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Provides a method to determinate automatically and unbiasedly distributions of protein across cellular compartments. PatternUnmixer is built on a machine-learning approach that calculates the amount of fluorescent signal in different subcellular compartments without the need of hand tuning and by only requiring the acquisition of separate training images of markers for each compartment. This software suits for high-throughput microscopy and works well on real images obtained form mixed patterns.
Automated Image Analysis of Protein Localization in Budding Yeast
Computational methods were developed to automatically analyze the images created by the University of California, San Francisco (UCSF) yeast GFP fusion localization project. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins.
An imaging system package relying on a closed-loop control algorithm to adapt the collection of a series of time-lapse images to optimize the measurement of gene expression data in individual cells. GenoSIGHT allows users to define their own functions to analyze cell properties like fluorescence, growth, shape, or intracellular distribution of proteins to any criteria defined by the user. GenoSIGHT is capable of detecting that cells are not behaving as expected and notify the operator in real-time so that the experiment can be restarted immediately.
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