Magee, N. | bioRxiv Science

bioRxiv2026

Survey and Evaluation of Applied Containerization Practices in Bioinformatics

Containerization enables portable, reproducible, and scalable scientific computing. However, container development, documentation, and deployment practices can vary widely, even within a single domain. Understanding patterns of how containers are implemented in real-world settings can inform community guidelines, quality scoring rubrics, and systems to automate software review. Focusing on bioinformatics as an exemplar domain, we surveyed published software tools to find applied containerization examples. After identifying >250 tools to review, we annotated metadata regarding version control, asset provenance, and general container image health and tested the ability to build and pull the container images in a purpose-built evaluation platform (https://github.com/vpnagraj/socr8s). The majority of images tested did not build successfully in our environment. Of the tool characteristics we tracked, the strongest predictor of build success was version control activity. Tools with commits in the preceding two years were roughly twice as likely to build. Deeper assessment of failures identified a variety of issues, including missing assets and broken dependency chains. While many tools had published images accessible in open registries, 24% could neither be built nor pulled. Images tended to be large, with median size of 1.57GB for those that were able to be built. Where image specification files were available, we found that more advanced container orchestration and build techniques were uncommon. Our results highlight areas of improvement in how containerized tools are built and maintained in bioinformatics and scientific computing software in general.

bioinformatics↗

bioRxiv2025

NOVA: a novel R-package enabling multi-parameter analysis and visualization of neural activity in MEA recordings

Multielectrode array (MEA) technology enables simultaneous recording of electrical signals from neuronal networks, producing complex datasets. Current analytical approaches typically examine a limited number of metrics such as mean firing rate and synchronicity, leaving much of the data underutilized. To address this gap, we created NOVA (Neural Output Visualization and Analysis), an accessible R-based computational tool for comprehensive MEA data interpretation and visualization. NOVA integrates dimensionality reduction through principal component analysis, hierarchical clustering with heatmap generation, and temporal trajectory mapping of network activity patterns. Our code offers both a userfriendly pipeline requiring minimal coding background as well as customizable advanced plotting modules for experienced users. Validation experiments using primary cortical neurons during development and pharmacological manipulation demonstrated NOVAs capacity to detect subtle activity shifts overlooked by conventional methods. Notably, our unbiased approach identified network burst duration as a stronger contributor to activity variance than commonly reported firing rate metrics, exemplifying NOVAs utility for discovering meaningful patterns and generating data-driven hypotheses.

neuroscience↗