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Bioinformatic pipelines in Python with Leaf.

Francesco Napolitano¹, Renato Mariani-Costantini, Roberto Tagliaferri

¹Department of Computer Science (DI),University of Salerno, Fisciano (SA) 84084, Italy. fnapolitano@unisa.it

BMC Bioinformatics

|June 22, 2013

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The Leaf system introduces pipeline formality to bioinformatic studies, enhancing structure and reproducibility. This approach balances planned and dynamic development for better data analysis management.

Area of Science:

Bioinformatics
Computational Biology
Software Engineering

Background:

Bioinformatic studies often use incremental, loosely planned development for custom data analysis.
This approach can compromise the maintainability, communicability, and replicability of the analysis process.
The Leaf system is proposed to address these issues by adding pipeline formality to dynamic development.

Purpose of the Study:

To introduce a system (Leaf) that integrates pipeline formality into dynamic bioinformatic development.
To provide a simple layer of software structuring with minimal overhead for programmers.
To improve the maintainability, communicability, and replicability of bioinformatic analyses.

Main Methods:

Developed the Leaf system, featuring a formal language for pipeline definition.

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Integrated pipeline code seamlessly into user's Python code.

Designed syntax to visually highlight pipeline dependencies.

Main Results:

Leaf enables transparent insertion of pipeline definitions into Python code.
The system visually represents pipeline dependencies.
Automated features include data persistence, consistency checks, processing optimization, and hypertext publication of protocols.

Conclusions:

Leaf provides a balance between plan-driven and change-driven development for bioinformatic pipelines.
It enhances the design, management, and communication of these pipelines.
Leaf presents a valuable alternative to existing tools for bioinformatic pipeline management.