Genome-wide prediction of DNase I hypersensitivity using gene expression.
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 are derivable from the shared data
- ✓Any deviation was negligible
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
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
Described well enough to reproduce 1:1 on the deterministic core. BIRD is a third-party-style deterministic C++ tool whose released exon-array model (BIRD-model v1.1, SHA f5d388...) + paper's own training data (BIRD-data v1.0 = GSE19090 exon arrays + ENCODE DNase) reproduce the paper's headline STRUCTURAL numbers exactly: 1,108,603 DHS loci x 57 cell types, genome-wide prediction lands on exactly those 1,108,603 loci and is bit-deterministic, and the loci cluster into 1000/2000/5000 clusters as described. The held-out CV ACCURACY (r_L=0.82, r_C=0.50) is the deliberately-skipped ~20%: the shipped .bin is the full 57-cell model, so only IN-SAMPLE correlations could be computed (r_L=0.908, r_C=0.756) -- upper bounds that exceed the held-out figures in the correct direction, corroborating but not 1:1-reproducing them. NOT attempted: 40/17 CV retraining (BigKmeans + R scripts + BIRD_build_library) and the 912,886-loci CV model. No fabrication signal: every reproduced value is regenerable from the public code+data at the pinned commit/SHAs.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 79assessed: 2026-06-14 ⛓ 05e98c3b24eb
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no 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: opusCan a biological sample's transcriptome (gene expression) be used to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH), and to what extent are regulatory elements' activities predictable from the whole transcriptome?
- ★ Gene expression substantially predicts genome-wide DNase I hypersensitivity (DH), demonstrating transcriptome-based prediction as a feasible approach for regulome mapping finding
- ★ BIRD (Big Data Regression for predicting DH) handles the ultra-high-dimensional prediction problem by clustering co-expressed genes and aggregating locus-level and pathway-level models method
- ★ Information useful for predicting DH is contained in the whole transcriptome rather than limited to a regulatory element's neighboring genes finding
- ★ BIRD-predicted DH can be used to predict transcription factor-binding sites (TFBSs), build a regulome database from GEO expression samples, predict differential regulatory element activities, and serve as pseudo-replicates resource
- DH correlates in trans with expression of TFs binding the locus and co-expressed genes, explaining why whole-transcriptome prediction outperforms neighboring-gene prediction mechanism
- BIRD produces the best prediction performance among compared methods while remaining computationally efficient for big data regression finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| DNase-seq (chromatin accessibility / DNase I hypersensitivity) | 57 distinct human cell types with normal karyotype (40 training, 17 test) from ENCODE | none | DH bin read count / DH signal level at genomic loci (912,886 DHSs) | ENCODE DNase-seq |
| Exon array (gene expression profiling) | 57 distinct human cell types from ENCODE (40 training, 17 test) | none | gene expression levels of 18,000+ genes used as predictors | Exon array (ENCODE) |
| Computational regression prediction (BIRD) | ENCODE human cell types; applied to GEO gene expression samples | none | predicted genome-wide DH levels per locus | BIRD algorithm |
- ▲ BIRD achieved high cross-locus prediction accuracy (mean P–T correlation r_L) in the 17 test cell types r_L = 0.82 (mean)
- ▲ BIRD predicted cross-cell-type DH variation, more challenging than cross-locus variation r_C = 0.50 (mean) vs r_L = 0.82
- ▲ Random/permuted models also yielded high cross-locus correlation due to locus-specific DH propensity, but BIRD exceeded them BIRD r_L = 0.82 vs random r_L = 0.65
- ▲ Random prediction models had cross-cell-type correlation centered around zero, while BIRD substantially increased r_C random r_C = -0.03 (mean)
- ▲ Whole-transcriptome BIRD prediction substantially outperformed best neighboring-gene approach across r_L, r_C, and τ
- – Fused lasso achieved similar accuracy to BIRD's locus-level model but was vastly slower >10^5 times slower
- – Cross-cell-type prediction accuracy varied greatly among loci, with a subset of loci showing high r_C 6% of loci (partial statement)
- correlation r_L = 0.82 (mean cross-locus P–T correlation for BIRD across 17 test cell types)
- correlation r_C = 0.50 (mean cross-cell-type P–T correlation for BIRD across genomic loci)
- correlation r_L = 0.65 (mean cross-locus correlation for random (permuted) prediction models)
- correlation r_C = -0.03 (mean cross-cell-type correlation for random prediction models)
- count 912,886 genomic loci (DHSs) (DHSs retained with unambiguous DNase-seq signal in at least one training cell type)
- count 57 human cell types (40 training, 17 test) (ENCODE cell types used for training and testing)
- pvalue p < 10^-15 (Two-sided Wilcoxon signed-rank test comparing methods on r_C)
- pvalue p < 10^-4 (Two-sided Wilcoxon signed-rank test comparing methods on r_L)
Statistical methods review
Model: opusA 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 that develops and benchmarks a prediction algorithm (BIRD) using a train/test design on ENCODE data: 57 human cell types were randomly partitioned into 40 training and 17 test cell types, and prediction accuracy was assessed with Pearson correlations between predicted and true DH (cross-locus r_L and cross-cell-type r_C) and a normalized squared prediction error (τ). Method-vs-method comparisons of these accuracy statistics were assessed with two-sided Wilcoxon signed-rank tests, and distributions were summarized with boxplots (median, quartiles, 1.5×IQR whiskers).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sided Wilcoxon signed-rank test | Comparing cross-locus P–T correlation (r_L) between methods (Fig. 2b) | 17 (test cell types) | not stated |
| Two-sided Wilcoxon signed-rank test | Comparing cross-cell-type P–T correlation (r_C) between methods (Fig. 2c) | 912,886 (genomic loci) | not stated |
| Pearson's correlation (predicted vs. true DH) | Evaluation metric for r_L (across loci) and r_C (across cell types) (Fig. 2) | — | not stated |
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Method comparisons used the two-sided Wilcoxon signed-rank test on paired accuracy statistics.↳ Could also: A paired t-test (when approximate normality holds) or a permutation/bootstrap test on the paired differences could also be used. — A paired t-test can offer more power when its assumptions hold, while a bootstrap can directly yield confidence intervals for the mean difference in accuracy.
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Significance was reported as p-value thresholds (e.g., p < 10^-4, p < 10^-15).↳ Could also: Reporting exact p-values alongside an effect-size measure (e.g., the median paired difference or a 95% CI for it) could also be presented. — Exact values and effect sizes convey the magnitude of improvement, not just that a difference exists, which is informative when n is very large and tiny differences become significant.
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Distributions of r_L and r_C were summarized with boxplots showing median and IQR.↳ Could also: Adding mean ± SD or a 95% CI, or overlaying the raw points, could also describe the spread. — These complementary summaries make central tendency and uncertainty explicit, which can aid comparison across methods.
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Prediction accuracy was evaluated on a single random 40/17 train/test split.↳ Could also: Cross-validation or repeated random splits could also be used to estimate accuracy. — Resampling provides a distribution of performance estimates and a sense of variability due to the particular partition chosen.
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Prediction agreement was quantified with Pearson's correlation between predicted and true DH.↳ Could also: Spearman's rank correlation or concordance/CCC, alongside the reported squared error, could also be used. — Rank-based or concordance measures are robust to nonlinearity and outliers and capture agreement (not just linear association), complementing the squared-error metric.
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For r_C, the test was based on n = 912,886 loci treated as the unit of comparison.↳ Could also: A multilevel/clustered analysis that accounts for correlation among co-activated loci (e.g., DHS 'pathways') could also be considered. — Modeling the dependence structure among loci can give a more conservative assessment of uncertainty when many loci co-vary.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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BIRD cross-cell-type DHS prediction (r_C = 0.50) dramatically outperforms random permuted models (r_C ≈ −0.03), demonstrating that expression-based predictors capture genuine cell-type variation in chromatin accessibility.other human cell types up 2017×1papers★ This paper is the founder (earliest)
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BIRD cross-locus DHS prediction accuracy (r_L = 0.82) exceeds random/permuted-label baseline (r_L = 0.65), confirming model captures genuine locus-specific signal beyond mean DHS propensity.other human cell types up 2017×1papers★ This paper is the founder (earliest)
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Fused lasso achieves comparable locus-level DHS prediction accuracy to BIRD but is more than 100,000-fold slower, making genome-wide application impractical.other human cell types 2017×1papers★ This paper is the founder (earliest)
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Cross-cell-type DHS prediction accuracy varies widely across genomic loci; approximately 6% of loci exhibit high cross-cell-type predictability.other human cell types mixed 2017×1papers★ This paper is the founder (earliest)
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Whole-transcriptome BIRD model outperforms the best neighboring-gene expression approach for DHS prediction across cross-locus, cross-cell-type, and rank-order accuracy metrics.other human cell types up 2017×1papers★ This paper is the founder (earliest)
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BIRD cross-cell-type DHS prediction (mean r_C = 0.50) is substantially harder than cross-locus prediction (r_L = 0.82), reflecting greater difficulty in capturing cell-type-specific variation.other human cell types mixed 2017×1papers★ This paper is the founder (earliest)
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BIRD model predicts genome-wide DNase I hypersensitivity with high mean cross-locus accuracy (r_L = 0.82) across 17 held-out human cell types from ENCODE.other human cell types up 2017×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
scope.md — pmid-29051481 (BIRD)
Paper: Zhou et al. 2017, "Genome-wide prediction of DNase I hypersensitivity using gene expression", Nat Commun. BIRD = a deterministic C++ regression tool + a pre-built model. The released model and training data ARE the paper's pipeline artifacts (third-party-tool-style reproduction, P16-valid).
In scope (pipeline-derived) — attempted
- Model/data structure (C1, C2, C4): number of DHS loci (1,108,603), number of cell types (57), and the DHS clustering (1000/2000/5000). Pipeline = the released BIRD-data v1.0 matrices + cluster files. DETERMINISTIC, exactly reproduced.
- Prediction (C3): running the released exon-array model (
BIRD_predict) on the shipped K562 example. Pipeline = the BIRD C++ predictor. DETERMINISTIC, reproduced. - Accuracy (C5, C6) — partial: cross-locus r_L and cross-cell r_C. Pipeline = BIRD prediction + correlation against measured DNase. Reproduced only IN-SAMPLE (full model on its own training cells); the paper's HELD-OUT CV values were not re-derived (see below).
In scope but NOT attempted (the hard ~20%)
- Held-out cross-validation (true r_L=0.82, r_C=0.50): requires retraining a BIRD
model on 40 cells and testing on 17 — BigKmeans +
R_script/get_param.r,get_DHS_cluster.r,get_model_data.r+BIRD_build_library. Feasible but the deliberately-skipped 20%. The 912,886-loci 40-cell CV model is not shipped as a.bin.
Out of scope (wet-lab / external / manual)
- Generation of the ENCODE DNase-seq and exon-array assays themselves.
- The PDDB web prediction database (2,000 GEO samples) and its server.
- Roadmap (70-cell) and ENCODE RNA-seq (167-cell) model variants — different releases, not the original paper's exon-array model.
Pipelines named per result
- C1/C2/C4: data inspection of BIRD-data v1.0 (.rds + cluster .txt).
- C3: BIRD
BIRD_predict(C++), exon-array full model. - C5/C6: BIRD
BIRD_predict+ R Pearson correlation vs measured DNase.
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.
The deterministic core reproduces exactly from the pinned public BIRD model+data: 1,108,603 DHS loci × 57 cell types, genome-wide prediction landing on exactly those loci and bit-deterministic across reruns, plus the 1000/2000/5000 clustering. The paper's held-out accuracy (r_L=0.82, r_C=0.50) was not 1:1 reproduced because only the full 57-cell .bin is shipped — so in-sample correlations (0.908, 0.756) were computed instead, which upper-bound the held-out values in exactly the expected direction, corroborating but not re-validating them. The 40/17 CV retraining is the deliberately-skipped hard 20%. No fabrication signal — every value is regenerable at the pinned SHAs; a solid partial limited by the accuracy not being held-out-reproduced.
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
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.