Clip peak calling

Version Compatibility

Reference examples tested with: CLIPper 2.0+, PureCLIP 1.3.1+, Piranha 1.2.1+, omniCLIP 0.2.0+, CTK 1.1.4+, CLAM 1.2+, Paraclu 9+, Skipper (commit 2023.05+), MACS3 3.0+, bedtools 2.31+, samtools 1.19+, idr 2.0.4+.

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 unexpected errors, introspect the installed binary and adapt the example to match the actual CLI rather than retrying. PureCLIP 2.x changed several flag names; Skipper is distributed as a Snakemake workflow with frequently-evolving paths.

CLIP-seq Peak Calling

"Call protein-RNA binding sites from my deduplicated CLIP BAM" -> Identify regions where read pile-up (and, for iCLIP/eCLIP, single-nucleotide truncations) exceed background from a size-matched input (SMInput) control. The choice of peak caller depends on (a) the CLIP variant (HITS-CLIP, iCLIP, eCLIP, PAR-CLIP), (b) whether SMInput is available, (c) the RBP binding mode (narrow motif vs broad zones vs repeat-binding), and (d) the goal (publication-comparable ENCODE peaks vs single-nucleotide crosslink sites vs high-recall site lists).

• CLI (ENCODE eCLIP canonical): clipper -b dedup.bam -s hg38 -o peaks.bed --save-pickle --FDR-alpha 0.05 --superlocal then eclip-norm peaks.bed -i sminput.bam for log2 fold-change against SMInput (--FDR-alpha is the CLIPper flag for the FDR cutoff; older docs sometimes shorten to --FDR)
• CLI (single-nucleotide crosslink sites, iCLIP/eCLIP): pureclip -i dedup.bam -bai dedup.bam.bai -g genome.fa -ibam sminput.bam -ibai sminput.bam.bai -o sites.bed -or regions.bed -nt 8 -dm 8
• CLI (high-recall, beta-binomial windowed; needs SMInput): Skipper Snakemake workflow with config matching cell type and SMInput BAM
• CLI (no SMInput, no truncation): Piranha -b 50 -p 0.01 -d ZeroTruncatedNegativeBinomial -s -o peaks.bed dedup.bam (Piranha takes the BAM as a positional argument, not via -s)
• CLI (CIMS/CITS single-nt from CTK): tag2cluster.pl dedup.bed cluster.bed --multi-tag-method coverage; then bedExtractCIMS.pl cluster.bed cims.bed
• CLI (multi-mapper rescue for repeat-binding RBPs): CLAM peakcaller -i unique.bam multimap.bam -o clam_out_dir/ --gtf gencode.gtf (CLAM peakcaller writes peaks into an OUTPUT DIRECTORY, not a single file)

The ENCODE eCLIP gold standard for "stringent" peaks is: log2(IP / SMInput) >= 3 AND -log10(p-value) >= 3. "Lenient" peaks use log2 >= 1 AND -log10 >= 2. Both filters operate on CLIPper peak output normalized against SMInput. Without SMInput, neither stringency level can be reproduced - the algorithm has no background term.

Algorithmic Taxonomy

Caller	Model	Resolution	SMInput required	Strength	Fails when
CLIPper (Yeo)	Poisson with 500 bp local lambda, cubic-spline interpolation	Peak (10-500 nt)	Not for calling; required for ENCODE normalization	ENCODE eCLIP standard; reproducible against published ENCODE peak sets	Assumes most reads not from binding; fails when IP signal dominates a gene; sensitive to highly expressed transcripts
PureCLIP (Krakau 2017)	Non-homogeneous HMM jointly modeling enrichment + truncation + sequence biases	Single-nucleotide CL + broad region	Optional via -ibam; recommended	Single-nt resolution; only caller that explicitly models the iCLIP truncation pattern	Misses broad binding zones; very focal (Skipper 2023 benchmark: only ~4 sites per CLIP on test set; F1 ~0.2)
Skipper (Boyle 2023)	GC-stratified beta-binomial, 100 bp feature-respecting windows	Window (~100 nt)	Mandatory	210-320% more sites than CLIPper; 8x faster; properly normalizes against input	Loses single-nt resolution; relatively new (2023); fewer published peak sets to compare
Piranha (Smith lab)	Zero-truncated negative binomial regression with optional covariates	Bin (50-200 nt)	Optional; pass as covariate	Mature, widely cited, handles count overdispersion	Biased toward high-expression transcripts; convergence fails with large covariate values (use -l log-space)
omniCLIP (Drewe-Boss 2018)	Non-homogeneous HMM with Dirichlet-multinomial of variants	Peak	Required	Models replicate variance; integrates background; can call peaks on any CLIP variant	Slow on deep libraries; F1 ~0.4 on standard benchmarks; fails for mitochondrial transcripts (Skipper benchmark: 0 chrM windows for FASTKD2)
CTK CIMS/CITS (Shah 2017)	Empirical FDR on crosslink-induced mutations or truncations	Single-nucleotide	No (uses background mutation rate from non-bound transcripts)	Single-nt resolution; works on HITS-CLIP deletions, PAR-CLIP T->C, iCLIP truncations	Empirical FDR less principled than HMM/beta-binomial; perl-based pipeline harder to integrate
CLAM peakcaller (Zhang & Xing 2017)	EM-assigned multi-mapper count + Piranha-like negative binomial	Peak	Optional	Only solution for repeat-binding RBPs; 10-30% additional sites in repeats	Inherits Piranha limitations on coverage-based stats; slower than Piranha
Paraclu (Kawaji)	Parametric clustering with min-density and max-density thresholds	Variable	No	Simple, parameter-tunable, works on bedgraph	Heuristic; no statistical significance; cluster boundaries sensitive to thresholds
PIPE-CLIP	Online CLIP pipeline with custom peak caller	Peak	Optional	Web interface; integrated end-to-end	Slow on cloud; less customizable; community has moved on
CLIPick (Park 2018)	Expression-deconvolved peak caller	Peak	No (RNA-seq used instead)	Models RNA-seq abundance as background	RNA-seq cannot capture nonspecific binding; less popular post-Skipper
Flipper (Tu 2024)	Negative-binomial differential test downstream of Skipper	Window	Yes	Companion differential tool to Skipper	Only meaningful in differential context; see clip-seq/differential-clip
MACS3 callpeak	Local Poisson	Peak	Optional (treats SMInput as ChIP-seq input)	Familiar, fast	Not designed for CLIP; misses truncation signal; produces wider-than-typical peaks

Methodology evolves; verify the current ENCODE eCLIP standard operating procedure (encodeproject.org/eclip) and the nf-core/clipseq pipeline configuration before locking on a single caller. The 2023 Skipper benchmark (Boyle Cell Genomics) is the most recent comprehensive comparison and is the rationale for many recent eCLIP reanalyses.

Critical Choice: Coverage-Based vs Crosslink-Site-Based vs Window-Based

Three fundamentally different statistical frameworks:

Coverage-based (CLIPper, Piranha, MACS3, Paraclu): Tally reads in a window; significance from local Poisson or negative binomial. Produces multi-nt peaks. Best when binding zones are broader than the read footprint (PUM2 3' UTR, HuR ARE elements).

Crosslink-site-based (PureCLIP, CTK CIMS/CITS, PARalyzer): Identify single nucleotides enriched in truncations (iCLIP/eCLIP) or specific mutations (PAR-CLIP T->C, HITS-CLIP deletions). Produces single-nt sites that can be aggregated into footprints. Best when single-nt resolution is essential (motif registration with mCross; allele-specific binding).

Window-based (Skipper): Tile transcriptome a priori into fixed-size feature-respecting windows; test each window's IP/IN ratio with beta-binomial. Produces window-level calls. Best when SMInput is available and the goal is high-recall, comparable-across-RBPs site sets.

Goal	Recommended caller	Why
Reproduce ENCODE published peaks	CLIPper + IP/SMInput normalization	The Yeo lab pipeline is the canonical reference
Maximum sensitivity, modern statistics, SMInput available	Skipper	210-320% more sites; explicit input normalization
Single-nucleotide crosslink-site map (motif registration, ASB)	PureCLIP (HMM) or CTK CITS (empirical)	True single-nt resolution
Repeat-binding RBP (Alu, LINE, LTR)	STAR multi-mapper + CLAM peakcaller	Only solution that uses multi-mappers
No SMInput control	Piranha or PureCLIP with no -ibam	Both work without input but lose specificity
PAR-CLIP T->C signature	PARalyzer or CTK CIMS (-substitution)	Designed for the T->C signal
HITS-CLIP deletion signature	CTK CIMS (-deletion)	Designed for the deletion signal
Custom statistical model needed	omniCLIP	Most flexible HMM among the options
Online interactive analysis	Galaxy CLIP-Explorer or PIPE-CLIP	Web interface

ENCODE eCLIP Stringency Thresholds

The Van Nostrand et al. ENCODE eCLIP standards define two stringency levels for CLIPper peaks normalized against SMInput:

Stringency	log2(IP / SMInput)	-log10(p-value)	Use case
Stringent (publication)	>= 3	>= 3	High-confidence binding sites; differential analysis; motif discovery
Lenient (discovery)	>= 1	>= 2	Catalogue of all enriched windows; sensitive analyses

Both thresholds are applied to the IDR-passing reproducible peak set. ENCODE requires:

• = 2 biological replicates

• = 1M unique fragments per replicate (or saturated peak detection)

• IDR rescue ratio and self-consistency ratio both < 2
• Narrow-binding RBPs: FRiP >= 0.005 (atypical-binding RBPs exempt)

# CLIPper -> SMInput normalization -> ENCODE thresholds
clipper -b dedup.bam -s hg38 -o peaks.bed --save-pickle --FDR-alpha 0.05 --superlocal

# Normalize against SMInput; eclip-norm is the Yeo lab tool
python overlap_peakfi_with_bam_PE.py peaks.bed dedup.bam sminput.bam dedup.bam.readnum.txt sminput.bam.readnum.txt peaks.normed.bed
python compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat.py peaks.normed.bed peaks.compressed.bed

# Stringent filter: log2 FC >= 3, -log10 p >= 3
awk 'BEGIN{FS=OFS="\t"} $4 >= 3 && $5 >= 3' peaks.compressed.bed > peaks.stringent.bed

# Lenient
awk 'BEGIN{FS=OFS="\t"} $4 >= 1 && $5 >= 2' peaks.compressed.bed > peaks.lenient.bed

The Yeo lab scripts (overlap_peakfi_with_bam_PE.py, compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat.py) live in the eclip-pipeline repository; they implement the exact log2 fold-change computation used for ENCODE data downloads.

Per-Caller Failure Modes

CLIPper -- High-expression transcript bias

Trigger: RBP that binds rare transcripts (snoRNAs, tRNAs, mitochondrial mRNAs) such as TROVE2 (Y RNA), NSUN2 (tRNA), or FASTKD2 (chrM).

Mechanism: CLIPper's Poisson local-lambda model assumes only a minority of reads come from binding. For rare-transcript binders, the IP reads ARE the majority signal on that transcript; the model treats them as background.

Symptom: Per-transcript peak count plummets compared to qualitatively-visible IP signal; chrM peaks called by Skipper but missed by CLIPper for FASTKD2.

Fix: Use Skipper for rare-transcript binders (beta-binomial against SMInput respects the actual IP/IN ratio independently of local lambda). Or pre-restrict CLIPper to the target transcript with --gene custom.bed.

CLIPper -- Requires SMInput downstream

Trigger: Submitting CLIPper output as "peaks" without IP/SMInput log2 normalization.

Mechanism: CLIPper's own p-value is uncorrected for SMInput background. ENCODE peak BED files distributed on the portal are CLIPper + SMInput log2 normalization combined.

Symptom: Peak count from the analysis is 5-10x higher than ENCODE values for the same RBP; the peak set includes obvious housekeeping-gene noise.

Fix: Always normalize CLIPper output against SMInput. The Yeo lab scripts (overlap_peakfi_with_bam_PE.py + compress_l2foldenrpeakfi_for_replicate_overlapping_bedformat.py) produce the canonical ENCODE-style peak.

PureCLIP -- Too focal; misses broad binding zones

Trigger: RBP with broad binding mode (PUM2 in 3' UTR clusters; SR proteins across exonic enhancer regions); using PureCLIP and disappointed.

Mechanism: PureCLIP's HMM emits single-nt crosslink-site state at high stringency. Skipper 2023 benchmark: PureCLIP F1 ~0.2 on RBFOX2 and similar bulk RBPs (only ~4 sites called per CLIP on test data).

Symptom: Site count 100x lower than expected; sites cluster within known binding regions but vast majority of region is "background" in PureCLIP output.

Fix: Use PureCLIP for single-nt CL maps (motif registration, ASB) but pair with CLIPper or Skipper for the broader binding-site list. PureCLIP's -or regions file gives broader output but is still focal compared to CLIPper.

Piranha -- Top-expression-decile bias

Trigger: Highly expressed transcript (GAPDH, ACTB, rRNA-flanking) appears in most-significant Piranha peaks even when RBP biology is elsewhere.

Mechanism: ZTNB regression weights peak significance by absolute count, not relative enrichment. Genes with 10x baseline expression dominate the top of the peak list.

Symptom: Top-100 Piranha peaks include housekeeping genes; motif enrichment in top peaks is weak; functional GO term analysis of peak-bearing genes returns generic "cellular metabolism."

Fix: Use Piranha with SMInput as a covariate (-c sminput_counts.bed -l) so the regression accounts for input pile-up. Or switch to Skipper for principled input normalization.

Piranha -- Convergence failure with covariates

Trigger: -c rna_seq.bed for expression normalization; large per-bin RNA-seq counts (> 10000).

Mechanism: ZTNB optimization fails on extreme covariate values; the iterative algorithm diverges.

Symptom: "Maximum iterations reached" or "Singularity in regression" error; or output BED file empty.

Fix: Pass -l to convert covariates to log-space; or pre-filter covariate BED to remove top-1% outlier bins; or switch to Skipper which handles input normalization internally.

omniCLIP -- Mitochondrial transcript blind spot

Trigger: Mitochondrial RBP (FASTKD2, LRPPRC) submitted to omniCLIP.

Mechanism: omniCLIP's HMM is trained on autosomal/X transcripts; chrM has different background statistics that the model treats as outlier (or excludes if blacklisted).

Symptom: chrM peaks return 0; FASTKD2 peak file has no peaks at all.

Fix: Use Skipper for chrM-binding RBPs (GC-stratified beta-binomial is per-bin and indifferent to chromosome). Or call peaks on chrM separately with PureCLIP.

CTK CIMS -- HITS-CLIP only; iCLIP/eCLIP misuse

Trigger: CTK CIMS (-mutation flag) applied to iCLIP/eCLIP data.

Mechanism: iCLIP/eCLIP use truncation (RT stops at adduct), not mutations. CIMS expects deletions/substitutions inside the read; CITS is the truncation-based companion.

Symptom: CIMS returns very few sites (mutation rate of CL is ~3-7% for HITS-CLIP, but only ~1-2% for iCLIP since most reads truncate before reaching the adduct).

Fix: For iCLIP/eCLIP, use CTK CITS (tag2cluster.pl ... -cs5 5 truncation-mode). For HITS-CLIP and PAR-CLIP, CIMS is correct (HITS deletions, PAR T->C).

CLAM peakcaller -- Multi-mapper BAM absent

Trigger: Ran STAR with --outFilterMultimapNmax 1 then tried CLAM; CLAM fails or returns identical output to Piranha.

Mechanism: CLAM's EM rescue requires the multi-mapper BAM from STAR (--outFilterMultimapNmax 100 --outSAMmultNmax -1). Without it, CLAM falls back to unique-only Piranha-style peak calling.

Symptom: CLAM output looks identical to Piranha; repeat peaks not rescued.

Fix: Re-align with multi-mapper-aware STAR parameters (see clip-seq/clip-alignment). Verify the multi-mapper BAM has alignments at non-unique positions before running CLAM.

MACS3 callpeak -- Wide peaks miss footprint

Trigger: MACS3 used because the user is familiar from ATAC/ChIP work.

Mechanism: MACS3 model is for DNA-protein binding at ~100-500 nt scale; CLIP-seq RBP footprints are 8-30 nt. MACS3 wide peaks (300+ nt) merge multiple distinct binding sites.

Symptom: MACS3 peak count is much lower than CLIPper/Skipper; mean peak width >> 100 nt; multiple known motif instances inside one MACS peak.

Fix: Use CLIP-specific callers. If MACS3 is forced (institutional pipeline), pass --nomodel --shift -1 --extsize 50 for narrower peaks and accept the limitation.

IDR for CLIP-seq

IDR (Li 2011) measures reproducibility of peak rankings across biological replicates. ENCODE eCLIP convention is per-rep + pooled + pseudoreplicates, identical to the ChIP-seq IDR pattern. The CLIP-specific consideration: rank by signalValue (column 7) NOT by score (column 5), because CLIPper's score is sparse and tied across many peaks.

# Per-replicate CLIPper + SMInput normalization yields .compressed.bed files
sort -k7,7nr rep1_peaks.compressed.bed > rep1.sorted
sort -k7,7nr rep2_peaks.compressed.bed > rep2.sorted

idr --samples rep1.sorted rep2.sorted \
    --input-file-type bed --rank 7 \
    --output-file idr.out --idr-threshold 0.05 \
    --plot --log-output-file idr.log

ENCODE IDR rules for eCLIP:

• Nt (true replicates) and Nself (pseudoreplicates): max(Nt, Nself) / min(Nt, Nself) <= 2 -> pass
• Both ratios > 2 -> library rejected
• IDR threshold 0.05 for true reps; 0.10 for pseudoreplicates

Decision Tree by Scenario

Scenario	Caller + parameters	Why
Standard eCLIP with SMInput, ENCODE-comparable	CLIPper + SMInput log2 norm + IDR	Reproducible against ENCODE peak files
Standard eCLIP with SMInput, maximum sensitivity	Skipper Snakemake workflow	210-320% more sites, beta-binomial input-norm
iCLIP/iCLIP2, want single-nt CL map	PureCLIP + SMInput (or empty bam if no SMI)	HMM jointly models enrichment + truncation
HITS-CLIP, want CIMS deletions	CTK parseAlignment.pl + tag2cluster.pl + bedExtractCIMS.pl	Empirical FDR on deletion-induced mutations
PAR-CLIP, T->C signal	PARalyzer or CTK CIMS -substitution T C	Designed for the T->C signature
Repeat-binding RBP (MATR3, LINE-1)	CLAM peakcaller on multi-mapper BAM	Only solution that recovers repeat peaks
No SMInput, mostly hopeless	Piranha -d ZeroTruncatedNegativeBinomial or PureCLIP w/o -ibam	Lose ENCODE comparability but get a peak set
Mitochondrial RBP (FASTKD2)	Skipper (window-based, chrM-friendly)	omniCLIP/CLIPper miss chrM
Highly multiplexed (SPIDR)	Custom Snakemake; no single-tool solution	Each barcode's reads are deep enough for CLIPper
Long-read CLIP (dirCLIP)	Custom analysis from BAM; long-read tools not yet mature	Isoform-resolved binding-site calls
Need maximum precision for motif analysis	PureCLIP (single-nt) + mCross downstream	mCross requires registered CL positions
Replicates with unequal library complexity (>2x diff in unique fragments)	Skipper (more robust to depth imbalance) OR samtools view -s down-sample to common depth then CLIPper + IDR	Skipper's GC-stratified beta-binomial handles per-sample depth; CLIPper without down-sampling lets the deeper replicate dominate joint analysis

Reconciliation: When Callers Disagree

Pattern	Likely cause	Action
Skipper >> CLIPper site count	Skipper better recovers low-abundance and rare-transcript binders	Trust Skipper for rare transcripts; trust CLIPper for ENCODE-comparable counts
PureCLIP << CLIPper	PureCLIP is single-nt focal; CLIPper is broad peak	Both correct; report at the resolution the downstream analysis needs
Piranha top peaks dominated by housekeeping	No input normalization	Add SMInput as -c covariate or switch to Skipper
CLAM > Piranha by 10-30%	CLAM rescued multi-mappers in repeats	Trust CLAM if RBP biologically binds repeats
omniCLIP wider peaks than CLIPper	HMM smoothes binding state across nearby positions	Both correct; choose by downstream analysis needs
Per-rep CLIPper calls peak; pooled does not	Replicates inconsistent; one rep dominates	Inspect with IGV; trust IDR-passing peaks only
MACS3 < CLIPper peak count	MACS3 wide peaks merge multiple CLIP sites	Use CLIP-specific caller
FASTKD2 chrM peaks: Skipper finds; CLIPper / omniCLIP find none	Coverage-based callers blind to chrM lambda	Trust Skipper for organelle-binding RBPs
chimeric eCLIP peaks for snoRNA: standard tools find few	snoRNA-targeting eCLIP needs custom processing	Use VanNostrandLab/snoRNA-chimeric pipeline

Operational rule for high-confidence reporting: Require a peak to pass (a) IDR <= 0.05 on true replicates, (b) log2(IP/SMInput) >= 3 AND -log10 p >= 3 (ENCODE stringent), (c) overlap with at least one independent caller within 100 nt. For motif analysis, additionally require PureCLIP single-nt sites within the peak. For ASB, require WASP-filtered alignment + PureCLIP CL sites overlapping heterozygous SNPs.

Strand-Specific Peak Calling

CLIP libraries are strand-specific by design. Most callers handle strands correctly when the BAM is stranded (read flags 99/147/83/163 for paired). If a caller bug or unstranded library forces manual split:

# Plus strand reads (F flag NOT 0x10)
samtools view -h -b -F 16 dedup.bam > plus.bam
samtools index plus.bam
# Minus strand reads (F flag 0x10)
samtools view -h -b -f 16 dedup.bam > minus.bam
samtools index minus.bam

# Call peaks on each strand independently, then merge
clipper -b plus.bam -s hg38 -o peaks_plus.bed
clipper -b minus.bam -s hg38 -o peaks_minus.bed
cat peaks_plus.bed peaks_minus.bed | sort -k1,1 -k2,2n > peaks_stranded.bed

For paired-end CLIP, mate orientation determines strand: RF (R1 reverse) means R2 5' is the truncation = CL site -1 on plus-strand transcripts. ENCODE eCLIP convention: read 2 of pair is the truncation-bearing read.

Multi-Mapper Rescue with CLAM (Repeat Binders)

# Assume STAR was run with --outFilterMultimapNmax 100 --outSAMmultNmax -1
samtools sort -o multimap.bam aligned_multimap.bam
samtools index multimap.bam

# Split unique vs multi
CLAM preprocessor -i multimap.bam -o clam_out/ --read-tagger-method median

# EM-based reassignment
CLAM realigner -i clam_out/unique.sorted.bam -o clam_out/ --winsize 50 --max-tags 0

# Peak calling with reassigned multi-mappers
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 rescues 10-30% additional peaks in repeat-rich regions. The trade-off: peak coordinates in repeat instances are probabilistic (single best from EM), so motif analysis on CLAM repeat peaks requires careful interpretation.

Quality Metrics for Peak Sets

import pandas as pd

def peak_qc(peaks_bed):
    peaks = pd.read_csv(peaks_bed, sep='\t', header=None,
                        names=['chrom','start','end','name','score','strand'])
    peaks['width'] = peaks['end'] - peaks['start']
    return {
        'n_peaks': len(peaks),
        'mean_width': peaks['width'].mean(),
        'median_width': peaks['width'].median(),
        'width_5p_95p': (peaks['width'].quantile(0.05), peaks['width'].quantile(0.95)),
        'chroms_with_peaks': peaks['chrom'].nunique(),
        'peaks_chrM': (peaks['chrom'] == 'chrM').sum()
    }

Metric	Expected	Interpretation
n_peaks (CLIPper stringent)	5k - 100k	<5k = under-IP or stringency too high; >>100k = noisy library
Mean peak width (CLIPper)	40-200 nt	Wider = caller over-merging; narrower = under-coverage
Peak width 5-95% range	20-500 nt	Tight range = consistent peaks; wide = mixed sharp + broad
FRiP (fraction reads in peaks)	>= 0.005	ENCODE narrow-binding RBP minimum; many RBPs higher
chrM peaks	0 unless mt-RBP	FASTKD2, LRPPRC expected to have chrM enrichment
TPM-rank distribution	Spread across deciles	Concentrated in top decile = caller has expression bias (Piranha symptom)

Common Errors

Error / symptom	Cause	Solution
CLIPper "no peaks found"	BAM not sorted/indexed; gene annotation missing	samtools sort && index; verify -s hg38 (or --gene custom.bed)
CLIPper out-of-memory on 50M BAM	--save-pickle keeps intermediates in RAM	Remove --save-pickle; or use machine with > 64 GB
PureCLIP slow (>24h)	-iv interval file missing	Restrict to one chromosome with -iv chr1 for testing; provide GTF
Piranha returns no peaks	ZTNB convergence failed silently	Add -l; or reduce -b bin size; or switch to Skipper
omniCLIP segmentation fault	Replicate BAMs differ in chromosome order	Re-sort all BAMs with identical reference dictionary
CTK CITS finds nothing on PAR-CLIP	Used CITS (truncation) for substitution data	Use CIMS -substitution T C for PAR-CLIP
Multi-mapper rescue returns 0 extra peaks	STAR was run unique-only; no multi-mapper BAM	Re-align with --outFilterMultimapNmax 100 --outSAMmultNmax -1
ENCODE stringent peak count = 0	Forgot SMInput normalization step	Run Yeo lab overlap_peakfi_with_bam_PE.py first; thresholds apply to normalized peaks
Strand information lost	samtools view -F 16 filter applied to merged BAM	Strand is encoded in BAM flags; do not pre-split
IDR returns no reproducible peaks	Wrong ranking column	Sort by signalValue / log2FC, not score; for narrowPeak format use --rank p.value

References

• Van Nostrand EL et al 2016 Nat Methods 13:508 (eCLIP, SMInput, ENCODE pipeline)
• Lovci MT et al 2013 Nat Struct Mol Biol 20:1434 (CLIPper algorithm)
• Krakau S et al 2017 Genome Biol 18:240 (PureCLIP HMM)
• Boyle EA et al 2023 Cell Genomics 3:100317 (Skipper, GC-stratified beta-binomial)
• Uren PJ et al 2012 Bioinformatics 28:3013 (Piranha, ZTNB)
• Drewe-Boss P et al 2018 Genome Biol 19:183 (omniCLIP)
• Shah A et al 2017 Bioinformatics 33:566 (CTK / CIMS / CITS)
• Zhang Z & Xing Y 2017 Nucleic Acids Res 45:9260 (CLAM multi-mapper)
• Frith MC et al 2008 Genome Res 18:1-12 (paraclu)
• Li Q et al 2011 Ann Appl Stat 5:1752 (IDR framework)
• Van Nostrand EL et al 2020 Nature 583:711 (Principles of RNA processing from 150 eCLIP maps)
• ENCODE eCLIP Standards (encodeproject.org/eclip) - canonical thresholds

Related Skills

• clip-seq/clip-preprocessing - Upstream preprocessing producing dedup BAM
• clip-seq/clip-alignment - Upstream alignment with crosslink-preserving parameters
• clip-seq/clip-qc - FRiP, library complexity, IDR before/after peak calling
• clip-seq/crosslink-site-detection - PureCLIP / CTK CITS / PARalyzer for single-nt CL sites
• clip-seq/differential-clip - DEWSeq / Flipper for cross-condition differential binding
• clip-seq/binding-site-annotation - Annotate called peaks to 5' UTR / CDS / 3' UTR / intron
• clip-seq/clip-motif-analysis - Motif discovery on called peaks
• clip-seq/ago-clip-mirna-targets - Chimeric eCLIP peak calling
• clip-seq/m6a-clip - miCLIP2-specific peak calling
• chip-seq/peak-calling - DNA-protein peak calling for comparison