A computational cloud framework for the study of gene families

点击翻译按钮获取中文摘要

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.