Building a continuous benchmarking ecosystem in bioinformatics

Area of Science:

• Computational biology
• Bioinformatics
• Software engineering

Background:

• Benchmarking is crucial for developing and validating new computational tools.
• Existing literature focuses on benchmark study design, but lacks guidance on computational platforms.
• Neutral comparisons of methods are essential in computational science.

Purpose of the Study:

• To propose a computational platform architecture for orchestrating benchmark studies.
• To outline key features for reproducible software environments and standardized workflows.
• To address the need for a dedicated platform to facilitate neutral method comparisons.

Main Methods:

• Discussion of requirements for reproducible software environments.
• Formal definition of benchmark datasets and metrics.
• Orchestration of standardized computational workflows.
• Integration with existing computing infrastructure.

Main Results:

• A conceptual framework for a computational benchmarking platform.
• Identification of essential components for reproducibility and standardization.
• Strategies for interfacing with diverse computing resources.

Conclusions:

• A dedicated computational platform can significantly enhance the rigor and reproducibility of benchmarking studies.
• Standardization of workflows and environments is key to neutral method comparisons.
• Future development should focus on implementing such platforms to advance computational tool validation.