skip to main content

DOE PAGESDOE PAGES

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

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.
Authors:
 [1] ;  [1] ;  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
Publication Date:
Grant/Contract Number:
DE-AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
Bioinformatics
Additional Journal Information:
Journal Volume: 31; Journal Issue: 2; Journal ID: ISSN 1367-4803
Publisher:
Oxford University Press
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1185410

Ahn, Tae-Hyuk, Chai, Juanjuan, and Pan, Chongle. Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance. United States: N. p., Web. doi:10.1093/bioinformatics/btu641.
Ahn, Tae-Hyuk, Chai, Juanjuan, & Pan, Chongle. Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance. United States. doi:10.1093/bioinformatics/btu641.
Ahn, Tae-Hyuk, Chai, Juanjuan, and Pan, Chongle. 2014. "Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance". United States. doi:10.1093/bioinformatics/btu641. https://www.osti.gov/servlets/purl/1185410.
@article{osti_1185410,
title = {Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillance},
author = {Ahn, Tae-Hyuk and Chai, Juanjuan and Pan, Chongle},
abstractNote = {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.},
doi = {10.1093/bioinformatics/btu641},
journal = {Bioinformatics},
number = 2,
volume = 31,
place = {United States},
year = {2014},
month = {9}
}