SciDAP: Scientific Data Analysis Platform

最近基于下一代测序(NGS)的用于分析表达的方法的激增， 染色质和蛋白质-DNA的相互作用为深入了解生物学创造了巨大的机会， 健康和疾病。然而，数据的分析需要许多生物学家不需要的计算专业知识 拥有。因此，在处理基因组数据时，大多数生物学家需要生物信息学家的帮助 即使是简单的任务。这使这些令人兴奋的方法超出了大多数生命科学家的能力范围。 DATIRIUM，LLC是俄亥俄州辛辛那提的一家初创企业，该公司的这份第二阶段提案遵循了第一阶段项目 开发了一个科学数据分析平台的原型(MVP)，该平台是一种新型的多 组学用户友好的数据分析平台，允许生物学家分析数据并实现协作 与生物信息学家合作。目前的第二阶段提案描述了一项继续发展本科学发展计划的计划。 创建用户友好的数据分析包的关键问题是添加新的或修改的困难 现有管道：由于管道和用户界面之间的紧密耦合，这需要在所有级别进行更改 软件。不幸的是，所有用户友好的生物信息学工具都存在同样的限制。鉴于那里有 有150多种基于NGS的方法和许多处理数据的方法，这解释了为什么通用和 人性化的数据分析平台还不存在。 我们假设我们可以通过以下方式创建一个既通用又用户友好的数据分析平台 将接口指令包含到计算流水线中。Platform将使用这些说明创建 图形界面。具体地说，我们正在使用使用通用工作流开发的集装箱化管道 语言(CWL)使我们的管道既可移植又可重现。在CWL之上，Datirium开发了一种 CWL扩展系统，允许描述CWL中的输入和输出可视化 工作流程。重要的是，我们的平台将通过(I)进行分析来提高计算分析的严谨性 由于CWL管道的便携性，生物信息学家可以重现和审计，并记录 将分析作为研究对象；(2)促进实验学家和计算生物学家之间的合作 通过为生物信息学家提供一种指导分析流程的方法，并为生物学家提供图形用户界面的便利； 包括开箱即用的管道，这些管道具有优化的参数和标记可能存在问题的可操作的质量控制指标。 在这项提议的第一个目标中，我们将开发一个版本的SciDAP，用于学术集群和 商业云。在第二个目标中，我们将与CCHMC的Salomonis博士合作，采用管道 MiRNA、WGS/WXS和scMultiome数据分析。在第三个阶段，我们将对本网站进行改进 为生物信息学家和实验者增加本科学数据处理程序的灵活性和可用性的界面。 该项目的成功完成将为研究界提供尖端、灵活和 生物学家友好的数据分析平台。

The recent proliferation of next-generation sequencing (NGS) - based methods for the analysis of expression, chromatin and protein-DNA interactions has created tremendous opportunities for gaining insights into biology, health, and disease. However, analysis of the data requires computational expertise that many biologists do not possess. Hence, when dealing with genomics data, majority of biologists require the help of bioinformaticians even for simple tasks. This places these exciting methods beyond the reach of the majority of life scientists. This phase II proposal from DATIRIUM, LLC, a start-up from Cincinnati, OH follows phase I project that resulted in the development of a prototype (MVP) of SciDAP (Scientific Data Analysis Platform), a novel multi- omics user-friendly data analysis platform that allows biologists to analyze the data and enables collaboration with bioinformaticians. The current phase II proposal describes a plan to continue SciDAP development. The key problem for creating user-friendly data analysis packages is the difficulty in adding new or modifying existing pipelines: due to the tight coupling between pipeline and user interface this required changes at all levels of software. Unfortunately, the same limitation exists for all user-friendly bioinformatics tools. Given that there are more than 150 NGS-based methods and many ways to process the data, this explains why a universal and user-friendly data analysis platform does not yet exist. We hypothesized that we can create a data analysis platform that is both universal and user-friendly by including interface instructions into computational pipelines. Platform will use these instructions to create a graphical interface. Specifically, we are using containerized pipelines developed using Common Workflow Language (CWL) making our pipelines both portable and reproducible. On top of CWL, Datirium developed a system of CWL extensions that allows to describe the inputs and outputs visualizations within the CWL workflows. Importantly, our platform will increase the rigor of computational analysis by (i) making the analysis reproducible and auditable by bioinformaticians due to CWL pipeline portability and recording each step of the analysis as Research Objects; (ii) enabling collaboration between experimentalists and computational biologists by providing bioinformaticians with a way to direct analysis flow and biologists with the convenience of GUI; (iii) Including out of the box pipelines with optimized parameters and actionable QC metrics that flag possible issues. In the first aim of this proposal we will develop a version of SciDAP for use on academic clusters and commercial clouds. In the second aim, in collaboration with Dr. Salomonis at CCHMC, we will adopt pipelines miRNA, WGS/WXS and scMultiome data analysis. In the third, we will develop improvements to SciDAP interface that will increase SciDAP flexibility and usability for bioinformaticians and experimentalists. Successful completion of this project will provide the research community with a cutting edge, flexible and biologist-friendly data analysis platform.