Happy to announce that Ilya Shabanov’s paper – “HARLEY mitigates user bias and facilitates efficient quantification and co-localization analyses of foci in yeast fluorescence images” has been published in Scientific reports. Link here.

This work has two main components. The first is a description of how much variability there is in quantifying yeast stress granules, both between users and even by the same user scoring the same dataset. The second part of the paper is the development and description of software (HARLEY) to accurately and reproducibly quantify yeast stress granules (and other foci) aided by a user-trained model. We also developed an array of useful co-localization tools and outputs. HARLEY runs in the chrome browser and is quite easy and intuitive to learn – you can download it freely, and access various training datasets and documents here – https://github.com/lnilya/harley

Congratulations Ilya on a job well done! We miss you and wish you well in your PhD in North Carolina.