5711877
doi
10.5281/zenodo.5711877
oai:zenodo.org:5711877
Ferdous Nasri
Hasso Plattner Institute
Melania Nowicka
Hasso Plattner Institute
Bernhard Y. Renard
Hasso Plattner Institute
DeePaC models for novel fungal pathogens and real-time detection of multiple pathogen classes
Jakub M. Bartoszewicz
Hasso Plattner Institute
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
fungi
novel pathogens
bioinformatics
deep learning
next-generation sequencing
<p>A collection of DeePaC ResNet models for</p>
<p>1) pathogenic potential prediction for novel fungal species (input Illumina read length: 250bp)</p>
<p>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).</p>
<p>See the code and manual at <a href="https://gitlab.com/dacs-hpi/deepac">https://gitlab.com/dacs-hpi/deepac</a>. Model weights for the fungal (-fun-) and multi-class (-multi4-) models in .h5 files and config .ini files.</p>
<p> </p>
<p>The models were trained on read sets hosted here: <a href="https://zenodo.org/record/5713153">https://zenodo.org/record/5713153</a> based on a curated database of pathogenic fungi (<a href="https://zenodo.org/record/5711852">https://zenodo.org/record/5711852</a>).</p>
<p> </p>
<p>See also the preprint: <a href="https://www.biorxiv.org/content/10.1101/2021.11.30.470625v1">https://www.biorxiv.org/content/10.1101/2021.11.30.470625v1</a></p>
Zenodo
2021-11-18
info:eu-repo/semantics/other
5711876
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