Streamlining Data-Intensive Biology With Workflow Systems

Reiter, Taylor; Brooks, Phillip T.; Irber, Luiz; Joslin, Shannon E K; Reid, Charles M.; Scott, Camille; Brown, C. Titus; Pierce, Nicholas

doi:10.1101/2020.06.30.178673

Cited by 4 publications

(5 citation statements)

References 97 publications

(98 reference statements)

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“…Workspaces are the building blocks of Terra - a dedicated space where collaborators can access and organize the same data and tools and run analyses together. Each workspace is associated with a cloud bucket where data can be stored, such as data generated by a workflow analysis (Reiter et al, 2020) or notebook files for interactive computing. Workspaces also provide data tables for storing and maintaining structured data similar to a spreadsheet.…”

Section: Anvil System Architecturementioning

confidence: 99%

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

Schatz

Philippakis

Afgan

et al. 2021

Preprint

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The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

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Section: Anvil System Architecturementioning

confidence: 99%

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

Schatz

Philippakis

Afgan

et al. 2021

Preprint

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show abstract

“…Reiter et al [32] also recommend rapid prototyping, in this case for migrating to a chosen workflow management system. In particular they recommend that “When building a workflow for the first time, creating an initial workflow based on a subset of your sample data can help verify that the workflow, tools, and command line syntax function at a basic level.…”

Section: Resultsmentioning

confidence: 99%

Using rapid prototyping to choose a bioinformatics workflow management system

Jackson

Wallace

Kavoussanakis

2020

Preprint

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Workflow management systems represent, manage, and execute multi-step 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 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 the content of these workflows, their data analyses, and 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 re-use 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 and Toil (implementations of the Common Workflow Language) and Nextflow. An evaluation of each candidate, via rapid prototyping of a subset of the RiboViz workflow, was performed and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow best satisfied the authors’ requirements. This use of rapid 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.Author summaryData analysis involves many steps, as data are wrangled, processed, and analysed using a succession of unrelated software packages. Running all the right steps, in the right order, with 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, 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 selected three for “rapid prototype” implementations to evaluate their suitability for our project. We advocate this rapid prototyping as a low-cost (both time and effort) way of making an informed selection of a system for use within a project. We conclude that many similar multi-step data analysis workflows can be rewritten in a workflow management system.

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“…Reiter and colleagues [ 36 ] also recommend prototyping, in this case for migrating to a chosen workflow management system. In particular they recommend that “When building a workflow for the first time, creating an initial workflow based on a subset of your sample data can help verify that the workflow, tools, and command line syntax function at a basic level.…”

Section: Discussionmentioning

confidence: 99%

Using prototyping to choose a bioinformatics workflow management system

2021

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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.

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Streamlining Data-Intensive Biology With Workflow Systems

Cited by 4 publications

References 97 publications

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

Using rapid prototyping to choose a bioinformatics workflow management system

Using prototyping to choose a bioinformatics workflow management system

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