MinION nanopore sequencing identifies the position and structure of a bacterial antibiotic resistance island

Ashton, Philip; Nair, Satheesh; Dallman, Tim; Rubino, Salvatore; Rabsch, Wolfgang; Mwaigwisya, Solomon; Wain, John; O’Grady, Justin

doi:10.1038/nbt.3103

Cited by 413 publications

(367 citation statements)

References 35 publications

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“…Both the Nanocorrect assembler described in Loman et al (53) and ABruijn assembled the ECOLInano dataset into a single circular contig structurally concordant with the E. coli K12 genome with error rates 1.5 and 1.1%, respectively (2,475 substitutions, 9,238 insertions, and 40,399 deletions for ABruijn). We note that, in contrast to the more accurate Pacific Biosciences technology, Oxford Nanopore technology currently has to be complemented by hybrid coassembly with short reads to generate finished genomes (40)(41)(42)(43).…”

Section: Resultsmentioning

confidence: 99%

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Assembly of long error-prone reads using de Bruijn graphs

Lin

Yuan

Kolmogorov

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

281

220

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The recent breakthroughs in assembling long error-prone reads were based on the overlap-layout-consensus (OLC) approach and did not utilize the strengths of the alternative de Bruijn graph approach to genome assembly. Moreover, these studies often assume that applications of the de Bruijn graph approach are limited to short and accurate reads and that the OLC approach is the only practical paradigm for assembling long error-prone reads. We show how to generalize de Bruijn graphs for assembling long error-prone reads and describe the ABruijn assembler, which combines the de Bruijn graph and the OLC approaches and results in accurate genome reconstructions.de Bruijn graph | genome assembly | single-molecule sequencing T he key challenge to the success of single-molecule sequencing (SMS) technologies lies in the development of algorithms for assembling genomes from long but inaccurate reads. The pioneer in long reads technologies, Pacific Biosciences, now produces accurate assemblies from long error-prone reads (1, 2). Goodwin et al. (3) and Loman et al. (4) demonstrated that high-quality assemblies can be obtained from even less-accurate Oxford Nanopore reads. Advances in assembly of long errorprone reads recently resulted in the accurate reconstructions of various genomes (5-10). However, as illustrated in Booher et al. (11), the problem of assembling long error-prone reads is far from being resolved even in the case of relatively small bacterial genomes.Previous studies of SMS assemblies were based on the overlaplayout-consensus (OLC) approach (12) or a similar string graph approach (13), which require an all-against-all comparison of reads (14) and remain computationally challenging (see refs. 15-17 for a discussion of the pros and cons of this approach). Moreover, there is an assumption that the de Bruijn graph approach, which has dominated genome assembly for the last decade, is inapplicable to long reads. This is a misunderstanding, because the de Bruijn graph approach, as well as its variation called the A-Bruijn graph approach, was developed to assemble rather long Sanger reads (18). There is also a misunderstanding that the de Bruijn graph approach can only assemble highly accurate reads and fails when assembling long error-prone reads. Although this is true for the original de Bruijn graph approach to assembly (15-17), the A-Bruijn graph approach was originally designed to assemble inaccurate reads as long as any similarities between reads can be reliably identified. Moreover, A-Bruijn graphs have proven to be useful even for assembling mass spectra, which represent highly inaccurate fingerprints of amino acid sequences of peptides (19,20). However, although A-Bruijn graphs have proven to be useful in assembling Sanger reads and mass spectra, the question of how to apply A-Bruijn graphs for assembling long error-prone reads remains open.de Bruijn graphs are a key algorithmic technique in genome assembly (15,(21)(22)(23)(24). In addition, de Bruijn graphs have been used for sequencing by hybridization (...

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Section: Resultsmentioning

confidence: 99%

“…Recent studies of bacterial plankton (41), antibiotics resistance (42), and genome rearrangements (43) demonstrated that hybridSPades works well even for coassembly with less-accurate nanopore reads. Below we sketch the hybridSPAdes algorithm (40) and show how to modify the path extension paradigm to arrive at the ABruijn algorithm.…”

Section: For Details)mentioning

confidence: 99%

Assembly of long error-prone reads using de Bruijn graphs

Lin

Yuan

Kolmogorov

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

281

220

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“…With reported features of compact, inexpensive, long read length and fast sequencing data production, the MinION device offers great potential for genome analysis from structural variation perspectives. Figure 2a shows read length profiles from two datasets, E. coli [45] and yeast [46]. In the future, it would be interesting to know how to use long reads to accurately annotate complex SVs, such as the one shown in Figure 2b, where popular SV calling tools failed using short Illumina reads.…”

Section: Emerging Technologies Of 3rd Generation Sequencingmentioning

confidence: 99%

Structural Variation Detection from Next Generation Sequencing

Ye¹,

Hall²,

Ning³

2015

Next Generat Sequenc & Applic

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Structural variations (SVs) are the genetic variations in the structure of chromosome with different types of rearrangements. They comprise millions of nucleotides of heterogeneity within every genome, and are likely to make an important contribution to genetic diversity and disease susceptibility. In the genomics community, substantial efforts have been devoted to improving understanding of the roles of SVs in genome functions relating to diseases and researchers are working actively to develop effective algorithms to reliably identify various types of SVs such as deletions, insertions, duplications and inversions. Structural variant detection using Next-generation sequencing (NGS) data is difficult, and identification of large and complex structural variations is extremely challenging. In this short review, we mainly discuss various algorithms and computational tools for identifying SVs of different types and sizes with a brief introduction to complex SVs. At the end, we highlight the impact and potential applications of the 3rd generation sequencing data, generated from PacBio and Oxford Nanopore long read sequencing platforms.

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“…A third variant, which was released by Oxford Nanopore Technologies as the MinION nanopore sequencing product, is about the size of a USB memory stick. Early literature reports on the MinION imply that it may not yet be ready for wholesale sequencing (20), but it does have some compelling applications (21).…”

Section: Genomics and Nanotechnologymentioning

confidence: 99%

Nanotechnologies for biomedical science and translational medicine

Heath

2015

Proc. Natl. Acad. Sci. U.S.A.

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In 2000 the United States launched the National Nanotechnology Initiative and, along with it, a well-defined set of goals for nanomedicine. This Perspective looks back at the progress made toward those goals, within the context of the changing landscape in biomedicine that has occurred over the past 15 years, and considers advances that are likely to occur during the next decade. In particular, nanotechnologies for health-related genomics and single-cell biology, inorganic and organic nanoparticles for biomedicine, and wearable nanotechnologies for wellness monitoring are briefly covered.nanotechnology | biotechnology | nanomedicine

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MinION nanopore sequencing identifies the position and structure of a bacterial antibiotic resistance island

Cited by 413 publications

References 35 publications

Assembly of long error-prone reads using de Bruijn graphs

Assembly of long error-prone reads using de Bruijn graphs

Structural Variation Detection from Next Generation Sequencing

Nanotechnologies for biomedical science and translational medicine

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