bio-clip-seq-m6a-clip
Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
npx skills add BioTender-max/awesome-bio-agent-skills --skill m6a-clip --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: miCLIP2 pipeline (Kortel 2021), m6Aboost 1.0+, GLORI-tools (Liu 2023), Bullseye 1.0+, m6Anet 2.1+, EpiNano 1.2+, MeRIPSeq tools (exomePeak2 1.16+), nanocompore 1.0+, samtools 1.19+, bedtools 2.31+, R 4.3+. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - 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. # m6A CLIP (N6-Methyladenosine Profiling) **"Map m6A modifications at single-nucleotide resolution"** -> Profile m6A on RNA using one of three orthogonal approaches: antibody-based UV-CL (miCLIP/miCLIP2), antibody-free chemical conversion (GLORI), or enzyme-fusion editing (DART-seq with APOBEC1-YTH). Nanopore direct RNA (m6Anet, nanocompore, EpiNano) provides a fourth modality. The DRACH consensus motif (D=A/G/U, R=A/G, A=m6A, C=C, H=A/C/U) constrains plausible sites but is not exclusive - only a f
- Version Compatibility
- Methods Taxonomy
- Critical Choice: Antibody-Based vs Antibody-Free
- DRACH Motif Constraint
- Cross-Method Discordance
- miCLIP2 Workflow
- GLORI Workflow (Antibody-Free Stoichiometric)
- DART-seq Workflow (Editing-Based)
- m6Anet Workflow (Nanopore)
- Per-Method Failure Modes
- miCLIP / miCLIP2 -- Antibody specificity
- GLORI -- RNA degradation
- DART-seq -- Off-target editing
- m6Anet -- Coverage requirement
Step 1: Preprocessing (eCLIP-style - 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: Alignment (eCLIP-style) STAR --runMode alignReads --genomeDir STAR_index \ umi_tools dedup --method=unique --paired -I aligned.bam -S dedup.bam Step 3: Single-nt CL site detection - PureCLIP or custom pureclip -i dedup.bam -bai dedup.bam.bai -g genome.fa \ Step 4: m6Aboost ML scoring (Kortel 2021)
What does the bio-clip-seq-m6a-clip skill do?
Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill m6a-clip --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.