Biology subjects

Vikesland, P.

Publications and source records attributed to Vikesland, P..

11 recordsLinked to original sources

bioRxiv2025

ARGfore: A multivariate framework for forecasting antibiotic resistance gene abundances using time-series metagenomic datasets

BackgroundThe global spread of antibiotic resistance presents a significant threat to human, animal, and plant health. Metagenomic sequencing is increasingly being utilized to profile antibiotic resistance genes (ARGs) in various environments, but presently a mechanism for predicting future trends in ARG occurrence patterns is lacking. Capability of forecasting ARG abundance trends could be extremely valuable towards informing policy and practice aimed at mitigating the evolution and spread of ARGs. ResultsHere we propose ARGfore, a multivariate forecasting model for predicting ARG abundances from time-series metagenomic data. ARGfore extracts features that capture inherent relationships among ARGs and is trained to recognize patterns in ARG trends and seasonality. ConclusionARGfore outperformed standard time-series forecasting methods, such as N-HiTS, LSTM, and ARIMA, exhibiting the lowest mean absolute percentage error when applied to different wastewater datasets. Additionally, ARGfore demonstrated enhanced computational efficiency, making it a promising candidate for a variety of ARG surveillance applications. The rapid prediction of future trends can facilitate early detection and deployment of mitigation efforts if necessary. ARGfore is publicly available at https://github.com/joungmin-choi/ARGfore.

bioinformatics↗

bioRxiv2024

CIWARS: a web server for waterborne antibiotic resistance surveillance using longitudinal metagenomic data

The rise of antibiotic resistance (AR) is a major global health crisis, exacerbated by the overuse and misuse of antibiotics, leading to the rapid spread of antibiotic resistance genes (ARGs) in bacterial pathogens. This phenomenon poses significant threats to human and animal health, food security, and economic stability. Water bodies, particularly wastewater treatment plants (WWTPs), serve as critical reservoirs for ARGs, creating environments that favor the proliferation of resistant bacteria. Wastewater-based surveillance (WBS) has emerged as a cost-effective strategy for monitoring AR at the population level, providing real-time data to guide public health and policy decisions. Despite advancements in WBS, there are no comprehensive online analytical platforms for continuous environmental AR surveillance. This paper introduces CIWARS, a web server designed for AR analyses of longitudinal metagenomic data. CIWARS offers comprehensive ARG profiling, taxonomic annotation, and anomalous AR risk points detection. We demonstrate its capabilities through an interactive temporal data visualization, showcasing its potential for enhancing AR risk monitoring and guiding effective mitigation strategies. CIWARS is broadly applicable to longitudinal metagenomic data generated from any environment and aims to support global efforts in addressing the AR crisis by providing cyberinfrastructure for continuous AR surveillance. The web server is freely available at https://ciwars.cs.vt.edu/.

bioinformatics↗

bioRxiv2024

ProtAlign-ARG: Antibiotic Resistance Gene Characterization Integrating Protein Language Models and Alignment-Based Scoring

The evolution and spread of antibiotic resistance pose a global health challenge. Whole genome and metagenomic sequencing pose a promising approach to monitoring the spread, but typical alignment-based approaches for antibiotic resistance gene (ARG) detection are inherently limited in the ability to detect new variants. Large protein language models could present a powerful alternative but are limited by databases available for training. Here we introduce ProtAlign-ARG, a novel hybrid model combining a pre-trained protein language model and an alignment scoring-based model to expand the capacity for ARG detection from DNA sequencing data. ProtAlign-ARG learns from vast unannotated protein sequences, utilizing raw protein language model embeddings to improve the accuracy of ARG classification. In instances where the model lacks confidence, ProtAlign-ARG employs an alignment-based scoring method, incorporating bit scores and e-values to classify ARGs according to their corresponding classes of antibiotics. ProtAlign-ARG demonstrated remarkable accuracy in identifying and classifying ARGs, particularly excelling in recall compared to existing ARG identification and classification tools. We also extended ProtAlign-ARG to predict the functionality and mobility of ARGs, highlighting the models robustness in various predictive tasks. A comprehensive comparison of ProtAlign-ARG with both the alignment-based scoring model and the pre-trained protein language model demonstrated the superior performance of ProtAlign-ARG.

bioinformatics↗

bioRxiv2024

Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae), which represent urgent threats to human health because they tend to be multi-antibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns. The code for running the pipeline is publicly available at https://github.com/NazifaMoumi/PPI-ARG-ESKAPE

bioinformatics↗

bioRxiv2024

MetaCompare 2.0: Differential ranking of ecological and human health resistome risks

BackgroundWhile there is increasing recognition of numerous environmental contributions to the spread of antibiotic resistance, quantifying the relative contributions of various sources remains a fundamental challenge. Similarly, there is a need to differentiate acute human health risks corresponding to exposure to a given environment, versus broader ecological risk of evolution and spread of antibiotic resistance genes (ARGs) across microbial taxa. Recent studies have proposed various methods of harnessing the rich information housed by metagenomic data for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon the original MetaCompare pipeline by differentiating indicators of human health resistome risk (i.e., potential for human pathogens to acquire ARGs) from ecological resistome risk (i.e., overall mobility of ARGs across a given microbiome). ResultsTo demonstrate the sensitivity of the MetaCompare 2.0 pipeline, we analyzed publicly available metagenomes representing a broad array of environments, including wastewater, surface water, soil, sediment, and human gut. We also assessed the effect of sequence assembly methods on the risk scores. We further evaluated the robustness of the pipeline to sequencing depth, contig count, and metagenomic library coverage bias through comparative analysis of a range of subsamples extracted from a set of deeply sequenced wastewater metagenomes. The analysis utilizing samples from different environments demonstrated that MetaCompare 2.0 consistently produces lower risk scores for environments with little human influence and higher risk scores for human contaminated environments affected by pollution or other stressors. We found that the ranks of risk scores were not measurably affected by different assemblers employed. The Meta-Compare 2.0 risk scores were remarkably consistent despite varying sequencing depth, contig count, and coverage. ConclusionMetaCompare 2.0 successfully ranked a wide array of environments according to both human health and ecological resistome risks, with both scores being strongly impacted by anthropogenic stress. We packaged the improved pipeline into a publicly-available web service that provides an easy-to-use interface for computing resistome risk scores and visualizing results. The web service is available at http://metacompare.cs.vt.edu/

bioinformatics↗

bioRxiv2022

Environmental Stability of Enveloped Viruses is Impacted by the Initial Volume and Evaporation Kinetics of Droplets

Efficient spread of respiratory viruses requires the virus to maintain infectivity in the environment. Environmental stability of viruses can be influenced by many factors, including temperature and humidity. Our study measured the impact of initial droplet volume (50, 5, and 1 {micro}L) and relative humidity (RH: 40%, 65%, and 85%) on the stability of influenza A virus, bacteriophage, Phi6, a common surrogate for enveloped viruses, and SARS-CoV-2 under a limited set of conditions. Our data suggest that the drying time required for the droplets to reach quasi-equilibrium (i.e. a plateau in mass) varied with RH and initial droplet volume. The macroscale physical characteristics of the droplets at quasi-equilibrium varied with RH but not with initial droplet volume. We observed more rapid virus decay when the droplets were still wet and undergoing evaporation, and slower decay after the droplets had dried. Initial droplet volume had a major effect on virus viability over the first few hours; whereby the decay rate of influenza virus was faster in smaller droplets. In general, influenza virus and SARS-CoV-2 decayed similarly. Overall, this study suggests that virus decay in media is closely correlated with the extent of droplet evaporation, which is controlled by RH. Taken together, these data suggest that decay of different viruses is more similar at higher RH and in smaller droplets and is distinct at lower RH and in larger droplets. Importantly, accurate assessment of transmission risk requires use of physiologically relevant droplet volumes and careful consideration of the use of surrogates. FundingNational Institute of Allergy and Infectious Diseases, National Institute of Neurological Disorders and Stroke, National Institutes of Health; Department of Health and Human Services; Flu Lab. ImportanceDuring the COVID-19 pandemic, policy decisions were being driven by virus stability experiments involving SARS-CoV-2 applied to surfaces in large droplets at various humidity conditions. The results of our study indicate that determination of half-lives for emerging pathogens in large droplets likely over-estimates transmission risk for contaminated surfaces, as occurred during the COVID-19 pandemic. Our study implicates the need for the use of physiologically relevant droplet sizes with use of relevant surrogates in addition to what is already known about the importance of physiologically relevant media for risk assessment of future emerging pathogens.

microbiology↗

bioRxiv2021

mobileOG-db: a manually curated database of protein families mediating the life cycle of bacterial mobile genetic elements

Currently available databases of bacterial mobile genetic elements (MGEs) contain both "core" and accessory MGE functional modules, the latter of which are often only transiently associated with the element. The presence of these accessory genes, which are often close homologs to primarily immobile genes, limits the usability of these databases for MGE annotation. To overcome this limitation, we analysed 10,776,212 protein sequences derived from seven MGE databases to compile a comprehensive database of 6,140 manually curated protein families that are linked to the "life cycle" (integration, excision, replication/recombination/repair, transfer, and stability/defense) of all major classes of bacterial MGEs. We overlay experimental information where available to create a tiered annotation scheme of high-quality annotations and annotations inferred exclusively through bioinformatic evidence. We additionally provide an MGE-class label for each entry (e.g., plasmid, integrative element) derived from the source database, and assign a list of keywords to each entry to delineate different MGE functional modules and to facilitate annotation. The resulting database, mobileOG-db (for mobile orthologous groups), provides a simple and readily interpretable foundation for an array of MGE-centred analyses. mobileOG-db can be accessed at mobileogdb.flsi.cloud.vt.edu/, where users can browse and design, refine, and analyse custom subsets of the dynamic mobilome.

genomics↗

bioRxiv2019

MetaMLP: A fast word embedding based classifier to profile target gene databases in metagenomic samples

The functional profile of metagenomic samples allows the understanding of the role of the microbes in their environment. Such analysis consists of assigning short sequencing reads to a particular functional category. Normally, manually curated databases are used for functional assignment where genes are arranged into different classes. Sequence alignment has been widely used to profile metagenomic samples against curated databases. However, this method is time consuming and requires high computing resources. Although several alignment free methods based on k-mer composition have been developed in the recent years, they still require a large amount of memory. In this paper, MetaMLP (Metagenomics Machine Learning Profiler) a machine learning method that represents sequences into numerical vectors (embeddings) and uses a simple one hidden layer neural network to profile functional categories is proposed. Unlike other methods, MetaMLP enables partial matching by using a reduced alphabet to build sequence embeddings from full and partial kmers. MetaMLP is able to identify a slightly larger number of reads compared to Diamond (one of the fastest sequence alignment method) as well as to perform accurate predictions with 0.99 precision and 0.99 recall. MetaMLP can process 100M reads in around 10 minutes in a laptop computer which is 50x faster than Diamond. MetaMLP is free for use, available at https://bitbucket.org/gaarangoa/metamlp/src/master/.

bioinformatics↗

bioRxiv2018

NanoARG: A web service for identification of antimicrobial resistance elements from nanopore-derived environmental metagenomes

Direct selection pressures imposed by antibiotics, indirect pressures by co-selective agents, and horizontal gene transfer are fundamental drivers of the evolution and spread of antibiotic resistance. Therefore, effective environmental monitoring tools should ideally capture not only antibiotic resistance genes (ARGs), but also mobile genetic elements (MGEs) and indicators of co-selective forces, such as metal resistance genes (MRGs). Further, a major challenge towards characterizing potential human risk is the ability to identify bacterial host organisms, especially human pathogens. Historically, short reads yielded by next-generation sequencing technology has hampered confidence in assemblies for achieving these purposes. Here we introduce NanoARG, an online computational resource that takes advantage of long reads produced by MinION nanopore sequencing. Specifically, long nanopore reads enable identification of ARGs in the context of relevant neighboring genes, providing relevant insight into mobility, co-selection, and pathogenicity. NanoARG allows users to upload sequence data online and provides various means to analyze and visualize the data, including quantitative and simultaneous profiling of ARG, MRG, MGE, and pathogens. NanoARG is publicly available and freely accessible at http://bench.cs.vt.edu/nanoARG.

bioinformatics↗

bioRxiv2018

ARG-miner: A web platform for crowdsourcing-based curation of antibiotic resistance genes

Curation of antibiotic resistance gene (ARG) databases is a labor-intensive process that requires expert knowledge to manually collect, correct, and/or annotate individual genes. Correspondingly, updates to existing databases tend to be infrequent, commonly requiring years for completion and often containing inconsistences. Further, because of limitations of manual curation, most existing ARG databases contain only a small proportion of known ARGs (~5k genes). A new approach is needed to achieve a truly comprehensive ARG database, while also maintaining a high level of accuracy. Here we propose a new web-based curation system, ARG-miner, which supports annotation of ARGs at multiple levels, including: gene name, antibiotic category, resistance mechanism, and evidence for mobility and occurrence in clinically-important bacterial strains. To overcome limitations of manual curation, we employ crowdsourcing as a novel strategy for expanding curation capacity towards achieving a truly comprehensive, up-to-date database. We develop and validate the approach by comparing performance of multiple cohorts of curators with varying levels of expertise, demonstrating that ARG-miner is more cost effective and less time-consuming relative to traditional expert curation. We further demonstrate the reliability of a trust validation filter for rejecting confounding input generated by spammers. Crowdsourcing was found to be as accurate as expert annotation, with an accuracy >90% for the annotation of a diverse test set of ARGs. ARG-miner provides a public API and database available at http://bench.cs.vt.edu/argminer.

bioinformatics↗

bioRxiv2017

DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

Growing concerns regarding increasing rates of antibiotic resistance call for global monitoring efforts. Monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is of particular interest as these media can serve as sources of potential novel antibiotic resistance genes (ARGs), as hot spots for ARG exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequence-based monitoring has recently enabled direct access and profiling of the total metagenomic DNA pool, where ARGs are identified or predicted based on the \"best hits\" of homology searches against existing databases. Unfortunately, this approach tends to produce high rates of false negatives. To address such limitations, we propose here a deep leaning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two models, deepARG-SS and deepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Performance evaluation of the deep learning models over 30 classes of antibiotics demonstrates that the deepARG models can predict ARGs with both high precision (>0.97) and recall (>0.90) for most of the antibiotic resistance categories. The models show advantage over the traditional best hit approach by having consistently much lower false negative rates and thus higher overall recall (>0.9). As more data become available for under-represented antibiotic resistance categories, the deepARG models performance can be expected to be further enhanced due to the nature of the underlying neural networks. The deepARG models are available both in command line version and via a Web server at http://bench.cs.vt.edu/deeparg. Our newly developed ARG database, deepARG-DB, containing predicted ARGs with high confidence and high degree of manual curation, greatly expands the current ARG repository. DeepARG-DB can be downloaded freely to benefit community research and future development of antibiotic resistance-related resources.\n\nAbbreviations

bioinformatics↗