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      Fast and accurate long-read assembly with wtdbg2

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      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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          Identification of common molecular subsequences.

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            Is Open Access

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

            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.
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              Efficient Local Alignment Discovery amongst Noisy Long Reads

              Gene Myers (2014)
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                6 November 2019
                09 December 2019
                February 2020
                09 June 2020
                : 17
                : 2
                : 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
                NIHMS1541890
                10.1038/s41592-019-0669-3
                7004874
                31819265
                2ac63da2-a5e1-4449-9f2e-7c05cb09f20c

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                Life sciences
                Life sciences

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