A Primer on Infectious Disease Bacterial Genomics

Abstract

SUMMARYThe number of large-scale genomics projects is increasing due to the availability of affordable high-throughput sequencing (HTS) technologies. The use of HTS for bacterial infectious disease research is attractive because one whole-genome sequencing (WGS) run can replace multiple assays for bacterial typing, molecular epidemiology investigations, and more in-depth pathogenomic studies. The computational resources and bioinformatics expertise required to accommodate and analyze the large amounts of data pose new challenges for researchers embarking on genomics projects for the first time. Here, we present a comprehensive overview of a bacterial genomics projects from beginning to end, with a particular focus on the planning and computational requirements for HTS data, and provide a general understanding of the analytical concepts to develop a workflow that will meet the objectives and goals of HTS projects.

Genomic analysis of a multidrug‐resistant clinical Providencia rettgeri (PR002) strain with the novel integron ln1483 and an A/C plasmid replicon

“…It has been shown that HGT remains the most effective means of bacterial evolution, as it allows bacteria to rapidly acquire new genomic traits, such as virulence and resistance determinants with the help of MGEs . This acquisition of genetic material can lead to immune suppression, colonization, host cell invasion, immune evasion, as well as other genetic factors involved in persistence and infection . Host and pathogens repeated interactions with other microbes or the environment provide a platform for the exchange and acquisition of these MGEs that are known to influence microbial virulence and diseases .…”

“…9 This acquisition of genetic material can lead to immune suppression, colonization, host cell invasion, immune evasion, as well as other genetic factors involved in persistence and infection. 10 Host and pathogens repeated interactions with other microbes or the environment provide a platform for the exchange and acquisition of these MGEs that are known to influence microbial virulence and diseases. 11,12 To date, 24 P. rettgeri genomes from diverse sources and countries have been deposited in public databases (National Center for Biotechnology Information (NCBI)), including our PR002 strain.…”

chewBBACA: A complete suite for gene-by-gene schema creation and strain identification

“…Read mapping approaches using Single Nucleotide Polymorphisms (SNP)/Single Nucleotide Variants (SNV) have been used for studying bacterial genomes (Lynch et al 2016). However, gene-by-gene (GbG) approaches are advantageous in the context of genomic epidemiology as an expansion of Multilocus Sequence Typing (MLST) (Maiden et al 1998) thus allowing portability, scalability, and independence from a defined reference strain.…”

“…Variants (SNV) have been widely used for studying bacterial genomes [1]. However, gene-by-61 gene (GbG) approaches have also been advocated in the context of genomic epidemiology as 62 an expansion of Multilocus Sequence Typing (MLST) [2] allowing portability, scalability, and 63 independence from a defined reference strain.…”

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel

“…Notable examples of SNP-based phylogenomic pipelines include the CFSAN SNP Pipeline (Davis et al, 2015) used in the international GenomeTrakr network for detection of foodborne pathogens, the Lyve-SET hqSNP pipeline used for the analysis of enteric organisms at the U.S. Centers for Disease Control, the SNVPhyl pipeline used for bacterial phylogenomics by the Canadian Public Health Laboratory Network, and the SnapperDB pipeline (Dallman et al, 2018) used for national surveillance of enteric pathogens at Public Health England. Each of these pipelines has its own set of complex dependencies and assumptions that can influence the results Katz et al, 2017;Lynch et al, 2016). In a recent review of 41 different SNP detection pipelines, the authors demonstrated that the choice of reference genome for SNP calling had a substantial effect on the variants identified, which can influence the phylogeny and thus its scientific and epidemiological interpretation (Bush et al, 2019).…”