Biology subjects

Beiko, R. G.

Publications and source records attributed to Beiko, R. G..

15 recordsLinked to original sources

bioRxiv2026

Lateral gene transfer shapes the distribution of nitrogen fixation within a cosmopolitan clade of marine Thalassolituus

Biological nitrogen fixation converts dinitrogen gas into ammonia, supplying new bioavailable nitrogen to marine ecosystems, but the evolutionary processes shaping its distribution among heterotrophic bacteria remain unresolved. Thalassolituus, a genus within the family Oceanospirillaceae (order Oceanospirillales), is best known for hydrocarbon degradation, yet nitrogen fixation has been confirmed in only one cultured isolate. We analyzed 74 quality-filtered genomes assigned to Thalassolituus within a broader dataset of 421 Oceanospirillaceae genomes to reconstruct the distribution and evolutionary history of the minimal nifHDKENB gene set. Twenty-five genomes encoded complete or near-complete nif loci and occurred in four well-supported clades interspersed with genomes lacking the pathway. Statistical topology tests rejected the species-tree topology for concatenated NifHDK and NifHDKENB protein alignments, and eleven recombination events across nif loci were supported by at least four detection methods. The core nifHDK gene order remained broadly conserved, but accessory neighborhoods differed among clades, and structural nifHDK genes showed stronger codon adaptation than biosynthesis nifENB genes. Clade 2 combined species-gene tree congruence, conserved gene neighborhoods, and comparatively high nifH codon adaptation, whereas Clades 1 and 4 showed greater phylogenetic discordance, more recombination, and weaker codon adaptation. These results support a reticulate history in Thalassolituus, in which lateral acquisition introduced nitrogen fixation into distinct lineages, vertical inheritance preserved it within some clades, and homologous recombination continued to reshape nif loci. These processes help explain why nitrogen fixation is unevenly distributed among closely related marine heterotrophic bacteria.

microbiology↗

bioRxiv2026

Analysing antimicrobial resistance mobility patterns using a diverse dataset of over 8000 bacterial species

2.Advances in genomics have enhanced surveillance of antimicrobial resistance (AMR), yet the factors governing resistance gene mobility, and therefore their risk of spread, remain poorly characterized. Here, we analyzed AMR gene distributions across thousands of bacterial genomes from NCBI RefSeq, encompassing clinical, agricultural and environmental isolates, to quantify associations with plasmids and predicted mobile genomic islands. AMR genes were identified using the Resistance Gene Identifier, and in addition to plasmid identification, mobile chromosomal elements were predicted using IslandViewer 4. Analysing both the full dataset and subsets of data with corrections for sampling bias, we show that known AMR genes are significantly enriched in mobile regions overall. However, stratification by resistance mechanism supported marked heterogeneity: certain drug classes and mechanisms are strongly associated with mobile elements, whereas others are predominantly chromosomal and non-mobile. Notably, mechanisms with specialized functions showed higher mobility, consistent with their role as "ecological public goods" that need not be present in all cells to confer community-level benefit. Differences were also observed across bacteria with distinct cell envelope structures. Together, these findings lay the groundwork for predictive models of AMR gene mobility and provide a framework for incorporating gene-level mobility into AMR risk assessment and antimicrobial stewardship policy. 3. Impact statementUnderstanding which antimicrobial resistance (AMR) genes are most likely to spread is central to managing resistance in clinical, agricultural, and environmental settings. This study reports a large-scale, systematic analysis of antimicrobial resistance (AMR) gene mobility across >8000 bacterial species, spanning diverse clinical and other ecological sources. By linking resistance mechanisms to their genomic location (plasmids, genomic islands, or the rest of chromosomes), our study demonstrates that AMR genes are overall enriched in mobile elements, but that mobility varies substantially by mechanism and bacterial context. In particular, functionally specialized resistance mechanisms are more frequently associated with mobile elements, consistent with an "ecological public goods" model for AMR dissemination. These findings extend beyond gene-specific case studies to identify generalizable patterns that help explain why some resistance determinants spread more readily than others. The breadth of relevance spans clinical microbiology, evolutionary biology, environmental microbiology, agri-foods, and public health. The work provides evidence supporting an improved framework for AMR mobility risk assessment, and antimicrobial stewardship strategies, with direct implications for forecasting the durability of existing and future antibiotics. 4. Data summaryThe complete dataset and analysis code used to generate the results can be accessed via the Open Science Framework (OSF) repository under DOI:10.17605/OSF.IO/WE3TX

genomics↗

bioRxiv2025

Effect of bedrest on the human gut and oral microbiome: implications for frailty

The physiological effects of spaceflight resemble those of ageing and prolonged inactivity, and ground-based microgravity analogs have emerged as promising models for studying frailty. The human microbiome is increasingly recognised for its role in age-associated decline, although precise mechanisms remain unclear. Here, we evaluate the gut and oral microbiomes of twenty-two participants, aged 55-65, who were enrolled in a head-down tilt bedrest (HDBR) study, the first Canadian HDBR study conducted in an older cohort. Participants were randomly assigned to an inactivity or multi-modality exercise intervention group for fourteen days of HDBR, followed by seven days of rehabilitation and additional follow-up appointments. Gut (n=343) and oral (n=344) taxonomic profiles were generated using V4/V5 16S rRNA gene sequencing from fecal and salivary samples collected throughout the study. Gut functional profiles were generated using metagenomic (n=86) and metabolomic (n=83) data. Frailty was measured using a 36-item frailty index. Inactivity-associated changes to the gut microbiome during HDBR included decreasing -diversity, decreasing Akkermansia and Lactobacillus, and increasing Bacteroides. Exercise-associated changes included increasing gut Roseburia. Interestingly, oral microbiome {beta}-diversity was more strongly associated with frailty than the gut. We conclude that inactivity-associated changes to the human microbiome could contribute to the early stages of frailty development, and that exercise can serve as an effective countermeasure against these effects. These results may inform strategies to preserve the health of both older adults facing prolonged periods of inactivity, as well as astronauts during longer space exploration missions. IMPORTANCEHealthy ageing carries significant demographic, economic, and public health implications globally, and both physical activity and human-associated microorganisms play important roles in its trajectory. Using microgravity simulation in older adults as a human frailty model, we show that short periods of inactivity could have significant effects on these microbial communities, particularly in the gut, which could further contribute to multi-system physiological decline and frailty pathogenesis. Participants who completed a daily, multi-modality exercise intervention did not exhibit these changes, which may help protect both older adults facing periods of inactivity and astronauts in the advent of longer space exploration missions. Interestingly, our results suggested that oral bacteria may be a more sensitive indicator of individual frailty levels than gut bacteria. Future research should ensure that functional aspects of human-associated microbial communities are thoroughly investigated, as these may offer deeper insight into their contributions to age-related decline.

microbiology↗

bioRxiv2025

Automated eDNA and eRNA Profiling for Biodiversity Monitoring in Marine and Freshwater Ecosystems

Automated sampling enables the collection and analysis of eDNA from regions that are limited by site access, sampling times, and operator safety. eDNA sampling devices must be rigorously tested against existing technologies to demonstrate fitness across different operational settings and sample quality. The Dartmouth Ocean Technologies, Inc. (DOT) automated eDNA sampler preserves samples and can be deployed at a range of temperatures and depths. The DOT sampler has previously been tested in marine environments for up to three months, with validation against manual protocols. In this study we tested the DOT sampler in four water bodies in Nova Scotia, Canada, with an expanded set of genetic analyses. We successfully profiled prokaryotes, eukaryotes, and fish using the 16S, 18S, and 12S ribosomal RNA genes respectively, in a brackish pond, a freshwater lake, and two marine harbours. eDNA samples collected by the DOT sampler were statistically concordant with manual Niskin-bottle samples in a range of aqueous habitats. We detected taxonomic groups consistent with the salinity level of each sampled habitat, including invasive species such as smallmouth bass and chain pickerel in the freshwater lake. One marine harbour was sampled at pre-defined time intervals in the days following a significant rainfall event during which site access was limited. We detected ten times as many probable fecal-associated bacteria by proportion at this site relative to the other marine harbour. Onboard preservation of samples in RNAlater allowed the identification of groups with different levels of metabolic activity, and shotgun metagenomic analysis identified key metabolic pathways and a small number of sequences with homology to known antimicrobial-resistance genes.

ecology↗

bioRxiv2025

CARD k-mers: Unmasking the pathogen hosts and genomic contexts of antimicrobial resistance genes in metagenomic sequences

Antimicrobial resistance (AMR) is a global health crisis requiring rapid surveillance across human, agricultural, and environmental systems. A major challenge during outbreaks is not only detecting antimicrobial resistance genes (ARGs), but also unmasking their pathogen hosts and genomic context, as ARGs alone do not fully capture AMR risk. Pathogen identification is often essential for guiding effective treatment. While culture-based methods remain the diagnostic gold standard, they are slow and sometimes impractical. Faster metagenomic (mNGS) tools typically detect either ARGs, taxonomy, or genomic context, but rarely all three, resulting in fragmented surveillance. Existing k-mer classifiers like Kraken2 and CLARK, designed for general taxonomy, often perform poorly on AMR-specific sequences. We introduce CARD k-mers, the first tool built to jointly predict species-level taxonomy and genomic context (plasmid vs. chromosome) for ARGs in short metagenomic reads. Integrated with the Comprehensive Antibiotic Resistance Database (CARD), CARD k-mers enables rapid, context-aware assignment of ARGs to their likely pathogen and genomic element origin. In benchmarking with 103,456 in-silico pathogen-specific AMR alleles, CARD k-mers outperformed Kraken2 and CLARK by 10.85% and 15.2%, respectively, and correctly classified the genomic context of 4,590 chromosome- and 176 plasmid-specific ARGs. The tool operates at speeds exceeding 675,000 metagenomic reads per minute. By delivering fast, accurate, and context-rich classification of ARGs, CARD k-mers significantly advances untargeted AMR surveillance and is accessible to users with basic command-line experience for use in both clinical and environmental pipelines. CARD k-mers is available at: https://github.com/arpcard/rgi.

bioinformatics↗

bioRxiv2025

Comparison of spatio-temporal dynamics and composition in size-fractionated and unfractionated Northwestern Atlantic microbial communities

Size fractionation is a widely applied approach to target specific microbial size ranges, differentiating between larger particle-associated, and smaller free-living microorganisms. To characterize its impact on microbial diversity and its comparability to unfractionated samples, we analyzed 16 weekly ocean samples across five depths during a spring bloom. A universal marker was used to characterize prokaryotes, eukaryotes and chloroplasts comparing single (0.2 {micro}m) or sequential (3 {micro}m and 0.2 {micro}m) filtration. We analyzed unfractionated, fractionated (small and large fractions) and de-fractionated samples. The particle-associated fraction defines the most different community from the other fractions, and combining size fractions (de-fractionating) before or after sequencing produces a community that is most similar to unfractionated samples in terms of composition, and richness dynamics with the exception of very rare taxa. Between 75% and 97% of features are shared, but some discrepancies in relative abundances were unresolved, including for some lineages of free-living Proteobacteria like OM43. The richness trends were consistent, and ANCOM detected at most one significantly different feature between fractionated, and de-fractionated samples, highlighting the similarity in community composition and temporal dynamics between the sets.

molecular biology↗

bioRxiv2024

ParallelEvolCCM: Quantifying co-evolutionary patterns among genomic features

Concerted gains and losses of genomic features such as genes and mobile genetic elements can provide key clues into related functional roles and shared evolutionary trajectories. By capturing phylogenetic signals, a co-evolutionary model can outperform comparative methods based on shared presence and absence of features.We previously developed the Community Coevolution Model, which represents the gain/loss probability of each feature as a combination of its own intrinsic rate, combined the joint probabilities of gain and loss with all other features. Originally implemented as an R library, we have now developed a R wrapper that adds parallelization and several options to pre-filter the features to increase the efficiency of comparisons. Here we describe the functionality of EvolCCM and apply it to a dataset of 1000 genomes of the genus Bifidobacterium. ParallelEvolCCM is released under the MIT license and available at https://github.com/beiko-lab/arete/blob/master/bin/ParallelEvolCCM.R. Significance StatementPatchy phylogenetic distributions of genes, mobile genetic elements, and other genomic features can constitute evidence for lateral gene transfer. Comparing the presence/absence patterns of multiple features can reveal important associations among them, but the phylogenetic relationships must be taken into consideration in order to avoid spurious correlations. Our new ParallelEvolCCM software embeds these comparisons in a coevolutionary framework, offers a range of options to optimize the speed and comparisons, and offers helper scripts to visualize relationships among features.

bioinformatics↗

bioRxiv2024

PlasEval: a framework for comparing and evaluating plasmid detection tools

Plasmids play a major role in the transfer of antimicrobial resistance (AMR) genes among bacteria via horizontal gene transfer. The identification of plasmids in short-read assemblies is a challenging problem and a very active research area. Plasmid binning aims at detecting, in a draft genome assembly, groups (bins) of contigs likely to originate from the same plasmid. Several methods for plasmid binning have been developed recently, such as PlasBin-flow, HyAsP, gplas, MOB-suite, and plasmidSPAdes. This motivates the problem of evaluating the performances of plasmid binning methods, either against a given ground truth or between them. We describe PlasEval, a novel method aimed at comparing the results of plasmid binning tools. PlasEval computes a dissimilarity measure between two sets of plasmid bins, that can originate either from two plasmid binning tools, or from a plasmid binning tool and a ground truth set of plasmid bins. The PlasEval dissimilarity accounts for the contig content of plasmid bins, the length of contigs and is repeat-aware. Moreover, the dissimilarity score computed by PlasEval is broken down into several parts, that allows to understand qualitative differences between the compared sets of plasmid bins. We illustrate the use of PlasEval by benchmarking four recently developed plasmid binning tools - PlasBin-flow, HyAsP, gplas, and MOB-recon - on a data set of 54 E. coli bacterial genomes. PlasEval is freely available at https://github.com/acme92/PlasEval

bioinformatics↗

bioRxiv2023

Sarand: Exploring Antimicrobial Resistance Gene Neighborhoods in Complex Metagenomic Assembly Graphs

Antimicrobial resistance (AMR) is a major global challenge to human and animal health. The genomic element (e.g., chromosome, plasmid, and genomic islands) and neighbouring genes associated with an AMR gene play a major role in its function, regulation, evolution, and propensity to undergo lateral gene transfer. Therefore, characterising these genomic contexts is vital to effective AMR surveillance, risk assessment, and stewardship. Metagenomic sequencing is widely used to identify AMR genes in microbial communities, but analysis of short-read data offers fragmentary information that lacks this critical contextual information. Alternatively, metagenomic assembly, in which a complex assembly graph is generated and condensed into contigs, provides some contextual information but systematically fails to recover many mobile genetic elements. Here we introduce Sarand, a method that combines the sensitivity of read-based methods with the genomic context offered by assemblies by extracting AMR genes and their associated context directly from metagenomic assembly graphs. Sarand combines BLAST-based homology searches with coverage statistics to sensitively identify and visualise AMR gene contexts while minimising inference of chimeric contexts. Using both real and simulated metagenomic data, we show that Sarand outperforms metagenomic assembly and recently developed graph-based tools in terms of precision and sensitivity for this problem. Sarand (https://github.com/beiko-lab/sarand) enables effective extraction of metagenomic AMR gene contexts to better characterize AMR evolutionary dynamics within complex microbial communities.

bioinformatics↗

bioRxiv2023

Profiling novel lateral gene transfer events in the human microbiome

Lateral gene transfer (LGT) is an important mechanism for genome diversification in microbial populations, including the human microbiome. While prior work has surveyed LGT events in human-associated microbial isolate genomes, the scope and dynamics of novel LGT events arising in personal microbiomes are not well understood, as there are no widely adopted computational methods to detect, quantify, and characterize LGT from complex microbial communities. We addressed this by developing, benchmarking, and experimentally validating a computational method (WAAFLE) to profile novel LGT events from assembled metagenomes. Applying WAAFLE to >2K human metagenomes from diverse body sites, we identified >100K putative high-confidence but previously uncharacterized LGT events ([~]2 per assembled microbial genome-equivalent). These events were enriched for mobile elements (as expected), as well as restriction-modification and transport functions typically associated with the destruction of foreign DNA. LGT frequency was quantifiably influenced by biogeography, the phylogenetic similarity of the involved taxa, and the ecological abundance of the donor taxon. These forces manifest as LGT networks in which hub species abundant in a community type donate unequally with their close phylogenetic neighbors. Our findings suggest that LGT may be a more ubiquitous process in the human microbiome than previously described. The open-source WAAFLE implementation, documentation, and data from this work are available at http://huttenhower.sph.harvard.edu/waafle.

bioinformatics↗

bioRxiv2022

A search for "perio-probiotics" by longitudinal dissection of oral bacterial community shifts during the onset and resolution of gingivitis

AimTo understand the spatiotemporal dynamics of bacterial succession during gingivitis, and to identify taxa with a critical role in gum health with prognostic value. Materials and methodsLongitudinal microbiome data were collected from 15 individuals after completely discontinuing all forms of oral hygiene, and subsequently reintroducing it for three and two weeks, respectively. Sequences from the 16S rRNA V4-V5 gene region from sub- and supra-gingival plaque, saliva, and tongue sites were annotated and mapped to a reference tree of Human Oral Microbiome Database sequences. ResultsSuspending oral hygiene induced gingivitis, which was resolved after its resumption to baseline. Most significant shifts in bacterial abundance were observed in dental plaque, but not in saliva and tongue sites. During gingivitis-induction, baseline microbiota dominated by Streptococcus, was superseded by increased Prevotella, Fusobacterium, Leptotrichia, and Porphyromonas genera. Converse to its decline during disease-induction, gum health restoration was accompanied by a significant increase in streptococci. ConclusionWe present the most comprehensive, spatiotemporal map of bacterial succession during gingivitis onset and resolution. We have identified taxa with potential as probiotic candidates for gum disease (i.e., perio-probiotics), and suggest tooth-associated plaque and not saliva or tongue plaque should be used in future prognostic tests.

microbiology↗

bioRxiv2022

Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents

Enterococcus faecium is a ubiquitous opportunistic pathogen that is exhibiting increasing levels of antimicrobial resistance (AMR). Many of the genes that confer resistance and pathogenic functions are localized on mobile genetic elements (MGEs), which facilitate their transfer between lineages. Here, features including resistance determinants, virulence factors, and MGEs were profiled in a set of 1273 E. faecium genomes from two disparate geographic locations (in the UK and Canada) from a range of agricultural, clinical, and associated habitats. Neither lineages of E. faecium nor MGEs are constrained by geographic proximity, but our results show evidence of a strong association of many profiled genes and MGEs with habitat. Many features were associated with a group of clinical and municipal wastewater genomes that are likely forming a new human-associated ecotype. The evolutionary dynamics of E. faecium make it a highly versatile emerging pathogen, and its ability to acquire, transmit, and lose features presents a high risk for the emergence of new pathogenic variants and novel resistance combinations. This study provides a workflow for MGE-centric surveillance of AMR in Enterococcus that can be adapted to other pathogens.

genomics↗

bioRxiv2020

Metagenome-Assembled Genome Binning Methods Disproportionately Fail for Plasmids and Genomic Islands

Metagenomic methods are an important tool in the life sciences, as they enable simultaneous characterisation of all microbes in a community without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods have emerged as a promising approach to recover individual genomes from metagenomic data. However, MAG binning has not been well assessed for its ability to recover mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), that have very high clinical/agricultural/environmental importance. Certain antimicrobial resistance (AMR) genes and virulence factor (VF) genes are noted to be disproportionately associated with MGEs, making studying their transmission a public health priority. However, the variable copy number and sequence composition of MGEs relative to the majority of the host genome makes them potentially problematic for MAG binning methods. To systematically investigate this, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated for recovery of MGE-associated AMR/VF genes. Here we show that while 82-94% of chromosomes could be correctly recovered and binned, only 38-44% of GIs were recovered and, even more notably, only 1-29% of plasmid sequences were found. Most strikingly, no plasmid-borne VF or AMR genes were recovered and within GIs, only between 0-45% of AMR or VF genes were identified. We conclude that short-read MAGs are largely ineffective for the analysis of mobile genes, including those of public-health importance like AMR and VF genes. We propose that microbiome researchers should instead primarily utilise unassembled short reads and/or long-read approaches to more accurately analyse metagenomic data

bioinformatics↗

bioRxiv2018

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

Whole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDAs data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.\n\nImpact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no \"one-size-fits-all\" genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.\n\nDATA SUMMARYO_LIData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.\nC_LI

bioinformatics↗

bioRxiv2016

SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology

MotivationThe recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research lab and into the front lines of surveillance and outbreak response requires user-friendly, reproducible, and scalable pipelines that have been well validated.\n\nResultsSNVPhyl (Single Nucleotide Variant Phylogenomics) is a bioinformatics pipeline for identifying high-quality SNVs and constructing a whole genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible, and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds, or in the presence of contamination.\n\nAvailabilitySNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

bioinformatics↗