Bacterial Genomic Data Research Articles

An update on novel taxa and revised taxonomic status of bacteria isolated from domestic companion and agricultural animals described in 2023.

With the proliferation of abundant bacterial genomic data comes the recognition of new organisms as well as a better understanding of the relatedness of known bacteria. Recognizing the associated taxonomic changes enhances communication and understanding about the significance of novel organisms and deeper understanding of known pathogens. This review addresses the addition of multiple gastrointestinal bacteria that form the normal microbiota in a variety of animals including honeybees as well as novel bacteria from domestic animals including an alpha-hemolytic Streptococcus species from guinea pigs, two Moraxella spp. from cows and goats, a new Capnocytophaga species from cats, a thermophilic Campylobacter species from pigs, and the new Exercitatus genus in Family Pasteurellaceae. Several revisions to the nomenclature also appeared in 2023 including the change of Clostridium spiroforme, which causes anorexia and diarrhea in domestic rabbits, to Thomasclavelia spiroformis comb. nov. and Mannheimia ovis to Mannheimia pernigra.

Seamless, rapid, and accurate analyses of outbreak genomic data using split k-mer analysis.

Sequence variation observed in populations of pathogens can be used for important public health and evolutionary 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. There is a need for tools that can process this growing volume of bacterial genome data, providing rapid results, but that 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 that 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 with reference-based methods, with no false positives. 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 reanalyze entire collections. With an inherent absence of reference bias, high accuracy, and a robust implementation, SKA2 has the potential to become the tool of choice for genotyping bacteria. SKA2 is implemented in Rust and is freely available as open-source software.

Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.

Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medicines. This resistance renders antibiotics and other antimicrobial drugs ineffective, making infections challenging or impossible to treat. This escalation in drug resistance heightens the risk of disease spread, severe illness, disability, and mortality. With datasets now containing hundreds or even thousands of pathogen genomes, machine learning techniques are on the rise for predicting antibiotic resistance in pathogens, prediction based on gene content and genome composition. Aim of this work is to combine and incorporate machine learning methods on bacterial genomic data to predict antimicrobial resistance, we will focus on the case of Klebsiella pneumoniae in order to support clinicians in selecting appropriate therapy.

Average nucleotide identity-based Staphylococcus aureus strain grouping allows identification of strain-specific genes in the pangenome.

We analyzed the genomic diversity of Staphylococcus aureus, a globally prevalent bacterial species that causes serious infections in humans. Our goal was to build a genetic picture of the different strains of S. aureus and which genes may be associated with them. We reprocessed >84,000 genomes and subsampled to remove redundancy. We found that individual samples sharing >99.5% of their genome could be grouped into strains. We also showed that a portion of genes that are present in intermediate frequency in the species are strongly associated with some strains but completely absent from others, suggesting a role in strain specificity. This work lays the foundation for understanding individual gene histories of the S. aureus species and also outlines strategies for processing large bacterial genomic data sets.

Design and Application of Intelligent Library Intelligence Authentication System Based on Information Entropy Early Warning Models

Abstract Identifying the authenticity of library intelligence has consistently posed a significant challenge in intelligence management. This paper aims to optimize the D-S evidence theory to address the issues of uncertainty, imprecision, and high conflict in library intelligence evidence fusion. To achieve this, DSmT is employed to extend the D-S evidence theory by eliminating the process of assigning conflicting information with normalization coefficients. Then, through the fuzzy information pseudo-feature analysis method, the library intelligent intelligence information entropy feature quantity is extracted, and semantic feature matching is used to construct the library intelligent intelligence true and false information recognition system. Finally, the recognition performance of the system is examined using a dataset to analyze the intelligence authenticity recognition of mis-spliced bacterial colony genomic data from a biological experiment that has been entered into the library intelligence database. Finally, it was found that the average recognition rate of this paper's system in the four classical datasets is 96.57%, which is higher than the 89.61% and 90.02% of the two evidence fusion classification schemes. The results of this paper's algorithm for high-conflict evidence are 0.9506, 0.0558, and 0.0097, reflecting the accuracy of the evidence support results. Finally, 35 colony genome splicing error intelligence were identified, of which 32 were genuine errors, with a precision rate of 91% and a recall rate of 94%. The library intelligence authenticity recognition system designed in this paper has excellent performance and provides an effective path and usable algorithmic model for library intelligence authentication.

Change in Urinary Inflammatory Biomarkers and Psychological Health with Gut Microbiome Modulation after Six Months of a Lifestyle Modification Program in Children.

Background: Obesity is a metabolic disorder that negatively impacts the quality of life. Long-term methods such as exercise and low-fat diets can help regulate this health issue, but 93.3 million Americans continue to struggle. Our research investigates if lifestyle changes can affect urinary inflammation markers and psychological aspects through the modification of gut microbiome composition. Methods: Our study included 16 healthy controls with normal BMI as a comparison group and 22 overweight/obese (OW/OB) adolescents. We collected demographic, clinical, psychological, stool, and urine sample data at enrollment and six months after implementing lifestyle modifications. Bacterial genomic data and inflammatory markers in these samples were analyzed. Results: The lifestyle interventions were associated with decreased inflammation and enhanced mental health among overweight teens. We observed differences in bacterial community compositions between healthy participants and those who underwent treatment, including exercise and dietary habit adjustments, although there was no significant change in bacterial species richness. Mental health correlated with gut microbiota compositions without any demographic influences. The research also uncovered connections between inflammatory markers, psychological factors, and gut microbiota phyla through carbohydrate metabolism alterations. Conclusion: Our findings demonstrate that lifestyle modifications are associated with improved mental health and a reduction in inflammation in overweight adolescents by adjusting the gut microbiota composition.

Prospects for New Antibiotics Discovered through Genome Analysis

Abstract: Antibiotic-resistant illnesses are on the rise worldwide, and the pipeline for developing new antibiotics is drying up. As a result, researchers need to create novel compounds with antimicrobial action. Recent decades have seen a dearth of novel antibiotics because of the reliance on conventional empirical screening procedures using both natural and synthetic chemicals to find them. There is hope that the massive amount of bacterial genome sequence data that has become accessible since the sequencing of the first bacterial genome more than 20 years ago might help lead to the development of new antibiotic drugs. Genes with significant levels of conservation both within and between bacterial species can be found using comparative genomic techniques; these genes may be involved in essential bacterial functions. Bioactive chemicals found in natural products have been successfully used in treating everything from infectious diseases to cancer, but over the past 20-30 years, the effectiveness of screening methods based on fermentation has decreased. Researchers urgently need answers to the unmet demand for bacterial infection resistance. Now more than ever, with the advent of cheap, high-throughput genomic sequencing technology, natural product discovery can be revitalized. Using bioinformatics, investigators may foretell whether or not a certain microbial strain would generate compounds with novel chemical structures, which may have novel modes of action in inhibiting bacterial growth. This manuscript describes how this potential might be utilised, with a particular emphasis on manipulating the expression of dormant biosynthetic gene clusters that are hypothesised to encode new antibiotics. Additionally, it consolidates the work of the past and the present to utilise bacterial genomic data in the identification and development of new antibiotics.

Genealogical inference and more flexible sequence clustering using iterative-PopPUNK.

Bacterial genome data are accumulating at an unprecedented speed due to the routine use of sequencing in clinical diagnoses, public health surveillance, and population genetics studies. Genealogical reconstruction is fundamental to many of these uses; however, inferring genealogy from large-scale genome data sets quickly, accurately, and flexibly is still a challenge. Here, we extend an alignment- and annotation-free method, PopPUNK, to increase its flexibility and interpretability across data sets. Our method, iterative-PopPUNK, rapidly produces multiple consistent cluster assignments across a range of sequence identities. By constructing a partially resolved genealogical tree with respect to these clusters, users can select a resolution most appropriate for their needs. We showed the accuracy of clusters at all levels of similarity and genealogical inference of iterative-PopPUNK based on simulated data and obtained phylogenetically concordant results in real data sets from seven bacterial species. Using two example sets of Escherichia/Shigella and Vibrio parahaemolyticus genomes, we show that iterative-PopPUNK can achieve cluster resolutions ranging from phylogroup down to sequence typing (ST). The iterative-PopPUNK algorithm is implemented in the "PopPUNK_iterate" program, available as part of the PopPUNK package.

Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach

The amount of data produced by genome sequencing experiments has been growing rapidly over the past several years, making compression important for efficient storage, transfer and analysis of the data. In recent years, nanopore sequencing technologies have seen increasing adoption since they are portable, real-time and provide long reads. However, there has been limited progress on compression of nanopore sequencing reads obtained in FASTQ files since most existing tools are either general-purpose or specialized for short read data. We present NanoSpring, a reference-free compressor for nanopore sequencing reads, relying on an approximate assembly approach. We evaluate NanoSpring on a variety of datasets including bacterial, metagenomic, plant, animal, and human whole genome data. For recently basecalled high quality nanopore datasets, NanoSpring, which focuses only on the base sequences in the FASTQ file, uses just 0.35–0.65 bits per base which is 3–6times lower than general purpose compressors like gzip. NanoSpring is competitive in compression ratio and compression resource usage with the state-of-the-art tool CoLoRd while being significantly faster at decompression when using multiple threads (> 4times faster decompression with 20 threads). NanoSpring is available on GitHub at https://github.com/qm2/NanoSpring.

Robust analysis of prokaryotic pangenome gene gain and loss rates with Panstripe.

Horizontal gene transfer (HGT) plays a critical role in the evolution and diversification of many microbial species. The resulting dynamics of gene gain and loss can have important implications for the development of antibiotic resistance and the design of vaccine and drug interventions. Methods for the analysis of gene presence/absence patterns typically do not account for errors introduced in the automated annotation and clustering of gene sequences. In particular, methods adapted from ecological studies, including the pangenome gene accumulation curve, can be misleading as they may reflect the underlying diversity in the temporal sampling of genomes rather than a difference in the dynamics of HGT. Here, we introduce Panstripe, a method based on generalized linear regression that is robust to population structure, sampling bias, and errors in the predicted presence/absence of genes. We show using simulations that Panstripe can effectively identify differences in the rate and number of genes involved in HGT events, and illustrate its capability by analyzing several diverse bacterial genome data sets representing major human pathogens.

Large-scale comparative genomics to refine the organization of the global Salmonella enterica population structure.

The White-Kauffmann-Le Minor (WKL) scheme is the most widely used Salmonella typing scheme for reporting the disease prevalence of the enteric pathogen. With the advent of whole-genome sequencing (WGS), in silico methods have increasingly replaced traditional serotyping due to reproducibility, speed and coverage. However, despite integrating genomic-based typing by in silico serotyping tools such as SISTR, in silico serotyping in certain contexts remains ambiguous and insufficiently informative. Specifically, in silico serotyping does not attempt to resolve polyphyly. Furthermore, in spite of the widespread acknowledgement of polyphyly from genomic studies, the prevalence of polyphyletic serovars is not well characterized. Here, we applied a genomics approach to acquire the necessary resolution to classify genetically discordant serovars and propose an alternative typing scheme that consistently reflect natural Salmonella populations. By accessing the unprecedented volume of bacterial genomic data publicly available in GenomeTrakr and PubMLST databases (>180 000 genomes representing 723 serovars), we characterized the global Salmonella population structure and systematically identified putative non-monophyletic serovars. The proportion of putative non-monophyletic serovars was estimated higher than previous reports, reinforcing the inability of antigenic determinants to depict the complexity of Salmonella evolutionary history. We explored the extent of genetic diversity masked by serotyping labels and found significant intra-serovar molecular differences across many clinically important serovars. To avoid false discovery due to incorrect in silico serotyping calls, we cross-referenced reported serovar labels and concluded a low error rate in in silico serotyping. The combined application of clustering statistics and genome-wide association methods demonstrated effective characterization of stable bacterial populations and explained functional differences. The collective methods adopted in our study have practical values in establishing genomic-based typing nomenclatures for an entire microbial species or closely related subpopulations. Ultimately, we foresee an improved typing scheme to be a hybrid that integrates both genomic and antigenic information such that the resolution from WGS is leveraged to improve the precision of subpopulation classification while preserving the common names defined by the WKL scheme.

Major histocompatibility complex genomic investigation of endangered Chinese alligator provides insights into the evolution of tetrapod major histocompatibility complex and survival of critically bottlenecked species

BackgroundThe major histocompatibility complex (MHC) gene family, a vital immune gene family in vertebrates, helps animals defend against pathogens. The polymorphism of MHC genes is important for a species and is considered to be caused by the numerous alleles of MHC antigen-presenting genes. However, the mechanism of this process is unclear due to the lack of data on the MHC structure. The evolutionary trajectories of the tetrapod MHC are also unclear because of insufficient studies on the reptile MHC architecture. Here, we studied the Chinese alligator (Alligator sinensis), which experienced a population bottleneck, but the population increased rapidly in the past 30 years and is proposed to have a unique MHC system to face pathogenic challenges.ResultsWe successfully constructed a 2 Mb MHC region using bacterial artificial chromosome (BAC) library and genome data of the Chinese alligator and checked the antigen-presenting genes using transcriptome data and the rapid amplification of cDNA ends (RACE) technique. The MHC architecture reported here uncovers adjacent Class I and Class II subregions and a unique CD1 subregion. This newly added information suggested that the Class I-II structure pattern was more ancient in tetrapods and helped reconstruct the evolution of the MHC region architecture. We also found multiple groups of MHC class I (MHC-I) (12 duplicated loci, belonging to three groups, two of which were novel) and MHC class II (MHC-II) (11 duplicated loci, belonging to two groups) inside the 2 Mb MHC region, and there were three more duplicated MHC-I loci outside it. These highly duplicated antigen-presenting loci had differences in expression, amino acid length of antigen-presenting exons, and splice signal of exon and intron, which together promoted the polymorphism of duplicated genes. Although the MHC antigen-presenting genes were identified as monomorphic or oligomorphic in our previous population study, the loci with high copy numbers and many differences can make up for this loss, presenting another mechanism for polymorphism in antigen presentation. These MHC-I and MHC-IIB loci with low polymorphism for each locus, but high numbers in all, may also contribute to MHC antigen-presenting binding variability in a population.ConclusionTo summarize, the fine MHC region architecture of reptiles presented in this study completes the evolutionary trajectories of the MHC structure in tetrapods, and these distinctive MHC gene groups in the Chinese alligator may have helped this species to expand rapidly in the past recent years.

Mycobacterium tuberculosis functional genetic diversity, altered drug sensitivity, and precision medicine.

In the face of the unrelenting global burden of tuberculosis (TB), antibiotics remain our most effective tools to save lives and control the spread of Mycobacterium tuberculosis (Mtb). However, we confront a dual challenge in our use of antibiotics: simplifying and shortening the TB drug regimen while also limiting the emergence and propagation of antibiotic resistance. This task is now more feasible due to the increasing availability of bacterial genomic data at or near the point of care. These resources create an opportunity to envision how integration of bacterial genetic determinants of antibiotic response into treatment algorithms might transform TB care. Historically, Mtb drug resistance studies focused on mutations in genes encoding antibiotic targets and the resulting increases in the minimal inhibitory concentrations (MICs) above a breakpoint value. But recent progress in elucidating the effects of functional genetic diversity in Mtb has revealed various genetic loci that are associated with drug phenotypes such as low-level MIC increases and tolerance which predict the development of resistance and treatment failure. As a result, we are now poised to advance precision medicine approaches in TB treatment. By incorporating information regarding Mtb genetic characteristics into the development of drug regimens, clinical care which tailors antibiotic treatment to maximize the likelihood of success has come into reach.

Estimated Roles of the Carrier and the Bacterial Strain When Methicillin-Resistant Staphylococcus aureus Decolonization Fails: a Case-Control Study.

ABSTRACTMethicillin-resistant Staphylococcus aureus (MRSA) is a common bacterial pathogen that frequently colonizes healthy individuals, with potential to cause invasive infection. In Denmark, to keep the prevalence low, MRSA carriers are recommended to undergo decolonization treatments, but achieving decolonization is challenging. Knowledge about the factors contributing to decolonization is scarce. We aimed to identify bacterial genome and clinical factors influencing MRSA decolonization. We identified all new MRSA patients above 2 years of age within the Hvidovre catchment area, Copenhagen, Denmark, in 2017 and 2018. Carriers were defined as chronic carriers (cases) if they were MRSA positive after two or more treatments and as nonchronic carriers (controls) if they were MRSA free after the first or second treatment. Using whole-genome sequencing (WGS), we constructed a pangenome of bacterial strains. With the incorporation of bacterial genome and clinical patient data, machine learning and multivariate analyses were performed to determine the factors associated with decolonization. A total of 477 MRSA carriers were included. An age of ≥13 years was significantly associated with nonchronic carriage. We identified 278 bacterial genetic features that were statistically significantly associated with chronic carriage (P < 0.05 by Fisher’s exact test). Chronic MRSA carriage was predicted with 68% accuracy using a combination of bacterial genome data and patient clinical data. Decolonization success is multifactorial. Apart from the 68% predicted accuracy found in this study, we estimate that the remaining 32% is a result of host factors and microbiome composition.IMPORTANCE Carriage of methicillin-resistant Staphylococcus aureus (MRSA) and other multiresistant bacteria is a prerequisite for infection and transmission. Successful decolonization treatment removes these risks. We aimed to identify bacterial genome and host clinical factors that influence MRSA decolonization to estimate the roles of the carrier and the bacterial strain, respectively, when decolonization fails. The long-term goal, beyond this study, is to optimize decolonization success, minimize MRSA transmission, and, ultimately, improve the quality of life of MRSA carriers.

A scalable analytical approach from bacterial genomes to epidemiology.

Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylogeny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phylogenetic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemiological analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research.This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’.

Within-Host Genetic Variation in Neisseria gonorrhoeae over the Course of Infection.

ABSTRACTKnowledge of within-host genetic variation informs studies on transmission dynamics. We studied within-host genetic variation in Neisseria gonorrhoeae over the course of infection and across different anatomical locations. Isolates were obtained during a clinical trial, and isolates from consecutive time points reflected persistent infections after treatment failure. We compared sequence types (STs) and recombination unfiltered- and filtered core genome single nucleotide polymorphism (SNP) distances in 65 within-host isolate pairs from the same anatomical location over time—obtained with a median interval of 7 days—and 65 isolate pairs across different anatomical locations at one time point. Isolates with different Multi-Locus Sequence Types (MLST), NG-Sequence Types for Antimicrobial Resistance (NG-STAR) and NG-Multi Antigen Sequence Types (NG-MAST) had a median of 1466 recombination filtered SNPs, whereas a median of 1 SNP was found between isolates with identical STs or a different NG-MAST only. The threshold for differentiating between strains was set at 10 recombination filtered SNPs, showing that isolates from persistent infections could have different NG-MASTs. Antibiotic pressure applied through treatment did not lead to an increase in genetic variation in specific genes or in overall extent of variation, compared to variation across anatomical locations. Instead, within-host genetic variation was proposedly driven by the host immune response, as it was concentrated in genomic regions encoding surface exposed proteins involved in host-microbe interaction. Ultimately, 15/228 (6.5%) between-host pairs contained a single strain, suggesting between-host transmission. However, patient reported data are needed to differentiate within-host persistence from between-host transmission.IMPORTANCE Understanding transmission dynamics of Neisseria gonorrhoeae (Ng) is based on the identification of transmission events. These can be identified by assessing genetic relatedness between Ng isolates, expressed as core genome SNP distances. However, a SNP threshold to differentiate between strains needs to be defined, using knowledge on within- and between-host genetic variation. Here, we assessed within-host genetic variation, using a unique set of within-host Ng isolates from the same anatomical location over time or across different anatomical locations at one time point. The insights in genetic variation that occurred during the infection period contribute to the understanding of infection dynamics. In addition, the obtained knowledge can be used for future research on transmission dynamics and development of public health interventions based on bacterial genomic data.

Missing Piece Study protocol: prospective surveillance to determine the epidemiology of group A streptococcal pharyngitis and impetigo in remote Western Australia

IntroductionGroup A β-haemolytic Streptococcus (GAS), a Gram-positive bacterium, causes skin, mucosal and systemic infections. Repeated GAS infections can lead to autoimmune diseases acute rheumatic fever (ARF) and rheumatic heart disease...

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN

Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high accuracy on some antibiotics but relatively low accuracy on others. Traditional machine learning (ML) approaches have been applied to classify drug resistance for MTB and have shown more stable performance. However, there is no study that uses deep learning architecture like Convolutional Neural Network (CNN) on a large and diverse cohort of MTB samples for AMR prediction. We developed 24 binary classifiers of MTB drug resistance status across eight anti-MTB drugs and three different ML algorithms: logistic regression, random forest and 1D CNN using a training dataset of 10,575 MTB isolates collected from 16 countries across six continents, where an extended pan-genome reference was used for detecting genetic features. Our 1D CNN architecture was designed to integrate both sequential and non-sequential features. In terms of F1-scores, 1D CNN models are our best classifiers that are also more accurate and stable than the state-of-the-art rule-based tool Mykrobe predictor (81.1 to 93.8%, 93.7 to 96.2%, 93.1 to 94.8%, 95.9 to 97.2% and 97.1 to 98.2% for ethambutol, rifampicin, pyrazinamide, isoniazid and ofloxacin respectively). We applied filter-based feature selection to find AMR relevant features. All selected variant features are AMR-related ones in CARD database. 78.8% of them are also in the catalogue of MTB mutations that were recently identified as drug resistance-associated ones by WHO. To facilitate ML model development for AMR prediction, we packaged every step into an automated pipeline and shared the source code at https://github.com/KuangXY3/MTB-AMR-classification-CNN.

Spatial and Genomic Data to Characterize Endemic Typhoid Transmission.

BackgroundDiverse environmental exposures and risk factors have been implicated in the transmission of Salmonella Typhi, but the dominant transmission pathways through the environment to susceptible humans remain unknown. Here, we use spatial, bacterial genomic, and hydrological data to refine our view of typhoid transmission in an endemic setting.MethodsA total of 546 patients presenting to Queen Elizabeth Central Hospital in Blantyre, Malawi, with blood culture–confirmed typhoid fever between April 2015 and January 2017 were recruited to a cohort study. The households of a subset of these patients were geolocated, and 256 S. Typhi isolates were whole-genome sequenced. Pairwise single-nucleotide variant distances were incorporated into a geostatistical modeling framework using multidimensional scaling.ResultsTyphoid fever was not evenly distributed across Blantyre, with estimated minimum incidence ranging across the city from <15 to >100 cases per 100 000 population per year. Pairwise single-nucleotide variant distance and physical household distances were significantly correlated (P = .001). We evaluated the ability of river catchment to explain the spatial patterns of genomics observed, finding that it significantly improved the fit of the model (P = .003). We also found spatial correlation at a smaller spatial scale, of households living <192 m apart.ConclusionsThese findings reinforce the emerging view that hydrological systems play a key role in the transmission of typhoid fever. By combining genomic and spatial data, we show how multifaceted data can be used to identify high incidence areas, explain the connections between them, and inform targeted environmental surveillance, all of which will be critical to shape local and regional typhoid control strategies.

MiDSystem: A comprehensive online system for de novo assembly and analysis of microbial genomes

The substantial reduction in experimental cost of next-generation sequencing techniques makes it feasible to assemble a bacterial genome of unknown species de novo and acquire substantial genetic information from environmental samples. Many bioinformatics tools and algorithms have also been developed for prokaryotes, but complex parameter settings and command line-based user interfaces cause a significant entry barrier for novices. Efficient construction of pipelines that integrate all the available genomic data poses a major challenge to the understanding of unknown pathogens. MiDSystem is a comprehensive online system for analyzing genomic data from microbiomes. With a user-friendly interface, MiDSystem supports both de novo assembly and metagenomic analysis pipelines. It is designed to automatically analyze whole genome shotgun sequencing data of bacteria submitted by users. Multiple analytical steps can be performed directly on the system, and the results generated from the embedded tools are visualized in an online summary report to make it more interpretable. Constructing a genome de novo has gradually become the foundation of bacterial studies. Taking both single species and metagenomic samples into consideration, MiDSystem can greatly reduce the time and effort for analysis of bacterial genomic data. Use of MiDSystem will enable more focus to be placed on understanding the etiology of bacterial infections and microorganism ecologies.