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      Fast and accurate long-read assembly with wtdbg2

      research-article
      Author(s): 1 , 2 , † , 3 , 4 , 5 , †
      Publication date (Electronic):
      Journal: Nature methods

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          Abstract

          Existing long-read assemblers require thousands of CPU hours to assemble a human genome and are being outpaced by sequencing technologies in terms of both throughput and cost. We developed a long-read assembler wtdbg2 ( https://github.com/ruanjue/wtdbg2) that is 2–17 times as fast as published tools while achieving comparable contiguity and accuracy. It paves the way for population-scale long-read assembly in future.

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          • Record: found
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          Identification of common molecular subsequences.

          T.F. Smith, M.S. Waterman (1981)
          Article 0 comments Cited 1772 times – based on 0 reviews     Review now
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            Is Open Access

            Structural variants identified by Oxford Nanopore PromethION sequencing of the human genome

            Wouter De Coster, Peter De Rijk, Arne De Roeck … (2019)
            We sequenced the genome of the Yoruban reference individual NA19240 on the long-read sequencing platform Oxford Nanopore PromethION for evaluation and benchmarking of recently published aligners and germline structural variant calling tools, as well as a comparison with the performance of structural variant calling from short-read sequencing data. The structural variant caller Sniffles after NGMLR or minimap2 alignment provides the most accurate results, but additional confidence or sensitivity can be obtained by a combination of multiple variant callers. Sensitive and fast results can be obtained by minimap2 for alignment and a combination of Sniffles and SVIM for variant identification. We describe a scalable workflow for identification, annotation, and characterization of tens of thousands of structural variants from long-read genome sequencing of an individual or population. By discussing the results of this well-characterized reference individual, we provide an approximation of what can be expected in future long-read sequencing studies aiming for structural variant identification.
            Article 0 comments Cited 75 times – based on 0 reviews     Review now
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              Efficient Local Alignment Discovery amongst Noisy Long Reads

              Gene Myers (2014)
              Article 0 comments Cited 31 times – based on 0 reviews
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101215604
                Journal ID (pubmed-jr-id): 32338
                Journal ID (nlm-ta): Nat Methods
                Journal ID (iso-abbrev): Nat. Methods
                Title: Nature methods
                ISSN (Print): 1548-7091
                ISSN (Electronic): 1548-7105
                Publication date Nihms-submitted: 6 November 2019
                Publication date (Electronic): 09 December 2019
                Publication date (Print): February 2020
                Publication date PMC-release: 09 June 2020
                Volume: 17
                Issue: 2
                Pages: 155-158
                Affiliations
                [1 ]Agricultural Genomics Institute, Chinese Academy of Agriculture Sciences, Shenzhen, China
                [2 ]Peng Cheng Laboratory, Shenzhen, China
                [3 ]Department of data sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                [4 ]Department of biomedical informatics, Harvard Medical School, Boston, MA 02115, USA
                [5 ]Broad Institute, Cambridge, MA 02142, USA
                Author notes

                Author contribution. J.R. conceived the project, designed the algorithm and implemented wtdbg2. H.L. contributed to the development and drafted the manuscript. Both authors evaluated the results and revised the manuscript.

                [† ]To whom correspondence should be addressed. ruanjue@ 123456caas.cn and hli@ 123456jimmy.harvard.edu .
                Article
                Manuscript ID: NIHMS1541890
                DOI: 10.1038/s41592-019-0669-3
                PMC ID: 7004874
                PubMed ID: 31819265
                SO-VID: 2ac63da2-a5e1-4449-9f2e-7c05cb09f20c
                License:

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Life sciences

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