ChIP-seq Data Processing and Relative and Quantitative Signal Normalization for Saccharomyces cerevisiae.
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
DESCRIBED WELL ENOUGH — full 1:1. Protocol paper (Alavattam et al., Bio-protocol 2025; ChIP-seq signal normalization for S. cerevisiae). Reproducible pipeline outputs are the normalization scaling coefficients. The repo (kalavattam/protocol_chipseq_signal_norm @958de78, MIT) ships an example bundle with both inputs and computed outputs for 12 samples. Ran the AUTHORS' OWN scripts (calculate_scaling_factor_alpha.py eqn=6nd; calculate_scaling_factor_spike.py) on the shipped inputs: ALL 12 siQ-ChIP alpha (C1) and ALL 12 spike-in sf (C2) match to rel_err 0.0 (full double precision). Genome length C3 = 12,157,105 bp confirmed (public R64 chrom lengths: 12,071,326 nuclear + 85,779 mito). 25/25 claims exact. Fabrication check PASS: every shipped coefficient is exactly derivable from shipped inputs via the documented equations. NOT ATTEMPTED (deliberate ~20%): full FASTQ->BAM alignment of the ~24 GSE288548 libraries to re-derive the depth/fragment-length/read-count inputs (heavy; no independent reference values printed by the paper); qualitative IGV browser-track figures (Figs 1-2); wet-lab masses (Table 5). HONEST FRAMING: this reproduces the normalization step on the protocol's own intermediates (deterministic) and confirms the example bundle is internally consistent, not a re-run of the entire pipeline. HUMMEL NOTE: core reproduction is deterministic stdlib arithmetic (control-plane class, not heavy compute); a best-effort «our HPC» job (run.sbatch: clone@pin on «infra», pinned conda env, FASTA-extraction genome cross-check) is staged + a poller is live, but the VPN tunnel needs a one-time operator SAML+2FA that has not yet occurred. The result does not depend on it.
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 100assessed: 2026-06-15 ⛓ 7fc55da36945
✎ I am an author of this paper
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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
- 2026-06-15
- 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: opusThis protocol does not test a hypothesis; it provides a reproducible bioinformatics workflow for ChIP-seq data processing in Saccharomyces cerevisiae, arguing that sans-spike-in quantitative ChIP-seq (siQ-ChIP) and normalized coverage are mathematically rigorous, more reliable alternatives to spike-in normalization for absolute and relative comparisons of ChIP-seq signal.
- ★ siQ-ChIP measures absolute protein–DNA interaction (IP efficiency) genome-wide without relying on exogenous spike-in chromatin, overcoming limitations of spike-in normalization. method
- ★ Normalized coverage enables relative comparisons of ChIP-seq data within and between samples. method
- ★ siQ-ChIP and normalized coverage are mathematically rigorous and more effective tools than semiquantitative spike-in scaling for ChIP-seq analysis. finding
- ★ The protocol provides a reproducible ChIP-seq data processing workflow (acquisition, trimming, alignment, processing, signal computation) for Linux and macOS, accessible to researchers with minimal bioinformatics experience. resource
- ★ An accompanying GitHub repository (protocol_chipseq_signal_norm) supplies driver/utility scripts, functions, and a workflow.md notebook implementing the workflow. resource
- siQ-ChIP introduces no additional experimental requirements beyond those inherent to ChIP-seq, instead highlighting factors like antibody behavior, chromatin fragmentation, and input quantification. mechanism
- Using FLAG-tagged endogenous (S. cerevisiae) and spike-in (S. pombe) proteins promotes uniform antibody affinity across chromatin sources, minimizing binding-efficiency variability. method
- The methods are broadly applicable to ChIP-seq data from any organism, including H. sapiens and M. musculus, not just S. cerevisiae rDNA. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| ChIP-seq (quantitative, siQ-ChIP) | S. cerevisiae strains yTT6336/yTT6337 (HHO1-2L-3FLAG, Hho1/histone H1) | FLAG epitope tagging; cell-cycle states G1, G2/M, Q | genome-wide protein–DNA interaction signal / absolute IP efficiency | mouse monoclonal anti-FLAG M2 antibody (Sigma, F1804) |
| ChIP-seq (quantitative, siQ-ChIP) | S. cerevisiae strains yTT7750/yTT7751 (HMO1-2L-3FLAG, Hmo1/HMG protein) | FLAG epitope tagging; cell-cycle states G1, G2/M, Q | genome-wide protein–DNA interaction signal / absolute IP efficiency | anti-FLAG M2 antibody (Sigma, F1804) |
| ChIP-seq spike-in control | S. pombe strain Sphc821 (abp1-3FLAG, ARS binding protein 1 / CENP-B homolog) | FLAG epitope tagging | spike-in normalization reference signal | anti-FLAG M2 antibody (Sigma, F1804) |
| High-throughput DNA sequencing (NGS) read alignment | concatenated S. cerevisiae (S288C, R64-5-1) and S. pombe (972h-) reference genomes | none | aligned reads/BAM files for signal track generation | Bowtie2 2.5.4 |
| FASTQ adapter and quality trimming | ChIP-seq sequencing reads | none | trimmed reads | Atria 4.0.3 (Julia 1.8.5) |
| Signal track generation and visualization | S. cerevisiae / S. pombe alignments | none | genome-wide ChIP-seq signal tracks with gene/feature annotations | Samtools 1.21; IGV 2.19.1 |
- – Spike-in controls often fail to reliably support comparisons within and between ChIP-seq samples, motivating siQ-ChIP/normalized coverage.
- – ChIP-seq experimental parameters (input/IP chromatin masses) were measured per sample to compute siQ-ChIP α proportionality constants.
- ▲ Quiescent (Q) Hho1 samples showed markedly higher IP chromatin masses (e.g., 116.9 and 70.6 ng) than G1/G2M samples (2.7–6.6 ng).
- other vol_in = 20 µL; vol_all = 300 µL (ChIP-seq input and total volumes (constant across all 12 S. cerevisiae samples))
- mean mass_ip = 2.7 and 5 ng (G1 Hho1, yTT6336/yTT6337) (IP chromatin mass, Hho1 G1 state)
- mean mass_ip = 116.9 and 70.6 ng (Q Hho1, yTT6336/yTT6337) (IP chromatin mass, Hho1 quiescent state)
- mean mass_in range 32.4–106.6 ng (input chromatin masses across all S. cerevisiae ChIP-seq samples)
- other GSE288548 (GEO accession for ChIP-seq datasets)
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 protocol/methods paper describing a bioinformatics workflow for ChIP-seq data processing in Saccharomyces cerevisiae; it does not present primary statistical analyses or hypothesis tests. The paper's central quantitative contribution is the introduction and advocacy of the siQ-ChIP normalization method (which computes absolute IP efficiency from physical experimental parameters) alongside normalized coverage for relative comparisons, contrasted with spike-in normalization. Experimental ChIP-seq data from two proteins (Hho1, Hmo1) across three cell-cycle states (G1, G2/M, Q) with n=2 biological replicates per condition are used as illustrative examples throughout the workflow. No inferential statistics, p-values, or formal comparisons between conditions are reported.
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Adapter and quality trimming is performed with Atria↳ Could also: Trimmomatic, fastp, or Cutadapt are widely used alternatives for adapter and quality trimming of paired-end sequencing data — fastp in particular offers comparable or faster performance with built-in QC reporting; Trimmomatic has a longer publication record and broad community familiarity, which may ease reproducibility in some settings
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Read alignment is performed with Bowtie2 against a concatenated S. cerevisiae + S. pombe reference↳ Could also: BWA-MEM or BWA-MEM2 are commonly used short-read aligners for ChIP-seq and produce similarly formatted BAM output — BWA-MEM can be preferred for longer reads or when sensitivity to structural variants is desired; comparing aligners on the same dataset can also serve as a reproducibility check
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Quantitative normalization uses siQ-ChIP, which derives absolute IP efficiency from physical experimental parameters (input/IP masses and volumes)↳ Could also: Spike-in normalization (e.g., adding a fixed amount of exogenous S. pombe chromatin) is the most widely used alternative for cross-sample scaling — Spike-in normalization requires no additional mathematical modeling beyond a scaling factor, making it conceptually straightforward; the paper itself discusses it for context, noting that siQ-ChIP addresses limitations of spike-in approaches when exogenous chromatin behaves inconsistently
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Relative comparisons use 'normalized coverage' (a within-sample scaling approach)↳ Could also: Reads Per Kilobase per Million mapped reads (RPKM/FPKM) or counts per million (CPM) are standard library-size normalization approaches used for relative ChIP-seq comparisons — CPM/RPKM are widely implemented in existing tools (deepTools bamCoverage, bedtools) and are familiar to many users; they provide an easy reference point for comparing the protocol's normalized coverage outputs against published datasets that used these conventions
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The workflow uses n=2 biological replicates per condition for demonstration↳ Could also: For studies making formal comparisons between conditions, n≥3 biological replicates per group is a widely recommended minimum, enabling use of tools such as DiffBind or ChIPseeker with appropriate statistical models (e.g., DESeq2 or edgeR-based differential binding analysis) — Larger n supports variance estimation and allows detection of differential binding with controlled false discovery rates; this is a standard consideration when moving from protocol demonstration to hypothesis-driven experiments
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Signal is visualized and compared by genome browser inspection in IGV↳ Could also: Quantitative differential binding analysis tools such as DiffBind (which integrates DESeq2 or edgeR) or MACS2/MACS3 peak calling followed by count-based differential analysis could be applied to identify statistically supported enrichment differences between conditions — Formal statistical testing with FDR control provides reproducible, quantitative calls of differential enrichment across cell-cycle states or protein targets, complementing visual inspection of signal tracks
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 — pmid-40364978
Paper: Alavattam KG, Dickson BM, Hirano R, Dell R, Tsukiyama T. ChIP-seq Data Processing and Relative and Quantitative Signal Normalization for Saccharomyces cerevisiae. Bio-protocol 2025;15(9):e5299. PMID 40364978 · PMCID PMC12067309 · DOI 10.21769/bioprotoc.5299.
Code: https://github.com/kalavattam/protocol_chipseq_signal_norm
pinned commit 958de7800ad11d3aeedd5149c56b40af906d0c50 (2026-03-30).
Data: GEO GSE288548 (FLAG-tagged Hho1/Hmo1 ChIP-seq in S. cerevisiae,
S. pombe abp1-3FLAG spike-in). License: MIT (code).
This is a protocol/methods paper. Its "results" are a demonstration of the described ChIP-seq processing + signal-normalization workflow on the authors' own data (browser tracks at the rDNA locus, Figs 1–2). The reproducible, pipeline-derived quantities are the signal-normalization scaling coefficients the protocol computes, and the processed reference-genome length. The repo ships an example results bundle containing both the inputs and the computed outputs, which makes a clean, deterministic 1:1 reproduction possible without re-running the heavy alignment.
IN SCOPE (pipeline-derived, attempted)
| # | Result | Pipeline / script | Where reported |
|---|---|---|---|
| C1 | 12 siQ-ChIP α scaling factors (equation 6nd) for WT G1/G2M/Q × Hho1/Hmo1 × 2 reps | scripts/calculate_scaling_factor_alpha.py (α = [mass_ip·len_in·vol_in] / [mass_in·len_ip·(vol_all−vol_in)]) |
repo data/processed/.../tables/example_…_siq.tsv (alpha col); protocol §siQ-ChIP, eqn 6 (PMID 37160995) |
| C2 | 12 spike-in scaling factors sf for the same samples |
scripts/calculate_scaling_factor_spike.py (sf = ratio_in/ratio_ip, ratio = spike/(main+spike)) |
repo …/example_…_spike.tsv (sf col) |
| C3 | Processed S. cerevisiae (S288C R64-5-1) genome length = 12,157,105 bp | genome-prep step (download_process_fasta_gff3.md) → data/genomes.tar.xz |
repo validate_siq_chip.md ("12,157,105 bp total") |
C1 and C2 are computed by the authors' own scripts run on the shipped input
columns of the example tables; outputs are compared to the shipped alpha/sf
columns. C3 sums sequence lengths in the shipped processed FASTA.
OUT OF SCOPE / not attempted (the hard ~20%)
- Full FASTQ→BAM alignment (Atria trim → Bowtie2 global, --mapq 1, flag 2 →
filter → sc/sp split) to regenerate the upstream intermediates
dep_ip, dep_in, len_ip, len_in(siq) andnum_mp, num_sp, num_mn, num_sn(spike). These are taken as given from the shipped example tables. Re-deriving them means downloading ~24 GSE288548 libraries and many CPU-hours of alignment, and the paper prints no independent reference values for them beyond the example tables themselves. The normalization step the protocol actually contributes (α/sf) is fully reproduced given those inputs — this is the deliberate 80/20 line. - Browser-track figures (Fig 1 norm/log2(IP/input); Fig 2 spike/siQ tracks at
chrXII 451,250–468,750).
validate_siq_chip.mdstates validation is a qualitative IGV comparison — no numeric assertion to grade. - Wet-lab quantities (Table 5 chromatin masses/volumes, ChIP benchwork) — manual/experimental, not pipeline-derived.
Honesty note (scope of the C1/C2 claim)
Reproducing α/sf from the shipped inputs verifies that (a) the documented normalization equations are implemented correctly and deterministically and (b) the shipped example outputs are internally consistent with the shipped inputs (a fabrication check on the example bundle). It does not re-verify the upstream alignment that produced those inputs. Stated precisely in AUDIT.md.
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 protocol paper whose reproducible outputs are signal-normalization scaling coefficients. Running the authors' own unmodified scripts (calculate_scaling_factor_alpha.py --eqn 6nd, calculate_scaling_factor_spike.py) on the repo's shipped example inputs reproduced all 24 coefficients to full double precision (rel_err 0.0) and confirmed the processed genome length of 12,157,105 bp against the known R64 total. Every shipped value is exactly derivable from the shipped inputs (fabrication check PASS), so there is no deviation on any side. The only honest caveat is scope — this reproduces the deterministic normalization step on shipped intermediates and confirms internal consistency, not the full FASTQ->BAM pipeline — but it does not affect the central claim, which 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
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.