Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🔴Could not use the authors’ exact input data
- 🔴Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🔴The deviation was non-trivial in magnitude
- 🟡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
PARTIAL reproduction. The paper's OWN computational results (C2-C7: scATAC cell/peak counts, bookmarking fraction, NF-YA overlap, R=0.94/0.70 correlations) are NOT reproducible from shipped artifacts: real code is github.com/QuKunLab/Mitosis (commit a8f91c6) = 5 plotting-only notebooks reading a .«path» dir of ~40 processed intermediate files that are deposited NOWHERE (not repo/Zenodo/GSA/supplementary), with no upstream pipeline scripts -> docs_insufficient for those. Real raw data is GSA CRA003844 (NOT the registry's GSE92846, a mis-harvest) = 131GB raw PE FASTQ only. Per the brief's third-party-tool rule (P16), ONE clearly-specified result was reproduced end-to-end on the paper's own raw data: NF-YA binding-site count (reported 5088) via fastp->bowtie2(end-to-end,very-sensitive,GRCh38_noalt_as)->samtools filter(MAPQ30,proper-pair,dedup)->MACS2 callpeak(BAMPE,q0.05) vs IgG, on runs CRR609413/CRR609414 vs CRR609415. The well-powered replicate NFYAR2 (33.1M pairs) gives 5188 peaks -> within ~2% of the reported 5088; the shallower NFYAR1 gives 2793; reproducible overlap 1845. Graded PARTIAL (not exact) because genome build, MACS version/thresholds, and single-rep-vs-pooled/IDR definition are unspecified in the paper, and the two replicates differ ~1.9x by depth -- but one replicate reproduces the headline number near-exactly, which is strong positive evidence and argues against fabrication of that value. Tooling: bowtie2 2.5.5, samtools 1.21, MACS2 2.2.9.1 (needed a __*_finite LD_PRELOAD math shim for glibc compat), fastp 1.1.0, bedtools 2.31.1. Compute on «our HPC» COMPUTE nodes («job» align + 2225104 peaks; 2220320/2221952 were failed earlier attempts, fixed); all data on «infra». NOT attempted: C2-C7 (out of scope, undocumented + absent inputs). No fabrication asserted.
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
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v1 current initial assessmentassessed: 2026-06-15 ⛓ 3a3bad389a91
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- Reproduced
- 2026-06-23
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15👤 1 human curator(s) · Level L2 2026-06-15
- 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: sonnetThe paper investigates whether and how chromatin accessibility is dynamically retained ('bookmarked') at specific genomic regions throughout mitosis, and what regulates the reestablishment of transcription after mitotic exit.
- ★ Chromatin accessibility continually decreases from mitotic entry until metaphase, then gradually increases as chromosomes segregate. finding
- ★ A subset of chromatin regions (~7%, n=2249) remain accessible throughout all of mitosis, defined as 'bookmarked regions'. finding
- ★ Bookmarked regions are enriched near promoters/TSS and are associated with genes that reactivate rapidly (first-wave genes) after mitotic exit. finding
- ★ NF-YA preferentially occupies bookmarked regions and functions as a mitotic bookmarking transcription factor contributing to post-mitotic transcriptional reactivation. mechanism
- ★ Pseudotime-aligned single-cell ATAC-seq (scATAC-seq) reveals mitotic bookmarking dynamics that bulk ATAC-seq cannot detect. method
- TFs split into mitotically lost (e.g., CTCF, PAX5, ASCL1) and mitotically enriched (e.g., RUNX2, MYC, NF-YA) groups based on correlation of motif enrichment with pseudotime. finding
- Opening rate of chromatin regions positively correlates with proportion and expression of first-wave reactivated genes. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| scATAC-seq | L02 human liver cells (unsynchronized H3pS10+ and RO3306/MG132/blebbistatin-synchronized) | drug synchronization (RO3306, MG132, blebbistatin) / FACS sorting | chromatin accessibility peaks across pseudotime | APEC algorithm, Slingshot pseudotime inference |
| Immunofluorescence imaging | L02 cells (FACS-sorted H3pS10+ population) | none | mitotic phase purity/staging | — |
| ATAC-see (imaging-based ATAC) | L02, HepG2, HUH7 cells | none (staged by mitotic phase) | chromatin accessibility signal quantified vs DAPI | — |
| Bulk ATAC-seq | L02 cells | nocodazole arrest vs unsynchronized | chromatin accessibility peaks | — |
| Motif enrichment / TF occupancy analysis | L02 scATAC-seq peaks (JASPAR database) | none (bioinformatic) | TF enrichment score correlated with pseudotime | hypergeometric test-based motif scanning |
| Public ATAC-seq dataset comparison | 135 tissues/cell lines (ENCODE) | none | overlap with bookmarked regions | ENCODE |
| Pol II ChIP-seq dataset comparison | 34 tissues/cell lines (ENCODE) | none | overlap between bookmarked regions and Pol II binding sites | ENCODE |
| EU-RNA-seq (nascent transcript) reanalysis | human hepatoma cells and U2OS osteosarcoma cells (public datasets) | mitotic block release (timepoints 0-300 min) | gene expression of first-/second-wave reactivated genes | — |
- – Chromatin accessibility decreased by ~75% during the prophase-metaphase transition before increasing after metaphase ~75% decrease
- – ~7% of initially open chromatin regions (n=2249) remained accessible throughout mitosis, defined as bookmarked regions 7% (n=2249)
- – ~79% of bookmarked regions located in gene promoters near TSS 79%
- – ~94% of bookmarked regions were open in public ATAC-seq datasets across 135 tissues/cell lines 94%
- ▲ Significant overlap between bookmarked regions and common Pol II binding sites from ChIP-seq of 34 tissues/cell lines P<0.0001
- ▲ Gene expression near bookmarked regions was significantly higher than unbookmarked regions at 80 min after mitotic release, but not in interphase P<0.0001
- ▲ Opening rate of chromatin bins positively correlated with proportion and expression of first-wave genes R=0.94 (both)
- – Identified 110 mitotically lost TFs (ρ<0) and 131 mitotically enriched TFs (ρ>0), including known bookmarking factors RUNX2 and MYC 110 vs 131 TFs
- count 6538 (total mitotic L02 cells profiled by scATAC-seq)
- count 30,671 (total accessible chromatin peaks identified)
- count 2249 (number of bookmarked regions (~7% of open regions))
- fold_change ~75% decrease (chromatin accessibility decline during prophase-metaphase transition)
- correlation R = 0.94 (opening rate vs proportion of first-wave genes)
- correlation R = 0.94 (opening rate vs expression of reactivated genes at 80 min post-release)
- pvalue P < 0.0001 (overlap of bookmarked regions with Pol II ChIP-seq binding sites (chi-square test))
- pvalue P < 0.0001 (expression difference of reactivated genes near bookmarked vs unbookmarked regions (Student's t test))
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.
The study applied single-cell ATAC-seq (scATAC-seq) to 6538 mitotic L02 human liver cells, using the APEC algorithm for peak calling and the Slingshot algorithm for pseudotime trajectory inference to represent continuous mitotic progression from prophase to anaphase. Chromatin accessibility dynamics across 30,671 peaks were characterized along the pseudotime axis, and bookmarked versus unbookmarked regions were compared using chi-square tests and two-sided Student's t-tests. Transcription factor motif enrichment was scored via hypergeometric test-based motif scanning against the JASPAR database, and Pearson correlations were used to relate chromatin opening rates to gene reactivation dynamics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Chi-square test | Overlap between bookmarked regions and open sites from 181 ATAC-seq datasets (Fig. 2C) and overlap with common Pol II ChIP-seq binding sites from 34 tissues/cell lines (Fig. 2D) | — | not stated |
| Two-sided Student's t-test | Z-scaled expression of reactivated genes near bookmarked vs. unbookmarked vs. bulk-detected regions at 80 min after mitotic release (Fig. 2F) and in interphase cells (Fig. 2G) | — | not stated |
| Pearson correlation (R) | Opening rate of 13 chromatin bins vs. proportion of first-wave genes in associated genes (Fig. 2I) and vs. expression of reactivated genes at 80 min post-release (Fig. 2J) | 13 bins | not stated |
| Pearson correlation coefficient (ρ) | TF motif enrichment score vs. pseudotime for each of 241 TFs to classify mitotically lost (ρ < 0) vs. mitotically enriched (ρ > 0) TFs (Fig. 3A) | 241 TFs | not stated |
| Pearson correlation | TF enrichment time vs. proportion of bookmarked regions in TF-targeted regions across mitotically enriched TFs (Fig. 3C) | — | not stated |
| Hypergeometric test-based motif scanning | Inferring per-cell TF enrichment scores from JASPAR motifs across scATAC-seq peaks throughout the pseudotime trajectory | — | not stated |
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Two-sided Student's t-test was used to compare Z-scaled expression values between gene sets associated with bookmarked vs. unbookmarked chromatin regions↳ Could also: A Mann-Whitney U (Wilcoxon rank-sum) test could also have been applied — RNA-seq-derived expression values, even when Z-scaled, often have skewed distributions; a non-parametric rank-based test makes no normality assumption and is robust to outliers, which is a widely used alternative for comparing gene expression distributions in genomics
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Pearson correlation was used to relate chromatin bin opening rates to gene reactivation metrics across 13 bins↳ Could also: Spearman rank correlation could also have been used — With only 13 data points and a potentially monotonic but not strictly linear relationship, Spearman correlation does not assume linearity or homoscedasticity, and provides a complementary measure of association that is also commonly reported alongside Pearson R in genomic studies
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Chi-square tests were used to assess statistical significance of overlaps between bookmarked regions and large ENCODE ATAC-seq or ChIP-seq datasets↳ Could also: Fisher's exact test, or a permutation-based overlap test (e.g., as implemented in BEDTools shuffle or LOLA), could also have been used — Fisher's exact test does not rely on large-sample chi-square approximations and is exact by construction; permutation-based tests additionally account for genomic features such as GC content and mappability that affect the null distribution of region overlaps
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Multiple chi-square tests, t-tests, and Pearson correlations were performed across several comparisons without an explicitly stated multiple comparison correction↳ Could also: A Benjamini-Hochberg FDR correction could also have been applied across the family of tests within each analysis — When several related hypothesis tests are conducted, controlling the false discovery rate is a widely used approach to quantify the expected proportion of false positives among significant findings, and is standard practice in high-dimensional genomics studies
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Dispersion for ATAC-see signal quantification (n=3 per group across five mitotic phases) was reported as mean ± SD↳ Could also: A 95% confidence interval could also have been reported — At n=3 per group, a 95% CI directly conveys the uncertainty around the estimated mean and is often considered more informative for inferential purposes than SD, which describes sample spread rather than estimation precision
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Pseudotime trajectory inference was performed using the Slingshot algorithm in two-dimensional space↳ Could also: Other trajectory inference methods such as Monocle 3 (with UMAP embedding) or PAGA could also have been applied — Different trajectory algorithms make distinct assumptions about manifold topology, branching structure, and dimensionality reduction; applying an alternative method is a standard sensitivity check to assess the robustness of the inferred pseudotime ordering and downstream chromatin accessibility dynamics
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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Bulk ATAC-seq mitotic-retained regions are 95% unbookmarked by scATAC-seq; 85% of scATAC-defined bookmarked regions are detectable by bulk ATAC-seq, revealing method-dependent coverage differences.ATAC-seq human l02 mixed 2023×1papers★ This paper is the founder (earliest)
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~94% of mitotic chromatin bookmarked regions are accessible in at least one of 135 human ENCODE tissues/cell lines, indicating constitutive regulatory activity.ATAC-seq human none 2023×1papers★ This paper is the founder (earliest)
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Global chromatin accessibility decreases ~75% during the prophase-to-metaphase transition then recovers after metaphase exit.imaging human l02 mixed 2023×1papers★ This paper is the founder (earliest)
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Genes associated with bookmarked chromatin regions show significantly higher transcriptional reactivation than unbookmarked genes at 80 min post-mitotic release (P<0.0001).RNA-seq human hepatoma up 2023×1papers★ This paper is the founder (earliest)
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Chromatin bin opening rate positively correlates with reactivated gene expression levels at 80 min post-mitotic release (R=0.94).RNA-seq human hepatoma up 2023×1papers★ This paper is the founder (earliest)
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~7% of initially open chromatin regions (n≈2249) remain accessible throughout mitosis, constituting mitotic bookmarked regions.scATAC-seq human l02 none 2023×1papers★ This paper is the founder (earliest)
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Post-mitotic chromatin bin opening rate positively correlates with the proportion of first-wave transcriptionally reactivated genes (R=0.94).scATAC-seq human l02 up 2023×1papers★ This paper is the founder (earliest)
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~79% of mitotic chromatin bookmarked regions are located at gene promoters proximal to TSSs.scATAC-seq human l02 none 2023×1papers★ 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.
Downstream reach in the literature
2 downstream papers · 3 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
- Widespread Mitotic Bookmarking by Histone Marks and... 2017 · 127 cites
- H3K27ac bookmarking promotes rapid post-mitotic acti... 2021 · 94 cites
- H3K27ac bookmarking promotes rapid post-mitotic acti... 2021 · 94 cites
- H3K27ac bookmarking promotes rapid post-mitotic acti... 2021 · 94 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Reproduction scope — pmid-36696508
Paper: Yu Q et al. "Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution." Sci Adv 2023;9:eadd2175. PMID 36696508 · PMCID PMC9876548 · DOI 10.1126/sciadv.add2175.
Provenance correction (important)
- Registry/BRIEF listed data as
geo:GSE92846and code asgithub.com/QuKunLab/ATAC-pipe. Both are mis-harvested:- The paper's actual data is in GSA (CNCB/NGDC) accession
CRA003844(BioProject PRJCA004401) — not GEO. GSE92846 is an unrelated older GEO series; ATAC-pipe is only one tool the paper cites, not its repo. - The paper's actual analysis code repo is
github.com/QuKunLab/Mitosis(pinned commita8f91c62da7bbd476fe59113fcc0925a5b4ffd1e, v1.0.0, 2022-11-11), mirrored on Zenodo10.5281/zenodo.7313683(code-only zip, 2.8 MB). This is a provenance/harvest error, not an author fabrication.
- The paper's actual data is in GSA (CNCB/NGDC) accession
What the shipped code actually is
QuKunLab/Mitosis = 5 Jupyter notebooks (s003_Fig1..Fig5.ipynb),
plotting-only. Every notebook reads from a local .«path» directory of ~40
processed intermediate files (peak BEDs, count CSVs, annotation tables,
pseudotime/TF tables, e.g. genes_scored_by_TSS_peaks.csv, merge.bed,
idr_NFYA_p12_igg_bam_peaks.bed, results.deseq.csv, …).
These .«path» files are shipped NOWHERE — not in the repo, not in the
Zenodo snapshot, not in GSA (raw FASTQ only), and not as accessible supplementary.
No upstream pipeline scripts / workflow / parameters are provided to
regenerate them. The notebooks therefore cannot be executed as shipped.
In scope vs out of scope (pipeline-derived results)
| Reported result | Pipeline | In scope? | Why |
|---|---|---|---|
| 6,538 mitotic scATAC cells; 30,671 peaks | APEC v1.1.0.11 scATAC + MACS2, then bespoke QC | Out | needs full APEC reprocessing of thousands of per-cell FASTQ runs + bespoke cell QC; no scripts → hard >20% |
| ~2,249 bookmarked regions (~7%) | bespoke scATAC accessibility-dynamics calc | Out | depends on absent .«path» + undocumented definition |
| R=0.94 (opening rate vs first-wave genes); R=0.70 (NF-YA vs scATAC) | notebook Fig1/Fig3 on absent .«path» |
Out | inputs (genes_scored_by_TSS_peaks.csv, etc.) not shipped |
| Trajectory / Palantir / Slingshot | scATAC pipeline | Out | inputs not shipped |
DESeq2 RNA-seq DE (results.deseq.csv) |
STAR+HTSeq+DESeq2 | Out | needs full RNA reprocessing; no scripts |
| 5,088 NF-YA binding sites (NF-YA CUT&Tag/ChIP, MACS2) | bowtie2 + MACS2 (+IDR) | IN (attempted) | well-specified standard bulk pipeline; raw data available as a small GSA subset (NFYAR1/2 + IgG); third-party-tool-on-paper's-data per BRIEF rule P16 |
Attempted reproduction (the one clear, low-cost data point)
Third-party standard pipeline on the paper's own raw data:
- Data: GSA CRA003844 runs NFYAR1=CRR609413 (26.5M PE), NFYAR2=CRR609414 (33.1M PE), IgG control iggForYA=CRR609415 (29.3M PE). 150 bp PE NovaSeq.
- Pipeline: fastp → bowtie2 (
--end-to-end --very-sensitive, GRCh38_noalt_as) → samtools filter (proper-pair, MAPQ≥30, chr1-22/X/Y, dedup) → MACS2callpeak -f BAMPE -g hs -q 0.05(NFYA rep vs IgG) → IDR(0.05) across reps. - Compared against: paper's reported 5,088 NF-YA binding sites.
- Caveats: genome build unspecified in paper (assumed GRCh38); exact MACS2 q/IDR thresholds and read-filtering not fully specified → expect partial / same-order-of-magnitude agreement, not exact. «our HPC» job 2178083.
Bottom line
The paper's own pipeline outputs are not reproducible from shipped
artifacts (plotting-only code + absent processed inputs + no upstream scripts)
→ shipped-code path = docs_insufficient. We instead reproduce one clearly
specified result (NF-YA binding sites) via a standard third-party pipeline on the
paper's raw data, as an honest partial check. We do *
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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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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