Agent skill

Bio Clip Seq Clip Alignment

by GPTomics in GPTomics/bioSkills

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…

MITAuto-check passedResearch & Science

Install Bio Clip Seq Clip Alignment

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-alignment -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-clip-seq-clip-alignment --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/clip-seq/clip-alignment .claude/skills/bio-clip-seq-clip-alignment && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-clip-seq-clip-alignment
GitHub stars
1.2k
Used in
2 other repos
Token cost
~5k tokens
SKILL.md length
2,131 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Turning preprocessed CLIP FASTQ into a deduplicated
  • SKILL.md covers Version Compatibility, Algorithmic Taxonomy, Critical Choice:… and Per-Aligner Failure Modes, plus 10 more sections
  • Runs Shell scripts from its folder
  • MAPQ-filtered BAM ready for peak calling

What it does

Bio Clip Seq Clip Alignment is an agent skill from GPTomics/bioSkills. 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.

Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/align_clip.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Turning preprocessed CLIP FASTQ into a deduplicated
  • MAPQ-filtered BAM ready for peak calling
  • Crosslink-site detection

Example prompts

  • “/bio-clip-seq-clip-alignment”

Requirements

  • A Bash shell

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Clip Seq Clip Alignment loads about 5k tokens when it runs. Until then it costs about 118 tokens; SKILL.md has 2,131 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~118
When it runs · the whole SKILL.md, loaded when a task matches
~5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 2,131 words, ~4,980 tokens.

Download SKILL.mdSave it as .claude/skills/bio-clip-seq-clip-alignment/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-clip-seq-clip-alignment
description
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.
tool_type
cli
primary_tool
STAR

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 --outFilterMismatchNoverReadLmax 0.04 --outSAMtype BAM SortedByCoordinate --outSAMattributes All --outFileNamePrefix sample_
  • CLI (PAR-CLIP, mismatch ceiling raised for T->C signal): same as above but --outFilterMismatchNoverReadLmax 0.07
  • CLI (low memory, no splicing): bowtie2 -x genome_index -U R1.trim.fq.gz --very-sensitive -p 8 | samtools view -bS - | samtools sort -o aligned.bam
  • CLI (repeat-binding RBP, multi-mapper rescue): STAR ... --outFilterMultimapNmax 100 --outSAMmultNmax -1 then process with CLAM (see repeat-element section)

End-to-end alignment is non-negotiable. --alignEndsType Local (STAR default for some pipelines) soft-clips low-quality 5' bases and discards exactly the truncation signal. Mismatch ceiling 0.04 (4% of read length) excludes most sequencing-error reads; 0.07 is the PAR-CLIP override to retain T->C reads.

Algorithmic Taxonomy

AlignerSplice-awareMemory (human)CLIP-suitableStrengthFails when
STARYes~30 GB peak (sjdbOverhang 100)Yes (ENCODE eCLIP standard)Splice-aware, fast on deep libraries, excellent multi-mapper logsRAM hungry; small clusters or laptops cannot run human genome index
HISAT2Yes~8 GBYes (good alternative when STAR memory infeasible)Low memory; comparable splice accuracySlightly lower multi-mapper precision; smaller community adoption for CLIP
bowtie2No (no splice)~3 GBLimited (loses intronic and read-through reads at splice junctions)Fast, low memory, matureMisses spliced reads; not suitable for mRNA-binding RBPs (PTBP1, U2AF2)
bwa-mem2No~30 GB indexNot recommendedFast for DNA; no splice supportSame splicing issue as bowtie2; no CLIP-specific advantage
chromapNo~5 GBNot recommended for CLIPVery fast for ATAC/ChIPNo splice support; pre-applies fragment shift inappropriate for CLIP
novoalignYes (splice with -X)~10 GBAcceptableOld standard; some labs still useCommercial; community moved to STAR/HISAT2
Salmon (transcriptome)N/AlowNot suitablePseudo-alignmentDiscards intronic and unannotated reads, both common in CLIP

Methodology evolves; verify the current ENCODE eCLIP pipeline (encodeproject.org/eclip) before locking parameters. The ENCODE eCLIP SOP v2.2 pins STAR 2.5.2b (the earlier v1 SOP used 2.4.0i); modern reanalyses use STAR 2.7.x with the same parameter set.

Critical Choice: Unique-Mapper-Only vs Multi-Mapper Rescue

Two valid alignment strategies depend on the RBP biology:

Strategy A -- Unique-mapper-only (default, ENCODE standard): --outFilterMultimapNmax 1 discards every read that maps to more than one genomic location. Loses 5-15% of reads but produces unambiguous positions for downstream peak calling and crosslink-site detection.

Strategy B -- Multi-mapper rescue (for repeat-binding RBPs): --outFilterMultimapNmax 100 --outSAMmultNmax -1 retains up to 100 alignments per read; downstream EM-based assignment (CLAM, Xinglab) probabilistically allocates them. Required for RBPs that bind Alu, LINE-1, LTR, or other repetitive elements (e.g., MATR3, ILF3, FUS at LINE-1, HNRNPK at SINEs, PUM2 in some repeat contexts).

RBP classStrategyWhy
Splicing factors (PTBP1, U2AF2, RBFOX, SRSF1)A (unique)Bind defined intronic/exonic motifs, not repeats
3' UTR stability (HuR, PUM2, AUF1)A (unique)Binding sites are usually in unique 3' UTR sequence
Ribosomal proteins (RPS19, RPL35A)A (unique)Bind mRNA bodies and snoRNAs in unique sequence
Translation initiation (EIF3J, EIF2S2)A (unique)Bind 5' UTRs and snoRNAs
Repeat / TE binders (MATR3, ZFP36 isoforms, HNRNPK, LINE-1 ORFs)B (multi-mapper)Genuine biology is in repeat regions
Y-RNA / 7SK / vault RNAs (TROVE2, LARP7)A (unique) but with --outFilterMultimapNmax 50 for the ncRNA itselfThese small RNAs have a few unique copies; pure unique discards them
Mitochondrial RBPs (FASTKD2, LRPPRC, TFAM)A (unique) with chrM retainedchrM has unique sequence but must not be excluded by blacklists
Histone mRNA (SLBP)A (unique) but DO NOT pre-filter rRNA index that includes histoneReplication-dependent histone genes are repetitive in some indices

Per-Aligner Failure Modes

STAR -- Local alignment soft-clips the truncation site

Trigger: Pipeline copied from RNA-seq tutorial uses --alignEndsType Local (or omits the flag, letting STAR default).

Mechanism: Local alignment soft-clips up to 12% of the read end if scoring improves. In CLIP, the 5' read end is the truncation = CL site -1 base; if it carries even one mismatched base from RT errors, STAR soft-clips it.

Symptom: Crosslink-site density looks "smoothed"; PureCLIP / CTK CITS detect 5-30% fewer single-nt sites than expected; peak boundaries look fuzzy.

Fix: Always pass --alignEndsType EndToEnd. To confirm: samtools view dedup.bam | awk '{ if ($6 ~ /S/) print }' | wc -l should report < 1% of reads with soft-clip CIGAR operations.

STAR -- Mismatch ceiling discards PAR-CLIP signal

Trigger: PAR-CLIP library aligned with --outFilterMismatchNoverReadLmax 0.04 (the iCLIP/eCLIP default).

Mechanism: PAR-CLIP T->C conversion rate is 20-50% of T positions in crosslinked reads. For a 30 nt read with 8 Ts and 50% conversion, ~4 T->C mismatches occur (13% of read length). The 4% mismatch ceiling drops these reads.

Symptom: 40-70% read loss at alignment for PAR-CLIP; downstream PARalyzer / wavClusteR find few clusters.

Fix: For PAR-CLIP only, set --outFilterMismatchNoverReadLmax 0.07 (7%). Verify post-alignment: samtools view dedup.bam | awk '{ for(i=12;i<=NF;i++) if($i ~ /^MD:/) print $i }' | head should show many T->C-indicating MD tags.

STAR -- Multi-mappers silently discarded when needed

Trigger: Repeat-binding RBP (MATR3, LINE-1 binders) aligned with default --outFilterMultimapNmax 1.

Mechanism: Reads mapping to more than one Alu/LINE/LTR instance are removed; 10-30% of true binding sites are lost.

Symptom: Compared to published RBP literature for repeat binders, the analysis peak count is 3-10x lower; repeat-overlap fraction is < 5% when literature suggests 15-30%.

Fix: Raise --outFilterMultimapNmax to 50-100; emit all alignments with --outSAMmultNmax -1; downstream use CLAM (Xinglab) to probabilistically assign multi-mappers to a single best location via expectation-maximization. Or restrict to a non-repeat analysis and note the limitation.

bowtie2 -- Splice junctions missed silently

Trigger: Used bowtie2 instead of STAR for an mRNA-binding RBP (typical of PTBP1, U2AF2 intronic binding studies); BAM looks normal but introns/exons map poorly.

Mechanism: bowtie2 has no splice model; reads spanning exon-intron junctions soft-clip 5-20 nt or fail to align entirely.

Symptom: Lower read recovery (~70-80% vs STAR ~90-95%); peaks at splice sites under-called; intronic peaks over-represented (reads that would have aligned across an exon now align fully in the intron upstream).

Fix: Use STAR or HISAT2 for any RBP that binds mRNA, pre-mRNA, or splice signals. Reserve bowtie2 for non-coding RNA targets (snoRNA, 7SK, Y RNA) where short reads do not span junctions.

STAR -- Memory exhausted on small machine

Trigger: Human genome index loaded into < 32 GB RAM machine.

Mechanism: STAR's suffix-array index requires ~30 GB RAM for human/mouse. Smaller machines crash or swap.

Symptom: "STAR EXITED" with bus error; or alignment takes > 24h on a single sample.

Fix: Switch to HISAT2 (~8 GB peak); or build a STAR index with --genomeSAsparseD 2 (halves RAM at the cost of ~30% alignment slowdown); or use a public cluster with > 64 GB. Do NOT use bowtie2 as a memory workaround for splice-aware needs.

Read-2 5' end trimmed inadvertently

Trigger: A --clip5pNbases flag carried over from RNA-seq quality trimming; or a fastp --trim_front2 5 step in preprocessing.

Mechanism: Removes the eCLIP truncation base on R2 5'.

Symptom: Crosslink sites called from R2 5' positions cluster artificially at uniform offsets across genome; motif enrichment around crosslink sites collapses.

Fix: Verify the BAM with samtools view dedup.bam | awk '$2 ~ /83|163/ { print $4 }' (R2 5' positions on minus and plus strand) and confirm they map to the EXPECTED truncation sites, not shifted by 5 nt.

Show full SKILL.md (841 more words)Show less

ENCODE 4 eCLIP STAR Parameters (Reference)

bash
STAR --runMode alignReads \
    --runThreadN 16 \
    --genomeDir /path/to/STAR_hg38_index \
    --genomeLoad NoSharedMemory \
    --readFilesIn R1.trim.fq.gz R2.trim.fq.gz \
    --readFilesCommand zcat \
    --outFilterType BySJout \
    --outFilterMultimapNmax 1 \
    --alignEndsType EndToEnd \
    --outFilterMismatchNoverReadLmax 0.04 \
    --outSAMtype BAM SortedByCoordinate \
    --outSAMattributes All \
    --outFileNamePrefix sample_ \
    --outFilterScoreMinOverLread 0.66 \
    --outFilterMatchNminOverLread 0.66

The --outFilterScoreMinOverLread 0.66 / --outFilterMatchNminOverLread 0.66 (matched score / matched bases >= 66% of read length) are ENCODE-specific stringency added on top of the mismatch ceiling. These two flags are why ENCODE eCLIP BAMs are smaller than naive STAR output.

Post-Alignment Filtering

bash
# 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 \
    --stdin=sample_q10.bam \
    --stdout=sample_dedup.bam \
    --method=unique \
    --paired \
    --log=sample_dedup.log
samtools index sample_dedup.bam

MAPQ thresholds differ by aligner:

  • STAR: 255 = uniquely mapped (== --outFilterMultimapNmax 1 already filtered); lower MAPQ values mean multi-mappers
  • bowtie2: 42 = unique; 0-1 = multi-mapper; intermediate = ambiguous
  • HISAT2: 60 = unique; similar to bowtie2 scheme but tool-specific

For STAR -q 10 is conventional but redundant if --outFilterMultimapNmax 1 was already set. For bowtie2/HISAT2, -q 30 is a stricter unique-mapper proxy.

Multi-Mapper Rescue with CLAM (Repeat-Binding RBPs)

bash
# 1. Re-align permitting multi-mappers
STAR --runMode alignReads \
    --genomeDir STAR_index --readFilesIn R1.fq.gz R2.fq.gz \
    --readFilesCommand zcat --outFilterMultimapNmax 100 \
    --outSAMmultNmax -1 --alignEndsType EndToEnd \
    --outSAMtype BAM SortedByCoordinate --outFileNamePrefix mm_

samtools index mm_Aligned.sortedByCoord.out.bam

# 2. CLAM preprocessing splits unique vs multi-mapper reads
CLAM preprocessor -i mm_Aligned.sortedByCoord.out.bam -o clam_out/ --read-tagger-method median

# 3. EM-based multi-mapper realignment
CLAM realigner -i clam_out/unique.sorted.bam -o clam_out/ --winsize 50 --max-tags 0

# 4. Downstream peakcaller (CLAM peakcaller is a wrapper for Piranha+EM)
CLAM peakcaller -i clam_out/unique.sorted.bam clam_out/realigned.sorted.bam \
    -o clam_peaks.bed -p 8 --gtf gencode.v38.annotation.gtf

CLAM (Zhang & Xing 2017) rescues additional peaks in repeat regions (operationally ~10-30% more, dataset-dependent). It is the only EM-based multi-mapper solution actively maintained for CLIP as of 2025.

HISAT2 Low-Memory Alternative

bash
hisat2 --rna-strandness FR --no-softclip \
    -p 8 -x hisat2_grch38_index \
    -1 R1.trim.fq.gz -2 R2.trim.fq.gz \
    --no-unal \
    --score-min L,0,-0.2 \
    -S sample.sam 2> hisat2.log

samtools view -bS sample.sam | samtools sort -o sample.bam -
samtools index sample.bam

--no-softclip is the HISAT2 equivalent of STAR's --alignEndsType EndToEnd. --score-min L,0,-0.2 is roughly equivalent to STAR's --outFilterMismatchNoverReadLmax 0.04 (penalty scales linearly with read length, with -0.2 per mismatch slope).

Decision Tree by Use Case

ScenarioRecommended aligner + parametersWhy
eCLIP, ENCODE-comparable, 32+ GB RAMSTAR ENCODE pattern (above)Reproducible against ENCODE peak calls
iCLIP / iCLIP2, single-endSTAR same params, --readFilesIn R1.fq.gzSingle-end variant of ENCODE block
PAR-CLIPSTAR with --outFilterMismatchNoverReadLmax 0.07T->C is signal, not error
Repeat-binding RBP (MATR3, LINE-1 binders)STAR --outFilterMultimapNmax 100 --outSAMmultNmax -1 + CLAMEM rescue of multi-mappers
Low memory (< 16 GB)HISAT2 with --no-softclipSTAR index unloadable
Bacterial / yeast (small genome, no splicing)bowtie2 --very-sensitiveSplice-awareness wasted
snoRNA-only / 7SK-only analysisbowtie2 with custom indexAvoid genome-scale splicing tangle
Allele-specific CLIPSTAR ENCODE + WASP filter (--waspOutputMode SAMtag)Reference-allele mapping bias must be removed
Long-read CLIP (dirCLIP, nanopore)minimap2 -ax splice -uf -k14STAR cannot align long reads

Allele-Specific Alignment with WASP

CLIP-seq inherits reference-allele mapping bias from RNA-seq. For allele-specific binding analyses (BEAPR, ASPRIN), STAR's WASP integration removes reads where the alternative-allele version of the read would not have aligned at the same position.

bash
# STAR with WASP filter; --varVCFfile is the heterozygous SNP VCF
STAR --runMode alignReads \
    --genomeDir STAR_index --readFilesIn R1.fq.gz R2.fq.gz --readFilesCommand zcat \
    --alignEndsType EndToEnd --outFilterMultimapNmax 1 --outFilterMismatchNoverReadLmax 0.04 \
    --outSAMtype BAM SortedByCoordinate \
    --varVCFfile sample.het.vcf.gz \
    --waspOutputMode SAMtag \
    --outSAMattributes vA vG vW \
    --outFileNamePrefix wasp_

samtools view -b -e '[vW]==1' wasp_Aligned.sortedByCoord.out.bam > wasp_pass.bam

WASP filtering is MANDATORY for allele-specific binding analyses; reference-allele bias inflates REF allele frequency 1-5% in unfiltered CLIP data.

Reconciliation: When Alignment Outputs Disagree

PatternLikely causeAction
STAR retains more reads than bowtie2STAR handles splice junctions; bowtie2 fails on spliced readsTrust STAR for mRNA-binding RBPs
Read count after STAR < expected--outFilterMultimapNmax 1 discarding multi-mappersIf RBP binds repeats, switch to multi-mapper mode + CLAM
Crosslink-site density looks smoothedSoft-clip ON (alignEndsType Local)Re-align with --alignEndsType EndToEnd
40-70% read loss in PAR-CLIPT->C mismatches exceed 4% ceilingRaise --outFilterMismatchNoverReadLmax to 0.07
HISAT2 calls peaks STAR missesHISAT2 lower-stringency soft-clip behaviourRe-run HISAT2 with --no-softclip; differences should narrow to < 5%
Two STAR versions (2.4 vs 2.7) give different BAMsIndex sjdb format changed; parameter defaults driftPin STAR version for cross-study comparison; document

Operational rule: For ENCODE-comparable analysis, use STAR 2.5.2b (ENCODE eCLIP SOP v2.2) or 2.7.x with the ENCODE eCLIP parameter block; use --alignEndsType EndToEnd; use --outFilterMultimapNmax 1 unless the biology requires multi-mappers (then add CLAM); UMI-dedupe with umi_tools dedup --method=unique. Document any deviation in methods.

Common Errors

Error / symptomCauseSolution
STAR ERROR ... Could not allocate memoryIndex too large for RAMSwitch to HISAT2; or rebuild STAR index with --genomeSAsparseD 2
samtools sort: not enough memory after STARsort -m default 768M too smallsamtools sort -m 4G -@ 8
Crosslink density at uniform 5 nt offsets across genomeR2 5' trimmed by mistakeRecheck preprocessing; cutadapt -g and fastp --trim_front2 are banned for CLIP
90% of reads MAPQ < 10Genome index built for different speciesVerify with samtools view -h sample.bam | head against expected chromosome names
HISAT2 splice junctions miss-detectedUsed --no-splice (HISAT2 splice OFF)Remove --no-splice for mRNA studies
umi_tools dedup OOMToo many UMIs at one position; deep librarySwitch to --method=unique (less RAM than directional)
Multi-mapper count after STAR --outFilterMultimapNmax 100 = 0--outSAMmultNmax not setAdd --outSAMmultNmax -1 to emit all alignments
Reads MAPQ 0 dominate after bowtie2--very-sensitive ON but multi-mappers retainedAdd samtools view -q 30 post-filter
CLAM EM never convergesBackground regions empty; sparse coverageLower --max-tags to 5; provide gene model GTF; verify multi-mapper BAM exists

References

  • Van Nostrand EL et al 2016 Nat Methods 13:508 (eCLIP / ENCODE alignment parameter block)
  • Konig J et al 2010 Nat Struct Mol Biol 17:909 (iCLIP truncation-as-CL principle)
  • Dobin A et al 2013 Bioinformatics 29:15 (STAR aligner)
  • Kim D et al 2019 Nat Biotechnol 37:907 (HISAT2)
  • Langmead B & Salzberg SL 2012 Nat Methods 9:357 (bowtie2)
  • Zhang Z & Xing Y 2017 Nucleic Acids Res 45:9260 (CLAM multi-mapper assignment)
  • van de Geijn B et al 2015 Nat Methods 12:1061 (WASP allele-specific alignment)
  • Hafner M et al 2010 Cell 141:129 (PAR-CLIP T->C mismatch tolerance need)
  • West C et al 2023 Wellcome Open Res 8:286 (nf-core/clipseq alignment defaults)
  • clip-seq/clip-preprocessing - UMI extraction and adapter trimming before alignment
  • clip-seq/clip-qc - Post-alignment QC (library complexity, read distribution, FRiP)
  • clip-seq/crosslink-site-detection - Why the 5' end must be preserved
  • clip-seq/clip-peak-calling - Downstream peak calling on the dedup BAM
  • read-alignment/star-alignment - General STAR usage and indexing
  • read-alignment/bowtie2-alignment - General bowtie2 usage
  • alignment-files/duplicate-handling - Picard MarkDuplicates as UMI-less fallback
  • single-cell/scatac-analysis - Cross-reference for single-cell variants

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in clip-seq/clip-alignment of GPTomics/bioSkills.

  • SKILL.md
  • examples/align_clip.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Bio Clip Seq Clip Alignment next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bio Clip Seq Clip Alignment compared with similar skills
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Bio Clip Seq Clip Alignment this skillGPTomics/bioSkills1.2k2 repos~5kAutomated safety check: PassMIT
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13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

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    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Research & ScienceAuto-check: notes
  • MFA Pipeline Orchestrator

    aiming-lab/AutoResearchClaw

    Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

    15k GitHub stars~923 tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

More from GPTomics/bioSkills

All 559 skills in this repo
  • Bio Alignment Io

    GPTomics/bioSkills

    Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.

    1.2k GitHub starsUsed in 3 repos~4.9k tokens
    Auto-check passed
  • bioSkills Installer

    GPTomics/bioSkills

    Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.

    1.2k GitHub starsUsed in 1 repo~789 tokens
    Auto-check passed
  • Bio Write Sequences

    GPTomics/bioSkills

    Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.

    1.2k GitHub starsUsed in 3 repos~2.1k tokens
    Auto-check passed
  • Amplicon Primer Clipping

    GPTomics/bioSkills

    Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.

    1.2k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed
  • Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.

    1.2k GitHub starsUsed in 2 repos~3.6k tokens
    Auto-check passed
  • Bio Alignment Indexing

    GPTomics/bioSkills

    Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.

    1.2k GitHub starsUsed in 2 repos~2.4k tokens
    Auto-check passed

Questions about Bio Clip Seq Clip Alignment

What does Bio Clip Seq Clip Alignment 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…. Bio Clip Seq Clip Alignment is an agent skill from GPTomics/bioSkills. 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.

When should I use Bio Clip Seq Clip Alignment?

Bio Clip Seq Clip Alignment fits situations like: turning preprocessed CLIP FASTQ into a deduplicated; MAPQ-filtered BAM ready for peak calling; crosslink-site detection.

How do I install Bio Clip Seq Clip Alignment in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-alignment -a claude-code`. Or copy the skill folder (clip-seq/clip-alignment in GPTomics/bioSkills) into .claude/skills/bio-clip-seq-clip-alignment in your project. Claude Code loads it when a task matches its description.

How do I install Bio Clip Seq Clip Alignment in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-alignment -a codex`. Or copy the skill folder (clip-seq/clip-alignment in GPTomics/bioSkills) into .agents/skills/bio-clip-seq-clip-alignment in your project. Codex loads it when a task matches its description.

Can I use Bio Clip Seq Clip Alignment in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-clip-seq-clip-alignment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-clip-seq-clip-alignment, .gemini/skills/bio-clip-seq-clip-alignment, .github/skills/bio-clip-seq-clip-alignment and .opencode/skills/bio-clip-seq-clip-alignment in your project.

What does Bio Clip Seq Clip Alignment need to run?

Going by SKILL.md and its folder, Bio Clip Seq Clip Alignment needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Bio Clip Seq Clip Alignment access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bio Clip Seq Clip Alignment safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Clip Seq Clip Alignment use?

Bio Clip Seq Clip Alignment is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Clip Seq Clip Alignment use?

About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Clip Seq Clip Alignment?

Skills that share tags, products or a category with Bio Clip Seq Clip Alignment: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Clip Seq Clip Alignment?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,217 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.