Other Open Access
DeePaC models for novel fungal pathogens and real-time detection of multiple pathogen classes
Jakub M. Bartoszewicz;
Ferdous Nasri;
Melania Nowicka;
Bernhard Y. Renard
A collection of DeePaC ResNet models for
1) pathogenic potential prediction for novel fungal species (input Illumina read length: 250bp)
2) real-time detection of novel bacterial, viral and fungal pathogens (input Illumina read length: 25-250bp). Those models assume four classes: non-pathogens (i.e. commensal bacteria or non-human viruses), pathogenic bacteria, human-infecting viruses, and human-infecting fungi. Two alternative models are provided: we recommend either using the 'log' model (for faster inference), or an ensemble averaging predictions of both models (for better results).
See the code and manual at https://gitlab.com/dacs-hpi/deepac. Model weights for the fungal (-fun-) and multi-class (-multi4-) models in .h5 files and config .ini files.
The models were trained on read sets hosted here: https://zenodo.org/record/5713153 based on a curated database of pathogenic fungi (https://zenodo.org/record/5711852).
See also the preprint: https://www.biorxiv.org/content/10.1101/2021.11.30.470625v1
| Name | Size | |
|---|---|---|
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illu-fun-250bp-res18.h5
md5:c28a9c57509fe1594e5c6931570891cf |
50.0 MB | Download |
|
illu-fun-250bp-res18.ini
md5:fe22f697301be82a05507c9238068e8c |
6.0 kB | Download |
|
illu-multi4-lin-res18.h5
md5:d7c4ef008668cfc660e8e7f22f1f6aa3 |
50.0 MB | Download |
|
illu-multi4-lin-res18.ini
md5:942b1bfa1bdef7c482b4109ce680d05b |
6.0 kB | Download |
|
illu-multi4-log-res18.h5
md5:accce11f11d14d423bc40e5da5bb9a66 |
50.0 MB | Download |
|
illu-multi4-log-res18.ini
md5:9dc29af47db744fbc59655d147c95b9f |
6.0 kB | Download |
| All versions | This version | |
|---|---|---|
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- Publication date:
- November 18, 2021
- DOI:
-
- Keyword(s):
- fungi novel pathogens bioinformatics deep learning next-generation sequencing
- License (for files):
- Creative Commons Attribution 4.0 International