Abstract
High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface. Here I survey and compare the design philosophies of several current pipeline frameworks. I provide practical recommendations based on analysis requirements and the user base.
Highlights
Bioinformatic analyses invariably involve shepherding files through a series of transformations, called a pipeline or a workflow
High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata
Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface
Summary
Bioinformatic analyses invariably involve shepherding files through a series of transformations, called a pipeline or a workflow. Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface.
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