ViMRT: a text-mining tool and search engine for automated virus mutation recognition

Tong, Yuantao; Tan, Fanglin; Huang, Honglian; Zhang, Zeyu; Zong, Hui; Xie, Yujia; Huang, Danqi; Cheng, Shiyang; Wei, Ziyi; Fang, Meng; Crabbe, M. James C.; Wang, Ying; Zhang, Xiaoyan

doi:10.1093/bioinformatics/btac721

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…Moreover, virus mutation is another important factor in escaping the immune protection derived from a previous infection or vaccination [ 14 - 16 ]. Several studies have shown that people living at high altitudes may be less susceptible to developing severe adverse effects from COVID-19, along with reduced case fatality rates [ 4 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

Deji,

Tong,

Huang

et al. 2024

Interact J Med Res

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Background The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date. Objective The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms. Methods We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above. Results Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours. Conclusions By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.

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Section: Introductionmentioning

confidence: 99%

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

Deji,

Tong,

Huang

et al. 2024

Interact J Med Res

Self Cite

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“…Additionally, automatic gathering of mutation effect information may soon become necessary, e.g. , with automatic processes such as ViMRT [42] or crowd-sourcing-based methods such as CoVEffect [43] . However, ViMRT does not classify effects; instead, it extracts phenotype descriptions using regular expression-based methods.…”

Section: Discussionmentioning

confidence: 99%

RCoV19: A One-Stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-Warning

Li,

Ma,

Zou

et al. 2023

Genomics, Proteomics &Amp; Bioinformatics

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The Resource for Coronavirus 2019 (RCoV19) is an open-access information resource dedicated to providing valuable data on the genomes, mutations, and variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk pre-warning system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19. The complete contents of RCoV19 are available to the public at https://ngdc.cncb.ac.cn/ncov/.

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“…A very recent tool called ViMRT [ 35 ] employs ad hoc optimized rules and regular expressions for the extraction of viral mutations; a whole infrastructure is built with this sole purpose, demonstrating the complexity of the task, whose resolution remains largely uncovered.…”

Section: Related Workmentioning

confidence: 99%

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning

Serna García,

Al Khalaf,

Invernici

et al. 2022

GigaScience

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Background Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract—for each variant/mutation—its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. Results The proposed framework comprises (i) the provisioning of abstracts from a COVID-19–related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. Conclusions The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.

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ViMRT: a text-mining tool and search engine for automated virus mutation recognition

Cited by 5 publications

References 30 publications

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

RCoV19: A One-Stop Hub for SARS-CoV-2 Genome Data Integration, Variant Monitoring, and Risk Pre-Warning

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning

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