土壤动物的分子分类预测策略评估

doi:10.17520/biods.2022252

生物多样性 ›› 2022, Vol. 30 ›› Issue (12): 22252. DOI: 10.17520/biods.2022252

所属专题：土壤生物与土壤健康

土壤动物的分子分类预测策略评估

徐聪¹, 张飞宇¹, 俞道远², 孙新³, 张峰¹^,^*()

1.南京农业大学植物保护学院, 南京 210095
2.南京农业大学资源与环境科学学院, 南京 210095
3.中国科学院城市环境研究所, 福建厦门 130102

收稿日期:2022-05-09 接受日期:2022-08-18 出版日期:2022-12-20 发布日期:2022-11-25
通讯作者: *E-mail: fzhang@njau.edu.cn
基金资助:
国家科技基础资源调查专项(2018FY100303);国家自然科学基金(31970434);国家自然科学基金(32270470)

Performance evaluation of molecular taxonomy assignment tools for soil invertebrates

Cong Xu¹, Feiyu Zhang¹, Daoyuan Yu², Xin Sun³, Feng Zhang¹^,^*()

1. College of Plant Protection, Nanjing Agricultural University, Nanjing 210095
2. College of Resources and Environmental Science, Nanjing Agricultural University, Nanjing 210095
3. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 130102

Received:2022-05-09 Accepted:2022-08-18 Online:2022-12-20 Published:2022-11-25
Contact: *E-mail: fzhang@njau.edu.cn

摘要/Abstract

摘要：

土壤动物类群包含庞大的生物多样性, 由于传统的形态学鉴定技术很难满足该类群多样性调查和监测的巨大需求, 基于DNA等遗传物质的分子层面的鉴定技术(分子分类预测)逐渐登上舞台。然而, 分子分类预测能否在参考分子序列严重匮乏的土壤动物分类研究中实现有效鉴定、如何利用分子分类预测更为准确高效地获取土壤动物的分类信息, 是当下分子分类预测在土壤动物应用中的两大难题。为探究这两大难题, 本文基于宏条形码技术, 对5款常用的分子分类预测软件(VSEARCH、HS-BLASTN、EPA-NG、RAPPAS和APPLES; 前两款基于相似度算法, 其余基于系统发育位置算法)进行了准确性(科和属阶元)、运行速度和内存占用等性能的比较和评估。其中, 预测准确性的评估基于4类土壤动物(弹尾纲, 蜱螨亚纲, 环带纲和色矛纲)和3种分子标记(COI、16S和18S)展开。结果表明: EPA-NG在大部分场合下准确性最高, 尤其是在使用COI标记时, 准确性远高于其他工具。VSEARCH和HS-BLASTN准确性也较高, 基于16S和18S标记时, 它们的准确性和EPA-NG相当。此外, VSEARCH在所有软件中运行速度最快且内存占用最小, 这使得它在16S和18S的应用中比EPA-NG更具竞争力。RAPPAS和APPLES具有较低的假阳性, 但假阴性很高, 相对保守的算法使得它们无法将一些物种鉴定到低阶元。总体来说, 即使是在参考数据库缺少目标物种且小部分物种在分类上存在界定争议的前提下, 5款分子分类预测软件都能极为准确地将土壤动物预测至科级阶元, 因此分子分类预测在土壤动物应用中前景远大。COI标记在土壤动物科、属和种阶元上的覆盖度最广且能有效实现分子鉴定, 在目前最适合作为土壤动物尤其是土壤节肢动物的分子标记。在应用COI标记且参考数据库规模不大时, EPA-NG是分子分类预测的最佳选择; 而在应用16S、18S标记或参考数据库规模较大时, 更推荐使用VSEARCH。

关键词: 分子分类预测, 土壤动物, 生物信息学软件, 物种鉴定, 生物多样性

Abstract

Aims: Soil invertebrate communities are of extremely high diversity but still poorly studied in DNA-based diversity assessments. Since traditional morphological identifications have trouble in completing thousands of taxonomy assignments accurately with limited time, more and more biodiversity surveys turn to molecular taxonomy assignments. To promote biodiversity surveys on soil invertebrates, we made a comprehensive comparison for five popular taxonomy assignment tools (VSEARCH, HS-BLASTN, EPA-NG, RAPPAS and APPLES) targeting on different molecular markers (COI, 16S and 18S). Four soil invertebrate groups (Collembola, Acari, Clitellata and Chromadorea) were selected in the comparison representing three representative phyla of varied body-sizes.
Methods: The databases of four soil invertebrate groups using three molecular markers were built with a filtering step. The commands of five taxonomy assignment tools were integrated into a script which would finally output the taxonomic information of query sequences. All of assignment accuracy, running speed and memory usage of five tools were estimated and compared.
Results: Our results indicated that EPA-NG performed best in accuracy for most cases, especially for COI. VSEARCH and HS-BLASTN remained high accuracy and showed similar accuracy performance when utilizing 16S and 18S markers. Moreover, shorter running time and lower memory usage made VSEARCH more popular applying in 16S and 18S than EPA-NG. RAPPAS and APPLES showed unstable performances in accuracy and were often too conservative to identify some species at generic or familial levels.
Conclusion: This study concluded that molecular taxonomy assignment could accomplish identifications of soil invertebrates in an accurate and efficient manner. COI marker is the most recommended marker applied in molecular taxonomy assignment for soil invertebrates because of its abundant repositories of reference sequences reflected in all of species, genus and family levels. When COI is utilized as marker, EPA-NG is the most recommended tool unless the reference database is too large. When 16S or 18S is utilized as marker, VSEARCH is most highly recommended.

Key words: taxonomy assignment, soil invertebrate, bioinformatics tool, identification, biodiversity

徐聪, 张飞宇, 俞道远, 孙新, 张峰 (2022) 土壤动物的分子分类预测策略评估. 生物多样性, 30, 22252. DOI: 10.17520/biods.2022252.

Cong Xu, Feiyu Zhang, Daoyuan Yu, Xin Sun, Feng Zhang (2022) Performance evaluation of molecular taxonomy assignment tools for soil invertebrates. Biodiversity Science, 30, 22252. DOI: 10.17520/biods.2022252.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2022252

https://www.biodiversity-science.net/CN/Y2022/V30/I12/22252

图/表 6

表1 各个类群COI、16S和18S参考数据库中的物种、属和科阶元的数目

Table 1 The biodiversity showed in databases of different groups for COI, 16S and 18S

类群 Taxa	分子标记 Markers	物种数目 Species number	属数目 Genus number	科数目 Family number
弹尾纲 Collembola	COI	1,211	157	22
	16S	387	81	18
	18S	163	79	19
蜱螨亚纲 Acari	COI	1,675	460	190
	16S	456	76	28
	18S	635	459	220
环带纲 Clitellata	COI	1,297	255	39
	16S	972	214	28
	18S	342	203	42
色矛纲 Chromadorea	COI	939	249	86
	16S	170	64	28
	18S	1,042	430	135
4个类群合并 Merged	COI	5,122	1,121	337

图1 5款分类预测软件在4个类群和3种分子标记应用中的平均敏感度

Fig. 1 Mean sensitivity of five taxonomic assignment tools among four groups and three markers

图2 5款分类预测软件在4个类群和3种条码应用中的平均F1分数

Fig. 2 Mean F1-score of five taxonomic assignment tools among four groups and three markers

图3 5款分类预测软件在混合COI参考数据库应用中的平均敏感度(a)和平均F1分数(b)

Fig. 3 Mean sensitivity (a) and mean F1-score (b) of five taxonomic assignment tools with merged COI reference database

图4 5款分类预测软件在1,000量级(a, b)和5,000量级(c, d)参考数据库应用中的(相对)运行速度和内存占用

Fig. 4 Relative running speed and memory usage of five taxonomic assignment tools when applying reference databases with 1,000 sequences (a and b) and 5,000 sequences (c and d) respectively

表2 5款分类预测软件的推荐使用情况

Table 2 The recommendation on application of five taxonomic assignment tools

分类预测软件 Tools	推荐使用情况 Recommendation on application
EPA-NG	以COI作为分子标记且参考数据库不大的场合 COI is used as the marker and the reference database is small
VSEARCH	以16S或18S作为分子标记或者参考数据库较大的场合 16S/18S is used as the marker; the reference database includes thousands of sequences or more
HS-BLASTN	同VSEARCH, 但优先级不如VSEARCH Similar to VSEARCH
APPLES	仅预测较高阶元的场合 Predicting higher taxonomical hierarchy
RAPPAS	目标序列间长度差异较大的场合 When sequence lengths differ greatly

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土壤动物的分子分类预测策略评估

Performance evaluation of molecular taxonomy assignment tools for soil invertebrates

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