Using prototyping to choose a bioinformatics workflow management system.
Using prototyping to choose a bioinformatics workflow management system.
复制标题
利用原型法选择生物信息学工作流管理系统。
DOI：
10.1371/journal.pcbi.1008622
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Wallace EWJ
中科院分区：
文献类型：
--
作者：
Jackson M;Kavoussanakis K;Wallace EWJ
工作流管理系统代表、管理和执行多步骤计算分析，并为生物信息学家提供了许多好处。它们为描述分析工作流提供了一种通用语言，有助于再现性和构建可重用组件库。它们既可以支持增量构建，也可以支持重入——在存在额外输入或配置更改的情况下，有选择地重新执行工作流的某些部分，并从工作流先前停止的地方恢复执行。许多工作流管理系统通过支持使用容器、高性能计算（HPC）系统和云来增强可移植性。最重要的是，工作流管理系统允许生物信息学家将他们的工作流如何运行委托给工作流管理系统及其开发人员。这使得生物信息学家可以专注于这些工作流应该做什么，专注于他们的数据分析和他们的科学。RiboViz是一个从核糖体分析数据中提取生物学见解的软件包，有助于提高对蛋白质合成的理解。RiboViz的核心是一个用Python脚本实现的分析工作流。为了符合科学计算的最佳实践（推荐使用构建工具来自动化工作流和重用代码而不是重写它），作者在工作流管理系统中重新实现了这个工作流。为了选择一个工作流管理系统，我们对可用的系统进行了快速调查，并列出了候选系统：Snakemake、cwltool、Toil和Nextflow。通过快速构建RiboViz工作流程的一个子集，对每个候选产品进行了评估，最终选择了Nextflow。选择过程耗时10人日，对于保证Nextflow满足作者的要求来说，这是一个很小的成本。原型的使用可以提供一种低成本的方法，使在项目中使用的软件选择更加明智，而不是仅仅依赖于其他人的评论和建议。数据分析涉及许多步骤，因为数据是使用一系列不相关的软件包进行整理、处理和分析的。以正确的顺序运行正确的步骤，并将正确的输出放在正确的位置，这是挫败感的主要来源。工作流管理系统要求以结构化的方式“包装”每个数据分析步骤，描述其输入、参数和输出。通过编写这些包装，科学家可以专注于每一步的意义，以及它们如何组合在一起，这是有趣的部分。系统使用这些包装器来决定运行哪些步骤以及如何运行这些步骤，并负责运行这些步骤，包括报告错误。这使得重复运行分析和在不同计算机上透明地运行分析变得更加容易。为了选择一个工作流管理系统，我们调查了可用的工具，并选择了4个我们开发原型实现的工具，以评估它们对我们项目的适用性。我们得出的结论是，许多类似的多步骤数据分析工作流可以在工作流管理系统中重写，并且我们提倡将原型设计作为一种低成本（时间和精力）的方式，为研究项目中使用的软件做出明智的选择。
Workflow management systems represent, manage, and execute multistep computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy—the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting the use of containers, high-performance computing (HPC) systems, and clouds. Most importantly, workflow management systems allow bioinformaticians to delegate how their workflows are run to the workflow management system and its developers. This frees the bioinformaticians to focus on what these workflows should do, on their data analyses, and on their science. RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool, Toil, and Nextflow. Each candidate was evaluated by quickly prototyping a subset of the RiboViz workflow, and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow satisfied the authors’ requirements. The use of prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others. Data analysis involves many steps, as data are wrangled, processed, and analysed using a succession of unrelated software packages. Running the right steps, in the right order, and putting the right outputs in the right places, is a major source of frustration. Workflow management systems require that each data analysis step be “wrapped” in a structured way, describing its inputs, parameters, and outputs. By writing these wrappers, the scientist can focus on the meaning of each step, and how they fit together, which is the interesting part. The system uses these wrappers to decide what steps to run and how to run these and takes charge of running the steps, including reporting on errors. This makes it much easier to repeatedly run the analysis and to run it transparently upon different computers. To select a workflow management system, we surveyed available tools and chose 4 in which we developed prototype implementations to evaluate their suitability for our project. We conclude that many similar multistep data analysis workflows can be rewritten in a workflow management system, and we advocate prototyping as a low-cost (both time and effort) way of making an informed selection of software for use within a research project.
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DOI：
10.1093/bioinformatics/btq033
发表时间：
2010-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Quinlan AR;Hall IM
通讯作者：
Hall IM
DOI：
10.1016/j.future.2014.10.008
发表时间：
2015-05-01
影响因子：
7.5
作者：
Deelman, Ewa;Vahi, Karan;Wenger, Kent
通讯作者：
Wenger, Kent
影响因子：
3
作者：
Carja O;Xing T;Wallace EWJ;Plotkin JB;Shah P
通讯作者：
Shah P
DOI：
10.1016/b978-0-12-375114-0.00004-9
发表时间：
2010-01-01
期刊：
USER EXPERIENCE RE-MASTERED: YOUR GUIDE TO GETTING THE RIGHT DESIGN
影响因子：
--
作者：
Nielsen, Jakob
通讯作者：
Nielsen, Jakob
影响因子：
48
作者：
Kim D;Langmead B;Salzberg SL
通讯作者：
Salzberg SL