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

陆生植物是在进化过程中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，其中之一涉及有性繁殖的适应。在高通量DNA和RNA测序方面的最新进展使在基因组和转录组水平上对植物有性生殖进化的成本效益和详细研究成为可能。通过应用现代测序方法，甚至可以建立大的植物参考基因组，如大小约20 GB的云杉基因组。此外，RNA测序有助于分析基因表达，以比较不同条件下的样本。因此，可以通过比较不同进化阶段的基因组和转录组数据来追踪植物有性繁殖的进化。在5098的研究单位内，将产生大量的数据。在这个项目中，我们将改进现有的自动化和标准化的分析管道，用于对这些数据进行生物信息处理，并开发新的管道，以满足我们合作伙伴的特定需求。由于数据量大，我们将特别关注管道的可扩展性，并将其调整为在云计算基础设施中执行。在不同植物中进行的标准化实验将创建一个数据库，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供研究小组的所有成员使用。研究组的所有原始数据、处理管道和分析结果以及相关的元数据都将按照公平原则(可查找、可访问、可互操作、可重复使用)进行存储和提供。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，用户应该能够以交互和动态的方式可视化数据。此外，还可以选择在不同情况下在平行小组中查看数据。单个面板应该以这样的方式相互链接：一个面板中的调整会导致所有其他面板的同步。此外，我们会为研究小组成员提供各种培训课程，例如资讯科技基础设施或分析管道的使用。

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.