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Quality Assurance

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Health Information Technology and Healthcare Information System

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
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Quality Control

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Related Experiment Video

Updated: Oct 4, 2025

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

Improving bioinformatics software quality through incorporation of software engineering practices.

¹Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Peerj. Computer Science

|February 3, 2022

Summary

Bioinformatics engineers can improve scientific software quality by adopting software engineering principles. Integrating these practices into education and project management fosters better development and collaboration.

Keywords:

Bioinformatics Documentation Education Integration Reproducibility Requirements Software engineering Testing

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Area of Science:

Life Sciences
Computational Biology
Software Engineering

Background:

Bioinformatics software is crucial for analyzing large life science datasets.
Bioinformatics engineers often lack essential software engineering skills for robust software development.
Improving the quality of bioinformatics software is a key challenge.

Purpose of the Study:

To review and discuss efforts to enhance bioinformatics software quality.
To identify core software engineering concepts applicable to bioinformatics development.
To illuminate trends for viable solutions to development struggles.

Main Methods:

Systematic literature review.
Identification of core software engineering concepts (requirements, documentation, testing, integration).
Analysis of trends in research for bioinformatics software development.

Main Results:

Bioinformatics engineers benefit from incorporating software engineering principles.
Cultural changes and formal education in software engineering are recommended.
Open management improves software quality through collaboration.

Conclusions:

Progress has been made in addressing bioinformatics software development issues.
Further improvements are needed in formal education and management approaches.
Shifting culture and management practices are essential for continued advancement.