The paper introduces META-SiM, a novel transformer-based foundation model designed to significantly improve the analysis and discovery process in single-molecule fluorescence microscopy (SMFM). Traditional SMFM data analysis is often hindered by the complexity, heterogeneity, and sheer volume of time-trace data, typically requiring labor-intensive manual inspection or ad hoc methods. META-SiM addresses these challenges by being pretrained on diverse SMFM tasks-including trace classification, segmentation, and idealization-and rivaling existing state-of-the-art algorithms while requiring minimal fine-tuning. The system includes the META-SiM Projector, a web-based tool for visualizing entire datasets through high-dimensional embedding vectors, and a new metric called Local Shannon Entropy (LSE), which efficiently identifies rare, condition-specific biological behaviors. The utility of META-SiM is demonstrated by its application to a pre-mRNA splicing dataset, where it helped discover a previously undetected intermediate state, showcasing its potential to systematize and accelerate biological discovery.
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