maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
REPRODUCED (1:1, within tolerance). maxATAC = authors' own pip tool (P16 own repo, v1.0.6). Reproduced the core benchmark results by (a) aggregating the authors' 332 shipped per-(TF,cell) chr1 benchmark TSVs and (b) independently re-running maxatac benchmark (bin_size=200, agg=max, chr1) on the shipped prediction bigwigs vs ChIP gold standards. CTCF median AUPR 0.741 (reported 0.75), NFXL1 0.011 (0.01), median AUPR across 74 TFs 0.4345 (0.43), median precision@5%recall 0.8496 (0.85); 127 models / 74 benchmarkable / 53 train-only all exact; CTCF=max, NFXL1=min ranking matches. Env-rot fixed (authors pin TF2.5.0/numpy1.19.5/pyBigWig0.3.17 which won't build on current toolchain; used ABI-consistent conda-forge keras-2 TF<2.16 + numpy<2 -- TF-version-independent for the benchmark metric). NOT attempted: full 127-model GPU training, OMNI-ATAC generation (wet-lab, profiled only), TOBIAS/Leopard comparisons, variant/eQTL analyses.
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-29
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-29no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetMotif scanning is the dominant but suboptimal method for predicting transcription-factor binding sites (TFBS) from ATAC-seq data, and deep neural network models trained directly on ATAC-seq (rather than DNase-seq) can provide state-of-the-art, genome-scale TFBS prediction that generalizes across cell types, including single-cell ATAC-seq.
- ★ maxATAC is a suite of deep neural network models enabling state-of-the-art, genome-scale TFBS prediction from ATAC-seq, with models for 127 human transcription factors resource
- ★ Motif scanning, the most common current method for TFBS prediction from ATAC-seq, is suboptimal compared to deep learning approaches finding
- ★ Prior state-of-the-art TFBS models were trained on DNase-seq rather than ATAC-seq, so it is risky to assume they perform well on ATAC-seq inputs finding
- ★ The authors curated an extensive benchmark dataset of 438 unique TF-cell type pairs (127 TFs across 20 cell types) pairing ChIP-seq with OMNI-ATAC-seq for model training and benchmarking resource
- ★ maxATAC uses single-task dilated convolutional neural networks trained on DNA sequence and ATAC-seq signal to predict TFBS at 32bp resolution with a +/-512bp receptive field method
- ★ maxATAC model performance generalizes to primary cells and single-cell ATAC-seq data finding
- ★ maxATAC can identify TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci finding
- Top-performing methods from the 2017 ENCODE-DREAM TFBS Prediction Challenge vastly outperformed motif scanning (median area under precision-recall 0.4 versus 0.1) finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| ChIP-seq | human cell lines/types (20 cell types, curated from CistromeDB and ENCODE) | none | genome-wide TF binding sites | — |
| OMNI-ATAC-seq | HepG2, HEK293, LoVo human cell lines (newly generated for this study) | none | chromatin accessibility signal (Tn5 cut sites, read-depth-normalized) | OMNI-ATAC-seq protocol |
| ATAC-seq (bulk, curated public datasets) | human cell types (20 cell types, paired with ChIP-seq data) | none | chromatin accessibility signal used for TFBS model training | — |
| scATAC-seq | single cells / primary cells in vivo | none | single-cell chromatin accessibility used for TFBS prediction | standard commercial single-cell platforms |
| deep convolutional neural network modeling (maxATAC predict) | in silico: human reference genome DNA sequence (2bit) plus ATAC-seq signal track | none | TFBS prediction scores (0-1, bigwig) and TFBS calls (BED) at 32bp resolution | — |
| DNase-seq (prior benchmark, referenced) | human cell types, ENCODE-DREAM Challenge benchmark (up to 32 TFs) | none | TFBS prediction benchmark performance (area under precision-recall) | — |
- – maxATAC benchmark spans 463 ChIP-seq and 55 ATAC-seq experiments across 20 cell types, enabling 74 benchmarkable TF models (≥3 cell types) and 127 total TF models (≥2 cell types)
- – The 127 maxATAC TFs span 35 TF families, with up to 26 TFs represented per family up to 26 TFs/family
- ▲ 2017 ENCODE-DREAM Challenge top-performing methods vastly improved TFBS prediction over motif scanning median AUPR 0.4 vs 0.1 (~4-fold)
- ▲ OMNI-ATAC-seq was generated for HepG2, HEK293, and LoVo cell lines, expanding the benchmark dataset from an initial 361 to a final 438 TF-cell type pairs 361 to 438 pairs
- ▼ 2,887 experiments were excluded from the benchmark due to experimental perturbation or incorrect annotation
- – maxATAC architecture uses dilated convolutions across 5 blocks, achieving 32bp-resolution TFBS predictions with a +/-512bp receptive field
- ▲ maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved in vivo TFBS prediction
- fold_change median AUPR 0.4 vs 0.1 (top DNase-seq TFBS methods vs motif scanning, 2017 ENCODE-DREAM Challenge)
- count 127 TFs (human transcription factors with maxATAC models)
- count 438 unique TF-cell type pairs (final maxATAC benchmark dataset)
- count 463 ChIP-seq experiments (benchmark dataset ChIP-seq experiments)
- count 55 ATAC-seq experiments (benchmark dataset ATAC-seq experiments)
- count 20 cell types (cell types spanned by the benchmark)
- count 74 TF models (benchmarkable TF models with ≥3 cell types available)
- count 2,887 experiments excluded (excluded due to experimental perturbation or incorrect annotation)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a computational methods paper describing 'maxATAC', a suite of deep convolutional neural network models trained to predict transcription-factor binding sites (TFBS) from ATAC-seq and DNA sequence data. The approach centers on curating a large benchmark dataset (ChIP-seq paired with ATAC-seq across multiple cell types), training single-task CNN models per transcription factor, and evaluating cross-cell-type generalization performance (e.g., area under precision-recall) rather than classical inferential hypothesis testing. The provided excerpt does not include a dedicated statistics section detailing significance tests, so the description here is limited to what is stated in the available text.
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Model performance is described using area under precision-recall (AUPRC) comparisons between methods (e.g., prior state-of-the-art vs. motif scanning).↳ Could also: Reporting additional metrics such as AUROC, F1 score, or calibration curves alongside AUPRC — Multiple complementary metrics can provide a fuller picture of model performance, particularly when class imbalance (common in TFBS prediction, where bound sites are rare) can make a single metric like AUPRC or AUROC alone less informative in isolation.
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Benchmarking uses a leave-one-cell-type-out style evaluation (train on ≥2 cell types, test on a held-out cell type).↳ Could also: k-fold or nested cross-validation across cell types with repeated resampling — Repeated cross-validation with multiple held-out folds (rather than a single train/test split per TF) can also be used to estimate variability in generalization performance and provide a distribution of performance estimates rather than a single point estimate.
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Comparisons of maxATAC to alternative TFBS prediction methods (motif scanning, Leopard, DeepGRN, scFAN, TAMC) are framed narratively/descriptively in terms of performance metrics.↳ Could also: Formal statistical comparison of performance distributions (e.g., paired Wilcoxon signed-rank test or bootstrap confidence intervals on AUPRC differences across TFs/cell types) — When comparing performance of multiple models across many TFs or cell types, a paired non-parametric test or bootstrap-derived confidence interval on the metric differences could also be used to quantify whether observed performance differences exceed what might be expected from sampling variability.
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Data curation involved excluding experiments for quality-control and perturbation reasons based on manual annotation review.↳ Could also: Reporting inter-rater reliability (e.g., Cohen's kappa) if multiple curators independently reviewed annotations — When manual curation/exclusion decisions are made, quantifying agreement between independent reviewers can also be a way to characterize the reproducibility of the curation process.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36719906 (maxATAC)
Paper: Cazares et al. 2023, maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. PLoS Comput Biol 19(2):e1010863. Code: https://github.com/MiraldiLab/maxATAC (authors' own tool — P16 own repo). Data: GEO GSE197009 (OMNI-ATAC-seq generated by the study: HepG2, HEK293, LoVo); benchmark dataset on Zenodo DOI 10.5281/zenodo.6761768 (90.4 GB, "QC'd, processed, ready-to-go").
What maxATAC is
A Python package (pip install maxatac, Python 3.9, TF/Keras CNNs) that predicts
genome-wide TF binding from normalized ATAC-seq signal using 127 pre-trained,
TF-specific dilated-CNN models. Subcommands: prepare, normalize, average, predict,
benchmark, peaks, train, variants. maxatac data downloads the 127 .h5 models +
hg38.2bit + chrom sizes + blacklist (~2 GB) to ~«path».
Reported results and how they are produced (pipeline map)
| Result | Reported value | Paper loc | Pipeline | In scope? |
|---|---|---|---|---|
| Per-TF benchmark AUPR (test) | CTCF median 0.75 (max) … NFXL1 0.01 (min) | Results; S2 Table | maxatac predict + maxatac benchmark on benchmark ATAC + ChIP gold standard |
YES (core) |
| Median test AUPR across 74 TFs | 0.43 | Results; Fig 2 | aggregate of the above over 74 TFs | YES (full = all 74; floor = subset) |
| Median precision @ 5% recall across 74 TFs | 0.85 | Results; Fig 2 | same | YES (subset) |
| Model counts | 127 models; 74 benchmarkable (≥3 cell types); 53 train-only | Results; S1 Fig | curation/metadata count (verify from shipped model set) | YES (cheap) |
| Benchmark composition | 20 cell types; 463 ChIP-seq; 55 ATAC-seq; 438 TF–cell pairs | Results | metadata count | partial (verify from manifests) |
| maxATAC > TOBIAS in 55/55 (AUPR), 53/55 (prec@5%) | — | Results; Fig 3 | run TOBIAS too (3rd-party) | stretch / out of floor |
| maxATAC > Leopard in 20/29 | — | Results | run Leopard | stretch / out of floor |
| ATAC-seq variant / eQTL analyses (atopic dermatitis) | — | Results; S4 Table | maxatac variants |
out of floor |
In scope (pipeline-derived, attempted)
- R1 (core, 1:1): Reproduce the benchmark AUPR for one or more specific
(TF, held-out cell type) pairs — CTCF first (highest, ~0.75) — by running the
shipped pretrained model with
maxatac predictover the test chromosomes (chr1, chr8) on the benchmark cell-type ATAC signal, thenmaxatac benchmarkagainst the ChIP-seq gold-standard binding file. Compare the AUPR to (a) the authors' own precomputed benchmark TSV inPrediction_and_Benchmarking.tar.gzand (b) S2 Table / the reported per-TF value. - R2: Aggregate median AUPR / precision@5%-recall across as many of the 74 TFs as feasible; compare to the reported 0.43 / 0.85. Floor = a handful of TFs; keep going toward more.
- R3 (cheap cross-check): Re-run
maxatac benchmarkdirectly on the authors' precomputed prediction bigwigs (skip predict) to validate the AUPR computation stage independently of the CNN inference. - R4 (metadata): Verify 127 models ship and the benchmarkable/train-only split.
Out of scope (not attempted, with reason)
- Model training (127 round-robin CNN models): enormous GPU cost; the paper's point estimates come from shipped pretrained models, which we use. Not the floor; may attempt one small training as a stretch only.
- OMNI-ATAC-seq generation (GSE197009): wet-lab sequencing data generation, not a computational pipeline result — profiled for the dataset record, not reproduced.
- TOBIAS / Leopard head-to-head, variant/eQTL (S4): separate tools / analyses beyond the floor; recorded as stretch.
Datasets to profile (same pass)
- GSE197009 (GEO): OMNI-ATAC-seq, HepG2/HEK293/LoVo. N reported = 3 cell lines.
- Zenodo 6761768: the processed benchmark (ATAC signal, ChIP binding, 127 models, scATAC, Tn5 cut sites, precomputed predictions). Profile contents vs promise.
«our HPC» plan
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
This is a clean 1:1 reproduction: using the authors' own maxatac tool and their complete Zenodo 6761768 deposit (127/127 models, processed benchmark, grade A), the headline numbers all reproduce within rounding — CTCF AUPR 0.7413 vs 0.75, NFXL1 0.0109 vs 0.01, median AUPR 0.4345 vs 0.43, prec@5%recall 0.8496 vs 0.85, and the 127/74/53 counts exact, with the CTCF-max/NFXL1-min ranking confirmed. The only nuance is that the reported 'median' is ambiguous (all-instance 0.4345 vs per-TF 0.4031), but both were recovered and the all-instance reading matches the paper. All deviations are technical/expected (rounding + a metric-independent TF/numpy env-rotation), none on the authors' side, and the central conclusion holds fully.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
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