Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions

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buir.contributor.authorAlkan, Can
dc.citation.epage1559en_US
dc.citation.issueNumber4en_US
dc.citation.spage1542en_US
dc.citation.volumeNumber20en_US
dc.contributor.authorCali, D. S.en_US
dc.contributor.authorKim, J. S.en_US
dc.contributor.authorGhose, S.en_US
dc.contributor.authorAlkan, Canen_US
dc.contributor.authorMutlu, O.en_US
dc.date.accessioned2019-02-23T08:11:29Z
dc.date.available2019-02-23T08:11:29Z
dc.date.issued2018-04en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractNanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.en_US
dc.identifier.doi10.1093/bib/bby017en_US
dc.identifier.eissn1477-4054
dc.identifier.issn1467-5463
dc.identifier.urihttp://hdl.handle.net/11693/50560
dc.language.isoEnglishen_US
dc.publisherOxford University Pressen_US
dc.relation.isversionofhttps://doi.org/10.1093/bib/bby017en_US
dc.source.titleBriefings in Bioinformaticsen_US
dc.subjectNanopore sequencingen_US
dc.subjectGenome sequencingen_US
dc.subjectGenome analysisen_US
dc.subjectAssemblyen_US
dc.subjectMappingen_US
dc.titleNanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directionsen_US
dc.typeReviewen_US

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