Bioinformatics workflows for scalable analysis of plant “omics” data in cloud computing environments

陆生植物是由水生植物进化而来的。因此，它们必须适应全新的环境条件，其中之一涉及有性生殖的适应。高通量DNA和RNA测序技术的最新进展使得在基因组和转录组水平上对植物有性生殖的进化进行具有成本效益的详细研究成为可能。通过应用现代测序方法，甚至可以建立植物的大型参考基因组，例如大小为~20 Gb的欧洲云杉基因组。此外，RNA测序有助于分析基因表达，以比较不同条件下的样品。因此，通过比较不同进化阶段的基因组和转录组数据，可以追溯植物有性生殖的进化。在FOR 5098研究单元内，将产生大量数据。在这个项目中，我们将改进现有的自动化和标准化分析管道，以生物信息学处理这些数据，并开发新的管道，以满足我们合作伙伴的特定需求。由于大量的数据，我们将特别关注管道的可扩展性，并使其适应云计算基础设施中的执行。不同植物的标准化实验将建立一个数据库，使我们能够开发新的方法来比较不同植物物种之间的基因表达模式和基因相互作用网络。我们的信息技术基础设施将提供给研究小组的所有成员。所有原始数据，处理管道和分析结果以及研究小组的相关元数据将根据FAIR原则（可查找，可扩展，可互操作，可重复使用）进行存储和访问。计算密集型分析可以在我们的云计算环境中进行计算。我们将开发一个基于网络的用户界面，以可视化数据和探索分析结果。在这里，用户应该能够交互式和动态地可视化数据。此外，还可以选择在不同背景下的平行面板中查看数据。单个面板应相互连接，使一个面板的调整导致所有其他面板的同步。此外，我们将为研究小组的成员提供各种培训课程，例如使用IT基础设施或分析管道。

Terrestrial plants have evolved from aquatic plants in the course of evolution. Thereby, they had to adapt to completely new environmental conditions one of which involved adaption of sexual reproduction. Recent advances in high throughput DNA and RNA sequencing enable cost-effective and detailed research of the evolution of sexual reproduction in plants at the genome and transcriptome level. By applying modern sequencing methods, even large reference genomes of plants can be established, such as the genome of Picea abies with a size of ~20 Gb. In addition, RNA sequencing facilitates the analysis of gene expression to compare samples from different conditions. Thereby, the evolution of sexual reproduction of plants can be traced by comparing genome and transcriptome data from different evolutionary stages of development.Within the research unit FOR 5098, large amounts of data will be generated. In this project, we will improve existing automated and standardised analysis pipelines for bioinformatic processing of those data and develop new pipelines that are tailored to the specific needs of our our cooperation partners. Due to the large amounts of data, we will place special focus on the scalability of the pipelines and adapt them for execution in cloud computing infrastructures. Standardised experiments in the various plants will create a data pool that enables us to develop new methods for comparing gene expression patterns and gene interaction networks between different plant species.Our IT infrastructure will be available to all members of the research group. All raw data, processing pipelines and analysis results as well as the associated metadata from the research group will be stored and made accessible in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable). Computationally intensive analyses can be computed in our cloud computing environment. We will develop a web-based user interface to visualise data and explore analysis results. Here, users should be able to visualise the data interactively and dynamically. In addition, there will be the option of viewing the data in parallel panels in different contexts. The single panels should be linked to each other in a way that adjustments in one panel lead to synchronization of all other panels. Furthermore, we will offer various training courses for the members of the research group, for example on the use of the IT infrastructure or the analysis pipelines.