Recycler: An algorithm for detecting plasmids from de novo assembly graphs

Roye Rozov*, Aya Brown Kav, David Bogumil, Naama Shterzer, Eran Halperin, Itzhak Mizrahi, Ron Shamir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

89 Scopus citations

Abstract

Motivation: Plasmids and other mobile elements are central contributors to microbial evolution and genome innovation. Recently, they have been found to have important roles in antibiotic resistance and in affecting production of metabolites used in industrial and agricultural applications. However, their characterization through deep sequencing remains challenging, in spite of rapid drops in cost and throughput increases for sequencing. Here, we attempt to ameliorate this situation by introducing a new circular element assembly algorithm, leveraging assembly graphs provided by a conventional de novo assembler and alignments of paired-end reads to assemble cyclic sequences likely to be plasmids, phages and other circular elements. Results: We introduce Recycler, the first tool that can extract complete circular contigs from sequence data of isolate microbial genomes, plasmidome and metagenome sequence data. We show that Recycler greatly increases the number of true plasmids recovered relative to other approaches while remaining highly accurate. We demonstrate this trend via simulations of plasmidomes, comparisons of predictions with reference data for isolate samples, and assessments of annotation accuracy on metagenome data. In addition, we provide validation by DNA amplification of 77 plasmids predicted by Recycler from the different sequenced samples in which Recycler showed mean accuracy of 89% across all data types-isolate, microbiome and plasmidome.

Original languageEnglish
Pages (from-to)475-482
Number of pages8
JournalBioinformatics
Volume33
Issue number4
DOIs
StatePublished - 15 Feb 2017

Funding

FundersFunder number
International Business Machines Corporation
Center for Visual Science
Horizon 2020 Framework Programme
European Research Council
National Natural Science Foundation of China2015-18
Ministry of Aliyah and Immigrant Absorption
Israel Science Foundation317/13, 1425/13, 1313/13
Tel Aviv University
Israeli Centers for Research Excellence41/11
Horizon 2020640384

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