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Identification of species by multiplex analysis of variable-length sequences

机译:通过可变长度序列的多重分析鉴定物种

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摘要

The quest for a universal and efficient method of identifying species has been a longstanding challenge in biology. Here, we show that accurate identification of species in all domains of life can be accomplished by multiplex analysis of variable-length sequences containing multiple insertion/deletion variants. The new method, called SPInDel, is able to discriminate 93.3% of eukaryotic species from 18 taxonomic groups. We also demonstrate that the identification of prokaryotic and viral species with numeric profiles of fragment lengths is generally straightforward. A computational platform is presented to facilitate the planning of projects and includes a large data set with nearly 1800 numeric profiles for species in all domains of life (1556 for eukaryotes, 105 for prokaryotes and 130 for viruses). Finally, a SPInDel profiling kit for discrimination of 10 mammalian species was successfully validated on highly processed food products with species mixtures and proved to be easily adaptable to multiple screening procedures routinely used in molecular biology laboratories. These results suggest that SPInDel is a reliable and cost-effective method for broad-spectrum species identification that is appropriate for use in suboptimal samples and is amenable to different high-throughput genotyping platforms without the need for DNA sequencing.
机译:寻求一种通用且有效的鉴定物种的方法一直是生物学中的长期挑战。在这里,我们表明可以通过对包含多个插入/缺失变体的可变长度序列进行多重分析,来实现对生命所有领域中物种的准确识别。名为SPInDel的新方法能够区分18个分类组中的93.3%的真核生物。我们还证明,利用片段长度的数字轮廓识别原核和病毒物种通常很简单。提出了一个计算平台,以促进项目的计划,并包括一个大数据集,该数据集具有生命各个领域中物种的近1800个数字轮廓(真核生物为1556,原核生物为105,病毒为130)。最后,一种用于区分10种哺乳动物物种的SPInDel分析试剂盒已在具有物种混合物的高度加工食品上成功验证,并且证明很容易适应分子生物学实验室常规使用的多种筛选程序。这些结果表明,SPInDel是一种可靠且具有成本效益的广谱物种鉴定方法,适合用于次优样品,并且适用于不同的高通量基因分型平台,而无需进行DNA测序。

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