非视觉、可复现且无障碍的生物信息学的十条简单规则
Ten simple rules for non-visual, reproducible and accessible bioinformatics
AI总结：
该研究提出非视觉生物信息学的十条规则，以单细胞RNA-seq为例说明图的无障碍等效形式是结构化决策记录，旨在提升生物信息学的包容性、透明性与可审计性。
AI中文摘要：
生物信息学工作流程严重依赖视觉表示：质量控制图、细胞嵌入、热图、基因组浏览器轨迹及交互式仪表板等不仅是图示，更是用于做出分析决策的工具。对于使用屏幕阅读器、盲文显示器或基于音频的界面的盲人和低视力研究人员而言，这些工具形成了一道障碍：用于证明分析合理性的证据常以视觉形式编码，而背后的决策却未被记录。我们认为，非视觉可访问性与计算可复现性紧密相关，因为二者都要求分析过程透明，并记录决策依据。我们提出了非视觉生物信息学的十条简单规则，涵盖：作为决策记录的图、谨慎使用AI生成的图描述、无障碍计算环境、以文本为先的 literate programming（ literate编程）、结构化数据与元数据、紧凑对象摘要、无障碍发表格式、协作实践、共享社区基础设施，以及将可访问性纳入FAIR研究。目标受众是计算生物学家和开发者。我们以单细胞RNA-seq为运行示例，表明图的无障碍等效形式是结构化决策记录：即图的配套文档，不仅存储底层数据，还说明分析目的、定量证据及不确定性。我们认为，以这种方式对待可访问性，可使生物信息学更具包容性，同时也更透明、可审计。
英文摘要：
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.
