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Related Experiment Video

Updated: Jan 11, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Building a continuous benchmarking ecosystem in bioinformatics.

Izaskun Mallona^1,2, Charlotte Soneson^2,3, Ben Carrillo^1,2

¹Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.

Plos Computational Biology

|November 13, 2025

Summary

This summary is machine-generated.

Computational benchmarking requires robust platforms for neutral method comparisons. This perspective outlines a computational platform wish list for organizing reproducible environments and standardized workflows in benchmarking studies.

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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.