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用于基于系统发育的分类法可视化的上下文感知系统发育树

Context-Aware Phylogenetic Trees for Phylogeny-Based Taxonomy Visualization.

作者信息

Kaya Gizem, Ezekannagha Chisom, Heider Dominik, Hattab Georges

机构信息

Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.

出版信息

Front Genet. 2022 May 18;13:891240. doi: 10.3389/fgene.2022.891240. eCollection 2022.

Abstract

Sustained efforts in next-generation sequencing technologies are changing the field of taxonomy. The increase in the number of resolved genomes has made the traditional taxonomy of species antiquated. With phylogeny-based methods, taxonomies are being updated and refined. Although such methods bridge the gap between phylogeny and taxonomy, phylogeny-based taxonomy currently lacks interactive visualization approaches. Motivated by enriching and increasing the consistency of evolutionary and taxonomic studies alike, we propose Context-Aware Phylogenetic Trees (CAPT) as an interactive web tool to support users in exploration- and validation-based tasks. To complement phylogenetic information with phylogeny-based taxonomy, we offer linking two interactive visualizations which compose two simultaneous views: the phylogenetic tree view and the taxonomic icicle view. Thanks to its space-filling properties, the icicle visualization follows the intuition behind taxonomies where different hierarchical rankings with equal number of child elements can be represented with same-sized rectangular areas. In other words, it provides partitions of different sizes depending on the number of elements they contain. The icicle view integrates seven taxonomic rankings: domain, phylum, class, order, family, genus, and species. CAPT enriches the clades in the phylogenetic tree view with context from the genomic data and supports interactive techniques such as linking and brushing to highlight correspondence between the two views. Four different use cases, extracted from the Genome Taxonomy DataBase, were employed to create four scenarios using our approach. CAPT was successfully used to explore the phylogenetic trees as well as the taxonomic data by providing context and using the interaction techniques. This tool is essential to increase the accuracy of categorization of newly identified species and validate updated taxonomies. The source code and data are freely available at https://github.com/ghattab/CAPT.

摘要

下一代测序技术的持续发展正在改变分类学领域。已解析基因组数量的增加使传统的物种分类法过时。通过基于系统发育的方法,分类法正在不断更新和完善。尽管这些方法弥合了系统发育和分类学之间的差距,但基于系统发育的分类学目前缺乏交互式可视化方法。出于丰富和提高进化研究与分类学研究一致性的动机,我们提出了上下文感知系统发育树(CAPT)作为一种交互式网络工具,以支持用户进行基于探索和验证的任务。为了用基于系统发育的分类法补充系统发育信息,我们提供了两种交互式可视化的链接,它们构成了两个同步视图:系统发育树视图和分类学冰柱视图。由于其空间填充特性,冰柱可视化遵循分类学背后的直觉,即具有相同数量子元素的不同层次排名可以用相同大小的矩形区域表示。换句话说,它根据所包含元素的数量提供不同大小的分区。冰柱视图整合了七个分类等级:域、门、纲、目、科、属和种。CAPT用基因组数据中的上下文丰富了系统发育树视图中的进化枝,并支持诸如链接和刷选等交互式技术,以突出两个视图之间的对应关系。从基因组分类数据库中提取了四个不同的用例,使用我们的方法创建了四个场景。通过提供上下文并使用交互技术,CAPT成功地用于探索系统发育树以及分类学数据。该工具对于提高新鉴定物种分类的准确性和验证更新的分类法至关重要。源代码和数据可在https://github.com/ghattab/CAPT上免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2639/9158481/edae8191393d/fgene-13-891240-g001.jpg

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