bio-microbiome-amplicon-processing
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.
npx skills add majiayu000/claude-skill-registry --skill amplicon-processing-gptomics-bioskills-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Amplicon Processing with DADA2 ## Complete DADA2 Workflow ```r 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 profiles plotQualityProfile(fnFs[1:2]) plotQualityProfile(fnRs[1:2]) ``` ## 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) ``` ## Error Rate Learning ```r errF <- learnErrors(filtFs, multithread = TRUE) errR <- learnErrors(filtRs, multithread = TRUE) # Visualize error rates plotErrors(errF, nominalQ = TRUE) ``` ## Sample Inference (D
- Complete DADA2 Workflow
- Quality Filtering and Trimming
- Error Rate Learning
- Sample Inference (Denoising)
- Merge Paired Reads
- Construct Sequence Table
- Remove Chimeras
- Track Reads Through Pipeline
- ITS-Specific Processing
- Related Skills
What does the bio-microbiome-amplicon-processing skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill amplicon-processing-gptomics-bioskills-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.