Data & ReproBioinformatics & GenomicsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-microbiome-amplicon-processing

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-microbiome-amplicon-processing🧬 bioinformatics (gptomics bio-* suite)bioinformatics — metagenomics & microbiomeamplicon

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

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

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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, and chimera removal. R: dada2::filterAndTrim() → learnErrors() → dada() → removeBimeraDenovo().
plotQualityProfile(fnFs[1:2]) plotQualityProfile(fnRs[1:2]).

Source Doc

Excerpt From SKILL.md

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)