bio-clip-seq-ago-clip-mirna-targets
Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.
npx skills add BioTender-max/awesome-bio-agent-skills --skill ago-clip-mirna-targets --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: eCLIP pipeline (Yeo lab), chimeric eCLIP analysis scripts (Manakov 2022), HEAP pipeline (Li 2020), Hyb pipeline (Travis 2014), TargetScanHuman 8.0, miRDB 6.0, samtools 1.19+, bedtools 2.31+, pyHyb 0.4+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying. # AGO-CLIP and miRNA Target Identification **"Identify direct miRNA-target interactions experimentally"** -> Use Argonaute (AGO1-4) CLIP-seq variants to map miRNA-binding sites on mRNAs, then resolve which miRNA pairs with each site. Three approaches: (a) standard AGO-CLIP recovers AGO-bound sites but cannot say which miRNA; (b) chimeric methods (CLEAR-CLIP, chimeric eCLIP / miR-eCLIP) ligate the miRNA to its target during library prep, producing miRNA-mRNA chimeric reads that unambiguously assign miRNA-target pairs; (c) HEAP uses HaloTag-Ago2 for in vivo profiling. The chi
- Version Compatibility
- Methods Taxonomy
- Critical Choice: Chimeric vs Computational miRNA-Target Pairing
- miRNA-Target Pairing Rules
- Chimeric eCLIP / miR-eCLIP Workflow
- CLEAR-CLIP (Moore 2015) Analysis
- miR-eCLIP Probe Enrichment (Manakov 2022)
- Per-Method Failure Modes
- Standard AGO-CLIP -- Cannot assign miRNA
- Chimeric methods -- Low chimera rate
- Hyb -- BLAST sensitivity vs miRNA length
- Computational seed matching -- High false positive
- Non-canonical miRNA-target pairing missed
- HEAP -- Mouse-only
Step 1: eCLIP-style preprocessing (see clip-seq/clip-preprocessing) umi_tools extract --bc-pattern=NNNNNNNNNN \ cutadapt -a AGATCGGAAGAGCACACGTCT -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \ R1.umi.fq.gz R2.umi.fq.gz Step 2: Chimera-specific alignment Chimeric reads have miRNA sequence (21-23 nt) at 5' followed by target mRNA Step 2a: Trim 5' for miRNA portion + align miRNA part Step 2b: Trim 3' for target mRNA portion + align target part Use custom chimeric-eCLIP pipeline OR Hyb (Travis 2014) Hyb pipeline approach (CLEAR-CLIP and chimeric methods)
What does the bio-clip-seq-ago-clip-mirna-targets skill do?
Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill ago-clip-mirna-targets --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
