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Rapid mitochondrial genome sequencing based on Oxford Nanopore Sequencing and a proxy for vertebrate species identification

机译:基于牛津纳米孔测序的快速线粒体基因组测序和脊椎动物物种鉴定的代理

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Molecular information is crucial for species identification when facing challenging morphology‐based specimen identifications. The use of DNA barcodes partially solves this problem, but in some cases when PCR is not an option (i.e., primers are not available, problems in reaction standardization), amplification‐free approaches could be an optimal alternative. Recent advances in DNA sequencing, like the MinION device from Oxford Nanopore Technologies (ONT), allow to obtain genomic data with low laboratory and technical requirements, and at a relatively low cost. In this study, we explore ONT sequencing for molecular species identification from a total DNA sample obtained from a neotropical rodent and we also test the technology for complete mitochondrial genome reconstruction via genome skimming. We were able to obtain “de novo” the complete mitogenome of a specimen from the genus Melanomys (Cricetidae: Sigmodontinae) with average depth coverage of 78X using ONT‐only data and by combining multiple assembly routines. Our pipeline for an automated species identification was able to identify the sample using unassembled sequence data (raw) in a reasonable computing time, which was substantially reduced when a priori information related to the organism identity was known. Our findings suggest ONT sequencing as a suitable candidate to solve species identification problems in metazoan nonmodel organisms and generate complete mtDNA datasets.
机译:当面对基于挑战的形态的样本标识时,分子信息对于物种鉴定至关重要。使用DNA条形码部分解决了该问题,但在某些情况下,当PCR不是选项时(即,引物不可用,反应标准化的问题),可以是最佳替代的方法。 DNA测序的最新进展,如来自牛津纳米孔技术(ONT)的矿物装置,允许获得具有低实验室和技术要求的基因组数据,并以相对较低的成本。在这项研究中,我们探讨了从新靶啮齿动物获得的总DNA样品中的分子物种鉴定的ONT测序,并通过基因组撇渣来测试全部线粒体基因组重建的技术。我们能够从Melanomys(Cricetidae:sigmodontinae)的完全促成样品的完全发电阻量,使用INT的数据和组合多个组装例程来实现78倍的平均深度覆盖。我们的自动化物种的管道能够在合理的计算时间中使用未组装的序列数据(RAW)识别样品,当已知与生物体特征相关的先验信息时显着减少。我们的研究结果表明,作为合适的候选物,以解决美容蛋白酶非统计生物中的物种鉴定问题并产生完整的MTDNA数据集。

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