Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples.
Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples.
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DOI：
10.1038/sdata.2017.27
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发表时间：
2017-03-14
期刊：
影响因子：
9.8
通讯作者：
Knowlton N
中科院分区：
文献类型：
--
作者：
Machida RJ;Leray M;Ho SL;Knowlton N
线粒体编码基因越来越成为使用高通量测序方法来表征环境样本（例如浮游生物、小型动物、过滤水）中后生动物群落特征的研究的目标。然而，与核糖体 RNA 标记不同，迄今为止还没有可用于分类学分配的高质量参考数据集。在这里，我们从 GenBank 检索了所有后生动物线粒体基因序列，然后使用分类分配工具对数据集进行质量过滤和格式化，以进行分类分配。参考数据集“Midori 参考资料”可从 www.reference-midori.info 下载。提供了两个版本：(I) Midori-UNIQUE，包含与每个物种相关的所有独特单倍型；(II) Midori-LONGEST，包含每个物种的单个序列（最长）。总体而言，线粒体细胞色素氧化酶亚基 I 基因是序列最丰富的基因。然而，在一些门的大量物种中观察到线粒体大核糖体亚基 RNA 和细胞色素 b 脱辅基酶基因的序列。 Midori 参考与一些分类分配软件兼容。因此，使用这些数据集进行自动化高通量序列分类分配可能特别有效。
Mitochondrial-encoded genes are increasingly targeted in studies using high-throughput sequencing approaches for characterizing metazoan communities from environmental samples (e.g., plankton, meiofauna, filtered water). Yet, unlike nuclear ribosomal RNA markers, there is to date no high-quality reference dataset available for taxonomic assignments. Here, we retrieved all metazoan mitochondrial gene sequences from GenBank, and then quality filtered and formatted the datasets for taxonomic assignments using taxonomic assignment tools. The reference datasets—‘Midori references’—are available for download at www.reference-midori.info. Two versions are provided: (I) Midori-UNIQUE that contains all unique haplotypes associated with each species and (II) Midori-LONGEST that contains a single sequence, the longest, for each species. Overall, the mitochondrial Cytochrome oxidase subunit I gene was the most sequence-rich gene. However, sequences of the mitochondrial large ribosomal subunit RNA and Cytochrome b apoenzyme genes were observed for a large number of species in some phyla. The Midori reference is compatible with some taxonomic assignment software. Therefore, automated high-throughput sequence taxonomic assignments can be particularly effective using these datasets.