ArticlePLoS computational biology2018
TAMMiCol: Tool for analysis of the morphology of microbial colonies.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- [PSI]-CIC: A Deep-Learning Pipeline for the Annotation of Sectored Saccharomyces cerevisiae Colonies.Bulletin of mathematical biology · 2024Article
- An off-lattice discrete model to characterise filamentous yeast colony morphology.PLoS computational biology · 2024Article
- HYPHAEdelity: a quantitative image analysis tool for assessing peripheral whole colony filamentation.FEMS yeast research · 2022Article
- A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.Microbiology spectrum · 2022Article
- Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi.PLoS computational biology · 2019Article
- TAMMiCol: Tool for analysis of the morphology of microbial colonies.PLoS computational biology · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.
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Registered trials
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