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Erschienen in: Journal of Visualization 3/2020

04.03.2020 | Regular Paper

VEGA: visual comparison of phylogenetic trees for evolutionary genome analysis (ChinaVis 2019)

verfasst von: Tong Ge, Yonghua Lu, Kecheng Lu, Yunhai Wang, Xin Liu, Zhanglin Cheng, Yi Chen, Oliver Deussen, Baoquan Chen

Erschienen in: Journal of Visualization | Ausgabe 3/2020

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Abstract

In the field of evolutionary genome analysis, biologists seek to identify important genes or chromosome regions by comparing phylogenetic trees and analyzing the mutation at which locus might affect phenotypic traits. Unfortunately, the tree comparison and accompanying analysis are often performed manually. In this paper, we characterize the workflow of evolutionary genome analysis and present a task analysis for the fundamental questions asked by biologists during the analysis procedure. We propose two algorithms to enable quantitative tree comparison. One is to measure the differences between corresponding leaf nodes on two trees, and the other is to compute the classification inconsistency of each leaf node by comparing tree structure with a given biological classification. Configuring with the obtained difference and inconsistency, we present a visual analysis system, visual comparison of phylogenetic trees for evolutionary genome analysis, which not only enables biologists to intuitively explore trees but also identify locus which affects their traits by comparing SNP variants of selected leaf nodes. We conclude with case studies from two biologists who used our system to augment their previous manual analysis workflow and demonstrate that our system can reveal more insight.

Graphic abstract

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Metadaten
Titel
VEGA: visual comparison of phylogenetic trees for evolutionary genome analysis (ChinaVis 2019)
verfasst von
Tong Ge
Yonghua Lu
Kecheng Lu
Yunhai Wang
Xin Liu
Zhanglin Cheng
Yi Chen
Oliver Deussen
Baoquan Chen
Publikationsdatum
04.03.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Visualization / Ausgabe 3/2020
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI
https://doi.org/10.1007/s12650-020-00635-0

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