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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 8  |  Issue : 3  |  Page : 273-280

A multilocus sequence typing scheme for Mycobacterium abscessus complex (MAB-multilocus sequence typing) using whole-genome sequencing data


1 National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada; Department of Microbiology, University of Manitoba, Manitoba, Canada
2 Department of Microbiology, University of Manitoba, Manitoba, Canada
3 Department of Bioinformatics, National Microbiology Laboratory, Public Health Agency of Canada, Manitoba, Canada
4 Streptococcus and Sexually Transmitted Diseases, National Microbiology Laboratory, Public Health Agency of Canada, Manitoba, Canada
5 National Reference Centre for Mycobacteriology, National Microbiology Laboratory, Public Health Agency of Canada; Department of Medical Microbiology, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada

Date of Web Publication12-Sep-2019

Correspondence Address:
Dr Meenu Kaushal Sharma
National Reference Centre for Mycobacteriology, National Microbiology Laboratory @CSCHAH, Public Health Agency of Canada, 1015 Arlington St, Winnipeg, MB
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmy.ijmy_106_19

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  Abstract 


Background:Mycobacterium abscessus is a rapid growing nontuberculous mycobacteria (NTM) and a clinically significant pathogen capable of causing variable infections in humans that are difficult to treat and may require months of therapy/surgical interventions. Like other NTMs, M. abscessus can be associated with outbreaks leading to complex investigations and treatment of affected cases. Typing schemes for bacterial pathogens provide numerous applications; including identifying chain of transmission and tracking genomic evolution, are lacking or limited for many NTMs including M. abscessus. Methods: We extended the existing scheme from PubMLST using whole-genome data for M. abscessus by extracting data for 15 genetic regions within the M. abscessus genome. A total of 168 whole genomes and 11 gene sequences were used to build this scheme (MAB-multilocus sequence typing [MLST]). Results: All seven genes from the PubMLST scheme, namely argH, cya, gnd, murC, pta, purH, and rpoB, were expanded by 10, 14, 13, 10, 13, 10, and 9 alleles, respectively. Another eight novel genes were added including hsp 65, erm(41), arr, rrs, rrl, gyrA, gyrB, and recA with 16, 16, 25, 7, 32, 35, 29, and 15 alleles, respectively, with 85 unique sequence types identified among all isolates. Conclusion: MAB-MLST can provide identification of M. abscessus complex to the subspecies level based on three genes and can provide antimicrobial resistance susceptibility prediction based on results from seven genes. MAB-MLST generated a total of 85 STs, resulting in subtyping of 90 additional isolates that could not be genotyped using PubMLST and yielding results comparable to whole-genome sequencing (WGS). Implementation of a Galaxy-based data analysis tool, MAB-MLST, that simplifies the WGS data and yet maintains a high discriminatory index that can aid in deciphering an outbreak has vast applicability for routine diagnostics.

Keywords: Nontuberculous mycobacteria, MAB-multilocus sequence typing, multilocus sequence typing, Mycobacterium abscessus, rapid growing mycobacteria


How to cite this article:
Wuzinski M, Bak AK, Petkau A, B. Demczuk WH, Soualhine H, Sharma MK. A multilocus sequence typing scheme for Mycobacterium abscessus complex (MAB-multilocus sequence typing) using whole-genome sequencing data. Int J Mycobacteriol 2019;8:273-80

How to cite this URL:
Wuzinski M, Bak AK, Petkau A, B. Demczuk WH, Soualhine H, Sharma MK. A multilocus sequence typing scheme for Mycobacterium abscessus complex (MAB-multilocus sequence typing) using whole-genome sequencing data. Int J Mycobacteriol [serial online] 2019 [cited 2019 Nov 12];8:273-80. Available from: http://www.ijmyco.org/text.asp?2019/8/3/273/266485




  Introduction Top


Mycobacterium abscessus is a rapid growing mycobacteria (RGM) and an emerging pathogen that causes a wide spectrum of clinical infection in humans including pulmonary disease in patients with cystic fibrosis and soft-tissue infections.[1] Of particular concern is its resistance to a large number of antibiotics, making it one of the most resistant pathogenic RGMs.[2]

A prominent challenge with M. abscessus is its ever-evolving nomenclature. Until 1992, M. abscessus ((also known as Mycobacterium abscessus complex (MAB)) was classified under the Mycobacterium chelonae group. In 2013, the complex was divided into three subspecies, M. abscessus subsp. abscessus, M. abscessus subsp. massiliense, and M. abscessus subsp. bolletii.[3],[4],[5] More recent studies have further amended the species into a new genus, Mycobacteroides, belonging to the “Abscessus-Chelonae” clade.[6]

The three different subspecies are known to have different antimicrobial susceptibilities, and therefore, division between them is vital in clinical settings. As 16S rRNA sequencing is unable to subspeciate M. abscessus, other conserved genes such as the beta-subunit of RNA polymerase (rpoB) or heat-shock protein65 (hsp65) are utilized.[7],[8],[9] With the advancing and growing trend of whole-genome sequencing (WGS), there are less obstacles to using genome data for diagnostics and outbreak investigations.[9] Due to its high discriminatory power, WGS may be able to decipher complex investigations but often involves large datasets and complicated pipelines for data analysis and interpretation. There is a need for genotyping tools that are user-friendly and require limited technical and bioinformatics knowledge but maintain high discriminatory power.

Multilocus sequence typing (MLST) has been a staple in molecular biology for the past 20 years. Its usefulness extends from an epidemiological tool to analyzing pathogenicity, evolution, and surveillance.[10] Traditional MLST schemes are composed of multiple housekeeping genes, each of which is sequenced and analyzed separately to assign allele numbers for each unique sequence. Unique allele combinations are then used to build sequence types (STs), with each given an arbitrarily assigned number.[9] WGS may provide a cost-effective, less-labor intensive method in the future with a high discriminatory index (DI).[11] As it increases in popularity, novel tools are needed to be able to analyze and utilize the data.

The publically available PubMLST database for M. abscessus complex (https://pubmlst.org/mabscessus/) is split into two schemes, M. abscessus and M. massiliense. In this study, we present an expanded version of the M. abscessus scheme, MAB-MLST, with the ability to subspeciate the members of M. abscessus complex and predict antimicrobial resistance phenotypes.


  Methods Top


Constructing the genomic dataset

An online search was conducted for M. abscessus complex WGS from NCBI GenBank (https://www.ncbi.nlm.nih.gov/genbank/), the European Nucleotide Archive (https://www.ebi.ac.uk/ena), and the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra). A total of 294 whole genomes including 100 genomes from GenBank were downloaded for analysis in Galaxy.[12] This includes a reference genome for each subspecies, M. abscessus subsp. abscessus ATCC19977[CU458896], M. abscessus subsp . massiliense CCUG48898[NZ_AKVF00000000.1], and M. abscessus subsp. bolletii CCUG50184[MVHK00000000], with Mycobacterium chelonae type strain CCUG47445[CP007220.1] serving as a relative outsider of M. abscessus complex.

Quality and degree of relatedness of the downloaded genomes were assessed by determining the sequencing depth for all genomes by assembling the FASTQ data using the SPAdes genome assembler v. 3.11.1 (coverage >5). We used a read-mapping approach to assess the degree of relatedness of all genomes by simulating paired-end reads (FASTQ files) using ART v. 2014.11.03.0, with read coverage set at 40x.[13],[14] Reads were aligned to the genome ATCC19977 using SMALT v. 0.7.6 (https://www.sanger.ac.uk/science/tools/smalt-0), and sequences with <80% and <5x coverage were excluded (as determined FastQC v. 0.71 [http://www.bioinformatics.babraham.ac.uk/projects/fastqc/]). From the original 294 genomes downloaded, 168 passed all parameters [listed in Table S1] and were used in the development of MAB-MLST.



Multilocus sequence typing scheme creation

PubMLST (https://pubmlst.org/mabscessus/) contains a curated database for alleles of argH, cya, gnd, murC, pta, purH, and rpoB genes. In addition to the PubMLST scheme, unique alleles for each gene were added to the database and assigned a unique allele number. In addition, eight new genes were added, hsp65, erm(41), rrl, rrs, arr, gyrA, gyrB, and recA. Wild-type genes were obtained from type strains of M. abscessus subsps. abscessus, massiliense, and bolletii [Table 1].
Table 1: Alleles found in both multilocus sequence typing schemes along with MAB-multilocus sequence typing sequence diversity and percent identity of each locus

Click here to view


High-throughput analysis of all 168 assembled genomes was performed using custom in-house-developed R-scripts in RStudio v. 1.1.463, which used BLAST with the e-value cutoff option set to 10e-100.[15],[16] The scripts interrogated the assembled genomes using a reference gene to identify and extract the sequence corresponding to the novel allele. An additional NCBI BLAST search was done for all genes, with any unique sequences investigated. Sequence variations of the novel alleles were confirmed using AliView v. 1.23.[17]

All allelic profiles were analyzed and filtered into STs based on unique combinations which were then reformatted to work with an MLST software package (https://github.com/tseemann/mlst; v. 2.15.1) available within the Galaxy (galaxyproject.org) platform.[12] This MLST software was used to validate the allelic assignments. MAB-MLST is available for download on GitHub (github.com/phac-nml/mab_mabscessus).

Whole-genome phylogenetic analysis

Core single-nucleotide variant (SNV) phylogenetic analyses using SNVPhyl was conducted on all genomes using ATCC19977 as the reference with parameters: minimum SNV-coverage of 10, minimum mapping quality of 30, and minimum SNV abundance ratio of 0.75.[18] High SNV density regions were not removed to maintain the diversity present. The phylogenetic tree was visualized in GrapeTree [19] (github.com/phac-nml/mab_mabscessus).


  Results Top



  PubMLST Results Top


The PubMLST (https://pubmlst.org/mabscessus/) scheme is composed of seven genes (argH, cya, gnd, murC, pta, purH, and rpoB) which are compiled into 26 STs. All 168 genomes were run against PubMLST which resulted in 56 genomes (33%) being assigned an ST: ST5 (n = 35), ST9 (n = 18), and ST24 (n = 3), leaving 67% of genomes not matching an ST.


  MAB-Multilocus Sequence Typing Results Top


To develop a more robust, precise, and discriminatory typing scheme, eight new genes were added: hsp65, erm(41), rrs, rrl, arr, gyrA, gyrB, and recA, and alleles for the previous housekeeping genes were expanded. Similarity matrices, total number of alleles added, and sequence divergence of all genes can be found in [Table 1]. MAB-MLST contains a total of 85 unique STs. When all sequences were queried through Galaxy, 144/168 genomes resulted in 65 STs being identified. Due to the enhancements made in the typing scheme, the resulting STs were redefined from the original PubMLST scheme. ST-1, ST-2, and ST-3 correspond to the type strains for M. abscessus subsp . abscessus, M. abscessus subsp. bolletii, and M. abscessus subsp . massiliense, respectively. All remaining ST numbers were arbitrarily assigned based on unique allelic profiles. The allele expansion of each gene and the relevant allelic diversity of each gene are illustrated in [Table 1].

PubMLST was able to type 33% (56/168) of all samples while MAB-MLST was able to type 86% (144/168), each with their current allele databases. Further, the DI of both schemes (PubMLST vs. MAB-MLST) was calculated through Simpson's diversity index through the Comparing Partitions website (www.comparingpartitions.info/index.php?link = home).[19] Samples that were unable to be assigned a ST were given a ST of 0; the DIs with 95% confidence interval for each scheme can be found in [Table 2]. In addition, the adjusted Wallace coefficient was used to measure the congruence of the two schemes [Table 2].[20]
Table 2: Diversity indices and congruence values for MAB-multilocus sequence typing and PubMLST schemes*

Click here to view


Allelic assignments for hsp65, rpoB, and erm(41) genes with their associated subspecies name and mutations are listed in [Table 3], [Table 4], [Table 5]. [Figure 1] shows dendrograms of all hsp65, rpoB, and erm(41) alleles that were created and visualized with MegAlign™ version 15.0.0 (160), DNASTAR, Madison, WI, USA.
Table 3: Allelic assignments for hsp65 with associated subspecies identity and mutations

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Table 4: Allelic assignments for rpoB with associated subspecies identity and attributes

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Table 5: Allelic assignments for erm(41) with associated subspecies, mutations, relevant sequevar, and the predicted phenotype for macrolide resistance

Click here to view
Figure 1: Dendrogram of all hsp65, rpoB, and erm(41) alleles added to MAB-multilocus sequence typing. Made and visualized with MegAlign™ version 15.0.0 (160), DNASTAR. Madison, WI, USA. Underscored number corresponds to allele number

Click here to view


Based on the correlation of rpoB, hsp65, and erm (41) genes using information from Tables 35, 98 genomes were found to be M. abscessus subsp. abscessus, 10 as M. abscessus subsp. bolletii, 50 as M. abscessus subsp. massiliense, and 1 as M. chelonae. Nine genomes produced discrepant results based on identities produced by hsp65, rpoB, and erm(41) genes, as listed in [Table 6].
Table 6: Individual gene identities of discrepant genomes

Click here to view


Annotation of SNVPhyl with corresponding STs was done in GrapeTree to compare WGS and MLST clustering. This tree was further visualized in Microreact v. 5.99.0 (https://microreact.org/showcase),[21] as seen in [Figure 2].
Figure 2: Phylogenetic tree of all query genomes based on single-nucleotide variant analysis generated in Galaxy using SNVPhyl (minimum coverage to call a single-nucleotide variant of 10 reads, minimum mapping quality of 30, and minimum single-nucleotide variant abundance ratio of 0.75) with no removal of regions of high single-nucleotide variant density due to the large genetic distance between some of the genomes. Sequence types in MAB-multilocus sequence typing are seen in color. Each unique color refers to a different sequence type where NA is those that could not be assigned a sequence type. Tree file created from SNVPhyl and visualized with GrapeTree and exported to Microreact v. 5.99.0

Click here to view



  Discussion Top


Genotyping of M. abscessus complex is clinically relevant in the context of outbreak surveillance. A common genotyping method is MLST, which can be extremely helpful in clinical identification, evolution, and surveillance of pathogenic bacteria.[10] The PubMLST scheme for M. abscessus uses seven housekeeping genes: argH, cya, gnd, murC, pta, purH, and rpoB with 6, 5, 7, 8, 11, 7, and 4 alleles, respectively, and 26 unique STs. The PubMLST database for M. abscessus was able to assign STs to 33% of the collective genomes which were mainly categorized into ST5 (n = 35), ST9 (n = 17), and ST24 (n = 3) with 67% of the genomes (all subspecies) not matching any previously established STs.

An alternative to traditional MLST is to utilize WGS data where we can examine complete genomes of any bacterial isolates.[22] Its use in a routine laboratory will change as the speed of sequencing is dramatically increasing while the cost is decreasing.[21] WGS provides a wealth of information that can be harnessed with the correct pipeline but may pose challenges to diagnostic laboratory staff. Advancements in MLST methods have been made in the form of core-genome and whole-genome MLST schemes. While these methods take better advantage of the data that WGS provides, they are often composed of 1000s of genes and thus do not create results that are easy to interpret. Staff competency for the use of WGS in diagnostic/clinical setting is concerning as most data analysis pipelines provide bioinformatics results in formats that are not routinely utilized or recognized by general bacteriology laboratory staff (e.g., phylogenetic trees).

MAB-MLST takes advantage of the increased discrimination that WGS can provide and presents results in a user-friendly tabular output. Eight new genes, such as hsp65, erm(41), arr, rrs, rrl, gyrA, gyrB, and recA, were added to the scheme with 16, 16, 25, 7, 32, 35, 29, and 15 alleles, respectively. The housekeeping alleles originally found in the PubMLST scheme such as argh, cya, gnd, murC, pta, purH, and rpoB were expanded by the addition of 10, 14, 13, 10, 13, 10, and 9 alleles, respectively. We were able to raise the total number of STs from 26 to 85, allowing us to differentiate genomes using the presence/absence of a combination of 15 genes. The top STs for MAB-MLST were ST1 (n = 25), ST9 (n = 10), and ST56 (n = 9), with 86% of the study samples being assigned an ST. The other 14% (n = 24) contained at least one gene which could not be exactly matched to an allele type, due to contig assembly issues. A major advantage is the addition of antimicrobial resistance genes in the MLST scheme which increases clinically relevant information by helping predict antibiotic resistance profiles.

The erm(41) gene is important for M. abscessus for its association of inducible resistance to macrolides as well as differentiating the three subspecies.[23] M. abscessus subsp. massiliense has a large deletion in erm(41) which produces a nonfunctional erm protein.[24] On the other hand, both M. abscessus subsp. abscessus and subsp. bolletii have functional erm(41) genes with unique sequences. A functional erm(41) confers inducible resistance in isolates that may initially appear susceptible to clarithromycin. On extended incubation, they can demonstrate resistance to the drug.[23],[24] Furthermore, T28C mutation (position 154; allele-2, MAB-MLST) demonstrates a susceptible macrolide phenotype.[24] All of these factors are included and recognized by the scheme. One C28 sequevar was found in 15 genomes but had an additional mutation (allele-14). All alleles included in the scheme can be found in [Table 5].

Heat-shock protein 65 (hsp65) sequencing is a common method for differentiating not only the subspecies of the M. abscessus complex but also other nontuberculous mycobacteria species.[25] Hsp65 has a highly conserved region that is not easily transferred to other species.[26] A total of 15 alleles were added allowing for differentiation between M. abscessus subspecies. M. chelonae strains will produce hsp65 allele-9 (or ~ 9) and no other gene matches. The beta-subunit of RNA polymerase (rpoB) is also used for identification in Mycobacterium species.[27] Alleles from M. abscessus subsp. massiliense and bolletii type strains were added to the scheme, as well as any novel alleles found, resulting in a total of 13 alleles [Table 3].

The combination of erm (41), hsp65 and rpoB genes in the MAB-MLST scheme can better identify the M. abscessus subspecies compared to single-gene sequencing.[7],[8],[9] Using these three genes, 159 genomes were divided into their appropriate subspecies: 98 were M. abscessus subsp. abscessus, 10 were M. abscessus subsp. bolletii, 50 were M. abscessus subsp. massiliense, and 1 was M. chelonae. However, nine samples showed discrepant results that correspond to four different allelic combinations [Table 6]. Hypothesis of lateral gene transfer has been previously described, where multiple M. abscessus subsp. massiliense genomes were found containing an rpoB sequence that is normally found in M. abscessus subsp. abscessus.[28] If this hypothesis is correct, this would explain discrepant strains SRR332187, SRR5483260, CP014959, CP016191, and CP02112, as they all contain hsp65 and erm(41) alleles for M. abscessus subsp. massiliense, and an rpoB allele for M. abscessus subsp. abscessus. Closer analysis of ERR908849 erm(41) shows four single-nucleotide polymorphisms (SNPs) away from the M. abscessus subsp. bolletii type strain but two SNPs from the M. abscessus subsp. abscessus type strain (includes T28C). When looking at the phylogenetic tree [Figure 2], it clusters with M. abscessus subsp. abscessus. Based on this, a clear identity cannot be assigned to ERR908849. Under the current nomenclature, CP009447 is identified as M. abscessus subsp. massiliense by hsp65 and rpoB but M. abscessus subsp. bolletii by erm(41). In accordance with alternative taxonomic classification from Leao et al., this result would not be classified as a discrepant genome.[4]

M. abscessus complex's genome encodes a rifampicin ADP-ribosyltransferase (arr), which provides intrinsic resistance to the drug. When the arr gene was introduced into naturally susceptible organisms, it demonstrated rifampicin resistance.[29] This gene was found in all strains that were analyzed, but arr was not found in M. chelonae.[29] A total of 20 unique alleles were found in all genomes. Allele-18 contained a single base pair gap at position C350, but on excluding the gap, the sequence remained unique.

The rrl gene encodes the peptidyltransferase domain of the 23S rRNA and is involved in clarithromycin resistance.[30] While 25 different alleles were identified among the samples, most differed by only a SNP or two. Mutations in rrl have shown to be sufficient to produce a resistant phenotype in otherwise erm(41) susceptible phenotypes.[31] Mutations known to be associated with resistance are at 2058 (A to G, C, or T) and 2059 (A to G, C, or T). Six alleles showed a mutation at position 2058 (alleles-5, -15, -16, -22, -23, and -30) and one allele had A2059G (allele-14).

The rrs gene encodes the 16S ribosomal RNA, and resistance to aminoglycosides has been shown to be acquired through a single-point mutation at position A1408G.[32] This mutation was present in 3 of 7 alleles. Several other mutations in the 16S RNA have also been attributed to high levels of kanamycin resistance include T1406A and C1209G but were not observed in our dataset.[3]

Fluoroquinolone resistance is typically attributed to mutations in the gyrA and gyrB genes which encode for the alpha- and beta-subunits of DNA gyrase.[33] Despite 35 different alleles being identified, the sequence diversity among these genes was moderately low in comparison to other genes [Table 1]. Resistance occurs through mutations within the quinolone resistance-determining region which is a conserved region that interacts with the drugs.[34] The most prominent mutations in gyrA that confer resistance are in amino acid positions 83 and 87 (E. coli numbering); all alleles showed a S83A mutation, suggesting that they may be resistant. In the beta-subunit, all alleles had Arg447 and Asn464 and hence may suggest resistance.[35]

Recombinase-A (recA) is a housekeeping gene that functions in double-stranded DNA repair.[36] Antibiotic therapy can induce adaptive resistance mutations in M. abscessus complex by activating the SOS DNA repair pathway.[37] Three wild type alleles of recA were added to our scheme for the three subspecies, as well as 12 other unique alleles.

Technical issues that arose in scheme development can mostly be attributed to genome assembly and lack of closed genomes. A common issue results when a contig starts or ends within a gene of interest, which leads to an unresolvable allele call. However, this risk of an incomplete allele call is to be expected with draft genomes. This issue was identified in 24 of the 167 (excluding M. chelonae) genomes and thus was not assigned a specific ST. A minor issue can be from duplicate alleles, resulting in unassigned ST.

When comparing the WGS clustering with the MAB-MLST STs, there were no apparent cases in which the same ST was in separate clusters [Figure 2]. While different STs appear to cluster together, the scale on the tree is quite large, so small SNP differences cannot be seen at this level and require zooming. As expected, WGS was able to provide a higher level of discrimination than MAB-MLST.


  Conclusion Top


Outbreak investigations are known to be complicated due to many reasons, and the use of an appropriate genotyping tool that provides adequate discrimination is essential. Investigation of M. abscessus complex infections is also a clinical concern due to its ability to cause serious infections, particularly in postsurgical procedures. Resistance to macrolides can often result in delayed bacterial clearance. Identifying the source of infection is one of the tools that can prevent the spread of the outbreak when the source is environmental or a surgical tool/equipment. WGS tools have been widely applied for many outbreak investigations. These tools are becoming more and more common; however, technical expertise required for library preparation and data analysis (i.e., bioinformatics tools) limits their applicability for routine diagnostics. The development of pipelines requires bioinformatic expertise, and validation can require hundreds of isolates. Implementing a data analysis tool that simplifies the data and yet maintains a high DI that can aid in deciphering an outbreak has vast applicability for routine diagnostics.

MAB-MLST for M. abscessus complex in this study has shown an advantage over PubMLST through an increased Simpson's Index of Diversity making it a more robust typing scheme. By increasing the number of STs from 26 to 85, with addition of 79 new alleles in 15 genes, we were able to assign STs to an additional 90 isolates than were not genotyped by PubMLST. Incorporation of identification genes of M. abscessus complex, namely hsp65, rpoB, and erm(41), assisted in better taxonomic classification of isolates. Including the genes responsible for antimicrobial resistance helped predict resistance/susceptibility profiles of key antimicrobials. As this system is easier to implement by transferring databases to Galaxy, the output is user-friendly and can be interpreted with ease by diagnostic laboratorians. Implementation of such a system can have a high impact in clinical/outbreak surveillance.

Acknowledgment

The authors would like to thank Cindi Corbett, Director of Bacterial Pathogen Division, and Kym Antonation for providing space and resources and Adrian Zetner for providing initial R training.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

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