Abstract
BackgroundAn incremental, loosely planned development approach is often used in bioinformatic studies when dealing with custom data analysis in a rapidly changing environment. Unfortunately, the lack of a rigorous software structuring can undermine the maintainability, communicability and replicability of the process. To ameliorate this problem we propose the Leaf system, the aim of which is to seamlessly introduce the pipeline formality on top of a dynamical development process with minimum overhead for the programmer, thus providing a simple layer of software structuring.ResultsLeaf includes a formal language for the definition of pipelines with code that can be transparently inserted into the user’s Python code. Its syntax is designed to visually highlight dependencies in the pipeline structure it defines. While encouraging the developer to think in terms of bioinformatic pipelines, Leaf supports a number of automated features including data and session persistence, consistency checks between steps of the analysis, processing optimization and publication of the analytic protocol in the form of a hypertext.ConclusionsLeaf offers a powerful balance between plan-driven and change-driven development environments in the design, management and communication of bioinformatic pipelines. Its unique features make it a valuable alternative to other related tools.
Highlights
An incremental, loosely planned development approach is often used in bioinformatic studies when dealing with custom data analysis in a rapidly changing environment
In order to take into account the need to reduce planning and at the same time maintain high level structuring in bioinformatic analyses, we developed the Leaf [12,13] system (Figures 2 and 3)
Concepts we introduce the concepts that formalizes the idea of bioinformatic pipeline implemented in Leaf
Summary
Leaf includes a formal language for the definition of pipelines with code that can be transparently inserted into the user’s Python code. Its syntax is designed to visually highlight dependencies in the pipeline structure it defines. While encouraging the developer to think in terms of bioinformatic pipelines, Leaf supports a number of automated features including data and session persistence, consistency checks between steps of the analysis, processing optimization and publication of the analytic protocol in the form of a hypertext
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