bio-microbiome-functional-prediction
Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.
npx skills add majiayu000/claude-skill-registry --skill functional-prediction-gptomics-bioskills --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.
# Functional Prediction with PICRUSt2 ## Prepare Input Files ```r library(phyloseq) library(Biostrings) ps <- readRDS('phyloseq_object.rds') # Export ASV table (samples as columns) otu <- as.data.frame(otu_table(ps)) if (!taxa_are_rows(ps)) otu <- t(otu) write.table(otu, 'asv_table.tsv', sep = '\t', quote = FALSE) # Export ASV sequences as FASTA seqs <- refseq(ps) # Or extract from ASV names if stored there writeXStringSet(seqs, 'asv_seqs.fasta') ``` ## Run PICRUSt2 Pipeline ```bash # Full pipeline (place sequences, predict functions, metagenome inference) picrust2_pipeline.py \ -s asv_seqs.fasta \ -i asv_table.tsv \ -o picrust2_output \ -p 4 \ --stratified \ --per_sequence_contrib # Output files: # - pathway_abundance.tsv (MetaCyc pathways) # - KO_metagenome_out/pred_metagenome_unstrat.tsv (KEGG orthologs) # - EC_metagenome_out/pred_metagenome_unstrat.tsv (EC numbers) ``` ## Step-by-Step Pipeline ```bash # 1. Place sequences in reference tree place_seqs.py -s asv_seqs.fasta -o placed_seqs.tre -p 4 # 2. Hidden state prediction (gene content) hsp.py -i 16S -t placed_seqs.tre -o marker_nsti_predicted.tsv -m pic -n # 3. Predict gene families (KO) hsp.py -i KO -t placed_seqs.tre -o KO_
- Prepare Input Files
- Run PICRUSt2 Pipeline
- Step-by-Step Pipeline
- Quality Control: NSTI
- Analyze Pathway Output
- Add Pathway Descriptions
- KEGG Module Analysis
- Limitations
- Related Skills
Full pipeline (place sequences, predict functions, metagenome inference) picrust2_pipeline.py \ Output files: place_seqs.py -s asv_seqs.fasta -o placed_seqs.tre -p 4 hsp.py -i 16S -t placed_seqs.tre -o marker_nsti_predicted.tsv -m pic -n hsp.py -i KO -t placed_seqs.tre -o KO_predicted.tsv -m pic metagenome_pipeline.py \ pathway_pipeline.py \ Map pathway IDs to names add_descriptions.py \
What does the bio-microbiome-functional-prediction skill do?
Predict metagenome functional content from 16S rRNA marker gene data using PICRUSt2. Infer KEGG, MetaCyc, and EC abundances from ASV tables. Use when functional profiling is needed from 16S data without shotgun metagenomics sequencing.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill functional-prediction-gptomics-bioskills --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.