How to make big data accessible to plant biologists and beyond: Ten years of lessons from TBtools

Over the past two decades, omics and big data have shifted plant molecular biology from single-gene, hypothesis driven studies to systems level, data driven discovery. As datasets expand in scale and diversity, bioinformatics software has become essential for routine analysis and interpretation. However, the efficiency of data exploration and evidence integration has not kept pace with data growth, leaving many datasets underutilized and only slowly translated into biological insight. A central bottleneck is the widening gap between the limited data analysis skills of many experimental biologists and the increasing complexity of biological data. TBtools was developed to narrow this gap by providing low-barrier, interactive functions for common plant omics tasks, and it has been broadly adopted. Here, we use it as a decade-long case study to determine why certain local tools achieve broad adoption in plant omics, distill eight actionable design recommendations, and propose four capacity pillars for next-generation local workbenches: project-level data management, reproducible workflow construction, elastic remote computing, and AI-assisted navigation and automation. Together, these lessons provide a practical roadmap for accelerating the translation of omics data into biological insight.