Theys Kristof, Lemey Philippe, Vandamme Anne-Mieke, Baele Guy
Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven, Leuven, Belgium.
Front Public Health. 2019 Aug 2;7:208. doi: 10.3389/fpubh.2019.00208. eCollection 2019.
Genomic and epidemiological monitoring have become an integral part of our response to emerging and ongoing epidemics of viral infectious diseases. Advances in high-throughput sequencing, including portable genomic sequencing at reduced costs and turnaround time, are paralleled by continuing developments in methodology to infer evolutionary histories (dynamics/patterns) and to identify factors driving viral spread in space and time. The traditionally static nature of visualizing phylogenetic trees that represent these evolutionary relationships/processes has also evolved, albeit perhaps at a slower rate. Advanced visualization tools with increased resolution assist in drawing conclusions from phylogenetic estimates and may even have potential to better inform public health and treatment decisions, but the design (and choice of what analyses are shown) is hindered by the complexity of information embedded within current phylogenetic models and the integration of available meta-data. In this review, we discuss visualization challenges for the interpretation and exploration of reconstructed histories of viral epidemics that arose from increasing volumes of sequence data and the wealth of additional data layers that can be integrated. We focus on solutions that address joint temporal and spatial visualization but also consider what the future may bring in terms of visualization and how this may become of value for the coming era of real-time digital pathogen surveillance, where actionable results and adequate intervention strategies need to be obtained within days.
基因组和流行病学监测已成为我们应对新出现和持续存在的病毒性传染病疫情的一个组成部分。高通量测序技术不断进步,包括成本降低和周转时间缩短的便携式基因组测序,与此同时,推断进化历史(动态/模式)以及识别推动病毒在空间和时间上传播的因素的方法也在不断发展。传统上用于可视化代表这些进化关系/过程的系统发育树的静态性质也有所演变,尽管速度可能较慢。分辨率更高的先进可视化工具有助于从系统发育估计中得出结论,甚至可能有潜力为公共卫生和治疗决策提供更好的信息,但当前系统发育模型中嵌入的信息复杂性以及可用元数据的整合阻碍了设计(以及所展示分析内容的选择)。在本综述中,我们讨论了因序列数据量增加以及可整合的大量额外数据层而产生的病毒疫情重建历史的解释和探索方面的可视化挑战。我们关注解决联合时空可视化的方案,同时也考虑未来可视化可能带来的变化以及这在实时数字病原体监测的未来时代如何变得有价值,在这个时代,需要在数天内获得可采取行动的结果和适当的干预策略。