Bioinformatic Analyses of Whole-Genome Sequence Data in a Public Health Laboratory

Autor: Michelle Mendenhall, Kelly F. Oakeson, Robyn Atkinson-Dunn, Andreas Rohrwasser, Jennifer Wagner
Jazyk: angličtina
Rok vydání: 2017
Předmět:
0301 basic medicine
Microbiology (medical)
medicine.medical_specialty
Epidemiology
Computer science
In silico
030106 microbiology
public health laboratory
lcsh:Medicine
Genomics
Computational biology
infectious diseases
Genome
DNA sequencing
lcsh:Infectious and parasitic diseases
03 medical and health sciences
Public health surveillance
Utah
genomics
medicine
Humans
Public Health Surveillance
lcsh:RC109-216
antimicrobial resistance
genes
genome
Phylogeny
Bioinformatic Analyses of Whole-Genome Sequence Data in a Public Health Laboratory
Whole genome sequencing
Bacteria
Virulence
Whole Genome Sequencing
Computers
Public health
lcsh:R
Computational Biology
High-Throughput Nucleotide Sequencing
Bacterial Infections
bioinformatics
United States
Identification (information)
030104 developmental biology
whole-genome sequencing
Perspective
next-generation sequencing
Public Health
Laboratories
Genome
Bacterial
Software
Zdroj: Emerging Infectious Diseases, Vol 23, Iss 9, Pp 1441-1445 (2017)
Emerging Infectious Diseases
ISSN: 1080-6059
1080-6040
Popis: The ability to generate high-quality sequence data in a public health laboratory enables the identification of pathogenic strains, the determination of relatedness among outbreak strains, and the analysis of genetic information regarding virulence and antimicrobial-resistance genes. However, the analysis of whole-genome sequence data depends on bioinformatic analysis tools and processes. Many public health laboratories do not have the bioinformatic capabilities to analyze the data generated from sequencing and therefore are unable to take full advantage of the power of whole-genome sequencing. The goal of this perspective is to provide a guide for laboratories to understand the bioinformatic analyses that are needed to interpret whole-genome sequence data and how these in silico analyses can be implemented in a public health laboratory setting easily, affordably, and, in some cases, without the need for intensive computing resources and infrastructure.
Databáze: OpenAIRE
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