MentaLiST - A fast MLST caller for large MLST schemes

Multi-country and intersectoral assessment of cluster congruence between pipelines for genomics surveillance of foodborne pathogens

Different laboratories employ different Whole-Genome Sequencing (WGS) pipelines for Food and Waterborne disease (FWD) surveillance, casting doubt on the comparability of their results and hindering optimal communication at intersectoral and international levels. Through a collaborative effort involving eleven European institutes spanning the food, animal, and human health sectors, we aimed to assess the inter-pipeline clustering congruence across all resolution levels and perform an in-depth comparative analysis of cluster composition at outbreak level for four important foodborne pathogens: Listeria monocytogenes, Salmonella enterica, Escherichia coli, and Campylobacter jejuni. We found a general concordance between allele-based pipelines for all species, except for C. jejuni, where the different resolution power of allele-based schemas led to marked discrepancies. Still, we identified non-negligible differences in outbreak detection and demonstrated how a threshold flexibilization favors the detection of similar outbreak signals by different laboratories. These results, together with the observation that different traditional typing groups (e.g., serotypes) exhibit a remarkably different genetic diversity, represent valuable information for future outbreak case-definitions and WGS-based nomenclature design. This study reinforces the need, while demonstrating the feasibility, of conducting continuous pipeline comparability assessments, and opens good perspectives for a smoother international and intersectoral cooperation towards an efficient One Health FWD surveillance.

Virulence plasmids in edema disease: Insights from whole-genome analysis of porcine O139:H1 Shiga toxin-producing Escherichia coli (STEC) strains

This study investigates the plasmid sequences of porcine O139:H1 Shiga toxin-producing Escherichia coli (STEC) responsible for Edema Disease (ED). Whole-genome analysis reveals significant similarities between these strains and known plasmids, notably pW1316-2, which harbors key virulence genes like hemolysin (hlyA, hlyB) and adhesion factors (aidA-I, faeE). These genes contribute to the cytotoxicity and host colonization associated with ED. Additionally, similarities to plasmids from Shigella flexneri 2a highlight potential associations in virulence gene regulation, particularly via the Hha-H-NS complex. The identification of sequences resembling plasmid pB71 raises serious concerns about the emergence of highly pathogenic strains, as it includes tetracycline resistance genes (tetA, tetC, tetR). This research emphasizes the role of plasmid-like sequences in ED pathogenesis, indicating important implications for swine industry management and public health.

Genomic typing, antimicrobial resistance gene, virulence factor and plasmid replicon database for the important pathogenic bacteria Klebsiella pneumoniae

BACKGROUND

The infections of bacterial origin represent a significant problem to the public healthcare worldwide both in clinical and community settings. Recent decade was marked by limiting treatment options for bacterial infections due to growing antimicrobial resistance (AMR) acquired and transferred by various bacterial species, especially the ones causing healthcare-associated infections, which has become a dangerous issue noticed by the World Health Organization. Numerous reports shown that the spread of AMR is often driven by several species-specific lineages usually called the 'global clones of high risk'. Thus, it is essential to track the isolates belonging to such clones and investigate the mechanisms of their pathogenicity and AMR acquisition. Currently, the whole genome-based analysis is more and more often used for these purposes, including the epidemiological surveillance and analysis of mobile elements involved in resistance transfer. However, in spite of the exponential growth of available bacterial genomes, their representation usually lack uniformity and availability of supporting metadata, which creates a bottleneck for such investigations.

DESCRIPTION

In this database, we provide the results of a thorough genomic analysis of 61,857 genomes of a highly dangerous bacterial pathogen Klebsiella pneumoniae. Important isolate typing information including multilocus sequence typing (MLST) types (STs), assignment of the isolates to known global clones, capsular (KL) and lipooligosaccharide (O) types, the presence of CRISPR-Cas systems, and cgMLST profiles are given, and the information regarding the presence of AMR, virulence genes and plasmid replicons within the genomes is provided.

CONCLUSION

This database is freely available under CC BY-NC-SA at https://doi.org/10.5281/zenodo.11069018 . The database will facilitate selection of the proper reference isolate sets for any types of genome-based investigations. It will be helpful for investigations in the field of K. pneumoniae genomic epidemiology, as well as antimicrobial resistance analysis and the development of prevention measures against this important pathogen.

Unraveling the impact of genome assembly on bacterial typing: a one health perspective

BACKGROUND

In the context of pathogen surveillance, it is crucial to ensure interoperability and harmonized data. Several surveillance systems are designed to compare bacteria and identify outbreak clusters based on core genome MultiLocus Sequence Typing (cgMLST). Among the different approaches available to generate bacterial cgMLST, our research used an assembly-based approach (chewBBACA tool).

METHODS

Simulations of short-read sequencing were conducted for 5 genomes of 27 pathogens of interest in animal, plant, and human health to evaluate the repeatability and reproducibility of cgMLST. Various quality parameters, such as read quality and depth of sequencing were applied, and several read simulations and genome assemblies were repeated using three tools: SPAdes, Unicycler and Shovill. In vitro sequencing were also used to evaluate assembly impact on cgMLST results, for six bacterial species: Bacillus thuringiensis, Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus, Vibrio parahaemolyticus and Xylella fastidiosa.

RESULTS

The results highlighted variability in cgMLST, which not only related to the assembly tools, but also induced by the intrinsic composition of the genomes themselves. This variability observed in simulated sequencing was further validated with real data for six of the bacterial pathogens studied.

CONCLUSION

This highlights that the intrinsic genome composition affects assembly and resulting cgMLST profiles, and that variability in bioinformatics tools can induce a bias in cgMLST profiles. In conclusion, we propose that the completeness of cgMLST schemes should be considered when clustering strains.

Genotypic characterization and antimicrobial susceptibility of human Campylobacter jejuni isolates in Southern Spain

Campylobacter jejuni is the main cause of bacterial gastroenteritis and a public health problem worldwide. Little information is available on the genotypic characteristics of human C. jejuni in Spain. This study is based on an analysis of the resistome, virulome, and phylogenetic relationship, antibiogram prediction, and antimicrobial susceptibility of 114 human isolates of C. jejuni from a tertiary hospital in southern Spain from October 2020 to June 2023. The isolates were sequenced using Illumina technology, and a bioinformatic analysis was subsequently performed. The susceptibility of C. jejuni isolates to ciprofloxacin, tetracycline, and erythromycin was also tested. The resistance rates for each antibiotic were 90.3% for ciprofloxacin, 66.7% for tetracycline, and 0.88% for erythromycin. The fluoroquinolone resistance rate obtained is well above the European average (69.1%). CC-21 (n = 23), ST-572 (n = 13), and ST-6532 (n = 13) were the most prevalent clonal complexes (CCs) and sequence types (STs). In the virulome, the cadF, ciaB, and cdtABC genes were detected in all the isolates. A prevalence of 20.1% was obtained for the genes wlaN and cstIII, which are related to the pathogenesis of Guillain-Barré syndrome (GBS). The prevalence of the main antimicrobial resistance markers detected were CmeABC (92.1%), RE-cmeABC (7.9%), the T86I substitution in gyrA (88.9%), blaOXA-61 (72.6%), tet(O) (65.8%), and ant (6)-Ia (17.1%). High antibiogram prediction rates (>97%) were obtained, except for in the case of the erythromycin-resistant phenotype. This study contributes significantly to the knowledge of C. jejuni genomics for the prevention, treatment, and control of infections caused by this pathogen.IMPORTANCEDespite being the pathogen with the greatest number of gastroenteritis cases worldwide, Campylobacter jejuni remains a poorly studied microorganism. A sustained increase in fluoroquinolone resistance in human isolates is a problem when treating Campylobacter infections. The development of whole genome sequencing (WGS) techniques has allowed us to better understand the genotypic characteristics of this pathogen and relate them to antibiotic resistance phenotypes. These techniques complement the data obtained from the phenotypic analysis of C. jejuni isolates. The zoonotic transmission of C. jejuni through the consumption of contaminated poultry supports approaching the study of this pathogen through "One Health" approach. In addition, due to the limited information on the genomic characteristics of C. jejuni in Spain, this study provides important data and allows us to compare the results with those obtained in other countries.

Multi-locus sequence typing, antimicrobials resistance and virulence profiles of Salmonella enterica isolated from bovine carcasses in Minas Gerais state, Brazil

The aim of this study was to identify virulence and antimicrobial resistance profiles and determine the sequence type (ST) by multilocus sequence typing (MLST) of Salmonella enterica isolates from bovine carcasses from slaughterhouse located in Minas Gerais state, Brazil, and its relationship with bovine isolates obtained on the American continent based on sequence type profile. The MLST results were compared with all Salmonella STs associated with cattle on American continent, and a multi-locus sequence tree (MS tree) was built. Among the 17 S. enterica isolates, five ST profiles identified, and ST10 were the most frequent, grouping seven (41.2%) isolates. The isolates presented 11 different profiles of virulence genes, and six different antibiotics resistance profiles. The survey on Enterobase platform showed 333 Salmonella STs from American continent, grouped into four different clusters. Most of the isolates in the present study (13/17), were concentrated in a single cluster (L4) composed by 74 STs. As a conclusion, five different STs were identified, with ST10 being the most common. The isolates showed great diversity of virulence genes and antibiotics resistance profiles. Most of the isolates of this study were grouped into a single cluster composed by 74 STs formed by bovine isolates obtained on the American continent.

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

Introduction and benchmarking of pyMLST: open-source software for assessing bacterial clonality using core genome MLST

Core genome multilocus sequence typing (cgMLST) has gained in popularity for bacterial typing since whole-genome sequencing (WGS) has become affordable. We introduce here pyMLST, a new complete, stand-alone, free and open source pipeline for cgMLST analysis. pyMLST can create or import a core genome database. For each gene, the first allele is aligned against the bacterial genome of interest using BLAT. Incomplete genes are aligned using MAFT. All data are stored in a SQLite database. pyMLST accepts assembly genomes or raw data (with the option pyMLST-KMA) as input. To evaluate our new tool, we selected three genome collections of major bacterial pathogens (Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus) and compared them with pyMLST, pyMLST-KMA, ChewBBACA, SeqSphere and the variant calling approach. We compared the sensitivity, precision and false-positive rate for each method with those of the variant calling approach. Minimal spanning trees were generated with each type of software to evaluate their interest in the context of a bacterial outbreak. We found that pyMLST-KMA is a convenient screening method to avoid assembling large bacterial collections. Our data showed that pyMLST (free, open source, available in Galaxy and pipeline ready) performed similarly to the commercial SeqSphere and performed better than ChewBBACA and pyMLST-KMA.

Comparative genomic assessment of members of genus Tenacibaculum: an exploratory study

Tenacibaculosis is an ulcerative skin disorder that affects finfish. It is caused by members of the genus Tenacibaculum, resulting in eccentric behavioural changes, including anorexia, lethargy, and abnormal swimming patterns that often result in mortality. Currently, species suspected of causing fish mortality include T. ovolyticum, T. gallaicum, T. discolor, T. finnmarkense, T. mesophilum, T. soleae, T. dicentrarchi, and T. maritimum. However, pathogenic members and the mechanisms involved in disease causation, progression, and transmission are limited due to the inadequate sequencing efforts in the past decade. In this study, we use a comparative genomics approach to investigate the characteristic features of 26 publicly available genomes of Tenacibaculum and report our observations. We propose the reclassification of "T. litoreum HSC 22" to the singaporense species and assignment of "T. sp. 4G03" to the species discolor (species with quotation marks have not been appropriately named). We also report the co-occurrence of several antimicrobial resistance/virulence genes and genes private to a few members. Finally, we mine several non-B DNA forming regions, operons, tandem repeats, high-confidence putative effector proteins, and sortase that might play a pivotal role in bacterial evolution, transcription, and pathogenesis.

IRIDA-ARIES Genomics, a key player in the One Health surveillance of diseases caused by infectious agents in Italy

Pathogen genomics is transforming surveillance of infectious diseases, deepening our understanding of evolution and diffusion of etiological agents, host-pathogen interactions and antimicrobial resistance. This discipline is playing an important role in the development of One Health Surveillance with public health experts of various disciplines integrating methods applied to pathogen research, monitoring, management and prevention of outbreaks. Especially with the notion that foodborne diseases may not be transmitted by food only, the ARIES Genomics project aimed to deliver an Information System for the collection of genomic and epidemiological data to enable genomics-based surveillance of infectious epidemics, foodborne outbreaks and diseases at the animal-human interface. Keeping in mind that the users of the system comprised persons with expertise in a wide variety of domains, the system was expected to be used with a low learning curve directly by the persons target of the analyses' results, keeping the information exchange chains as short as possible. As a result, the IRIDA-ARIES platform (https://irida.iss.it/) provides an intuitive web-based interface for multisectoral data collection and bioinformatic analyses. In practice, the user creates a sample and uploads the Next-generation sequencing reads, then an analysis pipeline is launched automatically performing a series of typing and clustering operations fueling the information flow. Instances of IRIDA-ARIES host the Italian national surveillance system for infections by Listeria monocytogenes (Lm) and the surveillance system for infections by Shigatoxin-producing Escherichia coli (STEC). As of today, the platform does not provide tools to manage epidemiological investigations but serves as an instrument of aggregation for risk monitoring, capable of triggering alarms on possible critical situations that might go unnoticed otherwise.

Whole-Genome Sequencing Revealed the Fusion Plasmids Capable of Transmission and Acquisition of Both Antimicrobial Resistance and Hypervirulence Determinants in Multidrug-Resistant Klebsiella pneumoniae Isolates

Klebsiella pneumoniae, a member of the Enterobacteriaceae family, has become a dangerous pathogen accountable for a large fraction of the various infectious diseases in both clinical and community settings. In general, the K. pneumoniae population has been divided into the so-called classical (cKp) and hypervirulent (hvKp) lineages. The former, usually developing in hospitals, can rapidly acquire resistance to a wide spectrum of antimicrobial drugs, while the latter is associated with more aggressive but less resistant infections, mostly in healthy humans. However, a growing number of reports in the last decade have confirmed the convergence of these two distinct lineages into superpathogen clones possessing the properties of both, and thus imposing a significant threat to public health worldwide. This process is associated with horizontal gene transfer, in which plasmid conjugation plays a very important role. Therefore, the investigation of plasmid structures and the ways plasmids spread within and between bacterial species will provide benefits in developing prevention measures against these powerful pathogens. In this work, we investigated clinical multidrug-resistant K. pneumoniae isolates using long- and short-read whole-genome sequencing, which allowed us to reveal fusion IncHI1B/IncFIB plasmids in ST512 isolates capable of simultaneously carrying hypervirulence (iucABCD, iutA, _prmpA, peg-344) and resistance determinants (armA, bla_NDM-1 and others), and to obtain insights into their formation and transmission mechanisms. Comprehensive phenotypic, genotypic and phylogenetic analysis of the isolates, as well as of their plasmid repertoire, was performed. The data obtained will facilitate epidemiological surveillance of high-risk K. pneumoniae clones and the development of prevention strategies against them.

Long-Read Whole Genome Sequencing Elucidates the Mechanisms of Amikacin Resistance in Multidrug-Resistant Klebsiella pneumoniae Isolates Obtained from COVID-19 Patients

Klebsiella pneumoniae is a Gram-negative, encapsulated, non-motile bacterium, which represents a global challenge to public health as one of the major causes of healthcare-associated infections worldwide. In the recent decade, the World Health Organization (WHO) noticed a critically increasing rate of carbapenem-resistant K. pneumoniae occurrence in hospitals. The situation with extended-spectrum beta-lactamase (ESBL) producing bacteria further worsened during the COVID-19 pandemic, due to an increasing number of patients in intensive care units (ICU) and extensive, while often inappropriate, use of antibiotics including carbapenems. In order to elucidate the ways and mechanisms of antibiotic resistance spreading within the K. pneumoniae population, whole genome sequencing (WGS) seems to be a promising approach, and long-read sequencing is especially useful for the investigation of mobile genetic elements carrying antibiotic resistance genes, such as plasmids. We have performed short- and long read sequencing of three carbapenem-resistant K. pneumoniae isolates obtained from COVID-19 patients in a dedicated ICU of a multipurpose medical center, which belonged to the same clone according to cgMLST analysis, in order to understand the differences in their resistance profiles. We have revealed the presence of a small plasmid carrying aph(3′)-VIa gene providing resistance to amikacin in one of these isolates, which corresponded perfectly to its phenotypic resistance profile. We believe that the results obtained will facilitate further elucidating of antibiotic resistance mechanisms for this important pathogen, and highlight the need for continuous genomic epidemiology surveillance of clinical K. pneumoniae isolates.

Delineating Mycobacterium abscessus population structure and transmission employing high-resolution core genome multilocus sequence typing

Mycobacterium abscessus is an emerging multidrug-resistant non-tuberculous mycobacterium that causes a wide spectrum of infections and has caused several local outbreaks worldwide. To facilitate standardized prospective molecular surveillance, we established a novel core genome multilocus sequence typing (cgMLST) scheme. Whole genome sequencing data of 1991 isolates were employed to validate the scheme, re-analyze global population structure and set genetic distance thresholds for cluster detection and taxonomic identification. We confirmed and amended the nomenclature of the main dominant circulating clones and found that these also correlate well with traditional 7-loci MLST. Dominant circulating clones could be linked to a corresponding reference genome with less than 250 alleles while 99% of pairwise comparisons between epidemiologically linked isolates were below 25 alleles and 90% below 10 alleles. These thresholds can be used to guide further epidemiological investigations. Overall, the scheme will help to unravel the apparent global spread of certain clonal complexes and as yet undiscovered transmission routes.

Comparative Genomic Analysis Discloses Differential Distribution of Antibiotic Resistance Determinants between Worldwide Strains of the Emergent ST213 Genotype of Salmonella Typhimurium

Salmonella enterica constitutes a global public health concern as one of the main etiological agents of human gastroenteritis. The Typhimurium serotype is frequently isolated from human, animal, food, and environmental samples, with its sequence type 19 (ST19) being the most widely distributed around the world as well as the founder genotype. The replacement of the ST19 genotype with the ST213 genotype that has multiple antibiotic resistance (MAR) in human and food samples was first observed in Mexico. The number of available genomes of ST213 strains in public databases indicates its fast worldwide dispersion, but its public health relevance is unknown. A comparative genomic analysis conducted as part of this research identified the presence of 44 genes, 34 plasmids, and five point mutations associated with antibiotic resistance, distributed across 220 genomes of ST213 strains, indicating the MAR phenotype. In general, the grouping pattern in correspondence to the presence/absence of genes/plasmids that confer antibiotic resistance cluster the genomes according to the geographical origin where the strain was isolated. Genetic determinants of antibiotic resistance group the genomes of North America (Canada, Mexico, USA) strains, and suggest a dispersion route to reach the United Kingdom and, from there, the rest of Europe, then Asia and Oceania. The results obtained here highlight the worldwide public health relevance of the ST213 genotype, which contains a great diversity of genetic elements associated with MAR.

Systems-Based Approach for Optimization of Assembly-Free Bacterial MLST Mapping

Epidemiological surveillance of bacterial pathogens requires real-time data analysis with a fast turnaround, while aiming at generating two main outcomes: (1) species-level identification and (2) variant mapping at different levels of genotypic resolution for population-based tracking and surveillance, in addition to predicting traits such as antimicrobial resistance (AMR). Multi-locus sequence typing (MLST) aids this process by identifying sequence types (ST) based on seven ubiquitous genome-scattered loci. In this paper, we selected one assembly-dependent and one assembly-free method for ST mapping and applied them with the default settings and ST schemes they are distributed with, and systematically assessed their accuracy and scalability across a wide array of phylogenetically divergent Public Health-relevant bacterial pathogens with available MLST databases. Our data show that the optimal k-mer length for stringMLST is species-specific and that genome-intrinsic and -extrinsic features can affect the performance and accuracy of the program. Although suitable parameters could be identified for most organisms, there were instances where this program may not be directly deployable in its current format. Next, we integrated stringMLST into our freely available and scalable hierarchical-based population genomics platform, ProkEvo, and further demonstrated how the implementation facilitates automated, reproducible bacterial population analysis.

In vitro and in silico parameters for precise cgMLST typing of Listeria monocytogenes

Background

Whole genome sequencing analyzed by core genome multi-locus sequence typing (cgMLST) is widely used in surveillance of the pathogenic bacteria Listeria monocytogenes. Given the heterogeneity of available bioinformatics tools to define cgMLST alleles, our aim was to identify parameters influencing the precision of cgMLST profiles.

Methods

We used three L. monocytogenes reference genomes from different phylogenetic lineages and assessed the impact of in vitro (i.e. tested genomes, successive platings, replicates of DNA extraction and sequencing) and in silico parameters (i.e. targeted depth of coverage, depth of coverage, breadth of coverage, assembly metrics, cgMLST workflows, cgMLST completeness) on cgMLST precision made of 1748 core loci. Six cgMLST workflows were tested, comprising assembly-based (BIGSdb, INNUENDO, GENPAT, SeqSphere and BioNumerics) and assembly-free (i.e. kmer-based MentaLiST) allele callers. Principal component analyses and generalized linear models were used to identify the most impactful parameters on cgMLST precision.

Results

The isolate’s genetic background, cgMLST workflows, cgMLST completeness, as well as depth and breadth of coverage were the parameters that impacted most on cgMLST precision (i.e. identical alleles against reference circular genomes). All workflows performed well at ≥40X of depth of coverage, with high loci detection (> 99.54% for all, except for BioNumerics with 97.78%) and showed consistent cluster definitions using the reference cut-off of ≤7 allele differences.

Conclusions

This highlights that bioinformatics workflows dedicated to cgMLST allele calling are largely robust when paired-end reads are of high quality and when the sequencing depth is ≥40X.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08437-4.

Genomic Characterization of Clinical Acinetobacter baumannii Isolates Obtained from COVID-19 Patients in Russia

The coronavirus disease 2019 (COVID-19) pandemic has already affected all realms of public healthcare and, in particular, has led to increasing use of various antibiotics to treat possible bacterial coinfections even in cases for which such infections were not confirmed clinically. This could lead to an increase in the fraction and severity of multidrug-resistant bacterial isolates in healthcare facilities, especially in intensive care units (ICU). However, detailed epidemiological investigations, possibly including whole genome sequencing (WGS), are required to confirm the increase in antibiotic resistance and changes, if any, in the population and clonal structures of bacterial pathogens. In this study, we performed a comprehensive genomic and phenotypic characterization of selected multidrug-resistant A. baumannii isolates obtained from the patients of a dedicated COVID-19 ICU in Moscow, Russia. Hybrid short- and long-read sequencing allowed us to obtain complete profiles of genomic antimicrobial resistance and virulence determinants, as well as to reveal the plasmid structure. We demonstrated the genomic similarity in terms of cgMLST profiles of the isolates studied with a clone previously identified in the same facility. We believe that the data provided will contribute to better understanding the changes imposed by the COVID-19 pandemic on the population structure and the antimicrobial resistance of bacterial pathogens in healthcare facilities.

Application and Challenge of 3rd Generation Sequencing for Clinical Bacterial Studies

Over the past 25 years, the powerful combination of genome sequencing and bioinformatics analysis has played a crucial role in interpreting information encoded in bacterial genomes. High-throughput sequencing technologies have paved the way towards understanding an increasingly wide range of biological questions. This revolution has enabled advances in areas ranging from genome composition to how proteins interact with nucleic acids. This has created unprecedented opportunities through the integration of genomic data into clinics for the diagnosis of genetic traits associated with disease. Since then, these technologies have continued to evolve, and recently, long-read sequencing has overcome previous limitations in terms of accuracy, thus expanding its applications in genomics, transcriptomics and metagenomics. In this review, we describe a brief history of the bacterial genome sequencing revolution and its application in public health and molecular epidemiology. We present a chronology that encompasses the various technological developments: whole-genome shotgun sequencing, high-throughput sequencing, long-read sequencing. We mainly discuss the application of next-generation sequencing to decipher bacterial genomes. Secondly, we highlight how long-read sequencing technologies go beyond the limitations of traditional short-read sequencing. We intend to provide a description of the guiding principles of the 3rd generation sequencing applications and ongoing improvements in the field of microbial medical research.

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S. Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool’s output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

Genomic Characterization of hlyF-positive Shiga Toxin-Producing Escherichia coli, Italy and the Netherlands, 2000-2019

Shiga toxin–producing Escherichia coli (STEC) O80:H2 has emerged in Europe as a cause of hemolytic uremic syndrome associated with bacteremia. STEC O80:H2 harbors the mosaic plasmid pR444_A, which combines several virulence genes, including hlyF and antimicrobial resistance genes. pR444_A is found in some extraintestinal pathogenic E. coli (ExPEC) strains. We identified and characterized 53 STEC strains with ExPEC-associated virulence genes isolated in Italy and the Netherlands during 2000–2019. The isolates belong to 2 major populations: 1 belongs to sequence type 301 and harbors diverse stx₂ subtypes, the intimin variant eae-ξ, and pO157-like and pR444_A plasmids; 1 consists of strains belonging to various sequence types, some of which lack the pO157 plasmid, the locus of enterocyte effacement, and the antimicrobial resistance–encoding region. Our results showed that STEC strains harboring ExPEC-associated virulence genes can include multiple serotypes and that the pR444_A plasmid can be acquired and mobilized by STEC strains.

SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

The occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited. In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains. We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.

Decentralized Investigation of Bacterial Outbreaks Based on Hashed cgMLST

Whole-genome sequencing (WGS)-based outbreak investigation has proven to be a valuable method for the surveillance of bacterial pathogens. Its utility has been successfully demonstrated using both gene-by-gene (cgMLST or wgMLST) and single-nucleotide polymorphism (SNP)-based approaches. Among the obstacles of implementing a WGS-based routine surveillance is the need for an exchange of large volumes of sequencing data, as well as a widespread reluctance to share sequence and metadata in public repositories, together with a lacking standardization of suitable bioinformatic tools and workflows. To address these issues, we present chewieSnake, an intuitive and simple-to-use cgMLST workflow. ChewieSnake builds on the allele calling software chewBBACA and extends it by the concept of allele hashing. The resulting hashed allele profiles can be readily compared between laboratories without the need of a central allele nomenclature. The workflow fully automates the computation of the allele distance matrix, cluster membership, and phylogeny and summarizes all important findings in an interactive HTML report. Furthermore, chewieSnake can join allele profiles generated at different laboratories and identify shared clusters, including a stable and intercommunicable cluster nomenclature, thus facilitating a joint outbreak investigation. We demonstrate the feasibility of the proposed approach with a thorough method comparison using publically available sequencing data for Salmonella enterica. However, chewieSnake is readily applicable to all bacterial taxa, provided that a suitable cgMLST scheme is available. The workflow is freely available as an open-source tool and can be easily installed via conda or docker.

Closed Genome and Plasmid Sequences of Legionella pneumophila AW-13-4, Isolated from a Hot Water Loop System of a Large Occupational Building

Unused water in unoccupied buildings can become stagnant, with reductions in temperature and levels of disinfectant resulting in increased microbial growth. We report the closed and complete genome and plasmid of Legionella pneumophila strain AW-13-4 (serogroup 1), which was isolated from a hot water loop system of a large building.

Antimicrobial Resistance and Genomic Characterization of OXA-48- and CTX-M-15-Co-Producing Hypervirulent Klebsiella pneumoniae ST23 Recovered from Nosocomial Outbreak

Multidrug resistance (MDR) and hypervirulence (hv) have been long considered distinct evolutionary traits for Klebsiella pneumoniae (Kp), a versatile human pathogen. The recent emergence of Kp strains combining these traits poses a serious global threat. In this article, we describe the phenotypic and genomic characteristics of an MDR hvKp isolate, MAR14-456, representative of a nosocomial outbreak in Moscow, Russia, that was recovered from a postoperative wound in a patient who later developed multiple abscesses, fatal sepsis, and septic shock. Broth microdilution testing revealed decreased susceptibility of MAR14-456 to carbapenems (MICs 0.5–2 mg/L) and a high-level resistance to most β-lactams, β-lactam-β-lactamase-inhibitor combinations, and non-β-lactam antibiotics, except ceftazidime-avibactam, amikacin, tigecycline, and colistin. Whole-genome sequencing using Illumina MiSeq and ONT MinION systems allowed to identify and completely assemble two conjugative resistance plasmids, a typical ‘European’ epidemic IncL/M plasmid that carries the gene of OXA-48 carbapenemase, and an IncFIIK plasmid that carries the gene of CTX-M-15 ESBL and other resistance genes. MLST profile, capsular, lipopolysaccharide, virulence genes encoded on chromosome and IncHI1B/FIB plasmid, and the presence of apparently functional type I-E* CRISPR-Cas system were all characteristic of hvKp ST23, serotype K1-O1v2. Phylogenetic analysis showed the closest relatedness of MAR14-456 to ST23 isolates from China. This report highlights the threat of multiple resistance acquisition by hvKp strain and its spread as a nosocomial pathogen.

A Comparative Study of Fluoroquinolone-Resistant Escherichia coli Lineages Portrays Indistinguishable Pathogenicity- and Survivability-Associated Phenotypic Characteristics Between ST1193 and ST131

Background

Sequence type 1193 is a new such lineage among fluoroquinolone-resistant Escherichia coli, which has risen dramatically within the last several years. However, reasons for rapid emergence and successful spread of E. coli ST1193 remain unclear. The aim of this study was to compare the pathogenicity and survivability features of E. coli ST1193 with global epidemic lineage, ST131.

Methods

A total of 30 E. coli were used in this study. Isolates were divided into two groups, ST1193 (n=15) and ST131 (n=15). Adhesion and invasion to T24 cells and resistance to serum were quantified and compared among two groups. Biofilm formation capacity was assessed by crystal violet assay. Macrocolony formation was assessed on macrocolony formation plates. Resistance to hydrogen peroxide was performed by broth microdilution. RAW264.7 cells were used to assess the anti-phagocytic function of different isolates.

Results

Adhesion and invasion assays revealed that E. coli ST1193 could adhere and invade T24 cells (p <0.05). 93.3% of E. coli ST1193 could form biofilms. The majority of E. coli ST1193 (66.7%) possessed no curli/no cellulose on macrocolony formation plates. E. coli ST1193 showed significant growth in serum and hydrogen peroxide and illustrated higher anti-phagocytic function to RAW264.7 cells (p <0.05). Group analysis showed that E. coli ST1193 was similar to ST131 in pathogenicity- and survivability-associated phenotypic characteristics (p >0.05).

Conclusion

Our study provided more insights into pathogenicity and survivability features of E. coli ST1193, which was similar to ST131. Our study could be of great importance in understanding the emergence of global spread E. coli ST1193. Strategic and continued surveillance should be carried out to prevent the infections caused by E. coli ST1193.

STing: accurate and ultrafast genomic profiling with exact sequence matches

Genome-enabled approaches to molecular epidemiology have become essential to public health agencies and the microbial research community. We developed the algorithm STing to provide turn-key solutions for molecular typing and gene detection directly from next generation sequence data of microbial pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the computational overhead associated with the time-consuming steps of quality control, assembly, and alignment, required by more traditional methods. We compared STing to six of the most widely used programs for genome-based molecular typing and demonstrate its ease of use, accuracy, speed and efficiency. STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with larger genome-scale typing schemes, and it enables rapid automated detection of antimicrobial resistance and virulence factor genes. STing determines the sequence type of traditional 7-gene MLST with 100% accuracy in less than 10 seconds per isolate. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

Analytical Performance Validation of Next-Generation Sequencing Based Clinical Microbiology Assays Using a K-mer Analysis Workflow

Next-generation sequencing (NGS) enables clinical microbiology assays such as molecular typing of bacterial isolates which is now routinely applied for infection control and epidemiology. Additionally, feasibility for NGS-based identification of antimicrobial resistance (AMR) markers as well as genetic prediction of antibiotic susceptibility testing results has been demonstrated. Various bioinformatics approaches enabling NGS-based clinical microbiology assays exist, but standardized, computationally efficient and scalable sample-to-results workflows including validated quality control parameters are still lacking. Bioinformatics analysis workflows based on k-mers have been shown to allow for fast and efficient analysis of large genomics data sets as obtained from microbial sequencing applications. We here demonstrate applicability of k-mer based clinical microbiology assays for whole-genome sequencing (WGS) including variant calling, taxonomic identification, bacterial typing as well as AMR marker detection. The wet-lab and dry-lab workflows were developed and validated in line with Clinical Laboratory Improvement Act (CLIA) guidelines for laboratory-developed tests (LDTs) on multi-drug resistant ESKAPE pathogens. The developed k-mer based workflow demonstrated ≥99.39% repeatability, ≥99.09% reproducibility and ≥99.76% accuracy for variant calling and applied assays as determined by intra-day and inter-day triplicate measurements. The limit of detection (LOD) across assays was found to be at 20× sequencing depth and 15× for AMR marker detection. Thorough benchmarking of the k-mer based workflow revealed analytical performance criteria are comparable to state-of-the-art alignment based workflows across clinical microbiology assays. Diagnostic sensitivity and specificity for multilocus sequence typing (MLST) and phylogenetic analysis were 100% for both approaches. For AMR marker detection, sensitivity and specificity were 95.29 and 99.78% for the k-mer based workflow as compared to 95.17 and 99.77% for the alignment-based approach. Summarizing, results illustrate that k-mer based analysis workflows enable a broad range of clinical microbiology assays, potentially not only for WGS-based typing and AMR gene detection but also genetic prediction of antibiotic susceptibility testing results.

Investigation on the Evolution of Shiga Toxin-Converting Phages Based on Whole Genome Sequencing

Bacteriophages are pivotal elements in the dissemination of virulence genes. The main virulence determinants of Shiga Toxin producing E. coli, Shiga Toxins (Stx), are encoded by genes localized in the genome of lambdoid bacteriophages. Stx comprise two antigenically different types, Stx1 and Stx2, further divided into subtypes. Among these, certain Stx2 subtypes appear to be more commonly occurring in the most severe forms of the STEC disease, haemorrhagic colitis and haemolytic uremic syndrome (HUS). This study aimed at obtaining insights on the evolution of Stx2 bacteriophages, due to their relevance in public health, and we report here on the analysis of the genomic structure of Stx2 converting phages in relation with the known reservoir of the E. coli strains harboring them. Stx2-converting phages conveying the genes encoding different stx2 subtypes have been isolated from STEC strains and their whole genomes have been sequenced, analyzed and compared to those of other Stx2 phages available in the public domain. The phages' regions containing the stx2 genes have been analyzed in depth allowing to make inference on the possible mechanisms of selection and maintenance of certain Stx2 phages in the reservoir. The "stx regions" of different stx2 gene subtypes grouped into three different evolutionary lines in the comparative analysis, reflecting the frequency with which these subtypes are found in different animal niches, suggesting that the colonization of specific reservoir by STEC strains could be influenced by the Stx phage that they carry. Noteworthy, we could identify the presence of nanS-p gene exclusively in the "stx regions" of the phages identified in STEC strains commonly found in cattle. As a matter of fact, this gene encodes an esterase capable of metabolizing sialic acids produced by submaxillary glands of bovines and present in great quantities in their gastrointestinal tract.

High Prevalence of Genetically Related Clostridium Difficile Strains at a Single Hemato-Oncology Ward Over 10 Years

Aims: Clostridium difficile (C. difficile) infection (CDI) is the main cause of healthcare-associated infectious diarrhea. We used whole-genome sequencing (WGS) to measure the prevalence and genetic variability of C. difficile at a single hemato-oncology ward over a 10 year period. Methods: Between 2008 and 2018, 2077 stool samples were obtained from diarrheal patients hospitalized at the Department of Lymphoma; of these, 618 were positive for toxin A/B. 140 isolates were then subjected to WGS on Ion Torrent PGM sequencer. Results: 36 and 104 isolates were recovered from 36 to 46 patients with single and multiple CDIs, respectively. Of these, 131 strains were toxigenic. Toxin gene profiles tcdA(+);tcdB(+);cdtA/cdtB(+) and tcdA(+);tcdB(+);cdtA/cdtB(-) were identified in 122 and nine strains, respectively. No isolates showed reduced susceptibility to metronidazole and vancomycin. All tested strains were resistant to ciprofloxacin, and 72.9, 42.9, and 72.9% of strains were resistant to erythromycin, clindamycin, or moxifloxacin, respectively. Multi-locus sequence typing (MLST) identified 23 distinct sequence types (STs) and two unidentified strains. Strains ST1 and ST42 represented 31 and 30.1% of all strains tested, respectively. However, while ST1 was detected across nearly all years studied, ST42 was detected only from 2009 to 2011. Conclusion: The high proportion of infected patients in 2008-2011 may be explained by the predominance of more transmissible and virulent C. difficile strains. Although this retrospective study was not designed to define outbreaks of C. difficile, the finding that most isolates exhibited high levels of genetic relatedness suggests nosocomial acquisition.

PathOGiST: A Novel Method for Clustering Pathogen Isolates by Combining Multiple Genotyping Signals

In this paper we study the problem of clustering bacterial isolates into epidemiologically related groups from next-generation sequencing data. Existing methods for this problem mainly use a single genotyping signal, and either use a distance-based method with a pre-specified number of clusters, or a phylogenetic tree-based method with a pre-specified threshold. We propose PathOGiST, an algorithmic framework for clustering bacterial isolates by leveraging multiple genotypic signals and calibrated thresholds. PathOGiST uses different genotypic signals, clusters the isolates based on these individual signals with correlation clustering, and combines the clusterings based on the individual signals through consensus clustering. We implemented and tested PathOGiST on three different bacterial pathogens - Escherichia coli, Yersinia pseudotuberculosis, and Mycobacterium tuberculosis - and we conclude by discussing further avenues to explore.

Evaluation of Rapid Library Preparation Protocols for Whole Genome Sequencing Based Outbreak Investigation

Whole genome sequencing (WGS) has become the new gold standard for bacterial outbreak investigation, due to the high resolution available for typing. While sequencing is currently predominantly performed on Illumina devices, the preceding library preparation can be performed using various protocols. Enzymatic fragmentation library preparation protocols are fast, have minimal hands-on time, and work with small quantities of DNA. The aim of our study was to compare three library preparation protocols for molecular typing: Nextera XT (Illumina); Nextera Flex (Illumina); and QIAseq FX (Qiagen). We selected 12 ATCC strains from human Gram-positive and Gram-negative pathogens with %G+C-content ranging from 27% (Fusobacterium nucleatum) to 73% (Micrococcus luteus), each having a high quality complete genome assembly available, to allow in-depth analysis of the resulting Illumina sequence data quality. Additionally, we selected isolates from previously analyzed cases of vancomycin-resistant Enterococcus faecium (VRE) (n = 7) and a local outbreak of Klebsiella aerogenes (n = 5). The number of protocol steps and time required were compared, in order to test the suitability for routine laboratory work. Data analyses were performed with standard tools commonly used in outbreak situations: Ridom SeqSphere+ for cgMLST; CLC genomics workbench for SNP analysis; and open source programs. Nextera Flex and QIAseq FX were found to be less sensitive than Nextera XT to variable %G+C-content, resulting in an almost uniform distribution of read-depth. Therefore, low coverage regions are reduced to a minimum resulting in a more complete representation of the genome. Thus, with these two protocols, more alleles were detected in the cgMLST analysis, producing a higher resolution of closely related isolates. Furthermore, they result in a more complete representation of accessory genes. In particular, the high data quality and relative simplicity of the workflow of Nextera Flex stood out in this comparison. This thorough comparison within an ISO/IEC 17025 accredited environment will be of interest to those aiming to optimize their clinical microbiological genome sequencing.

Draft Genome Sequences of Seven Legionella pneumophila Isolates from a Hot Water System of a Large Building

Public health data show that a significant fraction of the nation's waterborne disease outbreaks are attributable to premise plumbing. We report the draft genome sequences of seven Legionella pneumophila serogroup 1 isolates from hot water lines of a large building. Genomic analysis identified the isolates as belonging to sequence type 1.

Genome-Based Prediction of Bacterial Antibiotic Resistance

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.

Rapid multi-locus sequence typing direct from uncorrected long reads using Krocus

Genome sequencing is rapidly being adopted in reference labs and hospitals for bacterial outbreak investigation and diagnostics where time is critical. Seven gene multi-locus sequence typing is a standard tool for broadly classifying samples into sequence types (STs), allowing, in many cases, to rule a sample out of an outbreak, or allowing for general characteristics about a bacterial strain to be inferred. Long-read sequencing technologies, such as from Oxford Nanopore, can produce read data within minutes of an experiment starting, unlike short-read sequencing technologies which require many hours/days. However, the error rates of raw uncorrected long read data are very high. We present Krocus which can predict a ST directly from uncorrected long reads, and which was designed to consume read data as it is produced, providing results in minutes. It is the only tool which can do this from uncorrected long reads. We tested Krocus on over 700 isolates sequenced using long-read sequencing technologies from Pacific Biosciences and Oxford Nanopore. It provides STs for isolates on average within 90 s, with a sensitivity of 94% and specificity of 97% on real sample data, directly from uncorrected raw sequence reads. The software is written in Python and is available under the open source license GNU GPL version 3.

Staphylococcus aureus viewed from the perspective of 40,000+ genomes

Low-cost Illumina sequencing of clinically-important bacterial pathogens has generated thousands of publicly available genomic datasets. Analyzing these genomes and extracting relevant information for each pathogen and the associated clinical phenotypes requires not only resources and bioinformatic skills but organism-specific knowledge. In light of these issues, we created Staphopia, an analysis pipeline, database and application programming interface, focused on Staphylococcus aureus, a common colonizer of humans and a major antibiotic-resistant pathogen responsible for a wide spectrum of hospital and community-associated infections. Written in Python, Staphopia's analysis pipeline consists of submodules running open-source tools. It accepts raw FASTQ reads as an input, which undergo quality control filtration, error correction and reduction to a maximum of approximately 100× chromosome coverage. This reduction significantly reduces total runtime without detrimentally affecting the results. The pipeline performs de novo assembly-based and mapping-based analysis. Automated gene calling and annotation is performed on the assembled contigs. Read-mapping is used to call variants (single nucleotide polymorphisms and insertion/deletions) against a reference S. aureus chromosome (N315, ST5). We ran the analysis pipeline on more than 43,000 S. aureus shotgun Illumina genome projects in the public European Nucleotide Archive database in November 2017. We found that only a quarter of known multi-locus sequence types (STs) were represented but the top 10 STs made up 70% of all genomes. methicillin-resistant S. aureus (MRSA) were 64% of all genomes. Using the Staphopia database we selected 380 high quality genomes deposited with good metadata, each from a different multi-locus ST, as a non-redundant diversity set for studying S. aureus evolution. In addition to answering basic science questions, Staphopia could serve as a potential platform for rapid clinical diagnostics of S. aureus isolates in the future. The system could also be adapted as a template for other organism-specific databases.

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

Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. chewBBACA performs the schema creation and allele calls on complete or draft genomes resulting from de novo assemblers. The chewBBACA software uses Python 3.4 or higher and can run on a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA.