bio-microbiome-functional-prediction

name	bio-microbiome-functional-prediction
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.
tool_type	cli
primary_tool	picrust2

Functional Prediction with PICRUSt2

Prepare Input Files

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

# 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

# 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

Quality Control: NSTI

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()}')

Analyze Pathway Output

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)

Add Pathway Descriptions

# Map pathway IDs to names
add_descriptions.py \
    -i pathway_abundance.tsv \
    -m METACYC \
    -o pathway_abundance_described.tsv

KEGG Module Analysis

# 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')

Limitations

• Predictions based on phylogenetic placement
• Novel taxa (high NSTI) have unreliable predictions
• 16S resolution limits species-level accuracy
• Cannot detect horizontal gene transfer events

Related Skills

• amplicon-processing - Generate ASV input
• metagenomics/functional-profiling - Direct shotgun-based profiling
• pathway-analysis/kegg-pathways - KEGG pathway enrichment