A computational cloud framework for the study of gene families

生命科学研究正日益成为数据密集型学科。新的高通量测序技术产生了大量的数字数据，需要对这些数据进行有效分析，以发现有趣的模式，从而获得新的生物学发现。大量的数据本身也会产生问题，因为需要使用大型计算资源和复杂的计算技能来存储和分析这些数据。许多生物实验室努力拥有和维护大型计算集群以满足其计算需求。云计算框架已经成为在按需付费模式上利用大型计算机能力的可行替代方案，并日益进入主流生物数据分析领域。iPlant-UK是由BBSRC资助的一项云计划，旨在为英国研究人员免费提供大型计算资源。iPlant-UK云是专门为满足生命科学社区的计算需求而量身定制的，并通过舒适的网络浏览器提供对大型计算基础设施的访问。通过这个提议，我们想要开发一个用于分析基因家族数据集的计算工具包。植物中基因家族的一个例子是R基因，也被称为抗性基因，负责植物的病原体识别和抗病反应。要了解一个物种中的一个基因家族，首先必须对该家族的所有成员进行编目，然后了解它们彼此之间以及相关物种之间的功能。这些数据集是通过下一代测序技术生成的，通常体积很大。我们的目标是开发专门的软件来分析iPlant-UK计算云上的基因家族数据集。通过这种方式，我们可以为研究人员提供访问大型免费计算资源的专用工具。此外，我们希望通过提供可通过网络浏览器访问的图形用户界面来简化工具包的使用，使湿实验室生物学家能够专注于他们的核心研究，而不是担心云平台上的复杂计算。在这个项目中开发的代码将在开放源代码许可下在公共领域免费提供。通过在iplant中构建工作流程，我们将确保其可持续性和可见性。

Life science research is increasingly turning into data-intensive discipline. New high-throughput sequencing technologies produce vast amounts of digital data that needs to be efficiently analyzed in order to discover interesting patterns to make new biological discoveries. The large volume of data produces a problem of its own, as it needs to be stored and analyzed using large computing resources and sophisticated computing skills. Many biology labs struggle to own and maintain large computing clusters for their computing needs. Cloud computing frameworks have emerged as feasible alternative for availing large computer power on a pay-as-you-go model and are increasingly making inroads into mainstream biological data analysis. iPlant-UK is a cloud initiative funded by BBSRC to make large computing resources available for free of cost to UK researchers. iPlant-UK cloud is specifically tailored to meet the computing requirements of life sciences community and provides access to large computing infrastructures through the comforts of web-browser. Through this proposal, we want to develop a computational toolkit for analysis of gene family datasets. An example of a gene family in plants is R genes, also known as Resistance genes that are responsible for pathogen recognition and disease resistance responses in plants. To understand a gene family in a species, one must first catalogue all members of the family, and then understand their function with respect to other each other, as well as related species. These datasets are generated through next generation sequencing techniques and are usually large in volume. We aim to develop specialized software for analysis of gene family datasets on the iPlant-UK compute cloud. This way, we can provide researchers access a specialized tool on a large and free computing resource. Further, we want to simplify the use of the toolkit by providing graphical user interfaces that can be accessed through web-browsers to enable wet-lab biologists to focus on their core research rather than worry about the complex computation on a cloud platform. The code developed in this project will be available in public domain for free under open-source license. By building the workflow in iplant we will ensure its sustainability and visibality beyond this proposal.