Bioinformatic methods for the investigation of cross-kingdom RNA communication

高通量RNA测序和生物信息学数据处理方面的技术进步使研究宿主-微生物相互作用中的跨王国RNA通信(Ck RNAi)成为可能。此外，还对植物及其相互作用的微生物的转录组的影响进行了研究。病原体和受益者)可以通过双重RNA测序方法以前所未有的详细程度进行研究。随着许多这样的数据集的建立，从比较的角度来评估不同的核糖核酸物种(例如小的(S)核糖核酸和信使核糖核酸)在不同的植物-微生物相互作用中的影响成为可能。在这个项目中，我们将自动化和标准化现有的分析方案，并纳入比较分析。我们将对数据和结果应用公平(可发现、可访问、可互操作、可重复使用)原则。加入该联盟有助于访问所需的大规模数据集和比较研究ck RNAi效应，而RU5116和该联盟的每个成员将受益于我们针对他们自己的项目的分析能力，以及对植物与微生物相互作用中的RNA通信的更全面的看法。更具体地说，我们将通过实施易于使用、处理、可视化和比较结果的自动化分析工作流来支持该研究小组中的合作伙伴，并且可以在云计算环境中高效地横向扩展。最后，我们还将评估用于对比共表达网络分析的开源工具，以扩展我们的管道。

Technological advances in terms of high-throughput RNA sequencing and bioinformatics data processing enable cost effective experiments for the investigation of cross-kingdom RNA communication (ckRNAi) in host-microbe interactions. In addition, the effects on the transcriptome of plants and their interacting microbes (viz. pathogens and beneficials) can be studied at an unprecedented level of detail by dual RNA sequencing approaches. With the establishment of many of these data sets, it becomes feasible to implement a comparative perspective to evaluate the effects of different RNA species (e.g. small (s)RNA and mRNA) in different plant-microbe interactions. Within this project, we will automate and standardize the existing analysis protocols and incorporate comparative analyses. We will apply the FAIR (Findable, Accessible, Interoperable, Reusable) principles on data and results. Participation in the consortium facilitates access to the required large-scale data sets and the comparative investigation of ckRNAi effects, whereas the RU5116 and each member of the consortium will benefit from our analysis capabilities for their own projects and for a more comprehensive view on RNA communication in plant-microbe interactions.More specifically, we will support our collaborating partners within this research group by implementing automated analysis workflows that are easy to use, that process, visualize, and compare the results, and that can be scaled-out efficiently in cloud computing environments. Finally, we will also evaluate open-source tools for comparative co-expression network analyses for the extension of our pipelines.