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首页> 外文会议>IEEE International symposium on computer-based medical systems >Challenges of Large-Scale Biomedical Workflows on the Cloud -- A Case Study on the Need for Reproducibility of Results
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Challenges of Large-Scale Biomedical Workflows on the Cloud -- A Case Study on the Need for Reproducibility of Results

机译：云上大规模生物医学工作流程的挑战-以结果可重复性需求为例

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Computational bioinformatics workflows are extensively used to analyse genomics data. With the unprecedented advancements in genomic sequence technology and opportunities for personalized medicines, it is essential that analysis results are repeatable by others, especially when moving into clinical environment. To cope with the complex computational demands of huge biological datasets, a shift to distributed compute resources is unavoidable. A case study was conducted in which three well established bioinformatics analysis groups across Australia were assigned to analyse exome sequence data from a range of patients with a rare condition: disorder of sex development. Initially these groups used their own in-house data processing pipelines, and subsequently used a common bioinformatics workbench based upon Galaxy and offered through the Australia-wide National eResearch Collaboration Tools and Resources (NeCTAR) Research Cloud. This paper describes the experiences in this work and the variability of results. We put forward principles that should be used to ensure reproducibility of scientific results moving forward.
机译：计算生物信息学工作流程被广泛用于分析基因组数据。随着基因组序列技术的空前发展和个性化药物的机会，至关重要的是其他人可以重复分析结果，尤其是在进入临床环境时。为了应付庞大的生物数据集的复杂计算需求，不可避免地要转向分布式计算资源。进行了一个案例研究，其中在澳大利亚分配了三个完善的生物信息学分析小组来分析来自一系列罕见病患者的外显子组序列数据：性发育障碍。最初，这些小组使用自己的内部数据处理管道，随后使用基于银河的通用生物信息学工作台，该工作台通过澳大利亚全国电子研究协作工具和资源（NeCTAR）研究云提供。本文介绍了这项工作的经验和结果的可变性。我们提出了一些原则，应使用这些原则来确保科学成果的可重复性。

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