bio-workflows-microbiome-pipeline
End-to-end 16S amplicon workflow from FASTQ reads to differential abundance. Orchestrates DADA2 ASV inference, taxonomy assignment, diversity analysis, and compositional testing with ALDEx2. Use when processing 16S/ITS amplicon data.
npx skills add majiayu000/claude-skill-registry --skill microbiome-pipeline-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.
# Microbiome Pipeline ## Pipeline Overview ``` Paired-End FASTQ (16S V4) │ ▼ ┌──────────────────────────────────────────────────┐ │ microbiome-pipeline │ ├──────────────────────────────────────────────────┤ │ 1. Quality Filtering (DADA2 filterAndTrim) │ │ 2. Error Learning & Denoising │ │ 3. Merge Pairs & Remove Chimeras │ │ 4. Taxonomy Assignment (SILVA) │ │ 5. Create phyloseq Object │ │ 6. Alpha/Beta Diversity │ │ 7. Differential Abundance (ALDEx2) │ │ 8. Visualization & Export │ └──────────────────────────────────────────────────┘ │ ▼ ASV Table + Taxonomy + Diversity Plots + Differential Taxa ``` ## Complete R Workflow ```r library(dada2) library(phyloseq) library(ALDEx2) library(vegan) library(ggplot2) # === CONFIGURATION === path <- 'raw_reads' silva_train <- 'silva_nr99_v138.1_train_set.fa.gz' silva_species <- 'silva_species_assignment_v138.1.fa.gz' metadata_file <- 'sample_metadata.csv' # === 1. READ FILES === 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) # Setup filtered files filtFs <- file.path('fil
- Pipeline Overview
- Complete R Workflow
- QC Checkpoints
- Output Files
- Workflow Variants
- ITS Fungal Workflow
- Different 16S Regions
- GTDB Taxonomy
- Related Skills
What does the bio-workflows-microbiome-pipeline skill do?
End-to-end 16S amplicon workflow from FASTQ reads to differential abundance. Orchestrates DADA2 ASV inference, taxonomy assignment, diversity analysis, and compositional testing with ALDEx2. Use when processing 16S/ITS amplicon data.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill microbiome-pipeline-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.