Open source libraries and frameworks for biological data visualisation: a guide for developers.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• High-throughput experiments generate massive, complex biological datasets.
• Data visualization is crucial for hypothesis generation, model evaluation, and tool implementation in bioinformatics.
• The increasing complexity of biological data necessitates effective visualization strategies.

Purpose of the Study:

• To review open-source and free data visualization libraries and frameworks.
• To guide bioinformaticians, programmers, and developers in selecting appropriate tools.
• To showcase software for creating diverse charts and graphs from biological data.

Main Methods:

• Focus on visualization software written in Java, JavaScript, and Python.
• Categorization of libraries and frameworks based on their capabilities.
• Exploration of tools for generating various types of charts and graphs.

Main Results:

• A curated collection of open-source visualization tools is presented.
• The review covers software suitable for diverse data visualization needs in biology.
• Emphasis on tools that facilitate clear, meaningful, and integrated data representation.

Conclusions:

• Selected software empowers scientists to translate complex data into compelling visual narratives.
• Effective data visualization enhances reasoning and communication in bioinformatics workflows.
• The reviewed tools offer significant potential for advancing biological data analysis and discovery.