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Updated: Jun 26, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

An innovative approach for testing bioinformatics programs using metamorphic testing.

Tsong Yueh Chen¹, Joshua W K Ho, Huai Liu

¹School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia. tychen@swin.edu.au

BMC Bioinformatics

|January 21, 2009

Summary

This summary is machine-generated.

Metamorphic testing (MT) offers a novel solution for validating complex bioinformatics software. This technique effectively detects errors by verifying relationships between test outputs, enhancing software reliability in bioinformatics.

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A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Published on: May 28, 2021

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Area of Science:

Bioinformatics
Software Engineering
Computational Biology

Background:

Sophisticated bioinformatics programs are essential but challenging to test due to complex outputs.
Current software testing methods struggle with verifying the correctness of bioinformatics program outputs.
This difficulty hinders systematic software testing and can lead to incorrect biological conclusions.

Purpose of the Study:

To introduce and evaluate metamorphic testing (MT) as a novel technique for testing bioinformatics programs.
To demonstrate the practical application and effectiveness of MT in identifying faults in bioinformatics software.
To highlight the potential of MT for improving software reliability across various bioinformatics domains.

Main Methods:

Applied metamorphic testing (MT), a technique that verifies relationships between test outputs (metamorphic relations or MRs) instead of individual output correctness.
Utilized MT to test two open-source bioinformatics programs: GNLab and SeqMap.
Developed and applied domain-specific metamorphic relations tailored for bioinformatics applications.

Main Results:

Metamorphic testing (MT) proved to be simple to implement and effective in detecting faults in both real-life and fault-seeded bioinformatics programs.
The application of MT significantly increased the number and variety of applicable test cases.
Case studies demonstrated the practical feasibility and fault-detection capabilities of MT.

Conclusions:

Metamorphic testing (MT) provides a simple, effective, and automated approach for systematically testing bioinformatics programs.
MT is particularly beneficial for complex programs used in large-scale simulation and data analysis, which are traditionally hard to test.
This work represents a significant advancement in ensuring software reliability within the field of bioinformatics.