Data integration and analysis for mapping malaria parasite traits

摘要 在疟疾中进行靶向遗传杂交的能力为揭示遗传学提供了令人兴奋的机会。 与耐药性有关的机制。核心B将为所有项目数据提供集中资源， 标准化的生物信息学分析，RP 02和RP 03所需的基于网络的综合分析。所有 这些数据和结果将存入相关的公共资源。 将收集每个后代克隆的广泛组学数据（即，转录物、蛋白质和代谢物）和 由核心C存放在公共档案馆。因为这些数据来自于大量的独特的重组 核心A/RP 01提供的后代在集成时更强大，核心B将保持所需的 计算基础设施来标准化项目元数据并将基因型与收集的表型联系起来。在 核心B与RP 02一起作为所有项目数据的焦点。因为我们的 努力也将产生 对于没有标准存档的专门元数据，我们将与EuPathDB密切合作，以确保及时 和相关社区访问所有项目数据。 提供标准分析和重要的硬件资源也将开辟集成的新途径。 项目内的数据分析，这反过来将通过EuPathDB共享。此外，这些重要的元数据- 标记的数据集合将为生物信息学家和计算科学家提供极其丰富的资源。 生物学家虽然这种范围和大小的数据集仅适用于癌症生物学， 它们在计算方法的发展中是无价的，例如，预测药物组合 效率我们想象我们的资源会同样受欢迎，并招募许多参与者来提高预测能力 疟疾寄生虫的药物反应和其他生物学特性。除了一封来自EupathDB的支持信之外， 关于社区获取，我们还包括一封来自非洲研究人员的信，他们已经对此非常感兴趣 数据资源

ABSTRACT The ability to conduct targeted genetic crosses in malaria provides exciting opportunities to uncover genetic mechanisms linked to drug resistance. Core B will provide a centralized resource for all project data, standardized bioinformatics analysis, the integrated, network-based analysis required by RP02 and RP03. All of these data and results will then be deposited in relevant public resources. Extensive –omics data will be collected for each progeny clone (i.e., transcripts, proteins and metabolites) and deposited in public archives by Core C. Because such data from the large numbers of unique recombinant progeny provided by Core A/RP01 are more powerful when integrated, Core B will maintain the required computational infrastructure to standardize project metadata and link genotypes to collected phenotypes. In tandem with RP02, Core B serves as the focal point of all project data. Because our efforts will also generate specialized metadata for which no standard archives exist, we will work closely with EuPathDB to ensure timely and relevant community access of all Project data. Providing standard analysis and significant hardware resources will also open up new avenues of integrated data analysis within the Project, which in turn will be shared via EuPathDB. Further, these significant, metadata- labeled collections of data will provide an extremely rich resource for bioinformaticians and computational biologists. Although publically available datasets of this scope and size are only available for cancer biology, they have been invaluable in the development of computational methods, e.g., predicting drug combination efficiency. We imagine our resource would be just as popular and recruit many participants to improve prediction of drug responses and other biological traits in malaria parasites. In addition to a letter of support from EupathDB on community access, we also include a letter from African researchers who are already highly interested in this data resource.