miRNA靶点工具

安装指南

1. ClawHub CLI 从源直接安装技能的最快方式。
   npx clawhub@latest install mirna-target-tools
2. 手动安装 将技能文件夹复制到以下位置之一
   全局模式
   ~/.openclaw/skills/
   工作区
   /skills/
   优先级：工作区 > 本地 > 内置
3. 提示词安装 将此提示词复制到 OpenClaw 即可自动安装。
   请帮我使用 Clawhub 安装 mirna-target-tools。如果尚未安装 Clawhub，请先安装（npm i -g clawhub）。
   更倾向于下载？
   获取包含原生技能文件的ZIP压缩包。
   下载技能 (.zip)

什么是

miRNA Target Tools 是一个端到端的生物信息学工具包，用于分析 miRNA 靶点，从成熟 miRNA 序列到注释的基因列表、富集结果、保守性图表和 Cytoscape 网络。它支持 TargetScan 和 miRanda 工作流程，并支持无需预构建 TargetScan 数据库的全转录组预测。内置的 Ensembl 转录组涵盖人类、小鼠、大鼠、绵羊、山羊、奶牛、猪和鸡；您还可以提供自己的转录组 FASTA 或基因组和 GTF。
该技能结合了种子位点定义与 miRanda 双链证据，提供可选的 RNAhybrid 精炼，并支持 3′UTR 限制性扫描。它还提供基因注释、带有 Benjamini-Hochberg FDR 的 GO 和 KEGG 富集、种子区域保守性分析以及出版质量的图形。Openclaw Skills 用户可以使用它来建立可重复的分析管道，同时将下载的参考数据缓存以供重复使用。

应用场景

当没有可用的预构建 TargetScan 数据库时，从成熟的 miRNA 序列预测靶点。
扫描内置物种转录组或分析自定义转录组 FASTA 或基因组加 GTF。
将预测限制在 3′UTR，然后用 miRanda 确认种子位点或用 RNAhybrid 精炼选定的命中位点。
合并 TargetScan 和 miRanda 结果以产生更高置信度的靶点列表。
注释靶基因并识别具有 FDR 调整结果的富集 GO 条目和 KEGG 通路。
跨物种比较 miRNA 同源物并量化保守性，尤其是跨种子区域。
创建出版级的富集和保守性图表或将调控网络导出到 Cytoscape。
在扩大分析规模之前对少量转录本进行快速烟雾测试。

工作原理

检查环境并在文本文件或 FASTA 文件中提供 miRNA 名称或成熟序列。
选择目标来源：内置的 Ensembl 物种、自定义转录组 FASTA 或带有 GTF 注释的基因组 FASTA。内置参考按需下载并缓存。
选择预测工作流程：规范种子扫描、miRanda 双链预测、用 miRanda 确认种子位点的混合工作流程，或记录在案的多引擎三重策略。
可选地将扫描限制在 3′UTR、调整评分和能量阈值、设置并行任务或使用 RNAhybrid 精炼顶级候选者。
审查位点级和转录本级 TSV 结果，包括预测证据、得分、位置和运行元数据。
在适当时合并预测，然后注释靶基因并使用原始 p 值和 Benjamini-Hochberg q 值运行 GO/KEGG 富集。
可选地分析跨物种 miRNA 保守性、生成图表并导出用于 Cytoscape 的 .sif 网络加节点属性。

配置指南

运行环境检查器；它会报告缺失的工具和安装指导：
python3 ~/.openclaw/skills/mirna-target-tools/scripts/check_env.py
为需要它们的 工作流程安装 Python 依赖项。全基因组预测和保守性核心仅使用 Python 标准库；pandas 和 numpy 用于合并和 FDR 工作流程，mygene 是基因注释的可选项。
bash scripts/install_dependencies.sh
对于默认的 R + ggplot2 绘图引擎，安装 R 和所需的 R 包。仅对必需包使用最小选项。Matplotlib 是可选的后备选项，--engine python 强制使用该渲染器。
bash scripts/install_r_deps.sh
bash scripts/install_r_deps.sh --minimal
如果您选择的工作流程需要，请确认诸如 TargetScan 或 miRanda 等外部预测工具可用；使用环境检查器获取特定于工具的指导。RNAhybrid 是用于精炼或基于 RNAhybrid 工作流程的可选项。
准备一个 miRNA 名称列表（.txt，每行一个名称）或成熟序列 FASTA（.fa）。对于全转录组分析，选择内置物种或提供 --mrna-fa，或同时提供 --genome-fa 和 --gtf。
使用内置物种的全转录组运行示例：
python scripts/predict_targets_genome.py --mirna-fa mirna.fa --species sheep \ --target-region utr --method hybrid \ --outdir results/genome --prefix sheep_targets 第一次下载内置参考需要网络访问。Ensembl 和 miRNA 数据库资源被缓存在 ~/.cache/mirna-target-tools/ 下以供以后重复使用和离线运行。

数据架构

结果按分析阶段组织，并使用制表符分隔的表格用于机器可读的预测、注释和统计数据。
阶段 输出文件 内容
靶点预测 {sample}_targetscan_results.txt, {sample}_miranda_results.txt 来自选定的 TargetScan 和 miRanda 工作流程的原始输出。
合并 {sample}_merged_high_confidence.txt 合并的高置信度靶基因列表。
全转录组预测 {prefix}_sites.tsv 每个预测位点一行，包括 miRNA、转录本、基因、位置、位点类型、权重和位点序列。
全转录组预测 {prefix}_targets.tsv 排名靠前的 miRNA–转录本对，具有总得分、每 kb 的位点密度或得分，以及适用的 miRanda 命中计数。
全转录组预测 {prefix}_miranda.tsv, {prefix}_run_info.tsv 使用时的原始 miRanda 范围、得分和 dG；运行来源、计数、参数和位点权重。
注释与富集 {sample}_gene_annotation.tsv, {sample}_enrichment_all.tsv 基因标识符和描述；所有测试的 GO/KEGG 条目带有原始 p 值、BH-FDR q_value 和显著性标志。
富集子集与图表 {sample}_GO_BP.tsv, {sample}_GO_CC.tsv, {sample}_GO_MF.tsv, {sample}_KEGG.tsv, {sample}_enrichment_dotplot.png 按来源分类的显著结果和出版质量的点图；可通过 --svg 获取 SVG。
保守性 {prefix}_aligned.fa, {prefix}_conservation.tsv, {prefix}_summary.tsv 比对序列、每个位置的同一性和信息内容、碱基计数、覆盖范围以及种子/核心摘要统计数据。
保守性图表 {prefix}_sequence_logo.png/.svg, {prefix}_alignment.png/.svg 序列标志和多序列比对可视化。
Cytoscape 导出 {sample}_mirna_target_network.sif, {sample}_node_attributes.txt 用于导入 Cytoscape 的网络边和节点属性。
内置参考数据与结果文件分开缓存，位于 ~/.cache/mirna-target-tools/ 下；Ensembl 基因组和转录组文件按需下载。预测输出可以包括特定于所选引擎或工作流程的证据标签和评分字段。

高级特性

seed
、
miranda
、
hybrid
、
triple
auto
预测工作流程；如果可用，混合工作流程将规范种子位点定义与重叠的 miRanda 证据结合起来。
使用三阶段级联进行快速扫描，用 miRanda 确认，并仅用 RNAhybrid 精炼顶级候选者，或使用较慢的三重策略保留受所有三个引擎支持的靶点。 --target-region utr 进行 3′UTR 限制性预测，或扫描完整剪接的 cDNA；调整引擎阈值、最小 UTR 长度、转录本限制、顶级命中计数和并行作业。
通过 miRBase/RNAcentral 查找按名称或整个物种获取 miRNA，并带有本地 FASTA、缓存和数据库备用选项。
分析来自转录组 FASTA 的自定义装配或从基因组 FASTA 和 GTF 外显子重建转录本。
使用自由端缺口全局比对、香农信息内容、每个位置的同一性和可配置的种子区域统计数据来量化保守性。
默认使用 R 和 ggplot2 生成可编辑的 SVG 和 300 dpi PNG 图形，并具有自动或显式选择的 matplotlib 后备选项。
导出与 Cytoscape 兼容的
.sif
网络和节点属性，并在输出表中保留运行参数和预测证据以便下游审查。

name: mirna-target-tools version: 3.0.0
description: >- End-to-end miRNA target analysis from miRNA sequences alone: whole-transcriptome
target prediction across built-in species (human, mouse, rat, sheep, goat, cow,
pig, chicken) or your own transcriptome/genome, with TargetScan-style seed
sites, miRanda duplex confirmation and 3'UTR-restricted scanning; TargetScan
and miRanda workflows, gene annotation, GO/KEGG enrichment with
Benjamini-Hochberg FDR, seed-region conservation analysis, publication-quality
R/ggplot2 figures, and Cytoscape network export. Fetches miRNA sequences from
public databases itself (miRBase / RNAcentral), by name or for a whole species.

miRNA Target Tools — miRNA target gene prediction toolkit

Overview

An end-to-end toolkit for miRNA target analysis — from raw miRNA sequences to a Cytoscape-ready regulatory network:

Target prediction — TargetScan and miRanda workflows, or genome-wide seed-based prediction across an entire species transcriptome from miRNA sequences alone (no pre-built TargetScan database required)
Built-in species — human, mouse, rat, sheep, goat, cow, pig, chicken (Ensembl transcriptomes downloaded on demand and cached), plus your own transcriptome FASTA or genome + GTF annotation
Annotation and enrichment — gene-symbol annotation plus GO (BP/CC/MF) and KEGG enrichment reporting raw p-values and Benjamini-Hochberg FDR q-values computed over the full tested family
Conservation — multi-species homolog extraction from miRBase, free-end-gap multiple alignment, Shannon information content and seed-region statistics
Publication figures — every plot is drawn with R + ggplot2 in a clean journal-friendly style (300 dpi PNG plus editable-text SVG, matplotlib fallback), with a one-command installer for R and the required packages
Networks — .sif network plus node-attribute export for Cytoscape

Core features

1. TargetScan miRNA target prediction

Run TargetScan to predict target genes for input miRNA sequences and output the target gene list with scores.

Script: scripts/targetscan_predict.py

2. miRanda miRNA target analysis

Run miRanda for miRNA-mRNA binding prediction, with free-energy calculation and conservation analysis.

Script: scripts/miranda_predict.py

3. Result merging and deduplication

Merge predictions from multiple tools and take the intersection to raise confidence, producing a gene list.

Script: scripts/merge_targets.py

4. Target gene annotation

Functional annotation and enrichment analysis of high-confidence target genes:

Gene annotation: gene symbol -> Entrez ID / Ensembl ID / functional description (via the MyGene.info online API, no local database needed)
GO/KEGG enrichment: GO (BP/CC/MF) + KEGG pathway enrichment (via the g:Profiler REST API; standard-library implementation with zero third-party dependencies)
Species support: human / mouse / rat / sheep / goat / cow

Script: scripts/annotate_targets.py

5. Enrichment result visualization

Plot enrichment results as publication-grade dot plots (clusterProfiler style): terms ranked by significance, bubble size = genes in term, bubble colour = -log10(p) on a blue-to-red ramp, size legend + slim colour bar. 300 dpi PNG and (with --svg) an editable vector file.

Rendering engine: every figure in this skill is drawn with R + ggplot2 by default (script scripts/plot_enrichment.R). If R or the required packages are missing, install them with bash scripts/install_r_deps.sh; the scripts auto-detect that and fall back to a matplotlib implementation (--engine python forces it, --engine r requires R).

Script: scripts/plot_enrichment.py

6. Cytoscape network file generation

Generate .sif network files and attribute files that can be imported directly into Cytoscape for regulatory network visualization.

Script: scripts/format_cytoscape.py

7. miRNA conserved sequence analysis

Multi-species conservation analysis of a target miRNA (publication grade):

Homolog extraction: automatically extract the mature sequence of the target miRNA across multiple species from miRBase (exact mirbase-id matching, avoiding prefix collisions such as miR-21 vs miR-210)
Multiple sequence alignment: free-end-gap global alignment (a Needleman-Wunsch variant, pure standard library), with gaps pushed to the 5'/3' ends (isomiR variation) and the seed region strictly aligned
Conservation quantification: Shannon information content (bits, Schneider-Stevens 1990) + per-position percent identity + separate seed-region (default 2-8 nt) statistics
Publication-grade visualization: information-content sequence logo + multiple sequence alignment figure (300 dpi PNG + SVG vector, WebLogo colors)

Script: scripts/conservation_analysis.py

8. Whole-transcriptome target prediction (new in 2.0.0)

Predict every target site of user-supplied mature miRNA sequences across the entire mRNA set (transcriptome) of a species — no TargetScan database required.

Target source — built-in species presets, auto-downloaded from Ensembl on first use and cached, or your own sequences: --species human|mouse|rat|sheep|goat|cow|pig|chicken (aliases such as hsa/chi/oar work too; run --list-species) --mrna-fa my_transcripts.fa — your own mRNA/cDNA FASTA --genome-fa genome.fa --gtf annotation.gtf — your own genome + annotation (mRNA rebuilt by joining exons) miRNA source — any of: --mirna-fa mirna.fa — your own FASTA of mature sequences --mirna miR-21-5p,chi-miR-504-5p — resolve name(s) from the database (species prefix optional) --mirna-species goat (or chi) — fetch every mature miRNA of that species from the database database access goes through scripts/fetch_mirnas.py: local --mature-fa > cache > miRBase download (several URLs) > RNAcentral name lookup. Everything is cached under ~/.cache/mirna-target-tools/, so later runs work offline. If the network is blocked, python scripts/fetch_mirnas.py --species goat --mature-fa /path/to/mature.fa still works from a local copy.
Prediction engine (--method):
seed — canonical TargetScan seed sites (8mer / 7mer-m8 / 7mer-A1 / 6mer, weights 4/3/2/1). This is TargetScan's site definition only, not context++: that model needs pre-computed multi-species UTR alignments and does not exist for sheep/goat
miranda — run miRanda (Enright et al. 2003) over every miRNA x transcript pair: full duplex alignment with score and dG
hybrid — (recommended, and the default when miRanda is installed) seed sites are kept only when a miRanda alignment overlaps them, and each confirmed site carries its miRanda score and dG; this combines the precise seed definition with thermodynamic evidence
auto — hybrid when miRanda is available, otherwise seed
Target region (--target-region): cdna (full spliced transcript) or utr (3'UTR only, sliced from the Ensembl GTF - miRNA targeting is 3'UTR-dominant, so this is the biologically faithful choice)
Tuning: --miranda-score (default 140), --miranda-energy (default -20 kcal/mol), --rnahybrid-energy (default -20 kcal/mol), --rnahybrid-dataset (p-value dataset; only fly/worm/human ship with the tool), --rnahybrid-max-target (default 10000 nt - RNAhybrid silently skips longer targets), --min-utr-length (default 30 nt), --jobs
Two documented strategies (pick one)

Strategy A - three-stage cascade (fast, recommended default)

# stage 1 seed scan (whole transcriptome, seconds) -> stage 2 miRanda confirmation
# -> stage 3 RNAhybrid exact-MFE refinement of the top N targets per miRNA
python predict_targets_genome.py --mirna-fa mirna.fa --species goat \

--target-region utr --method hybrid --refine-rnahybrid 200
--outdir results/genome --prefix goat_cascade

Cheap because RNAhybrid (by far the slowest engine) only ever sees a few hundred transcripts; mfe_verified in *_targets.tsv marks whether the refined target actually passed the MFE cutoff.

Strategy B - triple parallel + intersection (slow, most complete data)

# all three engines run over everything; only targets supported by ALL THREE survive
python predict_targets_genome.py --mirna-fa mirna.fa --species goat \

--target-region utr --method triple
--outdir results/genome --prefix goat_triple

Every surviving row carries all three evidence types (seed site types + miRanda score/dG + RNAhybrid MFE/p-value) in the evidence column (seed+miranda+rnahybrid). This is the most complete and most conservative result, but RNAhybrid over a whole transcriptome is ~60-100x slower than miRanda, so expect hours per handful of miRNAs - run it on 3'UTRs, not full cDNAs, and use --jobs. For a quick single-engine check use --method rnahybrid on a small candidate set.

Scale — seed mode is standard library only (~5 s for the goat transcriptome); hybrid/3'UTR mode needs the miRanda binary and downloads the Ensembl GTF alongside the cDNA

Script: scripts/predict_targets_genome.py

Runtime and hardware

Prediction time depends on your machine and the engine you pick, not only on the dataset. Reference measurements on the development box (16 cores, WSL2; Ensembl goat cDNA = 34,136 transcripts):

Step Reference time What it scales with

seed scan, 1 miRNA, full cDNA ~5 s single-threaded Python: CPU clock, total sequence length x miRNA count
seed scan, 1 miRNA, 3'UTR only ~1-2 s same, but 3'UTRs are far smaller than cDNAs
miRanda, 1 miRNA, full cDNA ~48 s (parallel with --jobs) C code, scales roughly linearly with cores
RNAhybrid, 1 miRNA, full cDNA 60-100x slower than miRanda exact MFE dynamic programming; cost grows with target length
Ensembl download (first run per species) goat: cDNA 20 MB + GTF 13 MB your network speed; cached afterwards

Practical notes:

--jobs parallelises miRanda/RNAhybrid across miRNAs; use min(cores, #miRNAs). Scanning is memory-bound: all target transcripts are held in RAM (the goat transcriptome fits in a few hundred MB; the human cDNA set may need a few GB).
A slow laptop or a busy shared server can be 5-20x slower than the table above. Prefer --target-region utr and the cheaper engines first, then refine the top hits.
Nothing is downloaded twice: Ensembl cDNA/GTF and miRBase are cached under ~/.cache/mirna-target-tools/.

Full analysis workflow

Standard miRNA target prediction + annotation + regulatory network workflow:

Check the environment: confirm TargetScan, miRanda, and Python dependencies are installed

Provide the input miRNA list/sequence file

Predict targets with TargetScan and miRanda separately — or, if you have no TargetScan database, predict genome-wide directly from miRNA sequences with predict_targets_genome.py
Merge results and take the intersection to obtain high-confidence targets
Annotate the target genes (gene annotation + GO/KEGG enrichment) and generate enrichment bubble plots
(Optional) Run multi-species conserved sequence analysis on the target miRNA to validate seed-region conservation
Generate Cytoscape network files (.sif)
Guide the user through importing into Cytoscape for visualization and clustering

Environment check

Run scripts/check_env.py before use to check whether the tools are installed:

python3 ~/.openclaw/skills/mirna-target-tools/scripts/check_env.py

If a tool is missing, the script prints installation steps.

Input formats

.txt file: one miRNA name per line (e.g. hsa-miR-21-5p) — for TargetScan/miRanda
.fa file: FASTA-format miRNA sequence file — usable by every script, including predict_targets_genome.py

Output files

Target prediction stage:

{sample}_targetscan_results.txt - raw TargetScan output
{sample}_miranda_results.txt - raw miRanda output
{sample}_merged_high_confidence.txt - merged high-confidence target gene list

Whole-transcriptome prediction stage (predict_targets_genome.py):

{prefix}_sites.tsv - one row per predicted site (miRNA, transcript, gene, position, site type, weight, site sequence)
{prefix}_targets.tsv - one row per (miRNA, transcript) ranked by total score, with site_density_per_kb / score_per_kb for a length-fair comparison (--sort-by density) and miRanda hit counts
{prefix}_miranda.tsv - raw miRanda hits (query/target ranges, score, dG) when the miranda or hybrid engine is used
{prefix}_run_info.tsv - run metadata (source, counts, parameters, site weights)

Annotation stage (output of annotate_targets.py):

{sample}_gene_annotation.tsv - gene annotation table (symbol -> Entrez/Ensembl ID + description)
{sample}_enrichment_all.tsv - every tested term with raw p-value, BH-FDR q_value, significant flag (GO:BP/CC/MF + KEGG)
{sample}_GO_BP.tsv / {sample}_GO_CC.tsv / {sample}_GO_MF.tsv / {sample}_KEGG.tsv - per-source results filtered to significant terms
{sample}_enrichment_dotplot.png (+ .svg with --svg) - enrichment dot plot (output of plot_enrichment.py)

Conservation stage (output of conservation_analysis.py):

{prefix}_aligned.fa - multiple sequence alignment (with gaps)
{prefix}_conservation.tsv - per-position conservation statistics (identity %, bits, base counts, coverage)
{prefix}_summary.tsv - summary statistics (seed/core region conservation, mean pairwise identity)
{prefix}_sequence_logo.png/.svg - information-content sequence logo
{prefix}_alignment.png/.svg - multiple sequence alignment figure

Network construction stage:

{sample}_mirna_target_network.sif - Cytoscape network file
{sample}_node_attributes.txt - node attribute file (importable into Cytoscape)

Example usage

Target prediction

python targetscan_predict.py --input mirna.txt --output results/targetscan.txt
python miranda_predict.py --mirna mirna.fa --mrna 3utr.fa --output results/miranda.txt
python merge_targets.py --targetscan targetscan.txt --miranda miranda.txt --output merged.txt

Whole-transcriptome prediction from miRNA sequences (new in 2.0.0)

# Built-in species (Ensembl cDNA auto-download + cache) - sheep example
python predict_targets_genome.py --mirna-fa mirna.fa --species sheep \

--outdir results/genome --prefix sheep_targets

# Which species are built in?
python predict_targets_genome.py --list-species

# Your own transcriptome FASTA (any species / assembly)
python predict_targets_genome.py --mirna-fa mirna.fa \

--mrna-fa my_transcripts.fa --outdir results/genome --prefix my_targets

# Your own genome + GTF annotation (mRNA rebuilt from exons)
python predict_targets_genome.py --mirna-fa mirna.fa \

--genome-fa genome.fa --gtf annotation.gtf
--outdir results/genome --prefix custom_targets

# Quick smoke test: first 2,000 transcripts only, keep top 20 targets per miRNA
python predict_targets_genome.py --mirna-fa mirna.fa --species goat \

--max-transcripts 2000 --top 20 --outdir results/genome --prefix quick

Target gene annotation

# Extract targets from the merged result, then annotate + run GO/KEGG enrichment (goat example)
python annotate_targets.py --input merged.txt --gene-col TargetGene \

--species goat --output-dir results/annotation

# Visualize enrichment results (dot plot, 300 dpi PNG + editable SVG)
python plot_enrichment.py --input results/annotation/merged_enrichment_all.tsv \

--output results/annotation/merged_dotplot.png --source KEGG --top 15 --svg

# Other useful knobs: --sort size, --wrap, --figsize "9,7", --title "...", --dpi 600

miRNA conserved sequence analysis

# Auto-extract multi-species homologs of miR-21-5p from miRBase, then run conservation analysis
python conservation_analysis.py --mirna miR-21-5p \

--reference-species hsa --outdir results/conservation --prefix miR-21

# Or supply your own multiple sequence FASTA
python conservation_analysis.py --input-fa homologs.fa \

--reference-species hsa --outdir results/conservation

# Restrict to a species allow-list + custom seed region
python conservation_analysis.py --mirna miR-21-5p \

--species hsa,mmu,rno,bta,ssc --seed-start 2 --seed-end 8

Network construction

python format_cytoscape.py --input merged.txt --output network.sif --attr-output node_attributes.txt

Dependency installation

R + ggplot2 (default plotting engine):

bash scripts/install_r_deps.sh # R itself + ggplot2, dplyr, stringr, scales (+ optional svglite, ggrepel, patchwork, ggsci, forcats)
bash scripts/install_r_deps.sh --minimal # required packages only

The installer puts R packages in the user library (~/R/library), so it needs no root; if R itself is missing it tries conda/mamba first and otherwise prints the apt/brew command to run. scripts/check_env.py reports whether the R engine is ready.

Python:

bash scripts/install_dependencies.sh # Ubuntu/Debian and macOS

pandas, numpy (required for the TargetScan/miRanda merge scripts and enrichment FDR)
mygene (optional; only needed for gene annotation - GO/KEGG enrichment is pure standard library with zero extra dependencies)
matplotlib (optional; fallback only - used when R is unavailable)

predict_targets_genome.py and the conservation core are pure standard library. Built-in species mRNA sets are downloaded on demand from Ensembl and cached under ~/.cache/mirna-target-tools/genomes/ (tens of MB compressed each; human ~ 70 MB), so they are not bundled with the skill — the first run for a species needs network access.

上下文轮换
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