SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines

The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized...

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Vydáno v:Gigascience Ročník 8; číslo 5
Hlavní autoři: Lampa, Samuel, Dahlö, Martin, Alvarsson, Jonathan, Spjuth, Ola
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Oxford University Press 01.05.2019
Témata:
Application programming interface
Automation
Bioinformatics
Bioinformatik
Computational Biology
Data logging
Flow-based Programming
Gene Library
Genomics
Golang
Informatics
Learning algorithms
Libraries
Machine Learning
Nested loops
Parameterization
Pipelines
Programming Languages
Reproducibility
Reusable components
Scientific Workflow Management Systems
Software
Task scheduling
Technical Note
Transcriptomics
Workflow
Workflow management systems
Workflow software
Workflow tools
Workflows
Golang
pipelines
flow-based programming
reproducibility
scientific workflow management systems
Go
machine learning
ISSN:2047-217X, 2047-217X
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Shrnutí:The complex nature of biological data has driven the development of specialized software tools. Scientific workflow management systems simplify the assembly of such tools into pipelines, assist with job automation, and aid reproducibility of analyses. Many contemporary workflow tools are specialized or not designed for highly complex workflows, such as with nested loops, dynamic scheduling, and parametrization, which is common in, e.g., machine learning. SciPipe is a workflow programming library implemented in the programming language Go, for managing complex and dynamic pipelines in bioinformatics, cheminformatics, and other fields. SciPipe helps in particular with workflow constructs common in machine learning, such as extensive branching, parameter sweeps, and dynamic scheduling and parametrization of downstream tasks. SciPipe builds on flow-based programming principles to support agile development of workflows based on a library of self-contained, reusable components. It supports running subsets of workflows for improved iterative development and provides a data-centric audit logging feature that saves a full audit trace for every output file of a workflow, which can be converted to other formats such as HTML, TeX, and PDF on demand. The utility of SciPipe is demonstrated with a machine learning pipeline, a genomics, and a transcriptomics pipeline. SciPipe provides a solution for agile development of complex and dynamic pipelines, especially in machine learning, through a flexible application programming interface suitable for scientists used to programming or scripting.
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ISSN:2047-217X
2047-217X
DOI:10.1093/gigascience/giz044