clip-seq-crosslink-site-detection

name	clip-seq-crosslink-site-detection
description	Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.
tool_type	cli
primary_tool	PureCLIP

Version Compatibility

Reference examples tested with: PureCLIP 1.3.1+, CTK 1.1.4+, PARalyzer 1.5+, wavClusteR 2.34+, pyCRAC 1.5+, samtools 1.19+, bedtools 2.31+, pysam 0.22+, R 4.3+.

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 to match the actual CLI rather than retrying.

CLIP-seq Crosslink-Site Detection

"Detect single-nucleotide crosslink sites in my CLIP data" -> Identify the exact base where the protein-RNA UV adduct caused the reverse transcriptase to stop (truncation, in iCLIP/eCLIP), to read through with a mutation (deletion in HITS-CLIP, T->C in PAR-CLIP), or to leave a multi-base signature (PARalyzer kernel density for PAR-CLIP). Single-nucleotide resolution is the foundation of motif registration (mCross), allele-specific binding (BEAPR/ASPRIN), variant-effect prediction, and the most rigorous comparisons across CLIP variants. The detection chemistry differs by CLIP type: iCLIP/eCLIP read 5' end is one nucleotide downstream of the crosslink (CITS); PAR-CLIP reads contain T->C transitions at the crosslink (CIMS substitution); HITS-CLIP reads contain deletions at the crosslink (CIMS deletion).

• CLI (HMM, all CLIP variants): pureclip -i dedup.bam -bai dedup.bam.bai -g genome.fa -ibam sminput.bam -ibai sminput.bam.bai -o crosslinks.bed -or regions.bed -nt 8 -dm 8
• CLI (CTK CITS truncations, iCLIP/eCLIP): parseAlignment.pl --map-qual 1 --min-len 18 --mutation-file mut.txt dedup.bam dedup.bed then tag2cluster.pl ... -cs5 5 -m 1 for truncation-cluster
• CLI (CTK CIMS deletions, HITS-CLIP): getMutationType.pl dedup.bed mut.txt -type del then CIMS.pl dedup.bed mut.txt -big -c -p 0.01 cims.bed
• CLI (CTK CIMS T->C, PAR-CLIP): getMutationType.pl dedup.bed mut.txt -type sub -nuc t -mut c then CIMS.pl dedup.bed t2c.mut -p 0.001 t2c_cims.bed
• CLI (PAR-CLIP kernel density): PARalyzer params.ini (parameters file defines read length, min reads per cluster, mutation rate threshold)
• CLI (PAR-CLIP wavClusteR R): wavClusteR::filterClusters(cl, snps=NULL, filterFC=FALSE) after wavelet clustering

Single-nt CL sites are the input to mCross motif registration (see clip-seq/clip-motif-analysis), to BEAPR/ASPRIN allele-specific binding analyses, and to RBPNet/DeepRiPe deep-learning models (see clip-seq/clip-deep-learning). They are NOT a replacement for broad peak calls; the two outputs are complementary. A common downstream error is passing a CLIPper peak BED to mCross instead of a PureCLIP single-nt BED - mCross requires the single-nt resolution to register motif position relative to the crosslink offset.

Crosslink Chemistry by CLIP Variant

The detection method must match the underlying chemistry of how the reverse transcriptase encountered the protein-RNA adduct:

CLIP variant	RT behavior at CL	Detection signature	Sensitivity (CL captured per read)	Sequence bias
iCLIP / iCLIP2 / iCLIP3 / eCLIP / irCLIP / FLASH	Truncates at adduct	5' end of cDNA = CL site - 1	~80% of reads truncate, 20% read through with deletion	Strong U bias (~60-80% of CL events at U)
HITS-CLIP	Reads through with deletion (3-7% of crosslinked positions)	Single-base deletion in read	~3-7% of reads contain deletion at CL site	U bias plus sequence-specific deletion rate
PAR-CLIP (4SU)	Reads through with T->C (20-50% of T positions in crosslinked reads)	T->C transition (or G->A on reverse strand)	Per-T rate 20-50%; per-read 1-5 conversions	Restricted to T positions; depends on 4SU incorporation rate
PAR-CLIP (6SG)	Reads through with G->A	G->A transition	Lower rate than 4SU T->C	Restricted to G positions
miCLIP / miCLIP2 (m6A)	Primarily truncation at the m6A site (RT stops at antibody-trapped m6A); secondary C->T transition observed in a subset of reads at m6A	5' end (CL -1); C->T rate is a feature used by m6Aboost ML in addition to truncation, not a stand-alone primary signal	Mixed; m6Aboost ML integrates truncation + sequence context to score	DRACH motif context required
STAMP	C->T editing on target (not crosslink)	C->T editing in mRNA reads	NA (editing-based; not crosslink-based)	N/A
TRIBE	A->I editing on target	A->G in cDNA (I read as G)	NA	Adjacent to ADAR consensus

Algorithmic Taxonomy

Tool	CLIP variant	Detection signal	Statistical model	Output resolution	Strength	Fails when
PureCLIP (Krakau 2017)	iCLIP/eCLIP/PAR-CLIP	Truncation + enrichment + CL motif	Non-homogeneous HMM	Single-nucleotide	The most comprehensive HMM model	F1 ~0.2 on broad-binding RBPs (only ~4 sites per CLIP on benchmark)
CTK CITS (Shah 2017)	iCLIP/eCLIP	Truncation only	Empirical FDR vs background	Single-nucleotide	Simple, well-validated for iCLIP	Less granular than PureCLIP; no HMM smoothing
CTK CIMS deletion (Shah 2017)	HITS-CLIP	Single-base deletions	Empirical FDR	Single-nucleotide	Standard for HITS-CLIP	Requires deletion-tolerant aligner (BWA -e)
CTK CIMS T->C (Shah 2017)	PAR-CLIP	T->C substitutions	Empirical FDR	Single-nucleotide	Alternative to PARalyzer	Less popular than PARalyzer
PARalyzer (Corcoran 2011)	PAR-CLIP	T->C transitions, kernel density	Kernel density estimation	Cluster (10-50 nt)	Field standard for PAR-CLIP	Cluster-level, not single-nt; needs careful parameter tuning
wavClusteR (Comoglio 2015)	PAR-CLIP	T->C with wavelet smoothing	Wavelet transform + clustering	Cluster	Robust to sequencing depth variability	Less single-nt; legacy R package
pyCRAC / kPLogo	CRAC (yeast)	Read truncation + deletion	Empirical	Single-nucleotide	Original CLIP analytics tool	Yeast-focused; perl/python legacy
Piranha bins	Any	Coverage in bins	ZTNB	Bin-level (50-200 nt)	Not crosslink-specific	Bin width too coarse for single-nt
HOMER tag2pos	Any	5' end position	None	Read-end positions only	Quick truncation site dump	No statistical filtering
omniCLIP	Any	Coverage + variant pattern	Dirichlet-multinomial HMM

Methodology evolves; PureCLIP 2.0 and CTK 1.2 have minor flag changes; PARalyzer parameters need careful tuning per-RBP. Cross-validate single-nt sites with at least two tools when high-confidence reporting is required.

Critical Choice: Truncation vs Mutation vs HMM

Three orthogonal approaches:

Truncation-based (CITS, PureCLIP) -- Find positions enriched in cDNA 5' ends. The RT enzyme stops at the adduct; the 5' end of the read maps to CL site - 1. Used for iCLIP/eCLIP. Pro: high yield (~80% of reads truncate). Con: U bias of crosslinking inflates U positions.

Mutation-based (CIMS for deletions/substitutions; PARalyzer for T->C) -- Find positions with crosslink-induced mutations. RT reads through the adduct with high mutation rate at the CL position. Used for HITS-CLIP (deletions, 3-7%) and PAR-CLIP (T->C, 20-50%). Pro: less U bias; PAR-CLIP signal is restricted to T positions. Con: lower yield (only mutated reads count).

HMM-based (PureCLIP, omniCLIP) -- Joint model of enrichment + CL signature + sequence context. State the most-likely posterior probability of CL state per nucleotide. Used across CLIP variants. Pro: integrates multiple signals; explicit input normalization. Con: F1 ~0.2 on bulk RBPs (very focal); single-nt resolution at the cost of breadth.

Goal	Method	Tool
Single-nt CL map for motif registration (mCross)	HMM	PureCLIP
Truncation-based ENCODE-compatible iCLIP	Truncation	CTK CITS
PAR-CLIP T->C signal	Mutation	PARalyzer (cluster) or CTK CIMS sub T->C (single-nt)
HITS-CLIP deletion signal	Mutation	CTK CIMS deletion
Cross-CLIP-variant comparable single-nt	HMM	PureCLIP (all variants)
Allele-specific CLIP (BEAPR)	Truncation + ASB	PureCLIP CL sites + WASP-filtered BAM
Variant effect (computational from CL)	HMM	PureCLIP + downstream DeepRiPe (see clip-deep-learning)
Yeast / CRAC	Truncation + deletion	pyCRAC

Per-Tool Failure Modes

PureCLIP -- HMM convergence on sparse coverage

Trigger: RBP with low IP enrichment; SMInput depth uneven; PureCLIP run on full genome without -iv interval.

Mechanism: PureCLIP HMM convergence is sensitive to coverage depth. On regions with < 5 reads/100 bp the HMM defaults to background state; if half the genome is sparse, the state distributions become bimodal and the entire run takes > 24 h or runs out of memory.

Symptom: PureCLIP "Convergence not reached" warning; or output has 0 CL sites; or runtime > 24 h on standard server.

Fix: Restrict analysis to expressed transcripts: -iv expressed_tx.bed. Or filter the BAM to high-coverage regions first. Test on chr22 first to verify parameters before genome-wide run.

PureCLIP -- Misses broad binding zones

Trigger: RBP with broad binding (PUM2 3' UTRs, SR proteins exonic enhancers); using PureCLIP exclusively.

Mechanism: PureCLIP HMM emits the CL state at very high stringency. Skipper 2023 benchmark reports F1 ~0.2 on bulk RBPs - only ~4 sites per CLIP on test data.

Symptom: PureCLIP CL site count is 100x lower than CLIPper / Skipper peak count.

Fix: Use PureCLIP for single-nt CL maps AND CLIPper/Skipper for broad-zone peak calls. They are complementary, not substitutes. PureCLIP -or regions output is broader than -o sites but still focal.

CITS truncation -- Misapplied to PAR-CLIP

Trigger: CTK CITS run on PAR-CLIP data.

Mechanism: PAR-CLIP RT reads through the adduct (T->C mutation), not truncates. The 5' end of PAR-CLIP reads is at the fragment 5' end, not the CL site.

Symptom: CITS returns few sites; the sites that ARE returned do not align with the T->C mutation positions.

Fix: For PAR-CLIP use PARalyzer or CTK CIMS substitution mode (-type sub -nuc t -mut c). CITS is iCLIP/eCLIP only.

CIMS deletion -- Misapplied to iCLIP

Trigger: CTK CIMS run with -type del on iCLIP data.

Mechanism: iCLIP RT truncates, doesn't delete. The deletion rate is < 1% in iCLIP (background level), so CIMS finds nothing significant.

Symptom: CIMS output BED has < 100 sites genome-wide.

Fix: Use CTK CITS for iCLIP (truncation-based) or PureCLIP. CIMS deletion is HITS-CLIP / older PAR-CLIP only.

PARalyzer -- Parameter sensitivity

Trigger: PARalyzer default parameters on a new RBP; cluster count unexpected.

Mechanism: PARalyzer's kernel-density approach has 5+ parameters: minimum read depth, minimum mutation read count, mutation rate threshold, bandwidth, kernel type. Different combinations produce 5-100x different cluster counts.

Symptom: PARalyzer cluster count 5-100x off from literature reports for the same RBP.

Fix: Use the published PARalyzer parameter file for that RBP class (Hafner 2010 supplementary). Validate on a known-binding region with a reference dataset before publication.

Aligner deletion-tolerance for HITS-CLIP

Trigger: HITS-CLIP aligned with STAR or bowtie2 in standard mode; CIMS finds few deletion sites.

Mechanism: Standard STAR/bowtie2 are aggressive about handling deletions; CIMS expects the deletions to be visible as CIGAR D operations in BAM. STAR/bowtie2 may soft-clip or fail to align deletion-bearing reads.

Symptom: Few CIGAR D operations in BAM (samtools view dedup.bam | awk '$6 ~ /D/' | wc -l); CIMS deletion site count low.

Fix: Re-align HITS-CLIP with BWA-aln (Yeo lab convention) or STAR --scoreDelOpen -1 --scoreDelBase -1 --scoreInsOpen -1 --scoreInsBase -1 to permit deletions. Or use Novoalign which is more deletion-tolerant.

PAR-CLIP T->C mismatch ceiling

Trigger: PAR-CLIP aligned with STAR --outFilterMismatchNoverReadLmax 0.04; T->C reads discarded.

Mechanism: PAR-CLIP per-T conversion rate 20-50% means a 30 nt read with 8 Ts may have 4 T->C events = 13% mismatch rate, exceeding the 4% ceiling.

Symptom: 40-70% read loss at alignment for PAR-CLIP; downstream T->C site detection finds nothing.

Fix: Raise to 0.07 for PAR-CLIP only (see clip-seq/clip-alignment).

Strand mis-assignment for truncation site

Trigger: Single-end CLIP; using read 5' end as CL site without strand consideration.

Mechanism: For plus-strand RNA, the cDNA 5' end is downstream of the CL position. For minus-strand RNA, it is upstream. Without strand-aware coordinate conversion, half of CL sites are mis-positioned by ~1 nt.

Symptom: mCross / motif analysis shows the motif positioned 1 nt off from expected.

Fix: Verify the BED output handles strand correctly. CTK CITS, PureCLIP, and most modern tools do this automatically; custom scripts must add if strand == '-': cl_position = read_end_5p + 1.

Decision Tree by Use Case

Scenario	Tool	Parameters
Single-nt CL map for mCross motif registration	PureCLIP	-i dedup.bam -ibam sminput.bam -dm 8
iCLIP/eCLIP CITS truncation sites	CTK CITS	tag2cluster.pl -cs5 5 -m 1
HITS-CLIP deletion CIMS	CTK CIMS	CIMS.pl -big -c -p 0.01
PAR-CLIP T->C clusters	PARalyzer	Per-RBP params from Hafner 2010
PAR-CLIP T->C single-nt	CTK CIMS substitution	CIMS.pl -type sub -nuc t -mut c -p 0.001
Yeast CRAC	pyCRAC	pyCRAC.py with HTP-tagged protein
Variant effect at CL sites	PureCLIP + DeepRiPe	See clip-seq/clip-deep-learning
Allele-specific binding	WASP-filtered BAM + PureCLIP	See clip-seq/clip-alignment for WASP
Cross-CLIP comparison	PureCLIP (consistent across variants)	Same parameters
m6A miCLIP2 sites	miCLIP2-specific (m6Aboost)	See clip-seq/m6a-clip
STAMP edit sites	Not crosslink-based	See clip-seq/stamp-antibody-free

Reconciliation: When Detection Methods Disagree

Pattern	Likely cause	Action
PureCLIP << CTK CITS site count	PureCLIP HMM very focal; CITS empirical broader	Both correct; report which threshold
PARalyzer clusters << CIMS T->C sites for PAR-CLIP	PARalyzer is cluster-level; CIMS is single-nt	Aggregate CIMS to clusters for comparison
PureCLIP CL sites at unexpected positions	Strand mis-assignment OR aligner soft-clip	Check BED strand column; verify --alignEndsType EndToEnd
HITS-CLIP CIMS empty	Aligner not deletion-tolerant	Re-align with BWA-aln or STAR with adjusted scoring
PAR-CLIP CIMS << expected	Reads lost at mismatch ceiling	Raise STAR --outFilterMismatchNoverReadLmax to 0.07
eCLIP CITS positions shifted by 1 nt	Strand handling off-by-one	Verify R2 5' end position handling
Multiple CITS sites within 5 nt	RT stops near each other; same CL event	Cluster within 10 nt window post-detection
Motif registers 5 nt off in mCross	CL positions wrong by RT-stop offset	Confirm tool reports "CL - 1" position consistently

Operational rule: For motif registration and ASB, run PureCLIP with SMInput. For ENCODE-comparable truncation-based output, also run CTK CITS. Cross-validate by checking that mCross motif PWM is the same when fed PureCLIP sites vs CTK CITS sites. If they diverge by > 2 nt in motif position, suspect strand or off-by-one issue.

Workflow: PureCLIP -> mCross -> ASB

# Step 1: PureCLIP single-nt sites
pureclip \
    -i sample.dedup.bam -bai sample.dedup.bam.bai \
    -g genome.fa \
    -ibam sminput.dedup.bam -ibai sminput.dedup.bam.bai \
    -o sample.crosslinks.bed \
    -or sample.regions.bed \
    -nt 8 -dm 8 -iv expressed_tx.bed

# Step 2: mCross motif registration
mCross -i sample.crosslinks.bed -g genome.fa -k 7 -n 5 -o mcross_out

# Step 3: Intersect with heterozygous SNPs for allele-specific binding
bedtools intersect -wa -wb -s -a sample.crosslinks.bed -b het_snps.vcf > cl_at_hets.bed

# Step 4: Test allele bias with BEAPR or ASPRIN
# (see allele-specific CLIP literature; not in this skill)

Common Errors

Error / symptom	Cause	Solution
PureCLIP "Convergence not reached"	HMM didn't converge on sparse coverage	Restrict to expressed transcripts with -iv expressed.bed
PureCLIP > 24h runtime	Full genome on large library	Test on chr22 first; add -iv filter
CTK CIMS empty for HITS-CLIP	Aligner not deletion-tolerant	Re-align with BWA-aln
CITS truncation sites not single-nt	CIGAR S operations from soft-clip	Verify --alignEndsType EndToEnd upstream
PAR-CLIP CIMS empty	Reads lost at mismatch ceiling	Raise STAR mismatch tolerance
Off-by-one motif position	Strand handling differs	Verify tool documents whether output is CL or CL-1
Single-nt motif lost in HOMER on PureCLIP sites	Site BED too narrow; need flanking	Extend bedtools slop -b 15 before motif analysis
mCross "no crosslinks found"	Passed peak BED instead of CL BED	Use single-nt site output
PureCLIP output looks like uniform distribution	R2 5' end trimmed in preprocessing	Re-preprocess; -g and --trim_front2 banned for CLIP
Strand-specific motif inverted	BED column 6 wrong	Verify strand encoding; PureCLIP preserves correctly

References

• Konig J et al 2010 Nat Struct Mol Biol 17:909 (iCLIP truncation principle)
• Hafner M et al 2010 Cell 141:129 (PAR-CLIP T->C signature)
• Granneman S et al 2009 PNAS 106:9613 (CRAC, single-nt CL detection)
• Corcoran DL et al 2011 Genome Biol 12:R79 (PARalyzer)
• Comoglio F et al 2015 BMC Bioinformatics 16:32 (wavClusteR)
• Shah A et al 2017 Bioinformatics 33:566 (CTK / CIMS / CITS)
• Krakau S et al 2017 Genome Biol 18:240 (PureCLIP HMM)
• Sugimoto Y et al 2015 Nature 519:491 (iCLIP U-crosslink bias)
• Feng H et al 2019 Mol Cell 74:1189 (mCross requires CL sites)
• Wu B et al 2018 Nat Commun 9:5117 (BEAPR allele-specific binding)
• Van Nostrand EL et al 2020 Nature 583:711 (ENCODE eCLIP single-nt analyses)

Related Skills

• clip-seq/clip-preprocessing - 5' base preservation critical for truncation detection
• clip-seq/clip-alignment - End-to-end alignment for crosslink-preserving BAM
• clip-seq/clip-peak-calling - Peak vs CL site is a complementary distinction
• clip-seq/clip-motif-analysis - mCross consumes CL sites
• clip-seq/clip-deep-learning - RBPNet trained on CL count distributions
• clip-seq/m6a-clip - miCLIP2 has its own CL detection
• clip-seq/stamp-antibody-free - STAMP uses C->U editing, not crosslink
• alignment-files/sam-bam-basics - CIGAR string semantics for D operations