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  Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi

Vidal-Diez de Ulzurrun, G., Huang, T.-Y., Chang, C.-W., Lin, H.-C., & Hsueh, Y.-P. (2019). Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi. PLOS Computational Biology, 15(10): e1007428. doi:10.1371/journal.pcbi.1007428.

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Vidal-Diez de Ulzurrun, G, Author                 
Huang, T-Y, Author
Chang, C-W, Author
Lin, H-C, Author
Hsueh, Y-P1, Author                 
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1External Organizations, ou_persistent22              

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 Abstract: Filamentous fungi are ubiquitous in nature and serve as important biological models in various scientific fields including genetics, cell biology, ecology, evolution, and chemistry. A significant obstacle in studying filamentous fungi is the lack of tools for characterizing their growth and morphology in an efficient and quantitative manner. Consequently, assessments of the growth of filamentous fungi are often subjective and imprecise. In order to remedy this problem, we developed Fungal Feature Tracker (FFT), a user-friendly software comprised of different image analysis tools to automatically quantify different fungal characteristics, such as spore number, spore morphology, and measurements of total length, number of hyphal tips and the area covered by the mycelium. In addition, FFT can recognize and quantify specialized structures such as the traps generated by nematode-trapping fungi, which could be tuned to quantify other distinctive fungal structures in different fungi. We present a detailed characterization and comparison of a few fungal species as a case study to demonstrate the capabilities and potential of our software. Using FFT, we were able to quantify various features at strain and species level, such as mycelial growth over time and the length and width of spores, which would be difficult to track using classical approaches. In summary, FFT is a powerful tool that enables quantitative measurements of fungal features and growth, allowing objective and precise characterization of fungal phenotypes.

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 Dates: 2019-10
 Publication Status: Issued
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 Identifiers: DOI: 10.1371/journal.pcbi.1007428
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Title: PLOS Computational Biology
  Abbreviation : PLOS Comput Biol
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: 20 Volume / Issue: 15 (10) Sequence Number: e1007428 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1