Biogem: an effective tool based approach for scaling up open source software development in bioinformatics

R.J.P. Bonnal; G. Smant; J.C.P. Prins

doi:10.1093/bioinformatics/bts080

Biogem: an effective tool based approach for scaling up open source software development in bioinformatics

R.J.P. Bonnal, G. Smant, J.C.P. Prins

Laboratory of Nematology

Research output: Contribution to journal › Article › Academic › peer-review

24 Citations (Scopus)

Abstract

Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools, and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics.

Original language	English
Pages (from-to)	1035-1037
Journal	Bioinformatics
Volume	28
Issue number	7
DOIs	https://doi.org/10.1093/bioinformatics/bts080
Publication status	Published - 2012

Keywords

biology

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10.1093/bioinformatics/bts080

Development and application of high throughput computational methods for the identification of biologically significant genome-wide variation in host-microbe interactions
Prins, P., Bakker, J. & Smant, G.
1/10/07 → 5/10/15
Project: PhD

Cite this

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abstract = "Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools, and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics.",

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Biogem: an effective tool based approach for scaling up open source software development in bioinformatics

Abstract

Keywords

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Projects

Development and application of high throughput computational methods for the identification of biologically significant genome-wide variation in host-microbe interactions

Cite this