Estimate microbial gene functions and metabolic pathways from 16S rRNA sequences and abundance tables using PICRUSt2. The skill covers input preparation, prediction quality checks, and pathway comparisons when shotgun metagenomic sequencing data are unavailable.
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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.
安装前可以查看交给 AI 代理的指令内容。
Reference examples tested with: Biostrings 2.70+, ggplot2 3.5+, pandas 2.2+, phyloseq 1.46+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Predict functional pathways from my 16S data" → Infer metagenome functional content from marker gene (16S/ITS) ASV tables using phylogenetic placement and gene content prediction.
picrust2_pipeline.py -s seqs.fna -i table.biom -o output/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
# 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)
Goal: Predict functional metagenome content from 16S ASVs using the full PICRUSt2 pipeline with explicit control over each step.
Approach: Place ASV sequences into a reference tree, predict gene content via hidden-state prediction, infer per-sample metagenome abundances, and reconstruct MetaCyc pathways.
# 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_predicted.tsv -m pic
# 4. Metagenome inference
metagenome_pipeline.py \
-i asv_table.tsv \
-m marker_nsti_predicted.tsv \
-f KO_predicted.tsv \
-o KO_metagenome_out \
--strat_out
# 5. Pathway inference
pathway_pipeline.py \
-i KO_metagenome_out/pred_metagenome_contrib.tsv \
-o pathway_output \
-p 4
import pandas as pd
# NSTI = Nearest Sequenced Taxon Index
# Lower = more reliable prediction (< 2 is acceptable)
nsti = pd.read_csv('marker_nsti_predicted.tsv', sep='\t')
print(f'Mean NSTI: {nsti["metadata_NSTI"].mean():.3f}')
print(f'ASVs with NSTI > 2: {(nsti["metadata_NSTI"] > 2).sum()}')
library(ggplot2)
pathways <- read.delim('picrust2_output/pathways_out/path_abun_unstrat.tsv', row.names = 1)
metadata <- read.csv('sample_metadata.csv', row.names = 1)
# Normalize to relative abundance
pathways_rel <- sweep(pathways, 2, colSums(pathways), '/')
# Differential pathway analysis (use ALDEx2 or similar)
library(ALDEx2)
groups <- metadata[colnames(pathways), 'Group']
pathway_aldex <- aldex(as.data.frame(t(pathways)), groups, mc.samples = 128)
# Map pathway IDs to names
add_descriptions.py \
-i pathway_abundance.tsv \
-m METACYC \
-o pathway_abundance_described.tsv
# Analyze KEGG modules instead of individual KOs
ko_table <- read.delim('KO_metagenome_out/pred_metagenome_unstrat.tsv', row.names = 1)
# Use KEGGREST for module mapping
library(KEGGREST)
modules <- keggLink('module', 'ko')
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中文简介正在准备中。当前显示英语简介。