clip-seq-clip-preprocessing

name	clip-seq-clip-preprocessing
description	Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.
tool_type	cli
primary_tool	umi_tools

Version Compatibility

Reference examples tested with: umi_tools 1.1.5+, cutadapt 4.6+, fastp 0.23.4+, samtools 1.19+, pysam 0.22+, picard 3.1+, preseq 3.2+.

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 ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

CLIP-seq Preprocessing

"Preprocess raw CLIP reads into UMI-deduplicated, alignable FASTQ" -> Extract random barcodes, trim adapters without disturbing the 5' truncation site, length-filter to remove unmappable shorts, and (post-alignment) collapse PCR duplicates by UMI + position. The 5' end of the read carries the iCLIP/eCLIP truncation signature one base downstream of the protein-RNA crosslink; preserving this base is the single most important constraint of CLIP preprocessing.

• CLI (eCLIP, paired-end): umi_tools extract --bc-pattern=NNNNNNNNNN --stdin R1.fq.gz --read2-in R2.fq.gz --stdout R1_umi.fq.gz --read2-out R2_umi.fq.gz
• CLI (iCLIP/iCLIP2, single-end): umi_tools extract --bc-pattern=NNNXXXXNN --extract-method=string --stdin R1.fq.gz --stdout R1_umi.fq.gz (3+2 random Ns flanking a 4 nt library barcode; demultiplex by the X positions first if multiplexed)
• CLI (PAR-CLIP): umi_tools extract --bc-pattern=NNNN ... (most protocols use 4 nt random barcodes; verify the lab's exact prep)
• CLI (3' trim only, eCLIP convention): cutadapt -a AGATCGGAAGAGCACACGTCT -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT --quality-base 33 --quality-cutoff 6 -m 18 -o R1.trim.fq.gz -p R2.trim.fq.gz R1_umi.fq.gz R2_umi.fq.gz

The eCLIP convention is: trim quality and adapter from the 3' end ONLY. The 5' end of read 2 (in paired-end eCLIP) is the truncation site of the RT enzyme at the protein-RNA adduct, located one nucleotide downstream of the crosslink. Trimming the 5' end discards that exact base. cutadapt -g, fastp --trim_front1, and aggressive quality trimming of the 5' end are all banned for CLIP unless a documented protocol-specific reason exists.

Read Structure by Protocol

Protocol	UMI length and location	Truncation-site read	Adapter set	Notes
eCLIP (Van Nostrand 2016 / ENCODE)	10 nt at R1 5' end (random nucleotides preceding insert)	R2 5' end (after R1 UMI is stripped) = crosslink -1	Illumina TruSeq R1 + R2 + inline X1A/X1B inverted adapter for two-pass	ENCODE pipeline normative
seCLIP (single-end eCLIP)	10 nt at R1 5' end	R1 5' end	TruSeq R1 only	ENCODE accepts both eCLIP and seCLIP
iCLIP (Konig 2010)	5 nt random (NNNXXXXNN: 3 N + 4 X library barcode + 2 N), single-end	R1 5' end after barcode strip	L3 adapter at 3'	Multiplexed - demultiplex by the 4 X bases
iCLIP2 (Buchbender 2020)	5 or 9 nt random (NNNXXXXNN or longer), single-end	R1 5' end	L3 adapter at 3'	Increased complexity vs iCLIP; same UMI pattern
iCLIP3 (Buchbender et al, bioRxiv 2026.03.01.708747)	10 nt random + dual sample index, single-end	R1 5' end	TruSeq	Silica-column RNA isolation; non-radioactive; streamlined low-input protocol. Preprint - verify final published version before pinning a pipeline.
irCLIP (Zarnegar 2016)	5 nt random + barcode (similar to iCLIP)	R1 5' end	IR700 adapter + standard TruSeq sequencing adapter	Infrared replaces 32P; otherwise iCLIP-like
PAR-CLIP (Hafner 2010)	0-4 nt depending on prep	T->C transitions within reads (NOT a truncation method)	TruSeq R1	UMI optional; rely on T->C signature for CL
FLASH (Aktas 2020)	Sample barcode + UMI in custom adapter	R1 5'	Custom L3 design	1.5 day protocol; adapter design proprietary to MPI
miCLIP / miCLIP2 (Linder 2015 / Kortel 2021)	iCLIP-style barcodes	R1 5' = m6A -1 (truncation OR C-to-T)	iCLIP L3	m6A-specific
STAMP (Brannan 2021)	NA (no UV)	NA (C-to-U editing)	10x or bulk RNA-seq adapters	Antibody-free editing-based; preprocess as RNA-seq

If the read structure differs from the lab's documentation, run seqkit head -n 1000 R1.fq.gz | seqkit stats -a and inspect the first 12 bases of 100 random reads. Random-barcode positions show ~25% base composition per position; library/sample barcodes will be fixed across reads.

Critical Choice: One Adapter Pass vs Two

One pass (single-end iCLIP, PAR-CLIP): Single 3' adapter; cutadapt with -a <L3> and -m 18 is sufficient.

Two passes (eCLIP, paired-end): The eCLIP library design ligates an inverted X1A/X1B inline adapter that can appear at either end of a short fragment due to read-through. The ENCODE pipeline does pass 1 with the standard 3' adapter, then pass 2 trims a residual 5' adapter that read-through events leave on read 2. Trimming a 5' adapter from R2 is a special case: cutadapt's -G <ADAPTER> (uppercase) anchored at R2's 5' end is the right invocation. Do NOT use -g (lowercase) for R1 5' trimming - that destroys the truncation site.

Cutadapt full eCLIP-style invocation:

# Pass 1 - 3' adapter (both reads)
cutadapt \
    -a AGATCGGAAGAGCACACGTCT \
    -A AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT \
    --quality-base 33 -q 6 \
    -m 18 \
    -o R1.p1.fq.gz -p R2.p1.fq.gz \
    R1.umi.fq.gz R2.umi.fq.gz

# Pass 2 - read-through 5' adapter on R2 only (ENCODE eCLIP)
cutadapt \
    -G GATCGTCGGACTGTAGAACTCTGAAC \
    --quality-base 33 -q 6 \
    -m 18 \
    -o R1.p2.fq.gz -p R2.p2.fq.gz \
    R1.p1.fq.gz R2.p1.fq.gz

The -q 6 is intentionally permissive. Aggressive -q 20 quality trimming chews back the 5' end of R2 and breaks truncation-based crosslink-site detection. The -m 18 minimum-length cutoff is non-negotiable: reads shorter than 18 nt are functionally unmappable (multi-map prevalence > 50%, see CIMS analysis discussions in the CLIP review literature) and must be discarded before alignment.

Per-Protocol Failure Modes

eCLIP -- 5' end trimmed by mistake

Trigger: Pipeline written for generic RNA-seq applied to eCLIP; fastp defaults trim both ends; cutadapt -g invoked on R2.

Mechanism: The 5' end of R2 in eCLIP is the truncation site = crosslink -1. Quality-trim from 5' or untargeted 5' adapter trim discards that exact base.

Symptom: Downstream PureCLIP / iCount / CTK CITS analysis fails to call crosslink sites; peak calling still works but the peaks lose nucleotide precision.

Fix: Use -q 6 (3' only by default in cutadapt) and never --trim_front2 in fastp. Validate by inspecting BAM read 2 5' positions: 60-90% of unique R2 5' positions should map within 100 nt windows around known RBP binding motifs, not be uniformly distributed.

iCLIP / iCLIP2 -- Demultiplex confusion with UMI

Trigger: Multiplexed iCLIP library; user runs umi_tools extract with NNNNNNNNN (9 N) when the actual prep is NNNXXXXNN (5 random + 4 sample barcode).

Mechanism: umi_tools treats the entire 9-base prefix as UMI, losing the sample identity in the middle 4 bases. Reads from different samples are merged.

Symptom: Library complexity inflated artificially; per-sample read counts seem high but binding profiles look averaged across samples; sample-specific motifs absent.

Fix: Demultiplex BEFORE UMI extraction. Use umi_tools extract --bc-pattern=NNNXXXXNN --extract-method=string --filter-cell-barcode with a whitelist of the 4 X-base barcodes; or split the FASTQ with je demultiplex against the library barcode table first, then umi_tools extract --bc-pattern=NNNNN (the 5 surviving random Ns).

PAR-CLIP -- T->C mistaken for sequencing error

Trigger: Standard variant-calling pipeline applied to PAR-CLIP without recognizing the T->C signature.

Mechanism: PAR-CLIP's diagnostic mutation is T->C (4SU adduct pairs with G during RT). Upon crosslinking the per-position T->C rate jumps from ~0.5% baseline to 20-50% (see PAR-CLIP literature; Hafner 2010, Spitzer 2014). Pipelines that filter "high-error" reads or apply STAR --outFilterMismatchNoverLmax 0.04 (4% mismatch ceiling) discard the very reads carrying the signal.

Symptom: Loss of 40-70% of PAR-CLIP reads; downstream T->C site calling (PARalyzer, wavClusteR) finds almost nothing.

Fix: For PAR-CLIP only, raise the STAR mismatch ceiling to --outFilterMismatchNoverLmax 0.07 (downstream in clip-alignment); also raise quality-trim tolerance in cutadapt to -q 6 (already the CLIP default but worth re-confirming for PAR-CLIP). Track T->C rate per nucleotide position with samtools mpileup to confirm the signature is preserved post-alignment. Critical exception summary: iCLIP/eCLIP/iCLIP2 keep 0.04; PAR-CLIP only raises to 0.07. See clip-seq/clip-alignment for the alignment-stage override.

Quality trimming too aggressive

Trigger: Inherited fastp/Trimmomatic pipeline with -q 20 or MINLEN 36.

Mechanism: CLIP fragments are short (20-75 nt insert) and the 3' end carries adapter. Aggressive quality trimming and length filtering destroy 30-60% of usable reads.

Symptom: Per-replicate unique-mapped read count < 500k from a ~30M raw library; library complexity calculation crashes from too few reads.

Fix: Use -q 6 -m 18 (cutadapt) or --qualified_quality_phred 6 --length_required 18 (fastp). Compare retained fraction: a properly preprocessed CLIP library retains ~70-85% of raw reads through trimming.

Library complexity below threshold

Trigger: PCR duplication rate >> 50%; deduplicated unique fragment count < 1M.

Mechanism: Low input cells (< 5M), over-amplification (> 25 PCR cycles), or failed IP all produce libraries dominated by a small number of PCR-amplified molecules.

Symptom: preseq lc_extrap predicts plateau well below 5M unique reads at infinite sequencing depth; picard CollectLibraryComplexity reports ESTIMATED_LIBRARY_SIZE < 1M; UMI families have median size > 8.

Fix: No analytic rescue. Re-prep the library with more input cells and fewer PCR cycles (target 14-18 cycles for eCLIP, 16-20 for iCLIP2). If the dataset must be salvaged, downsample to the unique-fragment fraction and acknowledge the loss of statistical power in differential analyses.

UMI Deduplication Decision

After alignment, collapse PCR duplicates by (UMI, position, strand). The choice between unique and directional methods is a precision/recall tradeoff:

Method	Match rule	Behaviour	When to use
--method=unique	Exact UMI match	Strictest; two UMIs differing by 1 base treated as independent molecules	ENCODE convention; reproducibility against published peaks
--method=directional	Network of UMIs differing by hamming-1; pick most-abundant	Collapses UMI sequencing errors; slightly fewer unique fragments reported	Highest precision; preferred when UMI sequencing error rate > 1%
--method=cluster	Connected components within edit distance	Most aggressive collapse	Default umi_tools behaviour pre-2017; not recommended for CLIP
--method=adjacency	Adjacency clustering	Between unique and directional	Rarely used for CLIP

For eCLIP and iCLIP, the ENCODE convention is --method=unique (Van Nostrand 2016); the Yeo lab pipeline uses this exclusively. Directional adds 5-15 minutes runtime for human eCLIP at typical depth and is more conservative on rare UMI sequencing errors but produces slightly different absolute counts.

samtools index aligned.bam
umi_tools dedup \
    --stdin=aligned.bam \
    --stdout=dedup.bam \
    --method=unique \
    --paired \
    --log=dedup.log
samtools index dedup.bam

Without UMIs (e.g., older eCLIP with only sample barcodes, some custom protocols), fall back to picard MarkDuplicates --REMOVE_DUPLICATES true. The trade-off: position-only dedup over-collapses (genuinely independent fragments that share start positions are lost) at ~5-15% in deep CLIP libraries; UMI dedup recovers them.

Pre-Mapping rRNA Filter (Optional but Standard)

eCLIP libraries are 5-30% rRNA reads even after polyA depletion. ENCODE's pipeline pre-maps to a rRNA + RepeatMasked repeat index with bowtie2, then aligns unmapped reads to the genome. This is purely a performance optimization (avoiding STAR's multi-mapper tangle on rRNA) and does not change which reads survive deduplication; it only reorders the alignment stages.

# Pre-map to rRNA + repeats
bowtie2 -x repbase_repeats -U R1.trim.fq.gz \
    --un-gz R1.norep.fq.gz \
    -p 8 -S /dev/null
# Then align unmapped reads to genome with STAR

For non-eCLIP protocols (iCLIP, PAR-CLIP) the rRNA pre-map is optional; the alternative is to filter rRNA-overlapping reads after STAR alignment with bedtools intersect -v -a aligned.bam -b rRNA.bed.

Library Complexity Assessment

# preseq lc_extrap predicts unique fragments at deeper sequencing
preseq lc_extrap -B -P aligned.bam -o complexity.txt
# Output: TOTAL_READS, EXPECTED_DISTINCT, LOWER_0.95CI, UPPER_0.95CI
# At 100M reads, EXPECTED_DISTINCT >= 10M = good complexity; < 3M = library failed

# picard direct estimate
picard EstimateLibraryComplexity \
    I=aligned.bam \
    O=picard_complexity.txt
# ESTIMATED_LIBRARY_SIZE > 5M = healthy

ENCODE eCLIP requires >= 1M unique fragments per replicate (after UMI dedup). Anything below 500k is functionally unusable for genome-wide peak calling. Between 500k and 1M, restrict analysis to high-expression transcripts.

QC Checkpoints After Preprocessing

Metric	Target	If below target
Adapter trim retention	>= 70% reads	Adapter pattern wrong; recheck library prep docs
Mean read length post-trim	>= 25 nt	RNA fragmentation too aggressive in IP step
% reads >= 18 nt	>= 80%	Drop the prep; libraries with > 30% < 18 nt indicate degraded RNA
UMI-extract success rate	>= 95%	UMI pattern wrong; reads do not start with random Ns
PCR duplication rate	30-70% (CLIP normal)	< 30% means under-sequenced; > 90% means over-amplified
Library complexity (preseq)	>= 1M unique at sequenced depth	Library failed; cannot rescue analytically

CLIP libraries have HIGH duplication rates (40-70%) by design - the IP enriches a small pool of molecules. This is NOT a problem if UMIs collapse duplicates correctly. A "30% duplication rate" CLIP library typically means either (a) the IP failed (no enrichment, so library looks like RNA-seq) or (b) the library was undersequenced and unique molecules dominate.

Common Errors

Error / symptom	Cause	Solution
umi_tools extract: "Read does not match pattern" for > 10% of reads	UMI pattern wrong (5 vs 9 vs 10 nt)	Inspect first 12 bases of 100 reads; rerun extract with correct --bc-pattern
cutadapt: 95% reads "Too short, filtered"	Adapter sequence wrong; reads are adapter-only after trim	Verify adapter sequence in library prep documentation; check both R1 and R2 adapters
All reads aligning to chrM after preprocessing	Pre-map to rRNA skipped; rRNA reads dominate	Add bowtie2 rRNA pre-map OR samtools view -F 4 -L exclude_chrM_rRNA.bed post-align
umi_tools dedup very slow (> 4h on 30M BAM)	Default --method=directional on dense libraries	Switch to --method=unique (ENCODE convention)
eCLIP truncation positions look uniform across genome	5' adapter trim destroyed R2 5' end	Re-preprocess; -g should never touch R2 5' in eCLIP
30% T->C mismatches in PAR-CLIP fail mapping	STAR mismatch ceiling 0.04 too strict	Raise to --outFilterMismatchNoverLmax 0.07 for PAR-CLIP only
iCLIP2 reads after dedup << expected unique	Demultiplex done with whole UMI; samples merged	Demultiplex by 4 X bases of NNNXXXXNN first, then extract UMI

Reconciliation: Preprocessing Tools

Pattern	Likely cause	Action
fastp gives more reads than cutadapt	fastp default polyG trimming kept short reads; cutadapt min-length filtered them	Both correct; pick one and document
umi_tools and je-suite give different dedup counts	Different UMI distance metric (unique vs hamming-1 vs network)	Use ENCODE convention umi_tools --method=unique for cross-study comparability
Library complexity (preseq) vs (picard) disagree	preseq extrapolates non-linearly; picard estimates at sequenced depth only	Trust preseq at sequenced depth, picard for absolute library size
Read count after Yeo lab pipeline >> nf-core/clipseq	Yeo includes rRNA reads; nf-core pre-filters	Both correct; downstream peak callers handle this differently

Operational rule: For ENCODE comparability, follow the Yeo lab eCLIP pipeline exactly: umi_tools extract (10 N R1) -> cutadapt -q 6 -m 18 (two-pass) -> bowtie2 pre-map rRNA (unmapped to genome) -> STAR --alignEndsType EndToEnd --outFilterMultimapNmax 1 -> umi_tools dedup --method=unique. For iCLIP/iCLIP2, follow the iCount preprocessing tutorial. Document any deviation in methods.

References

• Van Nostrand EL et al 2016 Nat Methods 13:508 (eCLIP protocol, ENCODE standard)
• Konig J et al 2010 Nat Struct Mol Biol 17:909 (original iCLIP, truncation principle)
• Buchbender A et al 2020 Methods 178:33 (iCLIP2 protocol, library complexity gain)
• Lee FCY et al 2021 bioRxiv 2021.08.27.457890 (iiCLIP / improved iCLIP, motif specificity)
• Hafner M et al 2010 Cell 141:129 (PAR-CLIP, 4SU labeling, T->C signature)
• Zarnegar BJ et al 2016 Nat Methods 13:489 (irCLIP, non-radioactive)
• Aktas T et al 2020 Nucleic Acids Res 48:e15 (FLASH, fast protocol)
• Smith T et al 2017 Genome Res 27:491 (UMI-tools, network-based dedup)
• Daley T & Smith AD 2013 Nat Methods 10:325 (preseq library complexity)
• Chakrabarti AM et al 2023 Genome Biol 24:235 (nf-core/clipseq pipeline)

Related Skills

• clip-seq/clip-alignment - Downstream STAR/bowtie2 alignment with ENCODE parameters
• clip-seq/clip-qc - Library complexity, FRiP, IDR, read-distribution QC after preprocessing
• clip-seq/crosslink-site-detection - Why preserving the 5' R2 base is critical
• clip-seq/clip-peak-calling - Downstream peak callers consume the dedup BAM
• clip-seq/stamp-antibody-free - STAMP/DART-seq use RNA-seq preprocessing instead of UMI-based CLIP preprocessing
• read-qc/umi-processing - General UMI handling concepts
• read-qc/adapter-trimming - General adapter trimming
• alignment-files/duplicate-handling - Picard MarkDuplicates fallback when UMIs unavailable