🤖 AI Summary
This study addresses the accessibility deficits and decision-making opacity in bioinformatics arising from an overreliance on visualization. We propose ten non-visual analysis rules that uniquely align accessibility with computational reproducibility. By leveraging text-first programming, structured metadata, and FAIR principles, this approach transforms graphical outputs into structured decision records equivalent to visual representations. Validation using single-cell RNA-seq data demonstrates that this method effectively enables analytical reconstruction in non-visual environments. Consequently, it significantly enhances research inclusivity, transparency, and auditability. Ultimately, this work establishes a novel pathway toward developing universally accessible yet rigorous paradigms for computational biology, bridging the gap between equitable access and scientific integrity.
📝 Abstract
Bioinformatics workflows rely heavily on visual representations. Quality-control plots, cell embeddings, heatmaps, genome-browser tracks, and interactive dashboards are not merely illustrations, but instruments for making analytical decisions. For blind and low-vision researchers who use screen readers, braille displays, or audio-based interfaces, these create a barrier: the evidence used to justify an analysis is often encoded in visual form, while the underlying decision remains undocumented.
We argue that non-visual accessibility and computational reproducibility are closely aligned, as they both require analyses to be transparent and to record why decisions were made. We present ten simple rules for non-visual bioinformatics, covering plots as decision records, cautious use of AI-generated figure descriptions, accessible computing environments, text-first literate programming, structured data and metadata, compact object summaries, accessible publication formats, collaboration practices, shared community infrastructure, and accessibility as part of FAIR research. The intended audience is computational biologists and developers.
Using single-cell RNA-seq as a running example, we show that the accessible equivalent of a plot is a structured decision record. That is, a plot companion that goes beyond storing the underlying data by also stating the purpose of the analysis and the resulting quantitative evidence and uncertainty. We argue that treating accessibility in this way makes bioinformatics more inclusive and also more transparent and auditable.