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Updated: Jul 7, 2026
07:09
A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
Published on: May 28, 2021
A comparison of common programming languages used in bioinformatics.
Mathieu Fourment1, Michael R Gillings
1Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia. m.fourment@gmail.com
BMC Bioinformatics
|February 7, 2008
Summary
C and C++ offer the best performance for bioinformatics algorithms, balancing speed and memory usage. Language choice impacts development time and resource efficiency, crucial for computational biology tasks.
Area of Science:
- Computational biology
- Bioinformatics algorithm development
- Programming language performance analysis
Background:
- Previous programming language benchmarks focused on abstract algorithms, not standard bioinformatics methods.
- This study addresses the gap by evaluating performance using common bioinformatics tasks.
Purpose of the Study:
- To compare the execution speed and memory usage of six programming languages for bioinformatics algorithms.
- To inform developers about language selection for efficient bioinformatics tool development.
Main Methods:
- Implemented Sellers algorithm, Neighbor-Joining, and BLAST parsing in C, C++, C#, Java, Perl, and Python.
- Benchmarked performance across Windows and Linux operating systems.
Main Results:
- C and C++ demonstrated superior speed and minimal memory consumption.
- Java and C# offered a balance between performance and flexibility.
- Language performance remained consistent across operating systems.
Conclusions:
- Programming language choice is critical, requiring consideration of performance needs and library support.
- The benchmark provides valuable data for optimizing bioinformatics software development.
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