AI SummaryPredict 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.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "bio-microbiome-functional-prediction" skill in my project. Please run this command in my terminal: # Install skill into your project (2 files) mkdir -p .claude/skills/functional-prediction && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/functional-prediction/SKILL.md "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/functional-prediction/SKILL.md" && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/functional-prediction/metadata.json "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/functional-prediction/metadata.json" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
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.
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') `
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
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