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Significant taxon sampling gaps in DNA databases limit the operational use of marine macrofauna metabarcoding
Marine Biodiversity ( IF 1.5 ) Pub Date : 2020-08-17 , DOI: 10.1007/s12526-020-01093-5
Jon Thomassen Hestetun , Einar Bye-Ingebrigtsen , R. Henrik Nilsson , Adrian G. Glover , Per-Otto Johansen , Thomas G. Dahlgren

Significant effort is spent on monitoring of benthic ecosystems through government funding or indirectly as a cost of business, and metabarcoding of environmental DNA samples has been suggested as a possible complement or alternative to current morphological methods to assess biodiversity. In metabarcoding, a public sequence database is typically used to match barcodes to species identity, but these databases are naturally incomplete. The North Sea oil and gas industry conducts large-scale environmental monitoring programs in one of the most heavily sampled marine areas worldwide and could therefore be considered a “best-case scenario” for macrofaunal metabarcoding. As a test case, we investigated the database coverage of two common metabarcoding markers, mitochondrial COI and the ribosomal rRNA 18S gene, for a complete list of 1802 macrofauna taxa reported from the North Sea monitoring region IV. For COI, species level barcode coverage was 50.4% in GenBank and 42.4% for public sequences in BOLD. For 18S, species level coverage was 36.4% in GenBank and 27.1% in SILVA. To see whether rare species were underrepresented, we investigated the most commonly reported species as a separate dataset but found only minor coverage increases. We conclude that compared to global figures, barcode coverage is high for this area, but that a significant effort remains to fill barcode databases to levels that would make metabarcoding operational as a taxonomic tool, including for the most common macrofaunal taxa.

中文翻译:

DNA数据库中分类单元抽样的巨大空白限制了海洋大型动物元条形码的操作使用

通过政府资助或间接作为商业成本,在监测底栖生态系统上花费了巨大的精力,并且有人建议对环境DNA样本进行元条形码技术,以作为对当前形态学方法进行生物多样性评估的一种可能的补充或替代方法。在元条形码中,通常使用公共序列数据库将条形码与物种身份进行匹配,但是这些数据库自然是不完整的。北海石油和天然气行业在全球采样最频繁的海域之一中进行了大规模的环境监测计划,因此可以被认为是大型动物元条形码的“最佳案例”。作为测试案例,我们调查了两种常见的元条形码标记,即线粒体COI和核糖体rRNA 18S基因的数据库覆盖范围,有关北海监测区域IV所报告的1802个大型动物分类群的完整列表。对于COI,GenBank中物种级别的条形码覆盖率为50.4%,BOLD中公共序列的物种覆盖率为42.4%。对于18S,GenBank中的物种水平覆盖率为36.4%,SILVA中为27.1%。为了查看稀有物种是否代表性不足,我们将最常报告的物种作为单独的数据集进行了调查,但发现覆盖率仅增加了一点。我们得出的结论是,与全球数据相比,该地区的条形码覆盖率很高,但是仍然需要付出巨大的努力才能将条形码数据库填充到一定水平,以使元条形码可以作为一种分类工具(包括最常见的大型动物分类单元)进行操作。对于18S,GenBank中的物种水平覆盖率为36.4%,SILVA中为27.1%。为了查看稀有物种是否代表性不足,我们将最常报告的物种作为单独的数据集进行了调查,但发现覆盖率仅增加了一点。我们得出的结论是,与全球数据相比,该地区的条形码覆盖率很高,但是仍然需要付出巨大的努力才能将条形码数据库填充到一定水平,以使元条形码可以作为一种分类工具(包括最常见的大型动物分类单元)进行操作。对于18S,GenBank中的物种水平覆盖率为36.4%,SILVA中为27.1%。为了查看稀有物种是否代表性不足,我们将最常报告的物种作为单独的数据集进行了调查,但发现覆盖率仅增加了一点。我们得出的结论是,与全球数据相比,该地区的条形码覆盖率很高,但是仍然需要付出巨大的努力才能将条形码数据库填充到一定水平,以使元条形码可以作为一种分类工具(包括最常见的大型动物分类单元)进行操作。
更新日期:2020-08-17
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