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Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that der...

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Autores principales: Andreopoulos, William B, Geller, Alexander M, Lucke, Miriam, Balewski, Jan, Clum, Alicia, Ivanova, Natalia N, Levy, Asaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860608/
https://www.ncbi.nlm.nih.gov/pubmed/34871418
http://dx.doi.org/10.1093/nar/gkab1115
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author Andreopoulos, William B
Geller, Alexander M
Lucke, Miriam
Balewski, Jan
Clum, Alicia
Ivanova, Natalia N
Levy, Asaf
author_facet Andreopoulos, William B
Geller, Alexander M
Lucke, Miriam
Balewski, Jan
Clum, Alicia
Ivanova, Natalia N
Levy, Asaf
author_sort Andreopoulos, William B
collection PubMed
description Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids.
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spelling pubmed-88606082022-02-22 Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes Andreopoulos, William B Geller, Alexander M Lucke, Miriam Balewski, Jan Clum, Alicia Ivanova, Natalia N Levy, Asaf Nucleic Acids Res Methods Online Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid's ability to detect novel plasmids. Oxford University Press 2021-12-06 /pmc/articles/PMC8860608/ /pubmed/34871418 http://dx.doi.org/10.1093/nar/gkab1115 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Andreopoulos, William B
Geller, Alexander M
Lucke, Miriam
Balewski, Jan
Clum, Alicia
Ivanova, Natalia N
Levy, Asaf
Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
title Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
title_full Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
title_fullStr Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
title_full_unstemmed Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
title_short Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
title_sort deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8860608/
https://www.ncbi.nlm.nih.gov/pubmed/34871418
http://dx.doi.org/10.1093/nar/gkab1115
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