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
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:
Cong Xu1, Feiyu Zhang1, Daoyuan Yu2, Xin Sun3, Feng Zhang1,*()
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:
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。
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.
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 |
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
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 |
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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