1185410

Ahn, Tae-Hyuk ^[1]; Chai, Juanjuan ^[1]; Pan, Chongle ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division

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. In conclusion, 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: DE-AC05-00OR22725

OSTI ID:: 1185410

Journal Information:: Bioinformatics, Vol. 31, Issue 2; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 60 works

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