Iceberg representing the challenges of reproducing scientific research.

Is Your Research Really Reproducible? A Case Study in Bioinformatics

Elliot Brynn in Science & Nature December 2025 • 4 min read.

"Digging into the hidden challenges of scientific reproducibility and how to overcome them for reliable results."

In an era dominated by data and complex algorithms, the cornerstone of scientific progress—reproducibility—is increasingly under scrutiny. The ability to replicate experimental results is not just an academic ideal; it's fundamental to building trust and advancing knowledge. However, a recent survey highlighted a concerning trend: a significant number of researchers struggle to reproduce the findings of their peers, and even their own earlier work.

This challenge isn't new. Throughout history, scientific advancements have faced skepticism, often rooted in the difficulty of replicating complex setups. Yet, in computational science, the issue of reproducibility takes on a unique dimension, influenced by factors ranging from code availability to subtle differences in computing environments.

This article delves into a compelling case study that explores the multifaceted challenges of reproducing a published bioinformatics method. By examining the efforts to reimplement and validate a network-based stratification technique, we uncover practical insights and solutions for improving reproducibility and research efficiency.

The Underwater Iceberg: Hidden Challenges in Reproducibility

Iceberg representing the challenges of reproducing scientific research.

Imagine scientific research as an iceberg. What's visible—the published article—represents only a fraction of the work involved. Below the surface lies a complex web of adjustments, configurations, and dependencies that are often overlooked but crucial for successful replication. This 'underwater' portion can include:

The original study used a network-based stratification (NBS) method in cancer research, combining genetic profiles with protein-protein interaction networks to identify subgroups. While the original authors generously provided their data and code, the team encountered several hurdles in their attempt to reproduce the results.

Software Dependencies: The original code relied on specific versions of MATLAB and associated libraries, requiring significant effort to compile and configure for a different operating system.
Language Barriers: To gain deeper understanding and improve accessibility, the team reimplemented the method in Python, revealing subtle variations in default parameters and library functions that significantly impacted the results.
Metadata Mysteries: Understanding the structure and provenance of the original data files proved challenging, highlighting the need for clear and comprehensive metadata.
Parameter Puzzles: Key parameters, such as the graph regulator factor, were not clearly defined in the original article, requiring extensive experimentation to determine the optimal value.

These challenges underscore the importance of looking beyond the surface of published research and addressing the hidden complexities that can hinder reproducibility. Overcoming these obstacles requires a shift in mindset, from simply providing code and data to actively facilitating reuse and validation.

Practical Steps Towards Reproducible Research

The journey towards reproducible research requires a multi-pronged approach, combining individual best practices with community-level standards. By embracing these strategies, researchers can build a more reliable and impactful scientific ecosystem.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1093/gigascience/giy077, Alternate LINK

Title: Experimenting With Reproducibility: A Case Study Of Robustness In Bioinformatics

Subject: Computer Science Applications

Journal: GigaScience

Publisher: Oxford University Press (OUP)

Authors: Yang-Min Kim, Jean-Baptiste Poline, Guillaume Dumas

Published: 2018-06-28

Everything You Need To Know

What is scientific reproducibility and why is it important?

Reproducibility in scientific research is the ability to replicate experimental results. It's crucial for building trust and advancing knowledge, and it's often challenging in computational science due to factors like code availability and computing environment differences. A recent survey showed many researchers struggle to reproduce others' findings, highlighting the importance of addressing these challenges.

What is the 'underwater iceberg' in the context of this discussion?

The 'underwater iceberg' represents the hidden complexities of research, including software dependencies, language barriers, metadata, and parameter choices. These are often overlooked but essential for successful replication. For example, in a case study using the network-based stratification (NBS) method, issues arose from specific MATLAB versions, reimplementation in Python revealing parameter differences, unclear data file structures, and undefined parameters like the graph regulator factor. Addressing these 'underwater' elements is key to improving reproducibility.

Why are software dependencies a challenge to reproducibility?

Software dependencies are crucial for reproducibility because the original code may depend on specific versions of software and libraries. In the case study, the original code used MATLAB and required specific libraries. Compiling and configuring these for a different operating system proved challenging. Without addressing these dependencies, reproducing the results accurately becomes difficult, highlighting the need for careful documentation and environment management.

How does metadata impact the reproducibility of research?

Metadata, which is information about the data, is essential for reproducibility because it provides context and structure to the data files. Understanding the structure and origin of original data files can be challenging, as demonstrated in the case study. Comprehensive metadata ensures that other researchers can understand and use the data correctly. This includes details about data formats, sources, and processing steps.

What are parameter puzzles and why are they a problem for reproducibility?

Parameter puzzles occur when critical parameters are not clearly defined in the original publication. In the case study, the graph regulator factor was not clearly described, requiring extensive experimentation to determine the optimal value. Proper documentation of all parameters and their values are critical for others to replicate the study. Clearly defining parameters is crucial for successful replication of research results.