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      GetOrganelle: a fast and versatile toolkit for accurate de novo assembly of organelle genomes

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          Abstract

          GetOrganelle is a state-of-the-art toolkit to accurately assemble organelle genomes from whole genome sequencing data. It recruits organelle-associated reads using a modified “baiting and iterative mapping” approach, conducts de novo assembly, filters and disentangles the assembly graph, and produces all possible configurations of circular organelle genomes. For 50 published plant datasets, we are able to reassemble the circular plastomes from 47 datasets using GetOrganelle. GetOrganelle assemblies are more accurate than published and/or NOVOPlasty-reassembled plastomes as assessed by mapping. We also assemble complete mitochondrial genomes using GetOrganelle. GetOrganelle is freely released under a GPL-3 license ( https://github.com/Kinggerm/GetOrganelle).

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          Most cited references40

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          Reconstructing mitochondrial genomes directly from genomic next-generation sequencing reads—a baiting and iterative mapping approach

          We present an in silico approach for the reconstruction of complete mitochondrial genomes of non-model organisms directly from next-generation sequencing (NGS) data—mitochondrial baiting and iterative mapping (MITObim). The method is straightforward even if only (i) distantly related mitochondrial genomes or (ii) mitochondrial barcode sequences are available as starting-reference sequences or seeds, respectively. We demonstrate the efficiency of the approach in case studies using real NGS data sets of the two monogenean ectoparasites species Gyrodactylus thymalli and Gyrodactylus derjavinoides including their respective teleost hosts European grayling (Thymallus thymallus) and Rainbow trout (Oncorhynchus mykiss). MITObim appeared superior to existing tools in terms of accuracy, runtime and memory requirements and fully automatically recovered mitochondrial genomes exceeding 99.5% accuracy from total genomic DNA derived NGS data sets in <24 h using a standard desktop computer. The approach overcomes the limitations of traditional strategies for obtaining mitochondrial genomes for species with little or no mitochondrial sequence information at hand and represents a fast and highly efficient in silico alternative to laborious conventional strategies relying on initial long-range PCR. We furthermore demonstrate the applicability of MITObim for metagenomic/pooled data sets using simulated data. MITObim is an easy to use tool even for biologists with modest bioinformatics experience. The software is made available as open source pipeline under the MIT license at https://github.com/chrishah/MITObim.
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            A DNA barcode for land plants.

            DNA barcoding involves sequencing a standard region of DNA as a tool for species identification. However, there has been no agreement on which region(s) should be used for barcoding land plants. To provide a community recommendation on a standard plant barcode, we have compared the performance of 7 leading candidate plastid DNA regions (atpF-atpH spacer, matK gene, rbcL gene, rpoB gene, rpoC1 gene, psbK-psbI spacer, and trnH-psbA spacer). Based on assessments of recoverability, sequence quality, and levels of species discrimination, we recommend the 2-locus combination of rbcL+matK as the plant barcode. This core 2-locus barcode will provide a universal framework for the routine use of DNA sequence data to identify specimens and contribute toward the discovery of overlooked species of land plants.
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              Toward almost closed genomes with GapFiller

              De novo assembly is a commonly used application of next-generation sequencing experiments. The ultimate goal is to puzzle millions of reads into one complete genome, although draft assemblies usually result in a number of gapped scaffold sequences. In this paper we propose an automated strategy, called GapFiller, to reliably close gaps within scaffolds using paired reads. The method shows good results on both bacterial and eukaryotic datasets, allowing only few errors. As a consequence, the amount of additional wetlab work needed to close a genome is drastically reduced. The software is available at http://www.baseclear.com/bioinformatics-tools/.
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                Author and article information

                Contributors
                tingshuangyi@mail.kib.ac.cn
                dzl@mail.kib.ac.cn
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                10 September 2020
                10 September 2020
                2020
                : 21
                : 241
                Affiliations
                [1 ]GRID grid.9227.e, ISNI 0000000119573309, Germplasm Bank of Wild Species, Kunming Institute of Botany, , Chinese Academy of Sciences, ; Kunming, Yunnan 650201 China
                [2 ]GRID grid.458477.d, ISNI 0000 0004 1799 1066, Center for Integrative Conservation, , Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, ; Mengla, Yunnan 666303 China
                [3 ]GRID grid.9227.e, ISNI 0000000119573309, Center of Conservation Biology, Core Botanical Gardens, , Chinese Academy of Sciences, ; Mengla, Yunnan 666303 China
                [4 ]Southeast Asia Biodiversity Research Institute, Chinese Academy of Sciences, Yezin, Nay Pyi Taw 05282 Myanmar
                [5 ]GRID grid.29857.31, ISNI 0000 0001 2097 4281, Department of Biology, , The Pennsylvania State University, ; University Park, PA 16801 USA
                Author information
                http://orcid.org/0000-0001-7093-9564
                Article
                2154
                10.1186/s13059-020-02154-5
                7488116
                32912315
                a10640fb-4bd8-46a2-a882-efa3defdada9
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 January 2020
                : 24 August 2020
                Funding
                Funded by: Strategic Priority Research Program of the Chinese Academy of Sciences
                Award ID: XDB31000000
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31720103903
                Award ID: 31870196
                Award Recipient :
                Funded by: Chinese Academy of Sciences Large-scale Scientific Facilities
                Award ID: 2017-LSFGBOWS-02
                Award ID: 2017-LSFGBOWS-02
                Award Recipient :
                Funded by: the open research project of “Cross-Cooperative Team” of the Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences
                Funded by: CAS 135 Program
                Award ID: 2017XTBG-T03
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2020

                Genetics
                assembler,assembly graph,plastome,mitogenome,organelle genome
                Genetics
                assembler, assembly graph, plastome, mitogenome, organelle genome

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