Biology subjects

Chindelevitch, L.

Publications and source records attributed to Chindelevitch, L..

17 recordsLinked to original sources

bioRxiv2026

kamino: proteome-wide variant calling for amino acid phylogenomics

Amino acid-based phylogenetics usually relies on first clustering and aligning orthologous proteins. This approach is powerful but computationally demanding. Here, we present kamino, a reference- and alignment-free method that rapidly builds amino acid phylogenomic alignments directly from proteomes. As with similar algorithms, homologous regions are identified through shared sequences flanking variable regions. The method uses local changes in recoded k-mer occupancy to efficiently identify variable positions within these homologous regions and extract the corresponding pseudo-aligned sequences. It generates phylogenetically informative alignments across diverse prokaryotic and eukaryotic datasets. Phylogenetic analyses show that it accurately recovers Mycobacterium tuberculosis lineages, most curated GTDB taxa, and relationships consistent with published Drosophila and mammalian phylogenies, while producing signals broadly similar to BUSCO-based approaches. Runtimes are comparable to genome-based alignment-free methods and several orders of magnitude faster than classical marker-based pipelines, with moderate memory requirements. The method performs well across a broad range of divergence levels, from within-species comparisons to family-level prokaryotic and phylum-level eukaryotic datasets. kamino therefore provides a fast and simple route from proteomes to phylogenomic alignments across a broad range of evolutionary scales. The program is implemented in Rust and freely available at https://github.com/rderelle/kamino.

bioinformatics↗

bioRxiv2025

A comprehensive AMR genotype-phenotype database (CABBAGE)

Addressing the growing threat of antimicrobial resistance (AMR) requires the development of large-scale resources that link bacterial genomic data with phenotypic antimicrobial resistance profiles. Such datasets are essential for advancing genotype-based predictions of resistance to uncover novel resistance mechanisms, as well as identifying and tracking global trends. Here, we describe the development of the Comprehensive Assessment of Bacterial-Based AMR prediction from GEnotypes (CABBAGE) database, linking bacterial genomes to associated antibiotic susceptibility data and relevant metadata across WHO Bacterial Priority Pathogens, sourced from both publications and existing databases, and curated into a format that is compatible with, and extends, both NCBI and ENA formats. The resulting CABBAGE database, comprising over 170,000 unique sequenced isolates and approximately 1.7 million genome-phenotype pairs linked to extensive metadata, represents the largest database of its kind, consolidating existing AMR phenotype-genotype data into a single unified format. CABBAGE encompasses a broad range of antimicrobials, facilitating the analysis of global resistance trends as well as benchmarks of genotype-to-phenotype predictive methods, and empowering further research uses. The database is freely accessible at https://www.ebi.ac.uk/amr and is currently being integrated with the BioSample database, enabling easy access for the AMR research community.

genomics↗

bioRxiv2024

skalo: using SKA split k-mers with coloured de Brujin graphs to genotype indels

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment-free and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify variants between pathogen whole-genome sequencing data by traversing a coloured De Bruijn graph and building variant groups (ie, sets of variant combinations). Through in-silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in SNP calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).

microbiology↗

bioRxiv2024

Rapid and Accurate Estimation of Genetic Relatedness Between Millions of Viral Genome Pairs Using MANIAC

Average Nucleotide Identity (ANI) is a widely used metric to estimate genetic relatedness, especially in microbial species delineation. While ANI calculation has been well optimised for bacteria and closely related viral genomes, accurate estimation of ANI below 80%, particularly in large reference datasets, has been challenging due to a lack of accurate and scalable methods. To bridge this gap, here we introduce MANIAC, an efficient computational pipeline optimised for estimating ANI and alignment fraction (AF) in viral genomes with divergence around ANI of 70%. Using a rigorous simulation framework, we demonstrate MANIACs accuracy and scalability compared to existing approaches, even to datasets of hundreds-of-thousands of viral genomes. Applying MANIAC to a curated dataset of complete bacterial dsDNA viruses revealed a multimodal ANI distribution, with a distinct gap around 80%, akin to the bacterial ANI gap ([~]90%) but shifted, likely due to viral-specific evolutionary processes such as recombination dynamics and mosaicism. We then evaluated ANI and AF as predictors of genus-level taxonomy using a logistic regression model. We found that this model has strong predictive power (PR-AUC=0.981), but that it works much better for virulent (PR-AUC=0.997) than temperate (PR-AUC=0.847) bacterial viruses. This highlights the complexity of taxonomic classification in temperate phages, known for their extensive mosaicism, and cautions against over-reliance on ANI in such cases. MANIAC can be accessed under https://github.com/bioinf-mcb/MANIAC. ImportanceWe introduce a novel computational pipeline called MANIAC, designed to accurately assess Average Nucleotide Identity (ANI) and alignment fraction (AF) between diverse viral genomes, scalable to datasets of over 100k genomes. Through the use of computer simulations and real data analyses, we show that MANIAC could ac- curately estimate genetic relatedness between pairs of viral genomes around 60-70% ANI. We applied MANIAC to investigate the question of ANI discontinuity in bacterial dsDNA viruses, finding evidence for an ANI gap, akin to the one seen in bacteria but around ANI of 80%. We then assessed the ability of ANI and AF to predict taxonomic genus boundaries, finding its strong predictive power in virulent, but not in temperate phages. Our results suggest that bacterial dsDNA viruses may exhibit an ANI threshold (on average around 80%) above which recombination helps maintain population cohesiveness, as previously argued in bacteria.

bioinformatics↗

bioRxiv2024

Seamless, rapid and accurate analyses of outbreak genomic data using Split K-mer Analysis (SKA)

Sequence variation observed in populations of pathogens can be used for important public health and evolution genomic analyses, especially outbreak analysis and transmission reconstruction. Identifying this variation is typically achieved by aligning sequence reads to a reference genome, but this approach is susceptible to reference biases and requires careful filtering of called genotypes. Additionally, while the volume of bacterial genomes continues to grow, tools which can accurately and quickly call genetic variation between sequences have not kept pace. There is a need for tools which can process this large volume of data, providing rapid results, but remain simple so they can be used without highly trained bioinformaticians, expensive data analysis, and long term storage and processing of large files. Here we describe Split K-mer Analysis (SKA2), a method which supports both reference-free and reference-based mapping to quickly and accurately genotype populations of bacteria using sequencing reads or genome assemblies. SKA2 is highly accurate for closely related samples, and in outbreak simulations we show superior variant recall compared to reference-based methods, with no false positives. We also show that within bacterial strains, where it is possible to construct a clonal frame, SKA2 can also accurately map variants to a reference, and be used with recombination detection methods to rapidly reconstruct vertical evolutionary history. SKA2 is many times faster than comparable methods and can be used to add new genomes to an existing call set, allowing sequential use without the need to reanalyse entire collections. Given its robust implementation, inherent absence of reference bias and high accuracy, SKA2 has the potential to become the tool of choice for genotyping bacteria and can help expand the uses of genome data in evolutionary and epidemiological analyses. SKA2 is implemented in Rust and is freely available at https://github.com/bacpop/ska.rust.

bioinformatics↗

bioRxiv2024

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling

The detection of antimicrobial resistance (AMR) markers directly from genomic or metagenomic data is becoming a standard clinical and public health procedure. This has resulted in the development of a number of different bioinformatic AMR prediction tools. Although many may implement similar principles, these tools differ significantly in their supported inputs, search algorithms, parameterisation, and underlying reference databases. Each of these tools generates a report of detected AMR genes or variants in a distinct, non-standard, format. This presents a huge barrier to the comparison of results and to the modularity of tools for AMR gene prediction within bioinformatic workflows. In collaboration with 17 public health laboratories across 10 countries, the Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) data structures working group has developed and piloted a standardized output specification for the bioinformatic detection of AMR from microbial genomes. In this report, we discuss hAMRonization, a python package and command-line utility, which implements PHA4GEs AMR specification to combine the outputs of disparate antimicrobial resistance gene detection tools into a single unified format. hAMRonization can be easily extended and currently supports 18 different tools (both species-agnostic and species-specific) for the detection of genes and/or variants conferring AMR. The harmonized reports are available in tabular form, JSON format or through an interactive HTML file (e.g., https://maguire-lab.github.io/assets/interactive_report_demo.html) that can be opened within the browser for navigable data exploration. As of 2024-03-07 hAMRonization has been downloaded [~]12,500 times, incorporated into >9 public bioinformatic tools and workflows, and been internally adopted by several national and international public health groups. The hAMRonization tool and underlying specification are open-source and freely available through PyPI, conda and GitHub (https://github.com/pha4ge/hAMRonization).

bioinformatics↗

bioRxiv2023

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in-silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics. Data SummaryThe scripts used to run and fit the models can be found at https://github.com/gbatbiff/Kpneu_MIC_prediction. The Illumina sequences from Thorpe et al. are available from the European Nucleotide Archive under accession PRJEB27342. All the other genomes are available on https://www.bv-brc.org/ database. Impact statementKlebsiella pneumoniae is a leading cause of hospital and community acquired infections worldwide, highly contributing to the global burden of antimicrobial resistance (AMR). Ordinary methods to assess antibiotic resistance are not always satisfactory, and may not be effective in terms of costs and delays, so robust methods able to accurately predict AMR are increasingly needed. Genome-based prediction of minimum inhibitory concentrations (MICs) through machine learning methods is a promising tool to assist clinical diagnosis, also offsetting phenotypic MIC discordance between the different culture-based assays. However, benchmarking predictive models against phenotypic data is problematic due to inconsistencies in the way these data are generated and how they should be handled remains unclear. In this work, we focused on genome-based prediction of MIC and evaluated the performance of interpretable machine learning models across different genetic architectures and data encodings. Our workflow highlighted how MICs need to be treated as different types of data depending on the method used to measure them, in particular considering each antibiotic separately. Our findings shed further light on the factors affecting model performance, paving the way to future improvements of antibiotic resistance prediction.

bioinformatics↗

bioRxiv2023

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning

Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, and some have even set zero-tolerance thresholds for particular food products to minimise the risk of L. monocytogenes outbreaks. This emphasises that proper sanitation of food processing plants is of utmost importance. Consequently in recent years, there has been an increased interest in L. monocytogenes tolerance to disinfectants used in the food industry. Even though many studies are focusing on laboratory quantification of L. monocytogenes tolerance, the possibility of predictive models remains poorly studied. Within this study, we explore the prediction of tolerance and minimum inhibitory concentrations (MIC) using whole genome sequencing (WGS) and machine learning (ML). We used WGS data and MIC values to quaternary ammonium compound (QAC) disinfectants from 1649 L. monocytogenes isolates to train different ML predictors. Our study shows promising results for predicting tolerance to QAC disinfectants using WGS and machine learning. We were able to train high-performing ML classifiers to predict tolerance with balanced accuracy scores up to 0.97{+/-}0.02. For the prediction of MIC values, we were able to train ML regressors with mean squared error as low as 0.07{+/-}0.02. We also identified several new genes related to cell wall anchor domains, plasmids, and phages, putatively associated with disinfectant tolerance in L. monocytogenes. The findings of this study are a first step towards prediction of L. monocytogenes tolerance to QAC disinfectants used in the food industry. In the future, predictive models might be used to monitor disinfectant tolerance in food production and might support the conceptualisation of more nuanced sanitation programs. AUTHOR SUMMARYMicrobial contamination challenges food safety by potentially transmitting harmful microbes such as bacteria to consumers. Listeria monocytogenes is an example of such a bacteria, which is primarily transmitted through food and can cause severe diseases in at-risk groups. Fortunately, strict food safety regulations and stringent cleaning protocols are in place to prevent the transmission of Listeria monocytogenes. However, in recent years, there has been an increase in tolerance towards disinfectants used in the food industry, which can reduce their effectiveness. In this study, we used genome sequencing and phenotypic data to train machine learning models that can accurately predict whether individual Listeria monocytogenes isolates are tolerant to selected disinfectants. We were able to train models that are not only able to distinguish sensitive/tolerant isolates but also can predict different degrees of tolerance to disinfectants. Further, we were able to report a set of genes that were important for the machine learning prediction and could give information about possible tolerance mechanisms. In the future, similar predictive models might be used to guide cleaning and disinfection protocols to facilitate maximum effectiveness.

bioinformatics↗

bioRxiv2023

fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing

Lineage typing of the Mycobacterium tuberculosis complex (MTBC) has evolved from traditional phenotypic methods to advanced molecular and genomic techniques. In this study we present fastlin, a bioinformatics tool designed for rapid MTBC lineage typing. Fastlin utilises an ultra-fast alignment-free approach to detect previously identified barcode single nucleotide polymorphisms (SNPs) associated with specific MTBC lineages directly from fastq files. In a comprehensive benchmarking against existing tools, fastlin demonstrated high accuracy and significantly faster running times. Analysis of large MTBC datasets revealed fastlins capability not only to predict MTBC lineages, but also to detect mixed-lineage strain mixtures and estimate their proportions. Fastlin offers a user-friendly and efficient solution for MTBC lineage typing, complementing existing tools and facilitating large-scale analysis.

bioinformatics↗

bioRxiv2020

Predicting drug resistance in M. tuberculosis using a Long-term Recurrent Convolutional Networks architecture

MotivationDrug resistance in Mycobacterium tuberculosis (MTB) is a growing threat to human health worldwide. One way to mitigate the risk of drug resistance is to enable clinicians to prescribe the right antibiotic drugs to each patient through methods that predict drug resistance in MTB using whole-genome sequencing (WGS) data. Existing machine learning methods for this task typically convert the WGS data from a given bacterial isolate into features corresponding to single-nucleotide polymorphisms (SNPs) or short sequence segments of a fixed length K (K-mers). Here, we introduce a gene burden-based method for predicting drug resistance in TB. We define one numerical feature per gene corresponding to the number of mutations in that gene in a given isolate. This representation greatly reduces the number of model parameters. We further propose a model architecture that considers both gene order and locality structure through a Long-term Recurrent Convolutional Network (LRCN) architecture, which combines convolutional and recurrent layers. ResultsWe find that using these strategies yields a substantial, statistically significant improvement over state-of-the-art methods on a large dataset of M. tuberculosis isolates, and suggest that this improvement is driven by our methods ability to account for the order of the genes in the genome and their organization into operons. AvailabilityThe implementations of our feature preprocessing pipeline1 and our LRCN model2 are publicly available, as is our complete dataset3. Supplementary informationAdditional data are available in the Supplementary Materials document4.

bioinformatics↗

bioRxiv2020

Geographic heterogeneity impacts drug resistance predictions in Mycobacterium tuberculosis

The efficacy of antibiotic drug treatments in tuberculosis (TB) is significantly threatened by the development of drug resistance. There is a need for a robust diagnostic system that can accurately predict drug resistance in patients. In recent years, researchers have been taking advantage of whole-genome sequencing (WGS) data to infer antibiotic resistance. In this work we investigate the power of machine learning tools in inferring drug resistance from WGS data on three distinct datasets differing in their geographical diversity. We analyzed data from the Relational Sequencing TB Data Platform, which comprises global isolates from 32 different countries, the PATRIC database, containing isolates contributed by researchers around the world, and isolates collected by the British Columbia Centre for Disease Control in Canada. We predicted drug resistance to the first-line drugs: isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin. We focused on the genes which previous evidence suggests are involved in drug resistance in TB. We called single-nucleotide polymorphisms using the Snippy pipeline, then applied different machine learning models. Following best practices, we chose the best parameters for each model via cross-validation on the training set and evaluated the performance via the sensitivity-specificity tradeoffs on the testing set. To the best of our knowledge, our study is the first to predict antibiotic resistance in TB across multiple datasets. We obtained a performance comparable to that seen in previous studies, but observed that performance may be negatively affected when training on one dataset and testing on another, suggesting the importance of geographical heterogeneity in drug resistance predictions. In addition, we investigated the importance of each gene within each model, and recapitulated some previously known biology of drug resistance. This study paves the way for further investigations, with the ultimate goal of creating an accurate, interpretable and globally generalizable model for predicting drug resistance in TB. Author summaryDrug resistance in pathogenic bacteria such as Mycobacterium tuberculosis can be predicted by an application of machine learning models to next-generation sequencing data. The received wisdom is that following standard protocols for training commonly used machine learning models should produce accurate drug resistance predictions. In this paper, we propose an important caveat to this idea. Specifically, we show that considering geographical diversity is critical for making accurate predictions, and that different geographic regions may have disparate drug resistance mechanisms that are predominant. By comparing the results within and across a regional dataset and two international datasets, we show that model performance may vary dramatically between settings. In addition, we propose a new method for extracting the most important variants responsible for predicting resistance to each first-line drug, and show that it is to recapitulate a large amount of what is known about the biology of drug resistance in Mycobacterium tuberculosis.

bioinformatics↗

bioRxiv2020

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotics mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that whether a drug acts as a bacteriostatic or bactericidal agent has little influence on resistance selection. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strains minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.

pharmacology and toxicology↗

bioRxiv2020

An interpretable classification method for predicting drug resistance in M. tuberculosis

MotivationPrediction of drug resistance and identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Solving this problem requires a transparent, accurate, and flexible predictive model. The methods currently used for this purpose rarely satisfy all of these criteria. On the one hand, approaches based on testing strains against a catalogue of previously identified mutations often yield poor predictive performance; on the other hand, machine learning techniques typically have higher predictive accuracy, but often lack interpretability and may learn patterns that produce accurate predictions for the wrong reasons. Current interpretable methods may either exhibit a lower accuracy or lack the flexibility needed to generalize them to previously unseen data. ContributionIn this paper we propose a novel technique, inspired by the group testing and Boolean compressed sensing, which yields highly accurate predictions, interpretable results, and is flexible enough to be optimized for various evaluation metrics at the same time. ResultsWe test the predictive accuracy of our approach on five first-line and seven second-line antibiotics used for treating tuberculosis. We find that it has a higher or comparable accuracy to that of commonly used machine learning models, and is able to identify variants in genes with previously reported association to drug resistance. Our method is intrinsically interpretable, and can be customized for different evaluation metrics. Our implementation is available at github.com/hoomanzabeti/INGOT_DR and can be installed via The Python Package Index (Pypi) under ingotdr. This package is also compatible with most of the tools in the Scikit-learn machine learning library.

bioinformatics↗

bioRxiv2019

Network science inspires novel tree shape statistics

1The shape of phylogenetic trees can be used to gain evolutionary insights. A trees shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.

bioinformatics↗

bioRxiv2018

MCS^2: Minimal coordinated supports for fast enumeration of minimal cut sets in metabolic networks

MotivationConstraint-based modeling of metabolic networks helps researchers gain insight into the metabolic processes of many organisms, both prokaryotic and eukaryotic. Minimal Cut Sets (MCSs) are minimal sets of reactions whose inhibition blocks a target reaction in a metabolic network. Most approaches for finding the MCSs in constrained-based models require, either as an intermediate step or as a byproduct of the calculation, the computation of the set of elementary flux modes (EFMs), a convex basis for the valid flux vectors in the network. Recently, Ballerstein et al. [BvKKH11] proposed a method for computing the MCSs of a network without first computing its EFMs, by creating a dual network whose EFMs are a superset of the MCSs of the original network. However, their dual network is always larger than the original network and depends on the target reaction. Here we propose the construction of a different dual network, which is typically smaller than the original network and is independent of the target reaction, for the same purpose. We prove the correctness of our approach, MCS2, and describe how it can be modified to compute the few smallest MCSs for a given target reaction. ResultsWe compare MCS2 to the method of Ballerstein et al. and two other existing methods. We show that MCS2 succeeds in calculating the full set of MCSs in many models where other approaches cannot finish within a reasonable amount of time. Thus, in addition to its theoretical novelty, our approach provides a practical advantage over existing methods. AvailabilityMCS2 is freely available at https://github.com/RezaMash/MCS under the GNU 3.0 license.

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↗

bioRxiv2017

MentaLiST - A fast MLST caller for large wgMLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing (WGS) data, MLST methods have evolved toward larger typing schemes, based on a few hundred genes (core genome MLST, cgMLST) to a few thousand genes (whole genome MLST, wgMLST). Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes.\n\nWe introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST scheme with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

bioinformatics↗