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Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Jun 9, 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 teamwork.

Katalin Ferenc¹, Ieva Rauluseviciute¹, Ladislav Hovan¹

¹Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Oslo 0318, Norway.

Bioinformatics (Oxford, England)

|October 22, 2024

Summary

This summary is machine-generated.

Bioinformatics software development often lacks quality standards. Adopting a collaborative team approach in computational biology groups improves coding skills, efficiency, and fosters new projects, enhancing overall software quality.

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Area of Science:

Bioinformatics
Computational Biology
Software Development

Background:

High-throughput techniques have increased demand for bioinformatics software.
Academic bioinformatics software development often lacks quality standards, leading to code that is difficult to test, reuse, and maintain.
The individualistic approach to scientific achievement hinders the adoption of best software development practices.

Purpose of the Study:

To investigate the impact of a collaborative approach on bioinformatics software development quality within academic research groups.
To promote the adoption of software development best practices in computational biology.

Main Methods:

Implemented a sustainable model involving all computational biology group members in learning, sharing, and discussing software development.
Maintained personal ownership of research projects and related software products within the collaborative framework.
Encouraged teamwork, knowledge sharing, and the establishment of coding standards.

Main Results:

Group members demonstrated improved coding skills and became more efficient bioinformaticians.
Participants gained detailed knowledge of peers' work, stimulating new collaborative projects.
The collective effort enhanced the quality and maintainability of bioinformatics software.

Conclusions:

A collective, team-based approach to software development significantly improves code quality, reusability, and maintainability in academic bioinformatics.
Fostering a collaborative software development culture is crucial for advancing computational biology research.
Institutes with three or more bioinformaticians should advocate for collective effort to enhance software development practices.