MolEpidPred: a novel computational tool for the molecular epidemiology of foot-and-mouth disease virus using VP1 nucleotide sequence data.

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Samarendra Das, Utkal Nayak, Soumen Pal, Saravanan Subramaniam
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引用次数: 0

Abstract

Molecular epidemiology of Foot-and-mouth disease (FMD) is crucial to implement its control strategies including vaccination and containment, which primarily deals with knowing serotype, topotype, and lineage of the virus. The existing approaches including serotyping are biological in nature, which are time-consuming and risky due to live virus handling. Thus, novel computational tools are highly required for large-scale molecular epidemiology of the FMD virus. This study reported a comprehensive computational tool for FMD molecular epidemiology. Ten learning algorithms were initially evaluated on cross-validated and ten independent secondary datasets for serotype prediction using sequence-based features through accuracy, sensitivity and 14 other metrics. Next, best performing algorithms, with higher serotype predictive accuracies, were evaluated for topotype and lineage prediction using cross-validation. These algorithms are implemented in the computational tool. Then, performance of the developed approach was assessed on five independent secondary datasets, never seen before, and primary experimental data. Our cross-validated and independent evaluation of learning algorithms for serotype prediction revealed that support vector machine, random forest, XGBoost, and AdaBoost algorithms outperformed others. Then, these four algorithms were evaluated for topotype and lineage prediction, which achieved accuracy ≥96% and precision ≥95% on cross-validated data. These algorithms are implemented in the web-server (https://nifmd-bbf.icar.gov.in/MolEpidPred), which allows rapid molecular epidemiology of FMD virus. The independent validation of the MolEpidPred observed accuracies ≥98%, ≥90%, and ≥ 80% for serotype, topotype, and lineage prediction, respectively. On wet-lab data, the MolEpidPred tool provided results in fewer seconds and achieved accuracies of 100%, 100%, and 96% for serotype, topotype, and lineage prediction, respectively, when benchmarked with phylogenetic analysis. MolEpidPred tool provides an innovative platform for large-scale molecular epidemiology of FMD virus, which is crucial for tracking FMD virus infection and implementing control program.

MolEpidPred:一个利用VP1核苷酸序列数据分析口蹄疫病毒分子流行病学的新型计算工具。
口蹄疫分子流行病学对实施包括疫苗接种和遏制在内的控制策略至关重要,这主要涉及了解病毒的血清型、拓扑型和谱系。包括血清分型在内的现有方法本质上是生物学的,由于要处理活病毒,这种方法既耗时又有风险。因此,对口蹄疫病毒的大规模分子流行病学研究迫切需要新的计算工具。本研究报道了一个全面的口蹄疫分子流行病学计算工具。10种学习算法在交叉验证和10个独立的辅助数据集上进行初步评估,使用基于序列的特征通过准确性、灵敏度和14个其他指标进行血清型预测。接下来,使用交叉验证对具有较高血清型预测精度的最佳算法进行拓扑型和谱系预测评估。这些算法在计算工具中实现。然后,在5个独立的二手数据集(以前从未见过)和主要实验数据上评估所开发方法的性能。我们对血清型预测的学习算法进行了交叉验证和独立评估,结果显示支持向量机、随机森林、XGBoost和AdaBoost算法优于其他算法。然后,对这四种算法进行拓扑型和谱系预测评估,在交叉验证的数据上,准确率≥96%,精密度≥95%。这些算法在web服务器(https://nifmd-bbf.icar.gov.in/MolEpidPred)中实现,这使得口蹄疫病毒的快速分子流行病学成为可能。MolEpidPred的独立验证分别观察到血清型、拓扑型和谱系预测的准确性≥98%、≥90%和≥80%。在湿实验室数据上,MolEpidPred工具在更短的时间内提供结果,当与系统发育分析作为基准时,在血清型、拓扑型和谱系预测方面分别达到100%、100%和96%的准确率。MolEpidPred工具为口蹄疫病毒的大规模分子流行病学研究提供了创新平台,对追踪口蹄疫病毒感染和实施控制方案具有重要意义。
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来源期刊
Briefings in Functional Genomics
Briefings in Functional Genomics BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
6.30
自引率
2.50%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Briefings in Functional Genomics publishes high quality peer reviewed articles that focus on the use, development or exploitation of genomic approaches, and their application to all areas of biological research. As well as exploring thematic areas where these techniques and protocols are being used, articles review the impact that these approaches have had, or are likely to have, on their field. Subjects covered by the Journal include but are not restricted to: the identification and functional characterisation of coding and non-coding features in genomes, microarray technologies, gene expression profiling, next generation sequencing, pharmacogenomics, phenomics, SNP technologies, transgenic systems, mutation screens and genotyping. Articles range in scope and depth from the introductory level to specific details of protocols and analyses, encompassing bacterial, fungal, plant, animal and human data. The editorial board welcome the submission of review articles for publication. Essential criteria for the publication of papers is that they do not contain primary data, and that they are high quality, clearly written review articles which provide a balanced, highly informative and up to date perspective to researchers in the field of functional genomics.
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