Reference: Neal ML, et al. (2024) Automated, image-based quantification of peroxisome characteristics with perox-per-cell. Bioinformatics 40(7)

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Abstract


Summary: perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics.

Availability and implementation: The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell.

Supplementary information: Supplementary data are available at Bioinformatics online.

Reference Type
Journal Article
Authors
Neal ML, Shukla N, Mast FD, Farré JC, Pacio TM, Raney-Plourde KE, Prasad S, Subramani S, Aitchison JD
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