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

Bioinformatics Pipeline

The data flow diagram is as follows

prepare

docker images

cd Docker/
docker build -t edna ./

refernce

mkdir ref/
python3 script/qiime_Greengene2_RefSeq_SILVA.py -v 138.2 -o ref/

The example command is as follows

python3 script/fastqc.py -p1 test_data/ERR2730388_1.fastq -p2 test_data/ERR2730388_2.fastq -o outdir/1.fastqc/

python3 script/fastp.py -p1 test_data/ERR2730395_1.fastq -p2 test_data/ERR2730395_2.fastq -p ERR2730395 -o outdir/2.fastp/

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

python3 script/dada2.py -s ref/silva-138.2-ssu-nr99-classifier.qza -g ref/2024.09.backbone.full-length.nb.qza -r ref/ncbi-refseqs-classifier.qza -i outdir/3.cutadapt/ -o outdir/4.dada2/

python3 script/usearch.py -p1 outdir/3.cutadapt/ERR2730388_no_primer_R1.fastq.gz,outdir/3.cutadapt/ERR2730391_no_primer_R1.fastq.gz -p2 outdir/3.cutadapt/ERR2730388_no_primer_R2.fastq.gz,outdir/3.cutadapt/ERR2730391_no_primer_R2.fastq.gz -p ERR2730388,ERR2730391 -o outdir/test/ -g ref/2024.09.backbone.full-length.nb.qza -r ref/ncbi-refseqs-classifier.qza

FROM mothur AS mifish RUN /opt/conda/bin/conda create -n MiFish python==3.9.13 &&
/opt/conda/bin/conda install -n MiFish --channel conda-forge --channel bioconda --channel defaults numpy
scikit-bio anaconda::pyqt ete3 duckdb cutadapt XlsxWriter biopython fastp flash seqkit vsearch blast fasttree gblocks &&
git clone https://github.com/billzt/MiFish.git && cd MiFish && /opt/conda/envs/MiFish/bin/python3 setup.py develop &&
/opt/conda/bin/conda clean -a -y https://github.com/tao-bioinfo/MiFish/archive/refs/heads/main.zip