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Sharing Programming Resources Between Bio* Projects.

Raoul J P Bonnal¹, Andrew Yates², Naohisa Goto³

¹Istituto Nazionale Genetica Molecolare INGM Romeo ed Enrica Invernizzi, Milan, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|July 7, 2019

Summary

This summary is machine-generated.

Call stack approaches offer superior performance for sharing bioinformatics software components across programming languages compared to remote procedure calls. This research provides practical examples and a Docker container for testing these methods.

Keywords:

Bioinformatics EMBOSS Java PAML Perl Python R RPC Ruby Web services

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Area of Science:

Bioinformatics
Computational Biology
Software Engineering

Background:

Open-source software is crucial in evolutionary bioinformatics, utilizing languages like Python, Java, and R.
Sharing components across Bio* projects (e.g., BioPerl, Biopython) avoids redundant coding.
Bridging different programming languages is essential for efficient bioinformatics workflows.

Purpose of the Study:

To compare the performance of different software-sharing strategies between programming languages in bioinformatics.
To evaluate remote procedure call (RPC), local call stack, and program-to-program communication methods.
To provide practical examples and a test environment for cross-language bioinformatics software integration.

Main Methods:

Compared three main approaches: remote procedure call (RPC), local call stack, and program-to-program calls.
Implemented cross-language examples for sequence translation.
Measured throughput using native Bio* implementations (BioPerl, Biopython, BioJava, BioRuby), RPC (RSOAP, Rserve), and call stack bindings (RPy, R bindings, Java VM).

Main Results:

Call stack approaches generally outperformed native Bio* implementations.
Native Bio* implementations showed better performance than RPC-based approaches.
Performance varied across specific interfaces and language combinations.

Conclusions:

Local call stack methods provide the most efficient strategy for sharing bioinformatics software components across diverse programming languages.
The findings guide developers in selecting optimal methods for inter-language bioinformatics tool integration.
A downloadable Docker container facilitates reproducible testing and comparison of these sharing strategies.