数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-microbiome-amplicon-processing

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Amplicon sequence variant (ASV) inference from 16S rRNA or ITS amplicon sequencing using DADA2. Covers quality filtering, error learning, denoising, and chimera removal. Use when processing demultiplexed amplicon FASTQ files to generate an ASV table for downstream analysis.

OpenClawNanoClaw分析处理复现实验bio-microbiome-amplicon-processing🧬 bioinformatics (gptomics bio-* suite)bioinformatics — metagenomics & microbiomeamplicon

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-microbiome-amplicon-processing

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：dada2：：filterAndTrim() → learnErrors() → dada() → removeBimeraDenovo()。
Process my 16S amplicon data to get ASVs" → Denoise amplicon sequencing reads into exact amplicon sequence variants (ASVs) through quality filtering，error model learning，、 chimera removal. R：dada2：：filterAndTrim() → learnErrors() → dada() → removeBimeraDenovo()。
plotQualityProfile(fnFs[1：2]) plotQualityProfile(fnRs[1：2])。

原始文档

SKILL.md 摘录

Complete DADA2 Workflow

library(dada2)

path <- 'raw_reads'
fnFs <- sort(list.files(path, pattern = '_R1_001.fastq.gz', full.names = TRUE))
fnRs <- sort(list.files(path, pattern = '_R2_001.fastq.gz', full.names = TRUE))
sample_names <- sapply(strsplit(basename(fnFs), '_'), `[`, 1)

## Quality Filtering and Trimming

```r
filtFs <- file.path('filtered', paste0(sample_names, '_F_filt.fastq.gz'))
filtRs <- file.path('filtered', paste0(sample_names, '_R_filt.fastq.gz'))
names(filtFs) <- sample_names
names(filtRs) <- sample_names

## Filter parameters depend on amplicon region and read length

out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs,
                     truncLen = c(240, 160),      # Trim to quality scores
                     maxN = 0,                     # No ambiguous bases
                     maxEE = c(2, 2),              # Max expected errors
                     truncQ = 2,                   # Truncate at first Q <= 2
                     rm.phix = TRUE,               # Remove PhiX
                     compress = TRUE,
                     multithread = TRUE)