A comparative evaluation of bioinformatic pipelines for invertebrate biodiversity profiling via environmental DNA metabarcoding

doi:10.17520/biods.2025369

Abstract

Abstract:

Aims: Environmental DNA (eDNA) technology has been increasingly applied in biodiversity research. However, its rapid development has also sparked methodological debates. A key issue involves the selection of bioinformatic pipelines, particularly for extremely biodiverse taxa such as invertebrates. Bioinformatic pipelines significantly affect eDNA-based biodiversity profiles, yet a systematic comparative evaluation of relevant pipelines is currently lacking. Therefore, this study aims to compare and evaluate bioinformatic pipelines commonly used for analyzing eDNA-derived invertebrate sequencing data.

Method: Invertebrate metabarcoding sequencing was carried out on freshwater eDNA samples, and the performance of various bioinformatic pipelines in processing invertebrate sequences was comparatively assessed. Four commonly used clustering or denoising methods (UPARSE, Swarm, UNOISE, and DADA2) and three taxonomic assignment methods (BOLDigger, BLASTN, and Naïve Bayesian Classifier) were selected, together constituting 12 bioinformatic pipelines.

Results: Of the 12 evaluated pipelines, the combination of DADA2 denoising and BOLDigger taxonomic assignment yielded the largest number of invertebrate molecular operational taxonomic units (MOTUs), along with the highest levels of taxonomic coverage and resolution. Among the four clustering or denoising methods, UNOISE and DADA2 denoising yielded more invertebrate MOTUs than UPARSE and Swarm clustering. Among the three taxonomic assignment methods, BOLDigger and BLASTN yielded higher taxonomic coverage and resolution than Naïve Bayesian Classifier.

Conclusion: These findings have significant implications for eDNA-based research of freshwater invertebrate biodiversity. Furthermore, our results suggest that bioinformatic pipelines should be adjusted according to different study taxa and barcode regions to obtain accurate and reliable biodiversity data.

Key words: environmental DNA, invertebrate biodiversity, bioinformatic pipeline, clustering, denoising, taxonomic assignment

Ziling Yan, Xiaoyu Chen, Meng Yao. A comparative evaluation of bioinformatic pipelines for invertebrate biodiversity profiling via environmental DNA metabarcoding[J]. Biodiv Sci, 2026, 34(1): 25369.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2025369

https://www.biodiversity-science.net/EN/Y2026/V34/I1/25369

Figures/Tables 9

Fig. 1 The specific steps and commands of 12 pipelines used in this study

Fig. 2 Rarefaction curve of invertebrate MOTU detected using different pipelines. Each curve represents a PCR product.

Table 1 Invertebrate MOTU, reads and taxonomic coverage obtained from 12 pipelines. The taxonomic coverage here refers to the coverage breadth of the outputs from different pipelines across each taxonomic level.

处理流程 Pipeline		MOTU (Reads)	门 Phylum	纲 Class	目 Order	科 Family	属 Genus	种 Species
UPARSE	BOLDigger	1,548 (1,475,851)	13	27	71	141	108	111
	BLASTN	984 (1,349,864)	11	22	46	107	92	98
	Bayesian	1,391 (1,689,419)	11	19	34	66	71	81
Swarm	BOLDigger	2,142 (1,765,422)	14	31	81	148	109	112
	BLASTN	1,541 (1,680,656)	11	22	47	108	94	104
	Bayesian	2,207 (2,095,227)	11	19	34	67	72	82
UNOISE	BOLDigger	2,392 (1,950,275)	14	31	81	153	108	111
	BLASTN	1,567 (1,797,455)	11	22	47	108	94	104
	Bayesian	2,158 (2,264,348)	11	19	34	67	73	82
DADA2	BOLDigger	2,777 (3,329,666)	14	32	83	164	118	120
	BLASTN	1,818 (2,678,329)	11	23	49	113	102	108
	Bayesian	2,683 (3,690,943)	11	19	35	71	78	87

Fig. 3 Venn diagrams of the number of invertebrate MOTU detected using different clustering or denoising methods (A) and the comparison of MOTU jointly detected using different clustering or denoising methods across different taxonomic assignment methods (B)

Fig. 4 The taxonomic resolution of invertebrate MOTU detected using different pipelines. The taxonomic resolution here refers to the taxonomic precision of the identification results from different pipelines.

Fig. 5 Venn diagrams of the number of invertebrate orders detected using different clustering or denoising methods

Fig. 6 Venn diagrams of the number of invertebrate orders detected using different taxonomic assignment methods

Fig. 7 The number of MOTU belong to major invertebrate orders detected using different pipelines. Only the top 10 orders with the largest number of MOTU are shown.

Fig. 8 PCoA analysis of invertebrate MOTU detected using different pipelines based on Jaccard dissimilarity index. The symbols with the same shape and color correspond to the results of eDNA samples from rivers and lakes.

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