Where next for the reproducibility agenda in computational biology?
Where next for the reproducibility agenda in computational biology?
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DOI：
10.1186/s12918-016-0288-x
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发表时间：
2016-07-15
影响因子：
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通讯作者：
Cooper J
中科院分区：
文献类型：
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作者：
Lewis J;Breeze CE;Charlesworth J;Maclaren OJ;Cooper J
再现性的概念是科学方法的基础。在过去的几十年里，随着快速而强大的计算机的到来，基于复杂的计算分析和模拟的结果激增。这些结果的可重复性主要是在精确的可重复性或数值等效性方面得到解决的，忽略了通过等效、扩展或替代方法得出结论的可重复性这一更广泛的问题。我们使用的案例研究，从我们自己的研究经验，以说明如何再现性的概念可能会应用在计算生物学。几个领域已经开发了“最小信息”清单，以支持计算模拟，分析和结果的完整报告，标准化的数据格式和模型描述语言可以方便地使用多个系统来解决相同的研究问题。我们注意到定义结果的关键特征的重要性，以及原始结果和后续结果之间的预期一致性。用于发布方法和结果的动态、可更新的工具正变得越来越普遍，但有时会以清晰的沟通为代价。一般来说，计算研究的可重复性正在改善，但将受益于额外的资源和激励措施。最后，我们提出了一系列相关的建议，通过沟通，政策，教育和研究实践来提高计算生物学的再现性。更多可重复的研究将导致更高质量的结论，更深入的理解和更有价值的知识。
The concept of reproducibility is a foundation of the scientific method. With the arrival of fast and powerful computers over the last few decades, there has been an explosion of results based on complex computational analyses and simulations. The reproducibility of these results has been addressed mainly in terms of exact replicability or numerical equivalence, ignoring the wider issue of the reproducibility of conclusions through equivalent, extended or alternative methods. We use case studies from our own research experience to illustrate how concepts of reproducibility might be applied in computational biology. Several fields have developed ‘minimum information’ checklists to support the full reporting of computational simulations, analyses and results, and standardised data formats and model description languages can facilitate the use of multiple systems to address the same research question. We note the importance of defining the key features of a result to be reproduced, and the expected agreement between original and subsequent results. Dynamic, updatable tools for publishing methods and results are becoming increasingly common, but sometimes come at the cost of clear communication. In general, the reproducibility of computational research is improving but would benefit from additional resources and incentives. We conclude with a series of linked recommendations for improving reproducibility in computational biology through communication, policy, education and research practice. More reproducible research will lead to higher quality conclusions, deeper understanding and more valuable knowledge.