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Version 7

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BioNumerics version 7.6 is a software application for the analysis and management of biological data. It provides tools for storage, processing, and comparison of various types of biological data.

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13 protocols using version 7

1

Molecular Epidemiology of Bacterial Isolates

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The genotype of each isolate was identified by compiling the genotype generated by each VNTR locus. The phylogenetic tree was generated using an unweighted pair group method with arithmetic mean (UPGMA) method35 (link) that is embedded in BioNumerics version 7.6 (Applied Maths, Austin, TX). The cluster analysis was performed using the UPGMA with a minimum spanning tree (MST) and distance matrices for categorical data by BioNumerics version 7.6. The Geographic distribution map template was downloaded from www.vectortemplates.com (Graphics Factory CC, Western Cape, Sourth Africa) with permission, and the added pie charts were constructed using BioNumerics version 7.6. Hunter and Gaston discrimination index (HGDI) was calculated using Ridom EpiCompare software version 1.0 (www.ridom.de/epicompare/) to elucidate the discriminatory power of the genotyping methods, which explained the probability of two unrelated and different isolates sampled from the test population grouping as different subtypes by a specific typing method28 (link). The 95% confidence intervals (CI) were calculated according to the method previously described36 (link).
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2

Molecular Subtyping of L. monocytogenes Isolates

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Clinical isolates of L. monocytogenes identified in hospital and private laboratories are submitted to the PHO Laboratory for molecular subtyping and surveillance purposes. Food samples and environmental swabs collected by public health unit officials in relation to a L. monocytogenes clinical case are also submitted to the PHO Laboratory, and testing is performed according to the Health Canada method MFHPB-30 [38 ]. During the outbreak investigations molecular subtyping by both pulsed field gel electrophoresis (PFGE) and WGS was performed in accordance with PulseNet Canada standard operating procedures; both methods were used as the PHO Laboratory was transitioning methods during this time. During the first outbreak, PFGE was performed for the clinical, food and environmental L. monocytogenes isolates at the PHO Laboratory and WGS was performed at the National Microbiology Laboratory (NML) in Winnipeg, Manitoba, Canada. PFGE and WGS for the second outbreak were both performed at the PHO Laboratory. Comparisons were generated using BioNumerics version 7.6 and nodes were calculated using UPGMA (Unweighted Pair Group Method with Arithmetic Mean), which is the standard analytical process for PulseNet Canada laboratories. Comparative analysis of WGS results from both outbreaks one and two were performed by the NML.
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3

Multilocus Sequence Typing and Ribotyping of C. difficile

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Multilocus sequence typing (MLST) was performed by sequencing seven house-keeping genes of C. difficile (adk, atpA, dxr, glyA, recA, sodA and tpi) as previously reported by Griffiths D et al. [11 (link)]. Sequence types (STs) and clades of C. difficile strains were confirmed by querying on http://pubmlst.org/ website. A minimum spanning tree generated from BioNumerics version 7.6 was used to show the genetic diversity of the MLST data derived from this study.
Capillary gel electrophoresis-based PCR ribotyping was implemented according to a previous report by Fawley WN et al. [17 (link)]. Ribotypes were identified by querying on the WEBRIBO web-based database (http://webribo.ages.at). The novel ribotype was named as “Chongqing Ribotype” (CQR) plus two Arabic numbers (e.g., CQR01).
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4

Multilocus Sequence Typing for Clostridioides difficile

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Multi-locus sequence typing (MLST) was carried out and evaluated as reported previously, and seven housekeeping genes (adk, atpA, dxr, glyA, recA, sodA, and tpi) were amplified and sequenced [28 ]. The result was uploaded to the C. difficile MLST database (https://pubmlst.org/organisms/clostridioides-difficile) and acquired the allele profile and ST. The MLST data is displayed by using the minimum spanning tree produced by BioNumerics version 7.6. In brief, the smallest generating tree represents the distribution and relationship of the MLST sequence type. The number of isolates of each related type is represented by the size of a circle. The illustration on the straight lines connecting the two circles shows different positions between them. The gray region covers the type that is less than or equal to two different spots. The colored region represents an evolutionary branch.
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5

Core Genome MLST Phylogenetic Analysis

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Core genome MLST (cgMLST) was performed based on assembled genomes in the software Ridom SeqSphere+ (Münster, Germany) with the integrated 1701 genes cgMLST scheme [17 (link)]. CgMLST allele coverage of at least 98% was set as quality threshold. CgMLST allelic profiles were imported into BioNumerics version 7.6 to perform single linkage clustering. Isolates with a maximum of ten allele differences from each other were assigned to the same cluster [17 (link)]. Trees were visualized and annotated in iTOL version 4 [18 (link)].
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6

Phylogenetic Analysis of Bifidobacterium longum

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Clustering analysis was also carried out using BioNumerics® version 7.6. Together with the 96 representative strains (Additional file 1: Table S3), the type strains of B. longum subsp. infantis (JCM 1222T) and B. longum subsp. longum (JCM 1217T) were also included in this analysis. Among the 2902 bp of the aligned nucleotide sequences of the seven housekeeping genes, 247 positions of the allelic profile were detected as those at which identical sequence was not shared among all of the strains used for this analysis (Additional file 1: Table S4). The categorical coefficient was calculated based on the allelic profiles and the dendrogram was constructed on the basis of the unweighted pair group method with arithmetic means (UPGMA) algorithm. The statistical reliability of the trees was evaluated by bootstrap analysis of 1000 replicates [37 (link)] and the nodes replicated at more than 95% were regarded as statistically reliable.
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7

Brucella Species Genotyping Analysis

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MLVA data were analyzed using BioNumerics version 7.6 software (Applied Maths, Belgium). Both categorical coefficient and un-weighted pair group methods with arithmetic mean algorithm (UPGMA) were applied to MLVA clustering analysis (Additional file 1: Table S1). Resultant genotypes were compared using the online Brucella 2016 MLVAbank. The MLVA-11 characters (combination of panels 1 and 2A loci) of the isolates were compared to those of strains in the 2016 MLVA bank to determine the geographic origin of each strain; minimum spanning tree (MST) based on complete MLVA-16 was used to investigation molecular relationships between strains in this study and 340 isolates including three species: B. melitensis (n = 296), B. suis (n = 15), and B. canis (n = 29) from other provinces of China (MLVAbank_V1.4.0) (Additional file 2: Table S2). MLVA-16 was used for genetic relationship investigation of both Brucella suis and canis on a global scale (n = 615) (Additional file 3: Table S3). The resulting MLST genotypes were compared using the web-based MLST database (https://pubmlst.org/brucella/) (Additional file 4: Table S4). MLST dendrogram was constructed by BioNumerics version 7.6 software. MLVA and MLST profiles of 40 isolates have been submitted to the MLVA bank_V1.4.0 (http://microbesgenotyping. i2bc. paris-saclay.fr/).
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8

Multilocus Sequence Typing of Burkholderia pseudomallei

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Multilocus sequence typing assays were performed as previously described (Price et al., 2016a (link)). Each allele was assigned a different number, and the allelic profile (a string of seven integers) was used to define the sequence type (ST) for that isolate (Supplementary Table S1). The allelic profiles of the isolates were imported into BioNumerics version 7.6, and the relatedness of the isolates was displayed as a dendrogram using the matrix of pairwise differences in the allelic profiles and UPGMA clustering. The genetic diversity and discriminatory power of each typing method were calculated based on the Hunter-Gaston diversity index (HGDI), according to a previously published method (Hunter, 1990 (link)).
The similarity of MLST profiles of isolates identified in this study or elsewhere in China (Supplementary Table S2) in B. pseudomallei MLST database was assessed using eBURST software as described previously (Kamthan et al., 2018 (link)) STs (Supplementary Table S3). The relationship of China STs to the global collection of STs was assessed using the eBURST algorithm with PHYLOViZ 2.0 (Nascimento et al., 2017 (link)) available at MLST site1. All MLST profiles have been submitted to the MLST DB2.
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9

Molecular Epidemiology of Brucellosis in Northwest China

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To clarify the molecular characteristics and evolutionary relationships of brucellosis in northwest China, all 66 isolates were analyzed using BioNumerics version 7.6. Using the same software, clustering analysis was performed using minimal spanning tree [12 , 13 (link)]. The resulting genotypes were compared using the web-based MLST database (https://pubmlst.org/brucella/). Genotypic diversification of Brucella in northwest China was analyzed using this study and published data [9 (link), 12 –14 (link)].
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10

MLST Genotyping of C. difficile Isolates

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MLST was performed on all recovered isolates using the primers and methods developed by Griffiths et al.49 (link). Seven housekeeping genes (adk, atpA, dxr, glyA, recA, sodA and tpi) were amplified and products were sent for bidirectional sequencing to TaKaRa, Japan. The complete allele sequences were analyzed using DNAStar and MEGA4 software and allele and ST assignments were performed using the C. difficile database at pubMLST (https://pubmlst.org/cdifficile). A minimum spanning tree was created using BioNumerics version 7.6. During the experiment, adk gene for seven strains couldn’t amplified, and we re-designed the primer (Table 4) based on the Clone Manager Professional Suite 8 software.
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