Usability evaluation of circRNA identification tools: Development of a heuristic-based framework and analysis
Aug 1, 2022·
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Guilherme Bauer-Negrini
Guilherme Cordenonsi da Fonseca
Carmem Gottfried
Juliana Herbert
Abstract
BACKGROUND AND OBJECTIVE: Circular RNAs (circRNAs) are endogenous molecules of non-coding RNA that form a covalently closed loop at the 3’ and 5’ ends. Recently, the role of these molecules in the regulation of gene expression and their involvement in several human pathologies has gained notoriety. The identification of circRNAs is highly dependent on computational methods for analyzing RNA sequencing data. However, bioinformatics software is known to be problematic in terms of usability. Evidence points out that tools for identifying circRNAs can have such problems, negatively impacting researchers in this field. Here we present a heuristic-based framework for evaluating the usability of command-line circRNA identification software. METHODS: We used heuristics evaluation to comprehensively identify the usability issues in a sample of circRNA identification tools. RESULTS: We identified 46 usability issues presented individually in four tools. Most of the issues had cosmetic or minor severity. These are unlikely to challenge experienced users but may cause inconvenience for novice users. We also identified severe issues with the potential to harm users regardless of their experience. The areas most affected were the documentation and the installability of the tools. CONCLUSIONS: With the proposed framework, we formally describe, for the first time, the usability problems that can affect users in this area of circRNA research. We hope that our framework can help researchers evaluate their software’s usability during development.
Type
Publication
Computers in Biology and Medicine
Status
Peer-reviewed

Authors
Biomedical Data Scientist
Computational neuroscientist working at the intersection of machine learning, biomedical
imaging, and human genetics in neurodegenerative disease. My work applies deep learning to
high-dimensional medical images, harmonizes imaging measurements across sites and acquisition
protocols, and integrates imaging with genomic, proteomic, and longitudinal clinical data to
characterise Alzheimer’s disease and related dementias, with particular focus on fluid and
imaging biomarkers of neurodegeneration.