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Bioinformatic pipelines in Python with Leaf

Francesco Napolitano1*, Renato Mariani-Costantini23 and Roberto Tagliaferri1

Author Affiliations

1 Department of Computer Science (DI),University of Salerno, Fisciano (SA) 84084, Italy

2 Department of Medicine, Dentistry and Biotechnology “G. d’Annunzio” University, Chieti-Pescara, Italy

3 Unit of General Pathology, Aging Research Center (CeSI) “G. d’Annunzio” University Foundation, Via Luigi Polacchi 15/17, Chieti 66100, Italy

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BMC Bioinformatics 2013, 14:201  doi:10.1186/1471-2105-14-201

Published: 21 June 2013

Abstract

Background

An 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.

Results

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.

Conclusions

Leaf 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.

Keywords:
Data analysis; Bioinformatic pipelines; Python