FridrichMethod 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