GPTomics Version Compatibility
Reference examples tested with: chrombpnet 0.1.7+, bpnet-lite 0.6+ (github.com/jmschrei/bpnet-lite), scBasset 0.1.0+ (basenji2 fork), tangermeme 0.1+, tfmodisco-lite 2.2+, DeepLIFT 0.6+, captum 0.7+, tensorflow 2.13+, pytorch 2.1+, kipoi 0.8+.
Verify before use:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --versionthen<tool> --helpto confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying. Deep-learning tooling evolves rapidly; method papers post 2023 may have superseded reference implementations.
Sequence-Based Deep Learning for ATAC-seq
"Score the effect of a GWAS SNP on chromatin accessibility" -> Train (or use pre-trained) sequence-to-accessibility CNNs that take 1-5 kb DNA windows and predict per-base Tn5 cleavage profiles. Outputs include: bias-corrected accessibility, single-base mutation effect predictions, and DeepLIFT contribution scores convertible to motifs via TF-MoDISco.
- CLI:
chrombpnet pipeline --bigwig signal.bw --bigwig-bias bias.bw ... - Python:
bpnet-litefor custom architectures;tangermemefor fast scoring - Python (single-cell):
scBassetfor per-cell sequence-based predictions - Python (long-context): Enformer pre-trained models via Kipoi
Sequence models are NOT a replacement for MACS+TOBIAS at every step. They excel at three specific tasks where classical pipelines struggle: (1) Tn5 bias correction in low-complexity sequence contexts, (2) variant effect prediction in non-genic regions, (3) cell-type-specific motif discovery beyond what JASPAR provides.
Algorithmic Taxonomy
| Tool | Architecture | Training | Output | Strength | Fails when |
|---|---|---|---|---|---|
| chromBPNet (Pampari 2024 bioRxiv) | Two-track CNN: bias model + accessibility model; bias trained on naked-DNA control or k-mer baseline, accessibility trained on chromatin signal | Per-cell-type, paired bias track | Bias-corrected per-base profile + total counts | Strongest bias correction of the compared tools; established in Kundaje lab pipelines | Requires GPU, ~24h training per cell type; needs >= 50M reads |
| BPNet (Avsec 2021 Nat Genet 53:354) | Original counts + profile dual-head CNN | TF ChIP-seq or ATAC | Per-base profile prediction | Foundational; widely cited; bpnet-lite reimpl maintained | Less polished than chromBPNet for ATAC; bias correction needs separate model |
| scBasset (Yuan & Kelley 2022) | Basenji2-derived CNN, per-cell projection layer | Single-cell ATAC | Per-cell sequence-derived peak score | First sequence model that predicts per-cell accessibility; outperforms chromVAR for cluster discrimination | Fixed architecture, hard to extend; benchmarks evolving |
| Enformer (Avsec 2021 Nat Methods 18:1196) | Long-context Transformer (196 kb input) | Reference epigenome (DNase + histones + CAGE) | Per-bin epigenome prediction | Best for distal regulation modeling; pre-trained available | Pre-trained models cell-line specific; finetuning on custom data is expensive |
| Borzoi (Linder 2025 Nat Genet) | Enformer extension trained on RNA + ATAC | Multi-tissue paired data | Sequence -> RNA + chromatin | Current best benchmark for variant effect on RNA via ATAC linkage | Newer; benchmarks still emerging |
| DeepATAC / Basset (legacy) | Earlier CNN architectures | -- | Binary peak prediction | Historical context; cited in older literature | Superseded by chromBPNet + Enformer; do not use for new work |
| tangermeme | Inference-only fast wrapper | Use any saved model | Marginal scoring of variants | Speeds up variant effect prediction 100x; works with chromBPNet/BPNet outputs | Inference only; cannot train |
Methodology evolves; verify against current Kundaje lab pipelines (chrombpnet GitHub), Greenleaf lab (scBasset), and Avsec / Linder publications before locking pipelines.
When Deep Learning Helps vs When Classical Pipelines Suffice
| Task | Classical | Deep Learning |
|---|---|---|
| Peak calling | MACS3 / Genrich (sufficient) | chromBPNet (overkill unless variant downstream) |
| Tn5 bias correction at TF motifs | TOBIAS ATACorrect (good) | chromBPNet (better at hard cases: low-complexity flanks, deep TF footprints) |
| Differential accessibility | DiffBind / DESeq2 (sufficient) | -- (no clear DL advantage) |
| GWAS variant effect prediction at causal SNPs | Limited (overlap heuristics) | chromBPNet / Enformer (essential) |
| Motif discovery from de novo data | MEME / HOMER (good) | chromBPNet + TF-MoDISco (better; finds composite + cooperative motifs) |
| Per-cell TF activity | chromVAR (sufficient at the cluster level) | scBasset (better at fine-grained cell states) |
| Cross-cell-type accessibility prediction | -- | Enformer / Borzoi (only option) |
| Predicting cell-type-specific enhancer activity from sequence | -- | chromBPNet / Enformer (essential) |
For most standard ATAC analysis, classical pipelines remain primary. Deep learning enters when (a) variant interpretation is the goal, (b) cell-type prediction is needed beyond observed data, or (c) bias correction quality is paramount (low-input, FFPE, transcription factors with weak motifs).
Per-Tool Failure Modes
chromBPNet -- Bias model mismatch
Trigger: Training the bias model on a dataset different from the accessibility dataset (e.g. K562 bias model used on primary T cells).
Mechanism: chromBPNet's bias model captures sequence-specific Tn5 preference, which is mostly cell-type-invariant BUT contributions of chromatin context at cuts can vary. Cross-celltype bias models work but with degraded performance.
Symptom: Predicted footprints look correct at known TFs (CTCF) but fail on cell-type-specific regulators.
Fix: Train a per-cell-type bias model from naked-DNA control if available, OR use the chromBPNet authors' pre-trained k562 / GM12878 / HepG2 bias as a fallback (acknowledged degradation).
chromBPNet -- Insufficient training data
Trigger: Training on < 50M deduplicated nuclear reads, or < 30k peaks.
Mechanism: CNN training needs enough peaks for stable gradient updates and enough background regions for the dual-task loss.
Fix: Pool replicates before training; reduce model capacity (--num-filters); use pre-trained model on closest cell type and skip retraining.
BPNet / chromBPNet -- DeepLIFT vs Integrated Gradients confusion
Trigger: Computing per-base contributions for motif discovery.
Mechanism: DeepLIFT (RevealCancel rule) and Integrated Gradients (50 baseline samples) give different attribution patterns. DeepLIFT preserves additivity; IG is stochastic.
Fix: Use DeepLIFT rescale-rule (chromBPNet default) for TF-MoDISco. IG only when DeepLIFT fails on saturating activations. Document the choice.
scBasset -- Cell projection layer instability
Trigger: Few cells per cluster; sparse training data.
Mechanism: scBasset learns a per-cell projection vector; with < 100 cells per cluster the projection is noisy.
Fix: Aggregate cells to clusters before training, OR use chromBPNet trained on pseudobulks per cluster instead.
Enformer -- Pre-trained models lack target cell type
Trigger: Using Enformer for variant effects in a cell type not in its training set (e.g. GTEx tissues are covered; novel primary cell types are not).
Mechanism: Enformer's outputs are per-track predictions; if the target cell type wasn't trained, the agent can use a similar track as proxy but accuracy degrades.
Fix: Use a similar tissue track as proxy (HepG2 for liver biology; GM12878 for B-cell-like) OR fine-tune Enformer on custom data (expensive). Document the proxy.
tangermeme -- Marginal vs in silico mutagenesis confusion
Trigger: Asking for a "variant effect score" without specifying the formula.
Mechanism: Marginal effects = ref vs alt at the SNP only. ISM = saturation across all positions in the window (every base mutated). Different magnitudes; different questions.
Fix: Define which calculation. For GWAS variant prediction, use marginal at the SNP (matches phenotype-genotype coupling). For motif discovery, use ISM.
Decision Tree by Goal
| Goal | Recommended approach |
|---|---|
| Score 100 GWAS SNPs for chromatin effects | Pre-trained chromBPNet model on closest cell type; tangermeme for fast scoring |
| Score 1 lead SNP at high resolution | chromBPNet + tangermeme + ISM saturation map |
| Identify TF binding motifs from a new cell type's ATAC | chromBPNet train + DeepLIFT contributions + TF-MoDISco-lite |
| Predict accessibility in a cell type not in training | Enformer pre-trained (best for ENCODE cell types) or scBasset for sc state interpolation |
| Bias-correct a low-input ATAC library before footprinting | chromBPNet bias model output as --bias to TOBIAS or directly use chromBPNet corrected track |
| Cell-type-specific enhancer prediction | chromBPNet trained on each cell type; per-cell-type ISM at candidate loci |
| Replace TOBIAS bias correction | chromBPNet corrected bigWig as input to TOBIAS ScoreBigwig; skip ATACorrect |
chromBPNet Standard Pipeline
Goal: Train a bias-corrected CNN that predicts per-base accessibility from sequence, then score variants with it.
Approach: Generate train/valid/test chromosome splits, train the Tn5 bias model on background regions, train the accessibility model with bias correction, then run the standalone variant-scorer repo to predict ref-vs-alt effects at SNPs.
# 1. Generate train / valid / test chromosome splits (output is a JSON file with chrom assignments)
chrombpnet prep splits \
-c hg38.chrom.sizes \
-tcr chr1 chr3 chr6 \
-vcr chr8 chr20 \
-op splits/fold_0
# Train chromosomes are auto-inferred (whatever is not in -tcr/-vcr). The `-tecr` flag does NOT exist.
# 2. Train bias model from background regions
chrombpnet bias pipeline \
-ibam atac.bam \
-d ATAC \
-g hg38.fa \
-c hg38.chrom.sizes \
-p peaks.narrowPeak \
-n nonpeaks.bed \
-fl splits/fold_0.json \
-b 0.5 \
-o bias_model/
# 3. Train accessibility model with bias correction
chrombpnet pipeline \
-ibam atac.bam \
-d ATAC \
-g hg38.fa \
-c hg38.chrom.sizes \
-p peaks.narrowPeak \
-n nonpeaks.bed \
-fl splits/fold_0.json \
-b bias_model/models/bias.h5 \
-o output/
# 4. Variant effect prediction at GWAS SNPs uses the SEPARATE kundajelab/variant-scorer repo
# (the `chrombpnet snp_score` subcommand is commented out in current chrombpnet/parsers.py)
git clone https://github.com/kundajelab/variant-scorer
python variant-scorer/src/variant_scoring.py \
--model output/models/chrombpnet_nobias.h5 \
--list variants.tsv \
--genome hg38.fa \
--chrom_sizes hg38.chrom.sizes \
--out_prefix variants_predicted
# Output: variants_predicted.variant_scores.tsv with per-SNP log2FC magnitudes
-b 0.5 is the bias threshold factor (--bias-threshold-factor); chromBPNet docs recommend a start value of 0.5 for ATAC (0.8 for DNase). For variant scoring, use the standalone kundajelab/variant-scorer companion repo, NOT a chrombpnet subcommand. Verify exact flag names with python variant_scoring.py --help because the API evolves.
DeepLIFT + TF-MoDISco for Motif Discovery
The maintained version is tfmodisco-lite (jmschrei/tfmodisco-lite, pip install modisco-lite), which exposes a CLI rather than the deprecated v1 TfModiscoWorkflow Python API. The original kundajelab/tfmodisco package (with tfmodisco.tfmodisco_workflow.workflow.TfModiscoWorkflow) is unmaintained and incompatible with modisco-lite.
Goal: Discover de novo motifs from a trained chromBPNet model using per-base contribution scores.
Approach: Extract one-hot sequences and DeepLIFT/SHAP contributions from chromBPNet, run modisco-lite to cluster seqlets into motif patterns, then generate an annotated HTML report matched against a known motif database.
# Generate one-hot sequence and SHAP / DeepLIFT contribution score arrays from chromBPNet
# (chromBPNet `chrombpnet contribs_bw` writes hypothetical contributions; convert to numpy via shap_to_modisco)
# Run TF-MoDISco-lite via its CLI
modisco motifs \
-s ohe.npz \
-a shap.npz \
-n 2000 \
-w 500 \
-o modisco_results.h5
# Generate HTML report with discovered motifs matched to known databases
modisco report \
-i modisco_results.h5 \
-o modisco_report/ \
-m motifs_meme.txt \
-s modisco_report/
-n 2000 caps seqlets per metacluster; -w 500 is the window around each peak center considered for motif discovery (default 400; the seqlet-core sliding-window size is a separate flag, -z/--size, default 20). motifs_meme.txt (e.g. JASPAR or HOCOMOCO MEME-format) lets modisco report annotate clusters against known motifs.
In Silico Variant Effect Prediction
Goal: Score the effect of SNPs on predicted accessibility using a trained chromBPNet model.
Approach: Load the bias-free chromBPNet model, build ref and alt one-hot windows centered on each SNP, run tangermeme's substitution_effect to get paired predictions, then compute log2(alt/ref) per variant.
import numpy as np
# tangermeme's variant-effect API: substitution_effect for SNPs, marginalize for motif insertions
from tangermeme.variant_effect import substitution_effect
from tangermeme.predict import predict
# Load pre-trained chromBPNet model (saved as Keras .h5 or PyTorch state_dict).
# chromBPNet wraps Keras; load with tensorflow.keras.models.load_model and wrap for tangermeme.
# `load_chrombpnet_model` below is pseudocode -- substitute the actual loader for the installed version
# (e.g. tf.keras.models.load_model + tangermeme.io.adapter, or torch.load for PyTorch checkpoints).
model = load_chrombpnet_model('output/models/chrombpnet_nobias.h5')
# substitution_effect: per-SNP ref vs alt prediction across a sequence window
# X shape (N, 4, L); substitutions is a sparse-COO tensor of shape (-1, 3) where each row is
# [example_idx, position, new_base_idx] (new_base_idx 0-3 for ACGT)
y_ref, y_alt = substitution_effect(model, X, substitutions)
log2fc = np.log2(y_alt.sum(axis=-1) / y_ref.sum(axis=-1))
For motif marginalization (testing a candidate motif's effect by inserting it into background sequences), use tangermeme.marginalize.marginalize(model, X, motif). The motif argument is a one-hot tensor of shape (-1, 4, motif_length); convert string motifs via tangermeme.utils.one_hot_encode. Verify the exact signatures with help(tangermeme.marginalize.marginalize) because tangermeme is actively developed; marginal_predict is NOT a real function name.
log2fc magnitudes are unitless; |log2fc| > 1 typical for strong-effect SNPs in regulatory regions.
Reconciliation
| Pattern | Likely cause | Action |
|---|---|---|
| chromBPNet predicts strong effect; MACS does not call peak | Sequence model captures latent regulatory potential | Trust chromBPNet for variant effect; not for peak calling |
| Enformer prediction differs from chromBPNet at same locus | Different context windows (196 kb vs 1-2 kb); different cell types | Both can be correct at different scales; report both with their context size |
| TF-MoDISco motifs differ from JASPAR | Different methodology (sequence-based vs ChIP-validated) | TF-MoDISco can find composites and cooperative; check JASPAR for confirmation |
| chromBPNet bias correction differs from TOBIAS ATACorrect | Different bias models (CNN vs k-mer) | chromBPNet is more accurate but slower; TOBIAS still publishable for standard use |
Operational rule: For high-confidence variant prediction, agree across two approaches: chromBPNet + Enformer (or Borzoi). Single-tool calls should be reported as exploratory. For motif discovery, validate TF-MoDISco hits against JASPAR/HOCOMOCO before publication.
GPU and Compute Considerations
| Task | Hardware | Wall time |
|---|---|---|
| chromBPNet training (per cell type) | 1 A100 GPU, 80 GB RAM | ~24 h |
| chromBPNet inference at 1M variants | 1 A100 | ~4 h |
| Enformer pre-trained inference | 1 V100+ | ~30 min for 100k variants |
| Borzoi training | 1 A100, ~250 GB RAM | ~7 days |
| scBasset training (10k cells) | 1 V100, 32 GB RAM | ~12 h |
| TF-MoDISco on 1M peaks | CPU 32 cores | ~6 h |
For most labs without sustained GPU access: use pre-trained chromBPNet/Enformer models for inference; only train custom models when the cell type is not in the public model zoo (encodeproject.org/atac-seq pre-trained chromBPNet).
Common Errors
| Error / symptom | Cause | Solution |
|---|---|---|
chromBPNet bias.h5 missing |
Bias model training failed silently | Re-run chrombpnet bias pipeline with verbose; check input BAM size |
| Out of memory during training | Default batch size too large for GPU | --batch-size 64 or smaller; reduce --num-filters |
| Predicted profile is constant | Model collapsed (training too short) | Increase epochs; verify input peaks are non-empty |
| TF-MoDISco produces too many small clusters | target_seqlet_fdr too loose |
Tighten to 0.01; or increase flank_size |
| Enformer prediction has wrong shape | Pre-trained model expects 196 kb input | Pad input to exactly 196,608 bp |
| Variant effect predictions cluster near zero | SNP outside model's effective window | Predict on window-centered sequences (variant at the center) |
| chromBPNet model not converging | Peaks file contains chrM or blacklist | Pre-filter; chromBPNet does not auto-filter |
| scBasset training crashes on Apple Silicon | TensorFlow Metal incompatible with operations | Use CPU mode or run on Linux GPU |
References
- Pampari A et al 2024 bioRxiv 2024.12.25.630221 (chromBPNet; Tn5 bias correction with deep learning; preprint)
- Avsec Z et al 2021 Nat Genet 53:354-366 (BPNet; foundational sequence-to-profile)
- Avsec Z et al 2021 Nat Methods 18:1196-1203 (Enformer; long-context Transformer)
- Linder J et al 2025 Nat Genet (Borzoi; multi-tissue sequence-to-RNA+chromatin; consult current publication for exact volume/pages)
- Yuan H & Kelley DR 2022 Nat Methods 19:1088 (scBasset)
- Shrikumar A et al 2017 ICML (DeepLIFT)
- Schreiber J 2025 bioRxiv 2025.08.08.669296 (tangermeme; fast inference utilities)
- Shrikumar A et al 2018 arXiv:1811.00416 (TF-MoDISco)
- Kelley DR 2020 PLoS Comput Biol 16:e1008050 (Basenji2; cross-species precursor)
Related Skills
- atac-seq/atac-peak-calling - Classical peak calling input
- atac-seq/footprinting - Use chromBPNet bias correction as TOBIAS alternative
- atac-seq/motif-deviation - chromVAR vs scBasset for per-cell motif activity
- atac-seq/single-cell-atac - scBasset integration with sc workflow
- atac-seq/enhancer-gene-linking - Variant effect feeds enhancer scoring
- atac-seq/allele-specific-accessibility - DL-predicted variant effects vs observed allelic imbalance
- causal-genomics/fine-mapping - Downstream use of variant effect scores
- machine-learning/biomarker-discovery - General ML patterns
- gene-regulatory-networks/scenic-regulons - Combine motif discovery with TF networks