ArticleCommunications biology2025
EASI-ORC: A pipeline for the efficient analysis and segmentation of smFISH images for organelle-RNA colocalization measurements in yeast.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- YeastSAM: A Deep Learning Model for Accurate Segmentation of Budding Yeast Cells.bioRxiv : the preprint server for biology · 2025Article
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3 authors.
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Abstract
Analysis of single-molecule fluorescent in situ hybridization (smFISH) images is important to translate cellular image data into a quantifiable format. Although smFISH is the gold standard for RNA localization measurements, there are no freely available, user-friendly applications for assaying messenger RNA (mRNA) localization to organelles. EASI-ORC (Efficient Analysis and Segmentation of smFISH Images for Organelle-RNA Colocalization) is a novel pipeline for the automated analysis of multiple smFISH images of yeast cells. EASI-ORC automates the segmentation of cells and organelles, identifies bona fide smFISH signals, and measures mRNA-organelle colocalization. EASI-ORC is efficient, unbiased, and plots the colocalization data and statistical analyses. EASI-ORC utilizes existing ImageJ plugins and original scripts, thus allowing for free access and ease-of-use. To circumvent technical literacy issues, a step-by-step user guide is provided. EASI-ORC offers a robust solution to smFISH image analysis - one that saves time, effort and provides consistent measurements of mRNA-organelle colocalization in yeast.
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