bio-chipseq-chip-deep-learning

Version Compatibility

Reference examples tested with: chrombpnet 0.1.7+, BPNet 0.0.23+, TF-MoDISco-lite 2.0+, EnFormer (Avsec lab Colab + DeepMind release), tensorflow 2.13+, pytorch 2.0+, JASPAR 2026 deep-learning collection (released 2025).

Deep Learning for ChIP-seq

"Predict TF binding from sequence and quantify variant effects on binding" -> Train base-resolution convolutional / transformer models on ChIP-seq / ChIP-nexus / CUT&RUN profiles; predict reference and alternate-allele binding profiles for variants; extract motif syntax via TF-MoDISco from sequence-attribution scores.

• Python (modern): chrombpnet (bias-factorized; ATAC/DNase/ChIP)
• Python (canonical TF ChIP): BPNet (originally for ChIP-nexus; soft motif syntax)
• Python (long-range): EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input window, ~100 kb effective receptive field; tissue-aggregated training)
• Python (multi-task): DeepSEA (Zhou 2015; older but still used)
• Precomputed: JASPAR 2026 Deep Learning collection (1259 BPNet ChIP models from ENCODE; 240 TFs)

Deep-learning ChIP-seq models predict signal from sequence; their power is in counterfactual variant prediction (effect on binding from a SNP) and discovery of soft motif syntax that PWMs miss (cooperativity, spacing).

Model Taxonomy

Model	Year	Architecture	Receptive field	Best for
BPNet (Avsec 2021 Nat Genet 53:354)	2021	CNN with dilated convolutions	~1 kb	TF ChIP-nexus / ChIP-exo; base-resolution profile prediction; soft motif syntax
chromBPNet (Pampari A et al 2024 bioRxiv)	2024	Bias-factorized CNN	~1-2 kb	ATAC/DNase + ChIP base-resolution; bias-corrected variant effects
EnFormer (Avsec 2021 Nat Methods 18:1196)	2021	Transformer	~100 kb effective receptive field (input window 196 kb)	Long-range regulatory predictions; cross-tissue; variant effects spanning enhancer-gene
DeepSEA (Zhou 2015)	2015	CNN multi-task	1 kb	Predicts presence/absence across many chromatin features simultaneously
DeepBind (Alipanahi 2015)	2015	CNN binary classifier	~50-200 bp	TF binding presence (older, less precise than BPNet)
Basset (Kelley 2016)	2016	CNN	~600 bp	DNase / ATAC accessibility prediction
JASPAR 2026 Deep Learning collection	2025	Precomputed BPNet	~1 kb	1259 ENCODE TF ChIP-seq models; 240 TFs; ready-to-use

Decision Tree: Which Model

Goal	Model	Why
Predict variant effect on TF binding (cis-pQTL fine-mapping)	chromBPNet or EnFormer	Both predict ref/alt counterfactuals; chromBPNet base-resolution, EnFormer long-range
Discover motif syntax / TF cooperativity from existing ChIP	BPNet (ChIP-nexus data) or chromBPNet (regular ChIP) + TF-MoDISco	Attribution-based motif discovery captures soft syntax PWMs miss
Use precomputed model on new variant	JASPAR 2026 deep-learning collection	1259 BPNet ChIP models ready; no training needed
Predict ChIP signal from sequence in a new cell type	EnFormer (cross-tissue training)	Long-range receptive field; multi-tissue training
Integrate ATAC + ChIP into single model	chromBPNet	Bias-factorized handles both assays
TF binding presence/absence multi-task	DeepSEA	Older but simple multi-output

In Silico Mutagenesis Workflow

Goal: Predict whether a single-nucleotide variant changes transcription factor or histone modification binding.

Approach: Encode reference and alternate-allele sequences in the model's expected window (2114 bp for chromBPNet, centered on variant), predict per-base profile + total counts for each, compute log2 fold change in counts as the variant-effect score. Apply ensemble of 5-10 models for uncertainty.

The most clinically/translationally useful application: predict whether a variant changes TF binding.

import chrombpnet
import numpy as np
import tensorflow as tf

# Load trained chromBPNet model
model = tf.keras.models.load_model('chrombpnet_model.h5', compile=False)

# Reference and alternate-allele sequence around variant (2114 bp window typical)
ref_seq = encode_dna_one_hot('NNN...CCATGNNN...')   # 2114 bp; variant position central
alt_seq = encode_dna_one_hot('NNN...CCAAGNNN...')   # G->A at central position

# Predict base-resolution profiles
ref_profile, ref_counts = model.predict(ref_seq[None, ...])
alt_profile, alt_counts = model.predict(alt_seq[None, ...])

# Variant effect: log2 fold change in predicted total counts
log2_fc = np.log2(alt_counts / ref_counts)
print(f'Variant effect: log2_fc = {log2_fc}')

# |log2_fc| > 1 indicates strong-effect SNP per chromBPNet 2024 paper
# Concordance with EnFormer increases confidence for clinical interpretation

Variant effect interpretation:

• |log2_fc| > 1: strong effect; binding likely affected
• 0.3 < |log2_fc| < 1: moderate effect; investigate further
• |log2_fc| < 0.3: weak / no effect predicted
• Concordance between chromBPNet and EnFormer increases confidence

TF-MoDISco for Soft Motif Syntax

Standard PWM-based motif discovery misses:

• Cooperative motif interactions (TF dimers, ETS-RUNX, GATA-TAL)
• Soft motif syntax (variable spacing, weak co-binding)
• Long-range dependencies

TF-MoDISco extracts motifs from deep-learning attribution scores:

import numpy as np
import shap

# Compute DeepLIFT / DeepSHAP attribution scores
explainer = shap.DeepExplainer(model, background_seqs)
attribution_scores = explainer.shap_values(test_seqs)

# Save one-hot sequences + attributions for tfmodisco-lite
np.savez('ohe.npz', test_seqs)
np.savez('shap.npz', attribution_scores)

# tfmodisco-lite runs via its `modisco` CLI: -n max seqlets/metacluster, -w window (default 400)
modisco motifs -s ohe.npz -a shap.npz -n 2000 -w 400 -o modisco_results.h5
modisco report -i modisco_results.h5 -o report/ -m motifs.meme
# Output: motif patterns discovered from attribution (not from PWM matching)
# Often more interpretable than PWM motifs for cooperative TF binding

Training chromBPNet from Scratch

# Install
pip install chrombpnet

# Train bias model (control regions without TF binding).
# In the bias pipeline, -b is the bias_threshold_factor (float; ~0.5 ATAC, ~0.8 DNase)
# and -o is the required output directory.
chrombpnet bias pipeline \
    -ibam input.bam -d DNASE \
    -g hg38.fa -c chrom.sizes -p peaks.bed \
    -n nonpeaks.bed -fl fold_0.json \
    -b 0.8 -o bias_model_dir/

# Train main chromBPNet model. chromBPNet assay types are ATAC or DNASE only
# (there is no -d ChIP); use the DNASE bias model for point-source ChIP.
# In the main pipeline, -b is the path to the trained bias model .h5.
chrombpnet pipeline \
    -ibam chip.bam -d DNASE \
    -g hg38.fa -c chrom.sizes -p peaks.bed -n nonpeaks.bed \
    -fl fold_0.json \
    -b bias_model_dir/models/bias.h5 \
    -o output_dir/

# Output: trained model + per-locus base-resolution predictions

Training cost: 1-3 GPU days for a single chromBPNet model; multiple GPUs for EnFormer.

EnFormer Application

from enformer_pytorch import from_pretrained

model = from_pretrained('EleutherAI/enformer-official-rough')

# 196 kb input window
seq = torch.tensor(one_hot_encode(reference_seq))[None, ...]
predictions = model(seq)
# Predictions: per-bin signal across 5,313 ENCODE tracks
# Each variant effect = difference in target track prediction

# Variant effect at SNP
ref_pred = model(encode(ref_seq))[..., :, target_track_idx]
alt_pred = model(encode(alt_seq))[..., :, target_track_idx]
variant_effect = (alt_pred - ref_pred).mean()

EnFormer's 196 kb input (~100 kb effective receptive field) captures distal regulatory effects; useful when variant is far from TSS.

Using JASPAR 2026 Deep Learning Models (Precomputed)

JASPAR 2026 (released 2025) added 1259 BPNet models trained on ENCODE TF ChIP-seq:

from pyjaspar import jaspardb

jdb = jaspardb(release='JASPAR2026')

# pyjaspar returns PWM/motif objects (Bio.motifs.jaspar.Motif), e.g. CORE PWMs:
core_pwms = jdb.fetch_motifs(collection=['CORE'])

# The JASPAR 2026 BPNet deep-learning models (per-TF, ENCODE ChIP) are distributed
# as model files on the JASPAR website, NOT via pyjaspar; download them there and
# load (Keras H5 / PyTorch) for in silico mutagenesis on new variants.

This is the lowest-effort path for variant-effect prediction on canonical TFs (no training required).

Per-Tool Failure Modes

chromBPNet -- Bias model trained on wrong assay

Trigger: Using ATAC bias model on ChIP data.

Mechanism: ATAC bias model captures Tn5 sequence preferences; ChIP has different bias structure (sonication, fragmentation, antibody-driven).

Symptom: Variant effect predictions noisy; attribution scores dominated by Tn5 sequence preferences.

Fix: Train bias model on ChIP non-peak regions, not ATAC; or use chromBPNet's ChIP-specific bias correction.

BPNet -- Trained on insufficient peaks

Trigger: Training BPNet on a TF with <5000 high-confidence peaks.

Mechanism: Base-resolution profile prediction needs many examples per motif context.

Symptom: Model accuracy <0.6 Spearman correlation between predicted and observed profiles.

Fix: Combine replicates; use more permissive peak threshold; or fall back to PWM-based motif analysis.

TF-MoDISco -- Background sequences not representative

Trigger: Using random genomic sequences as background for attribution.

Mechanism: Genomic sequences include other TF binding sites; attribution conflates target TF with background TFs.

Fix: Use shuffled-input sequences as background (preserves dinucleotide); OR use peaks from a control ChIP (e.g., IgG) as background.

In silico mutagenesis -- Variant outside training distribution

Trigger: Predicting effect of a variant in a sequence context the model never saw (e.g., new TF site arrangement).

Mechanism: Deep-learning models extrapolate poorly; counterfactual prediction for novel contexts is unreliable.

Symptom: Variant effect estimate has huge variance across model replicates.

Fix: Train ensemble of 5-10 models; use disagreement as uncertainty estimate; for high-stakes claims, validate experimentally.

EnFormer -- Tissue-aggregated predictions

Trigger: Predicting variant effect for a specific cell line that has its own ChIP-seq.

Mechanism: EnFormer was trained on tissue-aggregated tracks; cell-line-specific resolution is limited.

Fix: For cell-line-specific predictions, fine-tune EnFormer on cell-line ChIP-seq OR use chromBPNet trained on cell-line data.

Memory / GPU requirements

Trigger: Training chromBPNet or EnFormer on a single GPU with insufficient memory.

Mechanism: chromBPNet (~10 GB GPU memory), EnFormer (~16-24 GB), batch sizes / sequence lengths affect memory.

Fix: Reduce batch size; use gradient checkpointing; for EnFormer, use the smaller 5x architecture variant.

Reconciliation with PWM-Based Analysis

Pattern	Likely cause	Action
TF-MoDISco motif matches JASPAR PWM	DL model recovered canonical motif	Confidence in DL model
TF-MoDISco motif doesn't match any PWM	Novel motif syntax OR DL model overfit	Validate with TOMTOM against larger DBs; check model ensemble agreement
chromBPNet predicts strong variant effect, JASPAR PWM scan does not	Soft syntax / cooperativity; DL captures more than PWM	DL prediction often more accurate; experimental validation ideal
EnFormer and chromBPNet disagree on variant effect	Different receptive fields capture different biology	Trust EnFormer for distal effects, chromBPNet for local
Variant effect ensemble disagrees within model	Training instability OR variant in extrapolation regime	Treat as low-confidence; do not publish without validation

Common Errors

Error / symptom	Cause	Solution
chrombpnet import fails	TensorFlow / Keras version mismatch	Use chrombpnet conda env with pinned tf 2.13
cuda out of memory	Batch size too large	Reduce batch_size in training config
Predictions all zero	Bias model corrupted or wrong assay	Re-train bias on matched assay
TF-MoDISco produces empty motif list	FDR too strict or attribution noisy	Lower target_seqlet_fdr to 0.10; increase model training depth
EnFormer "shape mismatch"	Sequence not exactly 196608 bp (default)	Pad or truncate to expected length
Variant effect for nucleotide outside ACGT	Model only handles ACGT	Skip variants with N or ambiguous nucleotides

References

• Avsec Ž et al 2021 Nat Genet 53:354 (BPNet; ChIP-nexus base-resolution model)
• Pampari A et al 2024 bioRxiv 2024.12.25.630221 (chromBPNet; bias-factorized base-resolution models of chromatin accessibility)
• Avsec Ž et al 2021 Nat Methods 18:1196-1203 (EnFormer; ~100 kb effective receptive field, 196 kb input window)
• Zhou J & Troyanskaya OG 2015 Nat Methods 12:931 (DeepSEA)
• Alipanahi B et al 2015 Nat Biotechnol 33:831 (DeepBind)
• Kelley DR et al 2016 Genome Res 26:990 (Basset)
• Shrikumar A et al 2018 (rev. 2020) arXiv:1811.00416 (TF-MoDISco)
• Shrikumar A et al 2017 ICML (DeepLIFT)
• Lundberg SM & Lee SI 2017 NeurIPS (SHAP)
• JASPAR Project 2026 (deep-learning collection)
• Karbalayghareh A et al 2022 Genome Res 32:930 (GraphReg; chromatin-interaction-aware regulatory modeling, cross-cell-type generalization)

Related Skills

• chip-seq/peak-calling - Source peaks for training DL models
• chip-seq/motif-analysis - PWM-based analysis (complementary to DL)
• chip-seq/allele-specific-binding - Validate DL variant predictions against ASB data
• atac-seq/deep-learning-atac - ATAC-specific chromBPNet workflow
• atac-seq/footprinting - TOBIAS footprints as comparison
• causal-genomics/fine-mapping - Variant-level functional annotation
• machine-learning/biomarker-discovery - DL variant scores as features
• machine-learning/model-validation - Ensemble agreement, cross-validation