Characterizing Bacterial Single Isolates with BaseSpace™ Sequence Hub Apps
A guest blog, written by GoSeqIt
In an increasingly globalized world, bacteria can spread rapidly and easily. Furthermore, they often contain genes that make them resistant to antibiotics or confer high virulence. Sequencing the entire genome of bacteria enables a thorough characterization and thus makes it possible for researchers to monitor the spread of particular strains of bacteria or sets of genes.
In collaboration with the Illumina BaseSpace Sequence Hub development team, GoSeqIt has published two apps for characterization of bacterial single isolates. Both of these apps are now available to BaseSpace Sequence Hub users:
The input for both apps is a bacterial complete or draft genome in FASTA format (only files with the extension .fa or .fasta are accepted).
Bacterial Analysis Pipeline App
The Bacterial Analysis Pipeline app will initially predict the species of the bacterial draft genome based on the number of kmers (oligonucleotides with the length k) co-occurring between the input genome and bacterial genomes in a reference database (1). Further, acquired antimicrobial resistance genes are identified using a BLAST-based approach, where the nucleotide sequence of the input genome is compared to the genes in the ResFinder database (2). Depending on the identified species, Multilocus Sequence Typing (MLST) is performed, also using a BLAST-based approach (3). One-hundred-twenty-five (125) MLST schemes are currently available.
If the input genome is recognized as belonging to Enterobacteriaceae or the gram positive bacteria (Enterococcus, Streptococcus, or Staphylococcus), BLAST is used to search for plasmid replicons using the PlasmidFinder database (4). Identified plasmids of the incF, IncH1, IncH2, IncI1, IncN, or IncA/C type are further subtyped by plasmid MLST (4). Finally, identified Escherichia coli, Enterococcus sp., Listeria sp., and Staphylococcus aureus are compared to the VirulenceFinder database containing known virulence genes (5). For more information, refer to the article titled “Bacterial Analysis Platform: An Integrated System for Analysing Bacterial Whole Genome Sequencing Data for Clinical Diagnostics and Surveillance.” Figure 1 illustrates the output for species prediction and MLST, while figure 2 illustrates the output for the prediction of acquired antimicrobial resistance genes.
Figure 1: Example of output from the Bacterial Analysis Pipeline app for species prediction and MLST of the input genome.
Figure 2: Example of output from the Bacterial Analysis Pipeline app for acquired antimicrobial resistance genes in the input genome.
E. coli Serotyping App
The E. coli Serotyping app uses a BLAST-based approach to predict the serotype of E. coli isolates by comparing the input genome with a database of specific O-antigen processing system genes for O typing and flagellin genes for H typing (7). The app outputs the predicted serotype along with the identified O-antigen genes (wzx, wzy, wzm, and wzt) and flagellin genes (fliC, flkA, fllA, flmA, and flnA).
Figure 3: Example of output from the E. coli Serotyping app. So far, only E. coli isolates can in this way be in silico serotyped.
Using the New Apps
The price for using the Bacterial Analysis Pipeline app is 5 iCredits per uploaded file plus the cost of computing. The E. coli Serotyping app costs 1 iCredit per uploaded file plus the cost of computing.
Both apps use methods that have been throughly described and published in renowned scientific journals.
1) Larsen MV, Cosentino S, Lukjancenko O, Saputra D, Rasmussen S, Hasman H, Sicheritz-Pontén T, Aarestrup FM, Ussery DW, Lund O. Benchmarking of methods for genomic taxonomy. J Clin Microbiol. 2014 May;52(5):1529-39. PMID: 24574292.
2) Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. Identification of acquired antimicrobial resistance genes. J Antimicrob Chemother. 2012 Nov;67(11):2640-4. PMID: 22782487.
3) Larsen MV, Cosentino S, Rasmussen S, Friis C, Hasman H, Marvig RL, Jelsbak L, Sicheritz-Pontén T, Ussery DW, Aarestrup FM, Lund O. Multilocus sequence typing of total-genome-sequenced bacteria. J Clin Microbiol. 2012 Apr;50(4):1355-61. PMID: 22238442.
4) Carattoli A, Zankari E, García-Fernández A, Voldby Larsen M, Lund O, Villa L, Møller Aarestrup F, Hasman H. In silico detection and typing of plasmids using PlasmidFinder and plasmid multilocus sequence typing. Antimicrob Agents Chemother. 2014 Jul;58(7):3895-903. PMID: 24777092.
5) Joensen KG, Scheutz F, Lund O, Hasman H, Kaas RS, Nielsen EM, Aarestrup FM. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coli. J Clin Microbiol. 2014 May;52(5):1501-10. PMID: 24574290.
6) Thomsen MC, Ahrenfeldt J, Cisneros JL, Jurtz V, Larsen MV, Hasman H, Aarestrup FM, Lund O. A Bacterial Analysis Platform: An Integrated System for Analysing Bacterial Whole Genome Sequencing Data for Clinical Diagnostics and Surveillance. PLoS One. 2016 Jun 21;11(6):e0157718. PMID: 27327771.
7) Joensen KG, Scheutz F, Lund O, Hasman H, Kaas RS, Nielsen EM, Aarestrup FM. Real-time whole-genome sequencing for routine typing, surveillance, and outbreak detection of verotoxigenic Escherichia coli. J Clin Microbiol. 2014 May;52(5):1501-10. PMID: 24574290.
For Research Use Only. Not for use in diagnostic procedures.