数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-microbiome-functional-prediction

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-microbiome-functional-prediction🧬 bioinformatics (gptomics bio-* suite)bioinformatics — metagenomics & microbiomepredict

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-microbiome-functional-prediction

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

CLI：picrust2_pipeline.py -s seqs.fna -i table.biom -o output/。
预测 functional pathways ，面向 my 16S data" → Infer metagenome functional content ，面向 marker gene (16S/ITS) ASV tables ，使用 phylogenetic placement 、 gene content prediction. CLI：picrust2_pipeline.py -s seqs.fna -i table.biom -o output/。
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)。

原始文档

SKILL.md 摘录

Prepare Input Files

library(phyloseq)
library(Biostrings)

ps <- readRDS('phyloseq_object.rds')

## 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

适用场景

适合在functional profiling is needed ，面向 16S data without shotgun metagenomics sequencing时使用。

不适用场景

Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。

上游相关技能

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

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