Interpretable deep learning for novel pathogen detection from DNA sequences

Regular emergence of novel pathogens is one of the greatest threats to global health. DNA sequencing enables ‘reading’ the genetic information of viruses and microbes. However, standard approaches for pathogen detection from sequencing data can only recognize already known pathogens, as they rely on comparing the obtained DNA sequences to databases of reference genomes or genetic markers. For the same reason, detecting a hypothetical engineered pathogen and screening synthetic DNA against potential threats remain difficult.

We solve this problem by training deep neural networks that predict if a DNA read originates from potential human pathogens (bacteria, viruses or fungi) or viruses and microbes that do not infect humans. Such networks generalize to novel strains and species, allowing fast detection of previously unknown threats. This is possible also in real time, as the DNA sequencer is running. Further, we develop new methods of interpreting neural networks, visualize the learned patterns and find regions of novel genomes that are associated with elevated pathogenic potential.

The associated python package, DeePaC, support easy training and evaluation of custom models for arbitrary DNA sequence data.

Code: [DeePaC] [DeePaC-Live]

Database of fungal pathogens: [DeePaC-F]

Contact: Jakub.Bartoszewicz(at)hpi.de

Related Publications

Bartoszewicz, J.M., Nasri, F., Nowicka, M., Renard, B.Y.: Detecting DNA of novel fungal pathogens using ResNets and a curated fungi-hosts data collection. Bioinformatics. 38, ii168-ii174 (2022).

[ BibTeX ]

@article{bartoszewicz_detecting_2022,
  abstract = {Emerging pathogens are a growing threat, but large data collections and approaches for predicting the risk associated with novel agents are limited to bacteria and viruses. Pathogenic fungi, which also pose a constant threat to public health, remain understudied. Relevant data remain comparatively scarce and scattered among many different sources, hindering the development of sequencing-based detection workflows for novel fungal pathogens. No prediction method working for agents across all three groups is available, even though the cause of an infection is often difficult to identify from symptoms alone.We present a curated collection of fungal host range data, comprising records on human, animal and plant pathogens, as well as other plant-associated fungi, linked to publicly available genomes. We show that it can be used to predict the pathogenic potential of novel fungal species directly from DNA sequences with either sequence homology or deep learning. We develop learned, numerical representations of the collected genomes and visualize the landscape of fungal pathogenicity. Finally, we train multi-class models predicting if next-generation sequencing reads originate from novel fungal, bacterial or viral threats.The neural networks trained using our data collection enable accurate detection of novel fungal pathogens. A curated set of over 1400 genomes with host and pathogenicity metadata supports training of machine-learning models and sequence comparison, not limited to the pathogen detection task.The data, models and code are hosted at https://zenodo.org/record/5846345, https://zenodo.org/record/5711877 and https://gitlab.com/dacs-hpi/deepac.Supplementary data are available at Bioinformatics online.},
  author = {Bartoszewicz, Jakub M and Nasri, Ferdous and Nowicka, Melania and Renard, Bernhard Y},
  journal = {Bioinformatics},
  keywords = {dacs imported jbartoszewicz projectdeepac},
  month = {sep},
  number = {Supplement\_2},
  pages = {ii168&ndash;ii174},
  title = {Detecting {DNA} of novel fungal pathogens using {ResNets} and a curated fungi-hosts data collection},
  volume = 38,
  year = 2022
}

Bartoszewicz, J.M., Seidel, A., Renard, B.Y.: Interpretable detection of novel human viruses from genome sequencing data. NAR Genomics and Bioinformatics. 3, (2021).

[ BibTeX ]

Bartoszewicz, J.M., Genske, U., Renard, B.Y.: Deep learning-based real-time detection of novel pathogens during sequencing. Briefings in Bioinformatics. 22, bbab269 (2021).

[ BibTeX ]

Bartoszewicz, J.M., Seidel, A., Rentzsch, R., Renard, B.Y.: DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks. Bioinformatics. 36, 81–89 (2020).

[ BibTeX ]

Interpretable deep learning for novel pathogen detection from DNA sequences

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