bio-clip-seq-clip-alignment
Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.
npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-alignment --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: STAR 2.7.11b+, bowtie2 2.5.3+, HISAT2 2.2.1+, samtools 1.19+, CLAM 1.2+, umi_tools 1.1.5+. Before using code patterns, verify installed versions match. If versions differ: - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws unexpected errors, introspect the installed binary (`<tool> -h`) and adapt the example to match the actual CLI rather than retrying. # CLIP-seq Alignment **"Align preprocessed CLIP reads to genome with crosslink-preserving parameters"** -> Map UMI-extracted, adapter-trimmed reads to the genome (NOT transcriptome) with end-to-end alignment, strict mismatch ceiling, and unique-mapper-only filtering by default. The 5' end of the read (R2 5' in paired-end eCLIP; R1 5' in iCLIP) carries the reverse-transcriptase truncation = crosslink site -1; any soft-clipping or 5' trimming during alignment destroys nucleotide resolution. - CLI (eCLIP / iCLIP / iCLIP2, ENCODE pattern): `STAR --runMode alignReads --genomeDir STAR_index --readFilesIn R1.trim.fq.gz R2.trim.fq.gz --readFilesCommand zcat --outFilterType BySJout --outFilterMultimapNmax 1 --alignEndsType EndToEnd --outFilterMismatchNoverRe
- Version Compatibility
- Algorithmic Taxonomy
- Critical Choice: Unique-Mapper-Only vs Multi-Mapper Rescue
- Per-Aligner Failure Modes
- STAR -- Local alignment soft-clips the truncation site
- STAR -- Mismatch ceiling discards PAR-CLIP signal
- STAR -- Multi-mappers silently discarded when needed
- bowtie2 -- Splice junctions missed silently
- STAR -- Memory exhausted on small machine
- Read-2 5' end trimmed inadvertently
- ENCODE 4 eCLIP STAR Parameters (Reference)
- Post-Alignment Filtering
- Multi-Mapper Rescue with CLAM (Repeat-Binding RBPs)
- HISAT2 Low-Memory Alternative
STAR --runMode alignReads \ Sort and index samtools index sample_Aligned.sortedByCoord.out.bam MAPQ filter (255 in STAR = unique; bowtie2 uses different scheme) samtools view -b -q 10 sample_Aligned.sortedByCoord.out.bam > sample_q10.bam samtools index sample_q10.bam UMI deduplication (see clip-preprocessing for `--method=unique` rationale) umi_tools dedup \ samtools index sample_dedup.bam samtools index mm_Aligned.sortedByCoord.out.bam
What does the bio-clip-seq-clip-alignment skill do?
Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill clip-alignment --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.