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4CAC: 4-class classification of metagenome assemblies using machine learning and assembly graphs

Lianrong Pu, View ORCID ProfileRon Shamir
doi: https://doi.org/10.1101/2023.01.20.524935
Lianrong Pu
Blavatnik School of Computer Science, Tel Aviv University ,
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  • For correspondence: lianrongpu@mail.tau.ac.il rshamir@tau.ac.il
Ron Shamir
Blavatnik School of Computer Science, Tel Aviv University ,
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  • ORCID record for Ron Shamir
  • For correspondence: rshamir@tau.ac.il lianrongpu@mail.tau.ac.il rshamir@tau.ac.il
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Abstract

Microbial communities usually harbor a mix of bacteria, archaea, phages, plasmids, and microeukaryotes. Phages, plasmids, and microeukaryotes, which are present in low abundance in microbial communities, have complex interactions with bacteria and play important roles in horizontal gene transfer and antibiotic resistance. However, due to the difficulty of identifying phages, plasmids, and microeukaryotes from microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or two minor classes from bacteria and archaea in metagenome assemblies, but none can classify all of the four classes simultaneously. Moreover, existing classifiers have low precision on minor classes.

Here, we developed for the first time a classifier called 4CAC that is able to identify phages, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost algorithms and further improves the classification using the assembly graph. Evaluation of 4CAC against existing classifiers on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers on short reads. On long reads, it shows an advantage unless the abundance of the minor classes is very low. It is also by far the fastest. The 4CAC software is available at https://github.com/Shamir-Lab/4CAC.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted January 21, 2023.
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4CAC: 4-class classification of metagenome assemblies using machine learning and assembly graphs
Lianrong Pu, Ron Shamir
bioRxiv 2023.01.20.524935; doi: https://doi.org/10.1101/2023.01.20.524935
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4CAC: 4-class classification of metagenome assemblies using machine learning and assembly graphs
Lianrong Pu, Ron Shamir
bioRxiv 2023.01.20.524935; doi: https://doi.org/10.1101/2023.01.20.524935

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