Journal
BIOINFORMATICS
Volume 33, Issue 4, Pages 475-482Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw651
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Funding
- Israel Science Foundation [1425/13, 317/13, 1313/13]
- Israel Science Foundation-National Natural Science Foundation of China
- European Research Council under the European Union's Horizon 2020 research and innovation program [640384]
- Israeli Center of Research Excellence (I-CORE), Gene Regulation in Complex Human Disease [41/11]
- Edmond J. Safra Center for Bioinformatics at Tel Aviv University, an IBM PhD fellowship
- Center for Absorption in Science
- Israel Ministry of Immigrant Absorption
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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.
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