Bio Chipseq Allele Specific Binding

Version Compatibility

Reference examples tested with: WASP 0.3.4+, RASQUAL 1.1+, BaalChIP 1.30+ (Bioconductor), AlleleSeq 2.0+, samtools 1.19+, bcftools 1.19+, GATK 4.5+, pysam 0.22+.

Allele-Specific Binding (ASB)

"Identify variants that affect transcription factor or histone modification binding in cis" -> Compare ChIP-seq read counts at the reference and alternate alleles of heterozygous variants in a single sample. Differential read counts (ALT vs REF at hetSNPs in peaks) reveal allele-specific binding.

• CLI (mandatory bias filter): WASP mapping pipeline to remove reference-allele mapping bias
• CLI (joint association): RASQUAL with --n-permutations for cis-QTL + ASB
• R (Bayesian beta-binomial): BaalChIP with copy-number-aware overdispersion
• CLI (personalized genome): AlleleSeq with phased diploid genome
• Statistical test: beta-binomial likelihood ratio or chi-squared on count tables

ASB analysis has three universal pitfalls: reference-allele mapping bias (universal across short-read aligners), imprinted loci (constitutively allele-skewed by biology), and copy-number variation (changes effective allele dose). All three must be addressed or results are unreliable.

Method Taxonomy

| Method | Year | Approach | Strength | Fails when |

|--------|------|----------|----------|------------|

| WASP (van de Geijn 2015) | 2015 | Map reads, swap alleles, re-map, drop discordant | Universal first step; aligner-agnostic; mandatory preprocessing | Drops 22-31% of reads; reduces power; not an analysis method itself |

| RASQUAL (Kumasaka 2016) | 2016 | Joint genotype-phenotype association with per-feature phi bias parameter | Improves QTL mapping; integrates bias correction; works for ChIP/ATAC/RNA-seq | Computationally intensive; assumes binomial bias structure |

| BaalChIP (de Santiago 2017) | 2017 | Bayesian beta-binomial; copy-number-aware overdispersion | Cancer genomes (copy-number imbalance); rigorous inference | Slower; assumes copy-number known |

| AlleleSeq (Rozowsky 2011) | 2011 | Personalized diploid genome alignment | Avoids reference bias completely; conceptually cleanest | Requires phased genotype + diploid genome construction; computational cost |

| MBASED (Mayba 2014) | 2014 | Meta-analysis-based ASE; gene-level | RNA-seq oriented; adapted for ChIP gene-body binning | Gene-level not peak-level; less precise for narrow TF peaks |

| AllelicImbalance (R package) | — | Bioconductor multi-method | Easy R workflow | Requires variants and BAM; less rigorous than BaalChIP |

| deepSEA / chromBPNet variant effects | 2015 / 2024 | Deep-learning predictions | Sequence-only; no chromatin sample needed | Predictive not measurement; see chip-deep-learning |

Universal First Step: WASP Reference-Bias Filter

Goal: Remove reads that show reference-allele mapping bias before any ASB testing.

Approach: Align reads, identify those overlapping heterozygous SNPs, swap alleles and re-align; reads that don't map consistently to the same position with both alleles are discarded. The output is a bias-corrected BAM at the cost of 22-31% read loss.

Reference-allele mapping bias is systematic: reads with the reference allele align more readily because the reference is the alignment target. This inflates REF allele frequency by 1-5% genome-wide. WASP fixes this:

# WASP mapping pipeline
# 1. Initial alignment
bowtie2 -x hg38 -1 R1.fq -2 R2.fq -S step1.sam
samtools view -bS step1.sam | samtools sort -o step1.bam
samtools index step1.bam

# 2. Identify reads overlapping hetSNPs; swap alleles; re-map
python /path/to/WASP/mapping/find_intersecting_snps.py \
    --is_paired_end \
    --is_sorted \
    --output_dir wasp_out/ \
    --snp_tab snps_tab.h5 \
    --snp_index snps_index.h5 \
    --haplotype haplotypes.h5 \
    --samples sample_list.txt \
    step1.bam

# 3. Re-map swapped reads
bowtie2 -x hg38 -1 wasp_out/step1.remap.fq.gz -S step2.sam
# (process step2.sam similarly)

# 4. Filter reads that don't map back consistently
python /path/to/WASP/mapping/filter_remapped_reads.py \
    step1.to.remap.bam step2.bam step1.keep.bam

# 5. Final WASP-filtered BAM (use this for all downstream ASB analysis)
samtools sort -o step1.wasp.bam step1.keep.bam
samtools index step1.wasp.bam

WASP always drops 22-31% of reads. This is the cost of bias correction; downstream power is reduced but ASB calls are trustworthy.

Alternative to WASP filter: RASQUAL's phi parameter models bias within the test rather than filtering reads. More sophisticated but assumes binomial bias structure.

Workflow: BaalChIP (Recommended for Cancer / Copy-Number-Imbalanced Samples)

library(BaalChIP)
library(BSgenome.Hsapiens.UCSC.hg38)

# Sample metadata
samples <- data.frame(
    SampleID = c('HCC1395_FOXA1_rep1', 'HCC1395_FOXA1_rep2'),
    Tissue = 'TNBC',
    Target = 'FOXA1',
    BAM = c('rep1.wasp.bam', 'rep2.wasp.bam'),
    Peaks = c('rep1_peaks.bed', 'rep2_peaks.bed'),
    Group = 'HCC1395'
)

# hetSNP file: VCF or BED with chrom, pos, ref, alt, allele frequencies
hetSNPs <- 'het_snps.bed'

# CNV file for copy-number-aware overdispersion (critical for cancer)
cnvs <- 'cnvs.bed'

# Initialize BaalChIP object
res <- BaalChIP(samplesheet = samples, hets = hetSNPs)

# Run filters and Bayesian test
res <- alleleCounts(res, min_base_quality = 10, min_mapq = 15)
res <- QCfilter(res, RegionsToFilter = c('blacklist_v2.bed'))
res <- mergePerGroup(res)
res <- filter1allele(res)
res <- getASB(res, Iter = 5000, conf_level = 0.95)
# Verify parameter names against the installed BaalChIP version (`?getASB`); some releases
# use `nIter` instead of `Iter`.

# Results
asb_table <- BaalChIP.report(res)
head(asb_table)

BaalChIP outputs per-hetSNP: allelic ratio, posterior, Bayes factor, ASB call.

Workflow: RASQUAL (Joint cis-QTL + ASB)

# Prepare input
# - BAM filtered by WASP
# - Genotype VCF (phased)
# - Peak BED

# Run RASQUAL
rasqual \
    --y peak_counts.txt \
    --x covariates.txt \
    --k offsets.txt \
    --n N_samples \
    --p N_peaks \
    --j 0 -i 0 \
    --vcf genotypes.vcf \
    --window 250000 \
    --t 8 \
    > rasqual_results.txt

# Output columns: chrom, peak_id, n_RSNPs, n_FSNPs, n_imputed, summarized_phi,
#                 summarized_overdispersion, summarized_pi, beta, log10_BF, ...

RASQUAL's phi parameter is the per-feature bias estimate; pi is the allelic ratio.

Workflow: AlleleSeq (Personalized Diploid Genome)

# Build personalized diploid genome from phased VCF
vcf2diploid -id SAMPLE -chr hg38.fa -vcf SAMPLE.phased.vcf -outDir personalized/

# Align reads to both maternal and paternal copies
bowtie2-build personalized/maternal.fa maternal_index
bowtie2-build personalized/paternal.fa paternal_index
bowtie2 -x maternal_index -1 R1.fq -2 R2.fq -S maternal.sam
bowtie2 -x paternal_index -1 R1.fq -2 R2.fq -S paternal.sam

# AlleleSeq pipeline
AlleleSeq2.pl SAMPLE maternal.sam paternal.sam genotype.vcf

# Output: per-hetSNP allelic counts and binomial test

Personalized genome avoids reference bias by construction. Cost: per-sample diploid genome generation and indexing.

Three Universal Pitfalls

Pitfall 1: Imprinted Loci Are Constitutively Skewed

Imprinted loci (H19, IGF2, MEG3, MEG8, KCNQ1OT1, etc.) show extreme allele bias by biology, not from differential binding.

# Filter imprinted loci before ASB analysis
wget https://imprintingdiseases.org/data/imprinted_loci_hg38.bed
bedtools intersect -v -a hetSNPs.bed -b imprinted_loci_hg38.bed > hetSNPs.non_imprinted.bed

Pitfall 2: X-Inactivation in Females

In female samples, X-linked genes show extreme allele skew because each cell silences one X chromosome. This appears as ASB at every X-linked hetSNP.

# Filter chrX in female samples
awk '$1 != "chrX"' hetSNPs.bed > hetSNPs.autosomal.bed
# Or analyze chrX separately with imprinting-aware methods

Pitfall 3: Copy-Number Imbalance (Cancer Genomes)

In cancer cells, copy-number gain of one allele alters effective allele dose; raw allelic ratios mix dose and binding effects. BaalChIP's copy-number-aware overdispersion handles this; other methods require pre-filtering CN-altered regions.

# Use ASCAT / Sequenza / FACETS to call allele-specific CNVs
# Exclude CN-altered regions from ASB analysis OR use BaalChIP

Per-Tool Failure Modes

WASP -- Reference panel mismatch

Trigger: Using a WASP SNP file from a different population than the sample.

Mechanism: WASP swaps alleles at known hetSNPs; if the variant isn't in the SNP file, no swap happens; reads retain reference bias.

Symptom: Sample-specific hetSNPs (not in 1KG) still show reference bias after WASP.

Fix: Build WASP SNP file from the sample's own genotype VCF, not a population panel; OR use RASQUAL which handles novel hetSNPs.

WASP -- Excessive read loss

Trigger: WASP filter removes >40% of reads.

Mechanism: Many reads span multiple hetSNPs; each must re-map consistently after every allele swap; combinatorial loss.

Fix: Accept the loss (genuine bias correction) OR switch to AlleleSeq (personalized genome avoids the swap-and-remap step) OR RASQUAL (no read filtering).

RASQUAL -- Convergence failure

Trigger: Sparse data (few hetSNPs per peak); strong copy-number imbalance.

Mechanism: EM convergence requires enough hetSNPs per feature; sparse data underspecifies the model.

Fix: Increase --imputation-r2-cutoff to require well-imputed SNPs; combine replicates; or switch to BaalChIP for sparse-data robustness.

BaalChIP -- CN file mismatch

Trigger: CN BED uses different naming convention (chrX vs X) than BAMs.

Mechanism: BaalChIP silently doesn't apply CN-aware overdispersion if CN positions don't match BAM chromosomes.

Symptom: ASB calls at CN-altered regions look bimodal (one allele appears 100% bound).

Fix: Verify chromosome naming matches across CN file, BAM, hetSNP VCF.

AlleleSeq -- Insufficient phasing

Trigger: Using unphased VCF for diploid genome construction.

Mechanism: AlleleSeq requires phased genotypes; without phasing, maternal and paternal genomes are randomly assigned.

Fix: Use trio or read-based phasing (HapCUT2, WhatsHap) before AlleleSeq.

Imprinted loci not filtered

Trigger: Reporting ASB at H19 or IGF2.

Mechanism: These loci are biologically allele-skewed; the "ASB" call is correct but uninformative.

Fix: Always filter imprinted loci before reporting / interpreting ASB.

Female chrX ASB artifacts

Trigger: Reporting ASB at chrX in female samples without X-inactivation correction.

Mechanism: Random X-inactivation silences one X per cell; population of cells shows extreme allele bias at any X-linked variant.

Fix: Filter chrX in female samples OR use methods that model X-inactivation (rare in standard ASB pipelines).

Reference allele bias not corrected

Trigger: Running BaalChIP / chi-squared test directly without WASP or RASQUAL bias handling.

Mechanism: 1-5% genome-wide REF allele over-representation produces false-positive REF-favoring ASB calls.

Symptom: ASB calls skewed toward REF allele.

Fix: Always apply WASP (or RASQUAL's phi parameter) before testing.

Reconciliation

| Pattern | Likely cause | Action |

|---------|--------------|--------|

| WASP filter applied; still REF-biased | Sample-specific hetSNPs not in WASP SNP file | Use sample's own genotype VCF for WASP |

| BaalChIP and RASQUAL disagree at sparse hetSNPs | Different sparse-data behavior | BaalChIP Bayesian more conservative for sparse; check posterior |

| ASB call at imprinted locus | Biology, not differential binding | Filter imprinted loci |

| ASB at chrX in female | X-inactivation | Filter chrX |

| ASB call where copy-number altered | Cancer dose effect | Use BaalChIP with CN file OR exclude CN-altered regions |

| chromBPNet predicts strong variant effect; ASB doesn't | Sample has low coverage at variant; chromBPNet predicts in counterfactual | Increase depth; ASB requires actual chromatin sample |

Common Errors

| Error / symptom | Cause | Solution |

|-----------------|-------|----------|

| WASP find_intersecting_snps.py fails | h5 SNP table format wrong | Convert from VCF via WASP's extract_vcf.py |

| BaalChIP "no overlap with peaks" | hetSNP and peak chrom naming mismatch | Standardize chrom prefixes |

| RASQUAL OOM | Window too large; too many features | Reduce --window; chunk feature list |

| AlleleSeq "diploid genome too large" | Many SVs in genome | Use small-variant only VCF; exclude SV-rich regions |

| ASB calls cluster at REF allele | WASP not applied OR insufficient | Re-run WASP with sample-specific SNP file |

| Many ASB at chrX in female | X-inactivation | Filter chrX |

| All "ASB" calls are at imprinted loci | Imprinting not filtered | Apply imprinted-loci BED |

References

• Rozowsky J et al 2011 Mol Syst Biol 7:522 (AlleleSeq)
• van de Geijn B et al 2015 Nat Methods 12:1061 (WASP)
• Kumasaka N et al 2016 Nat Genet 48:206 (RASQUAL)
• de Santiago I et al 2017 Genome Biol 18:39 (BaalChIP)
• Mayba O et al 2014 Genome Biol 15:405 (MBASED)
• Chen J et al 2016 Nat Commun 7:11101 (1000 Genomes ASB / ASE survey)

Related Skills

• chip-seq/peak-calling - Peak calling upstream
• chip-seq/chipseq-qc - QC before ASB analysis
• chip-seq/chip-deep-learning - Validate DL variant predictions against ASB
• chip-seq/peak-annotation - Annotate ASB variants to genes / cCREs
• atac-seq/allele-specific-accessibility - Parallel ATAC ASB workflow
• causal-genomics/fine-mapping - ASB as fine-mapping orthogonal evidence
• variant-calling/variant-annotation - Annotate hetSNPs before ASB
• phasing-imputation/haplotype-phasing - Required for AlleleSeq