tooluniverse-noncoding-rna - Claude MCP Skill

# Non-Coding RNA Analysis

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**:
1. **Class determines function** — miRNAs repress mRNA translation; lncRNAs have diverse mechanisms (scaffolds, guides, decoys, enhancers); rRNAs/tRNAs are structural
2. **Targets matter more than the ncRNA itself** — for miRNAs, the regulated mRNA targets determine the phenotype
3. **Expression context is critical** — ncRNAs are highly tissue/cell-type specific
4. **Conservation indicates function** — deeply conserved ncRNAs (miR-let-7, MALAT1) have well-established roles
5. **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.

---

## When to Use

- "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`.

---

## Core Tools

| 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_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_by_accession` | Detailed ncRNA annotations from 40+ databases |
| `RNAcentral_get_genome_locations` | Genomic coordinates (chrom/strand/start/end, assembly) of a URS accession in a given organism — different question from `get_by_accession` (annotation, not coordinates) |
| `RNAcentral_get_sequence` | Raw FASTA nucleotide sequence for a URS accession |
| `MODOMICS_search_modifications` / `MODOMICS_list_modifications` | Look up RNA chemical modifications (m6A, pseudouridine, m5C, etc.) by name/formula, or browse all 430+ | 
| `Rfam_get_family` | RNA family details (structure, alignment, species distribution) |
| `Rfam_search_sequence` | 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 |

---

## Workflow

```
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
```

### Phase 0: ncRNA Identity & Classification

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

**Genomic location and raw sequence** (once you have a URS accession from
`RNAcentral_search`): `RNAcentral_get_by_accession` gives cross-database
*annotation*; `RNAcentral_get_genome_locations`/`RNAcentral_get_sequence`
are the separate tools for genomic *coordinates* and the raw *FASTA
sequence* respectively — verified live:

```
tu.run_tool("RNAcentral_get_genome_locations", {"urs_id": "URS00003B7674", "taxid": 9606})
#   -> locations[] with chromosome, strand, start, end, assembly_id (GRCh38), gca_accession
tu.run_tool("RNAcentral_get_sequence", {"urs_id": "URS00003B7674"})
#   -> real verified response for let-7a-5p:
#      {"fasta_header": "URS00003B7674 ncRNA from 61 species",
#       "sequence": "UGAGGUAGUAGAUUGUAUAGUU", "length": 22}
```

**RNA chemical modifications (MODOMICS)** — for epitranscriptomic questions
(m6A, pseudouridine, m5C, etc.), especially relevant for snoRNA-guided rRNA
modification and mass-spec-based modification identification:

```
tu.run_tool("MODOMICS_search_modifications", {"query": "pseudouridine"})
#   -> matches by name/short-name/formula, e.g. id 8 "1-methyl-3-(3-amino-
#      3-carboxypropyl)pseudouridine"
tu.run_tool("MODOMICS_list_modifications", {"limit": 20})
#   -> browse all 430+ known modifications with formula/mass/reference_moiety
```

**`MODOMICS_get_modification` is broken — verified live, do not use for a
specific ID lookup.** Calling it with `modification_id` 58, 78, or 22 (all
distinct, well-known modifications per the tool's own description: m6A,
pseudouridine, m5C) returns the identical hardcoded record every time (id 2,
"5,2'-O-dimethylcytidine") — the parameter is silently ignored. `search`/
`list` correctly vary by query and return `name`/`short_name`/`formula`/
`mass_avg`, which covers most needs; only SMILES/monoisotopic-mass/
product-ion detail (unique to `get_modification`) is currently unobtainable
for a specific ID — say so rather than fabricating those fields.

### Phase 1: Target & Interaction Analysis

**For miRNAs** — the targets determine the biology:

**PRIMARY TOOL**: `ENCORI_get_miRNA_targets` looks up miRNA-target interactions from ENCORI/starBase (CLIP-seq-supported + computationally predicted), no download needed:

1. **miRNA → targets**: `ENCORI_get_miRNA_targets(mirna="hsa-miR-21-5p", clip_min=1)` — each hit reports `clip_experiments` (CLIP-seq support; higher = stronger experimental evidence) and `predicted_by` (which programs call it). Results are ranked by CLIP support, so the top rows are the best-supported targets.
2. **gene → miRNAs**: `ENCORI_get_miRNA_targets(gene="TP53")` — which miRNAs target a gene.

Supporting/fallback approaches:
3. **Literature** (for mechanism/validation context): `PubMed_search_articles(query="miR-21 target validation luciferase")`
4. **Cross-references**: `miRBase_get_mirna_xrefs(accession="MIMAT0000076")`
5. **For novel miRNAs** not in ENCORI: search PubMed for "[miRNA] target".

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 |

### Phase 2: Expression & Tissue Specificity

```python
GTEx_get_median_gene_expression(gene_symbol="MIR21")  # miRNA host gene expression
# Note: GTEx measures RNA-seq; miRNA expression may need miRNA-seq data from GEO
```

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

### Phase 3: Disease Associations

```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)

### Phase 4: Functional Interpretation

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

```python
# Collect validated target gene symbols
targets = ["PTEN", "PDCD4", "TPM1", "RECK", "SPRY1"]  # miR-21 targets

# Pathway enrichment
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)

---

## Limitations

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

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

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

# Step 2: Query for a specific miRNA family
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)]

# Step 3: Rank by cumulative weighted context++ score
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.

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

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

```python
# Option 1: Download from miRTarBase bulk export (requires browser download first)
# Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/
# Download: hsa_MTI.xlsx (human miRNA-target interactions)

# Option 2: Use the GitHub data dump
# https://github.com/jorainer/mirtarbase — R package with cached data

# Once you have the file:
import pandas as pd
mti = pd.read_excel("hsa_MTI.xlsx")  # or read_csv if TSV

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

# Filter by evidence strength
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.

---

## Limitations

- **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