Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 06, 2021
- Source ID
- 10.1093/nar/gkab1115
Entities
People
- Alexander M Geller
- Alicia Clum
- Asaf Levy
- Jan Balewski
- Miriam Lucke
- Natalia N. Ivanova
- William B Andreopoulos
Organizations
- Hebrew University of Jerusalem
- Israel Science Foundation
- Joint Genome Institute
- National Energy Research Scientific Computing Center
- San José State University
- University of Illinois Urbana–Champaign