研究方法：ZOTU 与 OTU 和 ASV 的比较

特性	OTU（97% 聚类）	ZOTU（UNOISE3）	ASV（DADA2/Deblur）
定义方法	相似性聚类（阈值依赖）	100% 相似性，基于去噪	精确去噪，单碱基分辨率
分辨率	较低，可能合并相似物种	高，可区分单碱基差异	高，可区分单碱基差异
嵌合体处理	可能保留部分嵌合体	自动检测并移除嵌合体	自动检测并移除嵌合体
跨项目可比性	受阈值和工具影响	高，基于精确序列定义	高，基于精确序列定义
计算需求	较低	中等	较高
常用工具	VSEARCH、USEARCH、Mothur	UNOISE3（USEARCH）	DADA2、Deblur、QIIME 2
去噪方法	不去噪（基于聚类）	去噪（规则和经验方法）	去噪（统计模型）

Operational Taxonomic Units:OTUs

Amplicon Sequence Variants:ASVs, also known as Exact Sequence Variants (ESVs)

zero-radius OTUs:ZOTUs

Resources Link

Li Z, Zhao W, Jiang Y, et al. New insights into biologic interpretation of bioinformatic pipelines for fish eDNA metabarcoding: A case study in Pearl River estuary[J]. Journal of Environmental Management, 2024, 368: 122136.

Hakimzadeh A, Abdala Asbun A, Albanese D, et al. A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses[J]. Molecular Ecology Resources, 2024, 24(5): e13847.

MiFish pipeline:https://mitofish.aori.u-tokyo.ac.jp/mifish/help/

dada2_custom_fungal:https://github.com/thierroll/dada2_custom_fungal

16s rRNA demo data:https://www.ncbi.nlm.nih.gov/bioproject/PRJEB27564

nemabiome(dada2_ITS):https://www.nemabiome.ca ITS：https://www.nemabiome.ca/sequencing

16S rDNA V3-V4 amplicon sequencing analysis using dada2, phyloseq, LEfSe, picrust2 and other tools.16S rDNA V3-V4 amplicon sequencing analysis using dada2, phyloseq, LEfSe, picrust2 and other tools.:https://github.com/ycl6/16S-rDNA-V3-V4/

Bioinformatic Methods for Biodiversity Metabarcoding:https://learnmetabarcoding.github.io/LearnMetabarcoding/index.html

kraken2:SILVA 16s https://ftp.arb-silva.de current version:138_2

mkdir -p ref/kraken2/16S_SILVA138_2
docker run -v /staging/fanyucai/eDNA/ref/kraken2/16S_SILVA138_2:/ref/ edna sh -c 'export PATH=/opt/conda/bin:$PATH && sed -i s:138_1:138_2: /opt/conda/share/kraken2-2.1.3-4/libexec/16S_silva_installation.sh && kraken2-build --special silva --db /ref/ --threads 16 --kmer-len 51'

Lu J, Salzberg S L. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2[J]. Microbiome, 2020, 8(1): 124.

Bioinformatics Pipeline

The data flow diagram is as follows:

step1:docker images

local

cd Docker/
docker build -t edna ./

or

docker pull fanyucai1/edna:latest
docker tag fanyucai1/edna:latest edna:latest

step2:build database

step3:download demo data as you needed

step4:The example command is as follows:

16s rRNA pair-end(advice)

python3 script/fastqc.py -p1 test_data/16s/ERR2730388_1.fastq -p2 test_data/16s/ERR2730388_2.fastq -o outdir/16s_advice/1.fastqc/
python3 script/fastqc.py -p1 test_data/16s/ERR2730395_1.fastq -p2 test_data/16s/ERR2730395_2.fastq -o outdir/16s_advice/1.fastqc/

python3 script/fastp.py -p1 test_data/16s/ERR2730388_1.fastq -p2 test_data/16s/ERR2730388_2.fastq -p ERR2730388 -o outdir/16s_advice/2.fastp/
python3 script/fastp.py -p1 test_data/16s/ERR2730395_1.fastq -p2 test_data/16s/ERR2730395_2.fastq -p ERR2730395 -o outdir/16s_advice/2.fastp/

python3 script/cutadapt.py -n 16s_rRNA_V3-v4_341F-785R -r script/primer.tsv -l 300 -o outdir/16s_advice/3.cutadapt/ -p1 outdir/16s_advice/2.fastp/ERR2730388.clean_R1.fastq -p2 outdir/16s_advice/2.fastp/ERR2730388.clean_R2.fastq -p ERR2730388
python3 script/cutadapt.py -n 16s_rRNA_V3-v4_341F-785R -r script/primer.tsv -l 300 -o outdir/16s_advice/3.cutadapt/ -p1 outdir/16s_advice/2.fastp/ERR2730395.clean_R1.fastq -p2 outdir/16s_advice/2.fastp/ERR2730395.clean_R2.fastq -p ERR2730395

python3 script/dada2.py -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -g ref/qiime/2024.09.backbone.full-length.nb.qza -r ref/qiime/ncbi-refseqs-classifier.qza -i outdir/16s_advice/3.cutadapt/ -o outdir/16s_advice/4.dada2/
python3 script/usearch.py -p1 outdir/16s_advice/3.cutadapt/ERR2730388_no_primer_R1.fastq.gz,outdir/16s_advice/3.cutadapt/ERR2730395_no_primer_R1.fastq.gz -p2 outdir/16s_advice/3.cutadapt/ERR2730388_no_primer_R2.fastq.gz,outdir/16s_advice/3.cutadapt/ERR2730395_no_primer_R2.fastq.gz -p ERR2730388,ERR2730395 -o outdir/16s_advice/5.usearch -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -g ref/qiime/2024.09.backbone.full-length.nb.qza -r ref/qiime/ncbi-refseqs-classifier.qza

other:12s

python3 script/other.py -p1 test_data/12s/DRR030411_1.fastq,test_data/12s/DRR030422_1.fastq -p2 test_data/12s/DRR030411_2.fastq,test_data/12s/DRR030422_2.fastq -p DRR030411,DRR030422 -n 12s_rRNA_MiFish-U_MiFish-L -m script/primer.tsv -t 12s -g ref/qiime/2024.09.backbone.full-length.nb.qza -s12 ref/qiime/midori2-12s-classifier.qza -c ref/qiime/midori2-coi-classifier.qza -rfish ref/qiime/edna-fish-12S-16S-18S-classifier.qza -o outdir/12s -r ref/qiime/ncbi-refseqs-classifier.qza -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -i ref/qiime/unite_ver10_dynamic_s_all_19.02.2025-Q2-2024.10.qza -cfish ref/qiime/mitofish_COI-classifier.qza -rbcL /staging/fanyucai/eDNA/ref/qiime/NCBI_rbcL_Viridiplantae_classifier_2021_06_14.qza

other:CO1

python3 script/other.py -p1 test_data/CO1/SRR9185140_1.fastq,test_data/CO1/SRR9185142_1.fastq -p2 test_data/CO1/SRR9185140_2.fastq,test_data/CO1/SRR9185142_2.fastq -p SRR9185140,SRR9185142 -n CO1_BF2R2 -m script/primer.tsv -t CO1 -g ref/qiime/2024.09.backbone.full-length.nb.qza -s12 ref/qiime/midori2-12s-classifier.qza -c ref/qiime/midori2-coi-classifier.qza -rfish ref/qiime/edna-fish-12S-16S-18S-classifier.qza -o outdir/CO1 -r ref/qiime/ncbi-refseqs-classifier.qza -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -i ref/qiime/unite_ver10_dynamic_s_all_19.02.2025-Q2-2024.10.qza -cfish ref/qiime/mitofish_COI-classifier.qza -rbcL /staging/fanyucai/eDNA/ref/qiime/NCBI_rbcL_Viridiplantae_classifier_2021_06_14.qza

other:16s rRNA

python3 script/other.py -p1 test_data/16s/ERR2730388_1.fastq,test_data/16s/ERR2730395_1.fastq -p2 test_data/16s/ERR2730388_2.fastq,test_data/16s/ERR2730395_2.fastq -p ERR2730388,ERR2730395 -n 16s_rRNA_V3-v4_341F-785R -m script/primer.tsv -t 16s_single -g ref/qiime/2024.09.backbone.full-length.nb.qza -s12 ref/qiime/midori2-12s-classifier.qza -c ref/qiime/midori2-coi-classifier.qza -rfish ref/qiime/edna-fish-12S-16S-18S-classifier.qza -o outdir/16s -r ref/qiime/ncbi-refseqs-classifier.qza -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -i ref/qiime/unite_ver10_dynamic_s_all_19.02.2025-Q2-2024.10.qza -cfish ref/qiime/mitofish_COI-classifier.qza -rbcL /staging/fanyucai/eDNA/ref/qiime/NCBI_rbcL_Viridiplantae_classifier_2021_06_14.qza

other:18s rRNA

python3 script/other.py -p1 test_data/18s/v9/SRR12793466_1.fastq,test_data/18s/v9/SRR12793467_1.fastq -p2 test_data/18s/v9/SRR12793466_2.fastq,test_data/18s/v9/SRR12793467_2.fastq -p SRR12793466,SRR12793467 -n 18s_rRNA_V9_1380F-1510R -m script/primer.tsv -t 18s -o outdir/18s -g ref/qiime/2024.09.backbone.full-length.nb.qza -s12 ref/qiime/midori2-12s-classifier.qza -c ref/qiime/midori2-coi-classifier.qza -rfish ref/qiime/edna-fish-12S-16S-18S-classifier.qza -r ref/qiime/ncbi-refseqs-classifier.qza -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -i ref/qiime/unite_ver10_dynamic_s_all_19.02.2025-Q2-2024.10.qza -cfish ref/qiime/mitofish_COI-classifier.qza -rbcL /staging/fanyucai/eDNA/ref/qiime/NCBI_rbcL_Viridiplantae_classifier_2021_06_14.qza

other:ITS

python3 script/other.py -p1 test_data/ITS/SRR30683883_1.fastq,test_data/ITS/SRR30683886_1.fastq -p2 test_data/ITS/SRR30683883_2.fastq,test_data/ITS/SRR30683886_2.fastq -p SRR30683883,SRR30683886 -n ITS2_ITS7-ITS4 -m script/primer.tsv -t ITS -o outdir/ITS -g ref/qiime/2024.09.backbone.full-length.nb.qza -s12 ref/qiime/midori2-12s-classifier.qza -c ref/qiime/midori2-coi-classifier.qza -rfish ref/qiime/edna-fish-12S-16S-18S-classifier.qza -r ref/qiime/ncbi-refseqs-classifier.qza -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -i ref/qiime/unite_ver10_dynamic_s_all_19.02.2025-Q2-2024.10.qza -cfish ref/qiime/mitofish_COI-classifier.qza -rbcL /staging/fanyucai/eDNA/ref/qiime/NCBI_rbcL_Viridiplantae_classifier_2021_06_14.qza

other:rbcL

python3 script/other.py -p1 test_data/rbcL/SRR12374561_1.fastq,test_data/rbcL/SRR12374563_1.fastq -p2 test_data/rbcL/SRR12374561_1.fastq,test_data/rbcL/SRR12374563_1.fastq -p SRR12374561,SRR12374563 -n rbcL2-F_rbcLa-R -m script/primer.tsv -t rbcL -o outdir/rbcL -g ref/qiime/2024.09.backbone.full-length.nb.qza -s12 ref/qiime/midori2-12s-classifier.qza -c ref/qiime/midori2-coi-classifier.qza -rfish ref/qiime/edna-fish-12S-16S-18S-classifier.qza -r ref/qiime/ncbi-refseqs-classifier.qza -s ref/qiime/silva-138.2-ssu-nr99-classifier.qza -i ref/qiime/unite_ver10_dynamic_s_all_19.02.2025-Q2-2024.10.qza -cfish ref/qiime/mitofish_COI-classifier.qza -rbcL /staging/fanyucai/eDNA/ref/qiime/NCBI_rbcL_Viridiplantae_classifier_2021_06_14.qza