HyPhy: A Skeletonization-Based Approach For Fungal Network Analysis
Madrigal, M.; Moseley, A. J.; Moseley, J. C.; Dowell, J.
Show abstract
PremiseTraditional methods to quantify mycelial growth rely on destructive sampling to quantify biomass. However, these approaches limit continuous observation and require a large enough mass to measure. Recent work examines hyphal network traits by reconstructing the hyphal network from spatial coordinates via images, providing information about branching patterns and spatial growth over time. Methods and ResultsWe developed HyPhy, a Python-based graphical user interface that skeletonizes images of hyphal networks and extracts biologically relevant structural parameters such as fractal dimension, a proxy for the complexity and branching structure of the hyphal network. Using a high-throughput pipeline method, we imaged three isolates of Botrytis cinerea grown under liquid culture for 72 hours, generating a dataset of 180 time series images. ConclusionsHyPhy enables efficient, non-destructive, and scalable quantification of hyphal growth and complexity from time-resolved image datasets, providing a powerful and user-friendly tool for studying fungal network dynamics.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Benchmarking fungal species classification using Oxford Nanopore Technologies long-read ITS metabarcodes 93%
- Quantitative single molecule RNA-FISH and RNase-free cell wall digestion in Neurospora crassa. 93%
- Exploring a novel genomic safe-haven site in the human pathogenic mould Aspergillus fumigatus. 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.