tooluniverse-noncoding-rna

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Non-Coding RNA Analysis

非编码RNA分析

Pipeline for identifying, annotating, and interpreting non-coding RNAs and their biological roles. Covers microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and other ncRNA classes.

Key principles:

Class determines function — miRNAs repress mRNA translation; lncRNAs have diverse mechanisms (scaffolds, guides, decoys, enhancers); rRNAs/tRNAs are structural
Targets matter more than the ncRNA itself — for miRNAs, the regulated mRNA targets determine the phenotype
Expression context is critical — ncRNAs are highly tissue/cell-type specific
Conservation indicates function — deeply conserved ncRNAs (miR-let-7, MALAT1) have well-established roles
Evidence grading — T1: validated targets (reporter assay, CLIP-seq), T2: high-confidence computational prediction, T3: expression correlation, T4: sequence-based prediction only

Type-based reasoning — look up, don't guess: Non-coding RNA function depends on type: miRNA silences target mRNAs (look up targets in miRTarBase/TargetScan), lncRNA has diverse functions (scaffolding, guiding, decoying — check literature for the specific lncRNA), circRNA may sponge miRNAs.

For any ncRNA query: first identify the class from the name/sequence, then select the appropriate evidence source. Do not assume function based on name alone — a gene named "LINC" may have a characterized mechanism, or none at all. Always search PubMed for the specific ncRNA before interpreting. For miRNAs, validated targets (T1) from miRTarBase outweigh any computational prediction — a predicted target with no experimental support is a hypothesis, not a finding. For lncRNAs, mechanism is almost always determined by experimental studies; use

PubMed_search_articles

with the lncRNA name + "mechanism" or "function" to find relevant evidence. For circRNAs, miRNA sponging is the most common proposed mechanism but is frequently over-claimed — look for CLIP-seq or reporter assay evidence before asserting it.

本流程用于鉴定、注释和解读非编码RNA及其生物学功能，涵盖microRNA（miRNA）、长链非编码RNA（lncRNA）及其他类型的ncRNA。

核心原则:

类别决定功能 — miRNA抑制mRNA翻译；lncRNA具有多种作用机制（支架、引导、诱饵、增强子）；rRNA/tRNA起结构作用
靶标比ncRNA本身更重要 — 对于miRNA，受调控的mRNA靶标决定表型
表达环境至关重要 — ncRNA具有高度的组织/细胞类型特异性
保守性指示功能 — 高度保守的ncRNA（如miR-let-7、MALAT1）具有明确的功能
证据分级 — T1：验证靶标（报告基因实验、CLIP-seq），T2：高置信度计算预测，T3：表达相关性，T4：仅基于序列的预测

基于类型的推理——查阅资料，而非猜测: 非编码RNA的功能取决于其类型：miRNA沉默靶标mRNA（在miRTarBase/TargetScan中查找靶标），lncRNA具有多种功能（支架、引导、诱饵——查阅该特定lncRNA的文献），circRNA可能充当miRNA海绵。

针对任何ncRNA查询：首先根据名称/序列确定其类别，然后选择合适的证据来源。不要仅根据名称假设功能——名为"LINC"的基因可能有明确的作用机制，也可能没有。在解读前务必在PubMed中搜索该特定ncRNA的相关信息。对于miRNA，miRTarBase中的验证靶标（T1）优先级高于任何计算预测——无实验支持的预测靶标仅为假设，而非结论。对于lncRNA，作用机制几乎总是通过实验研究确定；使用

PubMed_search_articles

，以lncRNA名称 + "mechanism"或"function"为关键词查找相关证据。对于circRNA，miRNA海绵是最常见的假设机制，但经常被过度宣称——在断言前需查找CLIP-seq或报告基因实验证据。

When to Use

适用场景

"What are the targets of miR-21?"
"Find lncRNAs associated with breast cancer"
"Is this lncRNA conserved across species?"
"What miRNAs regulate TP53?"
"Annotate these non-coding RNA IDs"
"Which miRNAs are biomarkers for [disease]?"

Not this skill: For mRNA expression analysis, use

tooluniverse-rnaseq-deseq2

. For CRISPR screens, use

tooluniverse-crispr-screen-analysis

"miR-21的靶标有哪些？"
"查找与乳腺癌相关的lncRNA"
"该lncRNA在不同物种间是否保守？"
"哪些miRNA调控TP53？"
"注释这些非编码RNA ID"
"哪些miRNA可作为[疾病]的生物标志物？"

不适用场景：如需进行mRNA表达分析，请使用

tooluniverse-rnaseq-deseq2

。如需进行CRISPR筛选分析，请使用

tooluniverse-crispr-screen-analysis

。

Core Tools

核心工具

Tool	Use For
`miRBase_search_mirna`	Search miRNAs by name, accession, or sequence
`miRBase_get_mirna`	Detailed miRNA info (sequence, genomic location, family)
`miRBase_get_mature_mirna`	Mature miRNA sequences and annotations
`PubMed_search_articles`	Search for validated miRNA targets in literature (e.g., "miR-21 target validation")
`LNCipedia_search_lncrna`	Search lncRNAs by name, gene symbol, or transcript ID
`LNCipedia_get_lncrna`	Detailed lncRNA transcript info (sequence, structure, conservation)
`LNCipedia_get_lncrna_xrefs`	lncRNA gene info with all transcript variants
`LNCipedia_search_ncrna_by_type`	List all transcripts for a lncRNA gene
`LNCipedia_get_lncrna_publications`	lncRNA sequence (FASTA format)
`RNAcentral_search`	Search all ncRNA types across databases
`RNAcentral_get_rna`	Detailed ncRNA annotations from 40+ databases
`Rfam_get_family`	RNA family details (structure, alignment, species distribution)
`Rfam_search`	Search RNA families by keyword
`DisGeNET_search_gene`	ncRNA-disease associations
`PubMed_search_articles`	ncRNA literature
`GTEx_get_median_gene_expression`	Tissue expression of ncRNA genes

工具	用途
`miRBase_search_mirna`	按名称、登录号或序列搜索miRNA
`miRBase_get_mirna`	获取miRNA详细信息（序列、基因组位置、家族）
`miRBase_get_mature_mirna`	获取成熟miRNA序列及注释
`PubMed_search_articles`	在文献中搜索验证后的miRNA靶标（例如："miR-21 target validation"）
`LNCipedia_search_lncrna`	按名称、基因符号或转录本ID搜索lncRNA
`LNCipedia_get_lncrna`	获取lncRNA转录本详细信息（序列、结构、保守性）
`LNCipedia_get_lncrna_xrefs`	获取包含所有转录本变体的lncRNA基因信息
`LNCipedia_search_ncrna_by_type`	列出某一lncRNA基因的所有转录本
`LNCipedia_get_lncrna_publications`	获取lncRNA序列（FASTA格式）
`RNAcentral_search`	跨数据库搜索所有类型的ncRNA
`RNAcentral_get_rna`	从40+数据库获取ncRNA详细注释
`Rfam_get_family`	获取RNA家族详情（结构、比对、物种分布）
`Rfam_search`	按关键词搜索RNA家族
`DisGeNET_search_gene`	获取ncRNA-疾病关联信息
`PubMed_search_articles`	搜索ncRNA相关文献
`GTEx_get_median_gene_expression`	获取ncRNA基因的组织表达情况

Workflow

分析流程

Phase 0: ncRNA Identity & Classification
  Name/ID → miRBase/LNCipedia/RNAcentral → class, sequence, genomic location
    |
Phase 1: Target & Interaction Analysis
  miRNA → target mRNAs; lncRNA → interacting proteins/RNAs/chromatin
    |
Phase 2: Expression & Tissue Specificity
  GTEx/GEO → where is it expressed? Tissue-specific or ubiquitous?
    |
Phase 3: Disease Associations
  DisGeNET/PubMed/CTD → ncRNA-disease links with evidence
    |
Phase 4: Functional Interpretation
  Pathway enrichment of targets → biological role → clinical significance

阶段0：ncRNA鉴定与分类
  名称/ID → miRBase/LNCipedia/RNAcentral → 类别、序列、基因组位置
    |
阶段1：靶标与相互作用分析
  miRNA → 靶标mRNA；lncRNA → 相互作用的蛋白质/RNAs/染色质
    |
阶段2：表达与组织特异性
  GTEx/GEO → 表达位置？组织特异性还是普遍表达？
    |
阶段3：疾病关联
  DisGeNET/PubMed/CTD → 带有证据的ncRNA-疾病关联
    |
阶段4：功能解读
  靶标通路富集分析 → 生物学作用 → 临床意义

Phase 0: ncRNA Identity & Classification

阶段0：ncRNA鉴定与分类

ncRNA classes by size and database:

miRNA (~22 nt, miRBase): Post-transcriptional silencing via 3'UTR binding
lncRNA (>200 nt, LNCipedia): Diverse — chromatin remodeling, transcription regulation, miRNA sponges
rRNA (120-5000 nt, RNAcentral/Rfam): Ribosome components
tRNA (~76 nt, RNAcentral): Amino acid delivery
snoRNA (60-300 nt, Rfam): rRNA modification (methylation, pseudouridylation)
snRNA (~150 nt, Rfam): Spliceosome components
piRNA (26-31 nt, RNAcentral): Transposon silencing in germline
circRNA (variable, RNAcentral): miRNA sponges, protein scaffolds (experimental evidence required)

Identification workflow:

Name starts with
```
miR-
```
or
```
hsa-mir-
```
→ search miRBase
Name starts with
```
LINC
```
,
```
MALAT
```
,
```
HOTAIR
```
,
```
XIST
```
, or ends in
```
-AS1
```
→ search LNCipedia
Any ncRNA type → search RNAcentral (aggregates all databases)
RNA family question → search Rfam

按大小和数据库划分的ncRNA类别：

miRNA（约22 nt，miRBase）：通过结合3'UTR实现转录后沉默
lncRNA（>200 nt，LNCipedia）：功能多样——染色质重塑、转录调控、miRNA海绵
rRNA（120-5000 nt，RNAcentral/Rfam）：核糖体组成部分
tRNA（约76 nt，RNAcentral）：转运氨基酸
snoRNA（60-300 nt，Rfam）：rRNA修饰（甲基化、假尿苷化）
snRNA（约150 nt，Rfam）：剪接体组成部分
piRNA（26-31 nt，RNAcentral）：生殖系中转座子沉默
circRNA（长度可变，RNAcentral）：miRNA海绵、蛋白质支架（需实验证据支持）

鉴定流程:

名称以
```
miR-
```
或
```
hsa-mir-
```
开头 → 在miRBase中搜索
名称以
```
LINC
```
、
```
MALAT
```
、
```
HOTAIR
```
、
```
XIST
```
开头，或以
```
-AS1
```
结尾 → 在LNCipedia中搜索
任何类型的ncRNA → 在RNAcentral中搜索（整合所有数据库）
RNA家族相关问题 → 在Rfam中搜索

Phase 1: Target & Interaction Analysis

阶段1：靶标与相互作用分析

For miRNAs — the targets determine the biology:

NOTE: There is no dedicated miRNA target lookup tool in ToolUniverse. To find miRNA targets:

Literature search (most reliable):

PubMed_search_articles(query="miR-21 target validation luciferase")

Cross-references:
```
miRBase_get_mirna_xrefs(accession="MIMAT0000076")
```
— may link to external target databases
Known targets for well-studied miRNAs: Use the reference table below, then validate via STRING/Reactome
For novel miRNAs: Search PubMed for "[miRNA] target" and extract validated targets from papers

Well-studied miRNA targets (for common oncomiRs/tumor suppressors):

miR-21: PTEN, PDCD4, TPM1, RECK, SPRY1, SPRY2, BTG2
miR-155: SOCS1, SHIP1, AID, TP53INP1
miR-122: SLC7A1, ADAM17 (also HCV IRES cofactor)
let-7: RAS, HMGA2, MYC, LIN28

Target interpretation framework:

Validated (T1): Luciferase reporter, CLIP-seq, degradome-seq — base conclusions on these
High-confidence prediction (T2): TargetScan conserved sites, DIANA-microT score > 0.9 — support validated findings
Prediction only (T3-T4): miRanda, PicTar, RNA22 — hypothesis generation only; do not report as findings

For lncRNAs — the mechanism varies:

lncRNA Mechanism	Example	How to Investigate
Chromatin modifier	HOTAIR, XIST	Check interacting proteins (PRC2, LSD1) via PubMed
Transcription regulator	NEAT1, MEG3	Check nearby genes (cis-regulation) via genomic location
miRNA sponge	MALAT1, circRNAs	Search for miRNA binding sites
Scaffold	NKILA, BCAR4	Check protein interactions
Enhancer RNA	eRNAs	Check ENCODE enhancer annotations

针对miRNA — 靶标决定其生物学功能:

注意：ToolUniverse中没有专门的miRNA靶标查询工具。如需查找miRNA靶标：

文献搜索（最可靠）：

PubMed_search_articles(query="miR-21 target validation luciferase")

交叉引用：
```
miRBase_get_mirna_xrefs(accession="MIMAT0000076")
```
— 可能链接到外部靶标数据库
已研究透彻的miRNA的已知靶标：使用下方参考表，然后通过STRING/Reactome验证
针对新型miRNA：在PubMed中搜索"[miRNA] target"，从论文中提取验证后的靶标

已研究透彻的miRNA靶标（常见癌基因miRNA/肿瘤抑制miRNA）：

miR-21：PTEN、PDCD4、TPM1、RECK、SPRY1、SPRY2、BTG2
miR-155：SOCS1、SHIP1、AID、TP53INP1
miR-122：SLC7A1、ADAM17（同时是HCV IRES辅助因子）
let-7：RAS、HMGA2、MYC、LIN28

靶标解读框架:

验证后（T1）：荧光素酶报告基因、CLIP-seq、降解组测序 — 结论基于此类证据
高置信度预测（T2）：TargetScan保守位点、DIANA-microT评分>0.9 — 支持验证后的结论
仅预测（T3-T4）：miRanda、PicTar、RNA22 — 仅用于生成假设；不作为结论报告

针对lncRNA — 作用机制多样:

lncRNA作用机制	示例	研究方法
染色质修饰因子	HOTAIR、XIST	通过PubMed查找相互作用蛋白质（PRC2、LSD1）
转录调控因子	NEAT1、MEG3	通过基因组位置查找邻近基因（顺式调控）
miRNA海绵	MALAT1、circRNA	搜索miRNA结合位点
支架	NKILA、BCAR4	查找蛋白质相互作用
增强子RNA	eRNAs	查阅ENCODE增强子注释

Phase 2: Expression & Tissue Specificity

阶段2：表达与组织特异性

python

GTEx_get_median_gene_expression(gene_symbol="MIR21")  # miRNA host gene expression

python

GTEx_get_median_gene_expression(gene_symbol="MIR21")  # miRNA宿主基因表达

Note: GTEx measures RNA-seq; miRNA expression may need miRNA-seq data from GEO

注意：GTEx检测RNA-seq；miRNA表达可能需要来自GEO的miRNA-seq数据


**Interpretation**: Tissue-restricted ncRNAs are often functionally important in that tissue. Ubiquitous ncRNAs (like MALAT1) tend to have housekeeping roles.


**解读**：组织限制性ncRNA通常在该组织中具有重要功能。普遍表达的ncRNA（如MALAT1）往往具有管家功能。

Phase 3: Disease Associations

阶段3：疾病关联

python

DisGeNET_search_gene(query="MIR21")  # miR-21 disease associations
PubMed_search_articles(query="miR-21 biomarker cancer")

Key ncRNA-disease associations (well-established T1 examples — always verify via DisGeNET or PubMed for the specific ncRNA):

miR-21: OncomiR in multiple cancers; targets PTEN, PDCD4, TPM1 (hundreds of T1 studies)
miR-155: B-cell lymphoma, inflammation — immune regulation
miR-122: Hepatitis C liver disease — HCV replication cofactor; therapeutic target (miravirsen)
let-7 family: Lung cancer, stem cell differentiation — tumor suppressor targeting RAS, HMGA2
HOTAIR: Breast/colorectal cancer — recruits PRC2, promotes metastasis
MALAT1: Lung cancer/metastasis — splicing regulation
XIST: X-inactivation, cancer — chromatin silencing
H19: Beckwith-Wiedemann syndrome, cancer — imprinted lncRNA, miR-675 host
ANRIL: CVD, diabetes, cancer — CDKN2A/B locus regulation (GWAS-validated)

python

DisGeNET_search_gene(query="MIR21")  # miR-21的疾病关联信息
PubMed_search_articles(query="miR-21 biomarker cancer")

关键ncRNA-疾病关联（已确立的T1示例 — 针对特定ncRNA，务必通过DisGeNET或PubMed验证）：

miR-21：多种癌症中的癌基因miRNA；靶标包括PTEN、PDCD4、TPM1（数百项T1研究）
miR-155：B细胞淋巴瘤、炎症 — 免疫调控
miR-122：丙型肝炎肝病 — HCV复制辅助因子；治疗靶点（miravirsen）
let-7家族：肺癌、干细胞分化 — 靶向RAS、HMGA2的肿瘤抑制因子
HOTAIR：乳腺癌/结直肠癌 — 招募PRC2，促进转移
MALAT1：肺癌/转移 — 剪接调控
XIST：X染色体失活、癌症 — 染色质沉默
H19：贝克威-威德曼综合征、癌症 — 印记lncRNA，miR-675宿主
ANRIL：心血管疾病、糖尿病、癌症 — 调控CDKN2A/B位点（GWAS验证）

Phase 4: Functional Interpretation

阶段4：功能解读

After identifying miRNA targets (Phase 1), run pathway enrichment:

python

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在确定miRNA靶标（阶段1）后，进行通路富集分析：

python

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Collect validated target gene symbols

收集验证后的靶标基因符号

targets = ["PTEN", "PDCD4", "TPM1", "RECK", "SPRY1"] # miR-21 targets

targets = ["PTEN", "PDCD4", "TPM1", "RECK", "SPRY1"] # miR-21靶标

Pathway enrichment

通路富集分析

ReactomeAnalysis_pathway_enrichment(identifiers="PTEN PDCD4 TPM1 RECK SPRY1") STRING_get_network(identifiers="PTEN\rPDCD4\rTPM1\rRECK\rSPRY1", species=9606)


**Interpretation**: If miR-21 targets are enriched in apoptosis and PI3K-AKT signaling → miR-21 is an oncomiR that promotes survival by simultaneously suppressing multiple tumor suppressors.

**Report structure**:
1. **ncRNA Identity** — class, sequence, genomic location, conservation
2. **Targets/Interactions** — validated targets with evidence grades
3. **Expression Profile** — tissue specificity, disease-specific expression changes
4. **Disease Associations** — evidence-graded disease links
5. **Pathway Analysis** — enriched pathways among targets
6. **Mechanistic Model** — how this ncRNA contributes to disease biology
7. **Clinical Potential** — biomarker utility, therapeutic target potential (antagomirs, ASOs)

---

ReactomeAnalysis_pathway_enrichment(identifiers="PTEN PDCD4 TPM1 RECK SPRY1") STRING_get_network(identifiers="PTEN\rPDCD4\rTPM1\rRECK\rSPRY1", species=9606)


**解读**：如果miR-21靶标在凋亡和PI3K-AKT信号通路中富集 → miR-21是一种癌基因miRNA，通过同时抑制多个肿瘤抑制因子促进细胞存活。

**报告结构**:
1. **ncRNA鉴定信息** — 类别、序列、基因组位置、保守性
2. **靶标/相互作用** — 带有证据分级的验证靶标
3. **表达谱** — 组织特异性、疾病特异性表达变化
4. **疾病关联** — 带有证据分级的疾病关联
5. **通路分析** — 靶标中富集的通路
6. **作用机制模型** — 该ncRNA如何参与疾病生物学过程
7. **临床潜力** — 生物标志物效用、治疗靶点潜力（抗miRNA寡核苷酸、反义寡核苷酸）

---

Limitations

局限性

Computational Procedure: TargetScan Predicted Targets (Download-and-Process)

计算流程：TargetScan预测靶标（下载并处理）

TargetScan provides the best computational miRNA target predictions but has no REST API. Download and process locally:

python

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TargetScan提供最佳的miRNA靶标计算预测，但无REST API。需下载并本地处理：

python

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Step 1: Download TargetScan predicted targets (one-time, ~10MB zipped)

步骤1：下载TargetScan预测靶标（一次性操作，压缩包约10MB）

URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip

import pandas as pd import zipfile, io, requests

url = "https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip" resp = requests.get(url, timeout=60) with zipfile.ZipFile(io.BytesIO(resp.content)) as z: fname = z.namelist()[0] df = pd.read_csv(z.open(fname), sep='\t')

import pandas as pd import zipfile, io, requests

Step 2: Query for a specific miRNA family

步骤2：查询特定miRNA家族

mirna = "miR-21-5p" # or "miR-21/590-5p" (TargetScan uses family names) targets = df[df['miRNA Family'].str.contains("miR-21", case=False, na=False)]

mirna = "miR-21-5p" # 或 "miR-21/590-5p"（TargetScan使用家族名称） targets = df[df['miRNA Family'].str.contains("miR-21", case=False, na=False)]

Step 3: Rank by cumulative weighted context++ score

步骤3：按累积加权context++评分排序

targets_ranked = targets.sort_values('Cumulative weighted context++ score', ascending=True) print(f"Top 20 predicted targets of {mirna}:") for _, row in targets_ranked.head(20).iterrows(): print(f" {row['Target Gene']:10s} score={row['Cumulative weighted context++ score']:.3f} " f"sites={row['Total num conserved sites']}")


**Interpretation**: More negative context++ score = stronger predicted repression. Conserved sites (>1) are higher confidence.

targets_ranked = targets.sort_values('Cumulative weighted context++ score', ascending=True) print(f"{mirna}的前20个预测靶标:") for _, row in targets_ranked.head(20).iterrows(): print(f" {row['Target Gene']:10s} score={row['Cumulative weighted context++ score']:.3f} " f"sites={row['Total num conserved sites']}")


**解读**：context++评分越负 → 预测的抑制作用越强。保守位点>1的靶标置信度更高。

Computational Procedure: miRTarBase Validated Targets (Download-and-Process)

计算流程：miRTarBase验证靶标（下载并处理）

miRTarBase has Cloudflare protection blocking programmatic access. Use the R/Bioconductor data package or bulk download:

python

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miRTarBase受Cloudflare保护，阻止程序化访问。可使用R/Bioconductor数据包或批量下载：

python

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Option 1: Download from miRTarBase bulk export (requires browser download first)

选项1：从miRTarBase批量导出下载（需先通过浏览器下载）

Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/

访问：https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/

Download: hsa_MTI.xlsx (human miRNA-target interactions)

下载：hsa_MTI.xlsx（人类miRNA-靶标相互作用）

Option 2: Use the GitHub data dump

选项2：使用GitHub数据转储

https://github.com/jorainer/mirtarbase — R package with cached data

https://github.com/jorainer/mirtarbase — 包含缓存数据的R包

Once you have the file:

获取文件后：

import pandas as pd mti = pd.read_excel("hsa_MTI.xlsx") # or read_csv if TSV

import pandas as pd mti = pd.read_excel("hsa_MTI.xlsx") # 若为TSV格式则用read_csv

Filter for your miRNA

筛选特定miRNA

mir21_targets = mti[mti['miRNA'].str.contains('hsa-miR-21', case=False, na=False)] print(f"miR-21 validated targets: {len(mir21_targets)}")

mir21_targets = mti[mti['miRNA'].str.contains('hsa-miR-21', case=False, na=False)] print(f"miR-21的验证靶标数量: {len(mir21_targets)}")

Filter by evidence strength

按证据强度筛选

strong = mir21_targets[mir21_targets['Support Type'].str.contains( 'Luciferase|Reporter|Western|CLIP', case=False, na=False )] print(f" Strong evidence (reporter/CLIP): {len(strong)}") for _, row in strong.head(10).iterrows(): print(f" {row['Target Gene']:10s} — {row['Support Type']}")


**When download is not available**: Use the built-in reference table in Phase 1 for well-studied miRNAs, or search PubMed for validated targets.

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strong = mir21_targets[mir21_targets['Support Type'].str.contains( 'Luciferase|Reporter|Western|CLIP', case=False, na=False )] print(f" 强证据（报告基因/CLIP）: {len(strong)}") for _, row in strong.head(10).iterrows(): print(f" {row['Target Gene']:10s} — {row['Support Type']}")


**无法下载时**：使用阶段1中已研究透彻的miRNA参考表，或在PubMed中搜索验证后的靶标。

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Limitations

局限性

miRNA target prediction is noisy — even the best algorithms have >50% false positive rates; always prioritize experimentally validated targets
lncRNA function is poorly characterized — only ~5% of annotated lncRNAs have known functions
Expression measurement varies — miRNA-seq, RNA-seq, and microarray capture different ncRNA classes; check the assay type
Species differences — miRNAs are often conserved but lncRNAs are frequently species-specific; cross-species lncRNA comparisons are unreliable

miRNA靶标预测噪音大 — 即使是最佳算法也有>50%的假阳性率；始终优先考虑实验验证的靶标
lncRNA功能研究不足 — 仅约5%的注释lncRNA具有已知功能
表达测量方法差异大 — miRNA-seq、RNA-seq和微阵列捕获的ncRNA类别不同；需检查检测类型
物种差异 — miRNA通常保守，但lncRNA往往具有物种特异性；跨物种lncRNA对比不可靠