mapPat: tracking pathogens evolution in space and time.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Bioinformatics advances Pub Date : 2025-02-07 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbaf015

Erika Ferrandi, Graziano Pesole, Matteo Chiara

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引用次数: 0

Abstract

Motivation: The COVID-19 pandemic highlighted the importance of genomic surveillance for monitoring pathogens evolution, mitigating the spread of infectious disorders, and informing decision-making by public health authorities. Since the need for the summarization and interpretation of large bodies of data, computational methods are critical for the implementation of effective genomic surveillance strategies.

Results: Here, we introduce mapPat, an R Shiny application for the interactive visualization of pathogens genomic data in space and time. mapPat is designed as a user-friendly dashboard and allows the dynamic monitoring of the evolution of variants, lineages, and mutations in the genome of a pathogen at glance through informative geographic maps and elegant data visuals. mapPat provides a fine-grained map of pathogens evolution and circulation and represents a useful addition to the catalogue of bioinformatics methods for the genomic surveillance of pathogens.

Availability and implementation: mapPat is available at GitHub (https://github.com/F3rika/mapPat.git).

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mapPat：追踪病原体在空间和时间上的进化。

动机：2019冠状病毒病大流行凸显了基因组监测在监测病原体进化、减轻传染性疾病传播和为公共卫生当局决策提供信息方面的重要性。由于需要对大量数据进行总结和解释，计算方法对于实施有效的基因组监测策略至关重要。结果：在这里，我们介绍了mapPat，一个R Shiny应用程序，用于病原体基因组数据在空间和时间上的交互式可视化。mapPat被设计为一个用户友好的仪表板，允许通过信息丰富的地理地图和优雅的数据视觉一目了然地动态监测病原体基因组中的变体、谱系和突变的演变。mapPat提供了病原体进化和循环的细粒度地图，是对病原体基因组监测的生物信息学方法目录的有益补充。可用性和实现：mapPat可在GitHub （https://github.com/F3rika/mapPat.git）获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Bioinformatics advances

CiteScore

1.60

自引率

0.00%

发文量