Bio Workflows Clip Pipeline

Version Compatibility

Reference examples tested with: umi_tools 1.1.5+, cutadapt 4.6+, fastp 0.23+, STAR 2.7.11b+, samtools 1.19+, bedtools 2.31+, CLIPper 2.0+, Skipper (commit 2023.05+), PureCLIP 1.3.1+, HOMER 4.11+, ChIPseeker 1.40+, preseq 3.2+, picard 3.1+, idr 2.0.4+, MultiQC 1.21+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags
• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws unexpected errors, introspect the installed tool and adapt the example rather than retrying.

CLIP-seq End-to-End Pipeline

"Analyze my CLIP-seq data from raw FASTQ to ENCODE-compliant binding sites" -> Orchestrate protocol-specific UMI extraction, 3'-only adapter trimming (preserving the R2 5' truncation = crosslink site -1), ENCODE STAR alignment, UMI-based deduplication, library complexity QC, peak calling against SMInput with stringent thresholds (log2 FC >= 3 AND -log10 p >= 3), single-nucleotide crosslink-site detection, ChIPseeker annotation with CLIP-appropriate tssRegion, motif discovery with GC-matched background and CL-position registration, and optional differential binding between conditions.

Pipeline Overview

FASTQ + SMInput
  -> [clip-preprocessing]    UMI extract + 3' adapter trim (-q 6 -m 18) + two-pass for eCLIP
  -> [clip-alignment]        STAR ENCODE block (alignEndsType EndToEnd, mismatch 0.04 or 0.07 for PAR-CLIP) + UMI dedup
  -> [clip-qc]               preseq, FRiP, IDR rescue + self-consistency, read distribution
  -> [clip-peak-calling]     CLIPper + SMInput log2 norm (stringent: log2 FC >= 3, -log10 p >= 3) OR Skipper (210-320% more sites)
  -> [crosslink-site-detection] PureCLIP or CTK CITS for single-nt CL positions
  -> [binding-site-annotation] ChIPseeker (tssRegion=c(-100,100), level=transcript) + RBP-Maps for splicing factors
  -> [clip-motif-analysis]   HOMER + mCross (registered) + RBNS Kd cross-check
  -> [differential-clip]     DEWSeq window-level NB with type:condition interaction (optional)

CLIP Variant Selection

Step 1: Quality Control of Raw FASTQ

# Initial QC
fastqc raw_R1.fq.gz raw_R2.fq.gz -o qc/raw/

# Inspect first 12 bases of 100 reads to verify UMI pattern matches the prep
zcat raw_R1.fq.gz | awk 'NR%4==2' | head -100 | cut -c1-12 | sort | uniq -c | sort -rn | head
# Random barcode positions show ~25% per base; library barcodes are fixed

Step 2: Preprocessing (Protocol-Specific)

Goal: Convert raw CLIP FASTQ into UMI-deduplicated, alignment-ready FASTQ while preserving the R2 5' end (= crosslink site -1) that drives single-nucleotide resolution downstream.

Approach: Use the protocol-matched UMI pattern (10 nt eCLIP, NNNXXXXNN iCLIP, 4 nt PAR-CLIP), run umi_tools extract to move random barcodes to read names, then apply cutadapt with 3'-only adapter trimming at -q 6 -m 18 (permissive 5' to protect the truncation base). eCLIP uses two-pass trimming to remove read-through inline adapters from R2 5' only; iCLIP and PAR-CLIP use single-pass.

# eCLIP: 10 nt UMI on R1; two-pass adapter trim for read-through
# See clip-seq/clip-preprocessing for protocol-specific patterns
umi_tools extract \
    --bc-pattern=NNNNNNNNNN \
    --stdin=raw_R1.fq.gz --read2-in=raw_R2.fq.gz \
    --stdout=R1.umi.fq.gz --read2-out=R2.umi.fq.gz \
    --log=qc/umi_extract.log

# Pass 1: 3' adapter on both reads
# -q 6 is intentionally permissive; aggressive trimming destroys R2 5' = CL site -1
cutadapt \
    -a AGATCGGAAGAGCACACGTCT \
    -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \
    --quality-base 33 -q 6 -m 18 \
    -j 8 \
    -o R1.p1.fq.gz -p R2.p1.fq.gz \
    R1.umi.fq.gz R2.umi.fq.gz \
    > qc/cutadapt_pass1.log 2>&1

# Pass 2: strip read-through 5' adapter from R2 only (NEVER -g on R1)
cutadapt \
    -G GATCGTCGGACTGTAGAACTCTGAAC \
    --quality-base 33 -q 6 -m 18 \
    -j 8 \
    -o R1.trim.fq.gz -p R2.trim.fq.gz \
    R1.p1.fq.gz R2.p1.fq.gz \
    >> qc/cutadapt_pass2.log 2>&1

For PAR-CLIP: same UMI extraction but downstream alignment raises --outFilterMismatchNoverReadLmax from 0.04 to 0.07 (the T->C signature would otherwise be filtered as sequencing error). See clip-seq/clip-preprocessing for full per-protocol guidance.

Step 3: Alignment (ENCODE STAR Block)

# ENCODE eCLIP convention. Sacred: --alignEndsType EndToEnd (soft-clip would destroy truncation = CL site -1)
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 \
    --outFilterScoreMinOverLread 0.66 \
    --outFilterMatchNminOverLread 0.66 \
    --outSAMtype BAM SortedByCoordinate \
    --outSAMattributes All \
    --outFileNamePrefix sample_

samtools index sample_Aligned.sortedByCoord.out.bam

# MAPQ >= 10 (255 = unique in STAR; lower = multi-mapper)
samtools view -b -q 10 sample_Aligned.sortedByCoord.out.bam > sample_q10.bam
samtools index sample_q10.bam

# UMI dedup. ENCODE convention: --method=unique
umi_tools dedup \
    --stdin=sample_q10.bam \
    --stdout=sample_dedup.bam \
    --method=unique \
    --paired \
    --log=qc/dedup.log
samtools index sample_dedup.bam

For PAR-CLIP: change --outFilterMismatchNoverReadLmax 0.04 to 0.07. For repeat-binding RBPs (MATR3, ZFP36, FUS at LINE-1, HNRNPK at SINEs): change --outFilterMultimapNmax 1 to 100 and add --outSAMmultNmax -1, then run CLAM downstream for EM-based multi-mapper assignment. See clip-seq/clip-alignment for full guidance.

Step 4: QC (Five Gates)

# Gate 1: preprocessing retention (cutadapt log, target >= 70%)
grep -E "passing filters|Pairs written" qc/cutadapt_pass1.log

# Gate 2: alignment rate (STAR Log.final.out, target >= 60% eCLIP, 70% iCLIP)
grep "Uniquely mapped reads %" sample_Log.final.out

# Gate 3: library complexity (preseq, target >= 1M unique at sequenced depth)
preseq lc_extrap -B -P sample_q10.bam -o qc/preseq.txt

# Gate 4: FRiP (after peak calling; target >= 0.005 narrow-binding RBP)
# Gate 5: IDR replicate reproducibility (after peak calling; target rescue and self-consistency < 2)

# Aggregate all QC into a single MultiQC report
multiqc qc/ -o qc/multiqc/

CLIP libraries have 40-70% PCR duplication BY DESIGN (the IP enriches a small molecule pool). Low duplication usually means failed IP, not a good library. The unique-fragment count after UMI dedup is the actual quality metric. See clip-seq/clip-qc for full five-gate diagnostic.

Step 5: Peak Calling

# CLIPper (ENCODE canonical) + SMInput log2 normalization
clipper \
    -b sample_dedup.bam \
    -s hg38 \
    -o peaks/sample.clipper.bed \
    --FDR 0.05 \
    --superlocal \
    --save-pickle \
    --processors 8

# ENCODE stringent: log2(IP/SMInput) >= 3 AND -log10 p >= 3
# (Yeo lab eclip-pipeline scripts implement the normalization; see clip-seq/clip-peak-calling)
python overlap_peakfi_with_bam_PE.py \
    peaks/sample.clipper.bed \
    sample_dedup.bam sminput_dedup.bam \
    sample_dedup.bam.readnum.txt sminput_dedup.bam.readnum.txt \
    peaks/sample.normed.bed

python compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat.py \
    peaks/sample.normed.bed \
    peaks/sample.compressed.bed

# Stringent filter
awk 'BEGIN{FS=OFS="\t"} $5 >= 3 && $6 >= 3' peaks/sample.compressed.bed > peaks/sample.stringent.bed

For maximum sensitivity (210-320% more sites than CLIPper for mRNA-binding RBPs), use Skipper Snakemake workflow with the same SMInput control. Mandatory for FASTKD2 / mt-RBPs which CLIPper misses on chrM. See clip-seq/clip-peak-calling for the full caller taxonomy.

Step 6: Single-Nucleotide Crosslink-Site Detection

# PureCLIP: HMM jointly modeling enrichment + truncation + CL motif
# Restrict to expressed transcripts to avoid HMM convergence issues
pureclip \
    -i sample_dedup.bam -bai sample_dedup.bam.bai \
    -g genome.fa \
    -ibam sminput_dedup.bam -ibai sminput_dedup.bam.bai \
    -o crosslinks/sample.sites.bed \
    -or crosslinks/sample.regions.bed \
    -nt 8 -dm 8 \
    -iv expressed_tx.bed

Single-nt CL sites feed mCross motif registration and allele-specific binding analyses. They are NOT a replacement for the broad peak list; complementary outputs. See clip-seq/crosslink-site-detection.

Step 7: IDR Across Replicates

# Sort each replicate's compressed BED by signal (log2 FC, column 5)
sort -k5,5gr peaks/rep1.compressed.bed > peaks/rep1.sorted.bed
sort -k5,5gr peaks/rep2.compressed.bed > peaks/rep2.sorted.bed

# True replicates threshold 0.05
idr --samples peaks/rep1.sorted.bed peaks/rep2.sorted.bed \
    --input-file-type bed --rank 5 \
    --output-file qc/idr.true.out \
    --idr-threshold 0.05 \
    --plot --log-output-file qc/idr.log

# ENCODE rule: rescue + self-consistency ratios both < 2 to pass
# Pseudo-replicate IDR (split BAM in half) at threshold 0.10

Step 8: Binding-Site Annotation

# CLIP-appropriate ChIPseeker (tssRegion tight; level=transcript)
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

peaks <- readPeakFile('peaks/sample.stringent.bed')
anno <- annotatePeak(
    peaks,
    TxDb = txdb,
    level = 'transcript',
    tssRegion = c(-100, 100),
    genomicAnnotationPriority = c('Promoter','5UTR','3UTR','Exon','Intron','Downstream','Intergenic')
)
plotAnnoPie(anno)

Default ChIPseeker tssRegion=c(-3000, 3000) over-extends for CLIP (would label 30-50% peaks as "Promoter"). Splicing factors additionally need RBP-Maps (Yeo lab) for the 1400 nt cassette-exon regulatory metagene. See clip-seq/binding-site-annotation.

Step 9: Motif Analysis (De Novo + CL-Registered)

# Extract peak sequences (strand-preserving)
bedtools getfasta -fi genome.fa -bed peaks/sample.stringent.bed -s -fo motifs/peaks.fa

# GC-matched 3' UTR background (NOT auto-shuffled, which biases to AU)
bedtools shuffle -i peaks/sample.stringent.bed -g chrom.sizes \
    -incl expressed_3utr.bed -seed 42 > motifs/background.bed
bedtools getfasta -fi genome.fa -bed motifs/background.bed -s -fo motifs/background.fa

# HOMER de novo
findMotifs.pl motifs/peaks.fa fasta motifs/homer \
    -rna -len 5,6,7,8 -p 8 -fasta motifs/background.fa

# mCross for CL-position-registered motif (requires single-nt CL sites)
mCross -i crosslinks/sample.sites.bed -g genome.fa -k 7 -n 5 -o motifs/mcross

UV254 crosslinking has a strong U bias (~60-80% CL events at U); naive logos centered on CL positions are U-enriched even for non-U-binding RBPs. mCross corrects this by registering motif relative to the CL offset. See clip-seq/clip-motif-analysis.

Step 10: Differential Binding (Optional, Across Conditions)

# DEWSeq window-level NB with the interaction-term design
# The interaction `~ type + condition + type:condition` tests whether IP/SMInput ratio shifts;
# naive `~ condition` confounds binding with expression changes.
library(DEWSeq)
counts <- read.table('counts/merged.tsv', sep='\t', header=TRUE, row.names=1)
colData <- data.frame(
    type = c('ip','ip','ip','ip','sminput','sminput','sminput','sminput'),
    condition = c('treat','treat','ctrl','ctrl','treat','treat','ctrl','ctrl')
)
dds <- DESeqDataSetFromSlidingWindows(
    countData=counts, colData=colData,
    annotObj='annotation_windows.bed',
    design = ~ type + condition + type:condition
)
dds <- DESeq(dds)
res <- results(dds, name='typeip.conditiontreat')

See clip-seq/differential-clip for full DEWSeq workflow and the htseq-clip preprocessing required upstream.

Quality Checkpoints

Per-Variant Adjustments

• PAR-CLIP: Raise STAR --outFilterMismatchNoverReadLmax from 0.04 to 0.07; downstream use PARalyzer or CTK CIMS substitution T->C
• iCLIP / iCLIP2 multiplexed: Demultiplex by inline library barcode (NNNXXXXNN) BEFORE umi_tools extract
• HITS-CLIP: Use deletion-tolerant aligner (BWA-aln); downstream CTK CIMS deletion mode
• Repeat-binding RBPs: STAR --outFilterMultimapNmax 100 --outSAMmultNmax -1 + CLAM EM rescue
• m6A profiling: Switch to clip-seq/m6a-clip (miCLIP2 + m6Aboost or GLORI)
• Antibody unavailable: Switch to clip-seq/stamp-antibody-free (STAMP or TRIBE)
• miRNA targets: Switch to clip-seq/ago-clip-mirna-targets (chimeric eCLIP / miR-eCLIP)
• Variant-effect prediction: Use clip-seq/clip-deep-learning (RBPNet or RNAProt)

Related Skills

• clip-seq/clip-preprocessing - UMI extraction and adapter trimming details
• clip-seq/clip-alignment - STAR ENCODE block + multi-mapper rescue
• clip-seq/clip-qc - Five-gate QC framework
• clip-seq/clip-peak-calling - CLIPper / Skipper / PureCLIP / CTK taxonomy
• clip-seq/crosslink-site-detection - Single-nt CL detection by chemistry
• clip-seq/binding-site-annotation - ChIPseeker + RBP-Maps
• clip-seq/clip-motif-analysis - HOMER + mCross + RBNS validation
• clip-seq/differential-clip - DEWSeq + Flipper for cross-condition
• clip-seq/m6a-clip - miCLIP2 / GLORI / DART for m6A modifications
• clip-seq/stamp-antibody-free - STAMP / TRIBE for antibody-free profiling
• clip-seq/ago-clip-mirna-targets - chimeric eCLIP for direct miRNA-target pairs
• clip-seq/clip-deep-learning - RBPNet / RNAProt for variant-effect prediction
• read-qc/quality-reports - FastQC / MultiQC upstream QC
• alternative-splicing/differential-splicing - Cassette exon tables for RBP-Maps