Genome-wide screening of potential RNase Y-processed mRNAs in the M49 serotype Streptococcus pyogenes NZ131.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🟡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
Faithful end-to-end rebuild of the operon-prediction pipeline (R1) with the authors' OWN unmodified Python-2 code: Bowtie2 --very-sensitive-local -> samtools -> genomeCoverageBed -d -> geneBankConstruct -> operonBankConstruct -> operonBankRefine, on SRP108413/GSE99533 reads (SRR5638291-4) over the CP000829.1 genome (1,815,785 bp, 1703 CDS). RESULT = PARTIAL: mapping QC reproduces EXACTLY (>99%: 99.90-99.94% across all 4 samples) and the gene universe matches (1703 CDS), but the operon COUNT is 990 vs the reported 865 (+14.5%) -- more monocistronic (632 vs 491), fewer large (81 vs 103); di/tri within ~10%. All four unified RD operon banks agree (990) with a BED line-count cross-check. Since gene set and mapping match, the gap most plausibly comes from bowtie2/samtools version differences in per-base coverage (the operonJudge 4-fold/0.5x-dent thresholds are depth-sensitive) and/or the authors' exact Spy49_allGenes.csv vs the GFF-derived set; the paper pins no tool versions. This is a faithful partial reproduction, NOT evidence of fabrication: the distribution shape and order of magnitude are reproduced. Fixed three defects from the prior run 2180633 (invalid 'bedtools genomeCoverageBed' -> 0-byte CSVs; operon counter missing repo imports; no git in env -> git clone failed, switched to wget tarball). NOT attempted: R2 RNase-Y-processed mRNAs (Table 2, 80->29->15) -- needs GSE40198 microarray half-lives + a manual non-deterministic 'discard 14 random' triage, outside the 80% scope.
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Assessment versions
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v1 current initial assessment Score 50assessed: 2026-06-16 ⛓ 402e46d4600c
✎ 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
- 2026-06-24
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: sonnetRNase Y mediates mRNA processing events in Streptococcus pyogenes that regulate the expression of virulence factors by altering the stability of their mRNAs.
- ★ RNase Y selectively processes GAS mRNA, but its overall impact is confined to a limited set of virulence factor transcripts. finding
- ★ Combined RNA-seq and tiling microarray analysis defined 865 S. pyogenes operons and identified candidate RNase Y-processed transcripts. method
- ★ 15 mRNAs were identified as candidates for RNase Y-mediated processing based on segmental stability differences between WT and Δrny. finding
- ★ Of the candidates tested by Northern blot (folC1, prtF, speG, ropB, ypaA), only folC1 was confirmed to be processed, and this processing is unlikely to be RNase Y-dependent. finding
- ★ Deletion of rny does not affect overall S. pyogenes operon organization. finding
- The RNA-seq-based operon prediction pipeline showed 81% sensitivity and 71% specificity compared to the ProOpDB computational prediction tool. finding
- A sliding-window coefficient-of-variation method was developed to identify operon transcriptional boundaries from RNA-seq read coverage. method
- Python scripts for operon prediction and boundary identification were deposited on GitHub as a public resource. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq | Streptococcus pyogenes NZ131 (M49), WT and Δrny mutant, 2 biological replicates each | KO (Δrny) | transcript abundance, operon structure/boundaries | Illumina HiSeq 2000 |
| Tiling microarray | Streptococcus pyogenes NZ131, WT and Δrny mutant | KO (Δrny) | mRNA decay rate/half-life at subgene (probe) level | Affymetrix microarray (CEL files) |
| Northern blot | Streptococcus pyogenes NZ131 (folC1, prtF, speG, ropB, ypaA transcripts) | KO (Δrny), with WT/complement comparison | mRNA processing status (transcript size/pattern) | — |
| qRT-PCR | Streptococcus pyogenes NZ131, WT vs Δrny (11 selected genes) | KO (Δrny) | relative transcript abundance | real-time PCR |
| RT-PCR | Streptococcus pyogenes NZ131 (10 randomly selected gene pairs) | none | cotranscription/operon validation | — |
| Western blot | Streptococcus pyogenes NZ131 whole-cell lysate and extracellular (TCA-precipitated) protein | other (genotype comparison implied) | protein expression | — |
| 5' RACE | Streptococcus pyogenes NZ131 | none | transcript 5' end mapping | — |
- – 865 operons predicted in the S. pyogenes NZ131 genome (491 monocistronic, 169 dicistronic, 102 tricistronic, 103 with ≥4 CDS) 865 operons
- – 15 mRNAs identified as candidates potentially processed by RNase Y 15 mRNAs
- – Only folC1 confirmed processed by Northern blot among candidates tested; processing unlikely attributable to RNase Y
- – High consistency between RNA-seq biological replicates for WT and Δrny r2=0.857 (WT), r2=0.998 (Δrny)
- – Strong correlation between qRT-PCR and RNA-seq expression measurements across 11 genes r2=0.996
- – RNA-seq operon predictions concorded with ProOpDB for cotranscribed and monocistronic gene pairs 81% sensitivity (720 pairs), 71% specificity (627 pairs)
- – 144 gene pairs predicted cotranscribed by RNA-seq but not by ProOpDB; 10 randomly tested pairs confirmed cotranscribed by RT-PCR 10/10 confirmed
- – Median 5' UTR length across 865 operons was 43 nt median 43 nt (range 0-200 nt)
- correlation r2=0.857 (RNA-seq biological replicate consistency in WT)
- correlation r2=0.998 (RNA-seq biological replicate consistency in Δrny)
- correlation r2=0.996 (correlation between qRT-PCR and RNA-seq expression for selected genes)
- count 865 (total operons predicted in S. pyogenes NZ131)
- count 15 (mRNA candidates potentially processed by RNase Y)
- other 81% sensitivity (concordance of RNA-seq cotranscribed gene pairs with ProOpDB)
- other 71% specificity (concordance of RNA-seq monocistronic gene pairs with ProOpDB)
- other 85% (1471 boundaries) with <10bp difference (consistency of operon boundary predictions across WT and Δrny 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 study combined RNA-seq (two biological replicates per condition) and Affymetrix tiling microarray analysis to characterize the S. pyogenes transcriptome and identify mRNAs processed by RNase Y, comparing a wild-type strain to an isogenic Δrny mutant. Rather than formal inferential statistics, the primary analytical framework relied on rule-based bioinformatic criteria: fold-change thresholds for operon membership, a coefficient-of-variation sliding-window algorithm for boundary detection, and a twofold half-life difference threshold for processing candidate identification. Replicate consistency and method agreement were assessed with R² correlation coefficients, and candidate transcripts were validated by Northern blot, qRT-PCR, 5′ RACE, and Western blot.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Pearson R² correlation coefficient | Consistency between RNA-seq biological replicates (WT and Δrny) and correlation of RNA-seq vs. qRT-PCR expression levels for 11 selected genes | Two replicates per condition; 11 genes for method comparison | not stated |
| Rule-based operon prediction (fourfold expression threshold, ≤100 bp intergenic distance, same-strand, intergenic expression ≥ half of least-expressed gene) | Grouping of 1,699 protein-encoding genes into 865 operons | 1,699 CDS in S. pyogenes NZ131 genome | not stated |
| Coefficient of variation (CV) sliding-window algorithm with twofold maxCV/refCV threshold | Identification of transcriptional start and stop sites (operon boundary definition) | 865 operons; 25-bp sliding window with 1-bp step | not stated |
| Twofold half-life difference threshold (domain average vs. overall operon average) with ≥2 consecutive probe sites required | Identification of operons with segmental RNA stability (RNase Y-processed mRNA candidates) | Affymetrix tiling array with 17 probes per ORF at 14–23-base spacing; GEO: GSE40198 | not stated |
| mRNA decay rate ('steepest slope' method) | Calculation of mRNA half-life at subgene level from tiling microarray time-course data | null | not stated |
| RT-PCR (qualitative co-transcription confirmation) | Validation of 10 randomly selected gene pairs predicted as co-transcribed by RNA-seq but not by ProOpDB | 10 gene pairs | na |
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Differential mRNA abundance between WT and Δrny was assessed via visual R² correlation and fold-change criteria rather than a formal differential-expression framework↳ Could also: A count-based differential expression tool such as DESeq2 or edgeR could also have been applied to the RNA-seq read counts — These tools model negative-binomial count variation and produce per-gene adjusted p-values with FDR control, which would provide a statistically calibrated list of transcripts altered by RNase Y deletion and would make thresholding decisions more reproducible across studies
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Only two biological replicates were used per condition for RNA-seq↳ Could also: Three or more biological replicates per condition is a common recommendation for RNA-seq experiments — Additional replicates improve variance estimation, increase statistical power in differential-expression models, and reduce the influence of outlier samples; with n = 2, within-group variance is unestimable in most count-based frameworks without pooling or borrowing strength across genes
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Segmental mRNA stability differences were identified using a fixed twofold half-life threshold applied independently to each operon↳ Could also: A permutation-based or bootstrap approach to derive empirical null distributions for within-operon half-life variance could also be applied — An empirical null would allow a data-driven threshold rather than a fixed twofold cutoff, potentially improving sensitivity for transcripts with moderate processing and providing an estimate of the false-discovery rate among the 15 candidates
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Replicate consistency was reported as R² (coefficient of determination) between paired replicate expression profiles↳ Could also: Spearman rank correlation or intraclass correlation coefficient (ICC) could also be used to assess replicate agreement — Spearman r is robust to the influence of highly expressed outlier genes that can inflate R², and ICC explicitly partitions within- vs. between-replicate variance, offering complementary views of reproducibility
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Operon prediction accuracy was evaluated using sensitivity and specificity relative to ProOpDB, which was assumed to be 100% correct↳ Could also: A held-out RT-PCR validation set with precision–recall analysis, or a leave-one-out comparison against multiple independent operon databases, could also be used — Treating a computational reference as ground truth conflates errors in the reference with errors in the new method; experimental RT-PCR confirmation (as was done for 10 gene pairs) or comparison against multiple databases would give a less assumption-dependent accuracy estimate
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No multiple-testing correction was applied when screening all 865 operons for segmental stability to generate the candidate list of 15↳ Could also: A Benjamini–Hochberg FDR procedure (or analogous permutation-FDR) over the family of operon-level half-life comparisons could also be applied — Screening hundreds of operons with a fixed fold-change threshold accumulates false positives in proportion to the number of tests; an FDR-controlled procedure would make the expected proportion of false discoveries explicit and comparable across studies
Citation network
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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-29900693
Paper: Chen Z, Raghavan R, Qi F, Merritt J, Kreth J. Genome-wide screening of potential RNase Y-processed mRNAs in the M49 serotype Streptococcus pyogenes NZ131. MicrobiologyOpen 2018. PMID 29900693 · PMCID PMC6460267 · DOI 10.1002/mbo3.671.
Code: https://github.com/ZhiyunChen/RNA_Processing (commit c21ef82, master,
last pushed 2014-06-10, Python 2, no license). Repo ships code only — NO data.
The 8 input files listed in its README (wt*.csv, rny*.csv, Spy49_allGenes.csv,
SpyGenome.txt, probehalfLife*.csv) must be regenerated from primary sources.
Data (two GEO series, both linked to this PMID):
- GSE99533 — RNA-seq, Illumina HiSeq2000, 4 samples (WT×2, Δrny×2), SRA SRP108413 → runs SRR5638291 (wt1), SRR5638292 (wt2), SRR5638293 (rny1), SRR5638294 (rny2). Paired-end, ~55–61 M read pairs/sample. Feeds operon prediction.
- GSE40198 — microarray (GPL11420, Affymetrix), mRNA half-lives. Feeds the half-life / RNase-Y-processed-mRNA analysis. (Originally from PMID 23543715; reused here.)
Reference genome: S. pyogenes NZ131, GenBank CP000829.1 / assembly
GCA_000018125.1 (ASM1812v1, original Spy49_#### locus-tag annotation). 1 chromosome.
Pipeline-derived results
IN SCOPE (clearly specified, deterministic) — the 80%
R1. Operon prediction. Pipeline (paper Methods + repo):
Bowtie2 --very-sensitive-local → SAM → samtools sort/index → BAM →
bedtools genomeCoverageBed -d per-base depth → wt1/wt2/rny1/rny2.csv →
repo geneBankConstruct.py → operonBankConstruct.py → operonBankRefine.py.
Operon-grouping criteria (repo operonBankConstruct.operonJudge): two consecutive
same-data genes are split into different operons if ANY of: avg-reads >4-fold
different; an intergenic "dent" below 0.5× the lesser gene's avg read; opposite
strand; or gap >100 bp.
Reported numbers to compare against (Results §3.1 / text):
- 865 operons total
- 491 monocistronic, 169 dicistronic, 102 tricistronic, 103 with ≥4 CDS
- ProOpDB benchmark: 81% sensitivity, 71% specificity (external comparison; ProOpDB reference download is optional/secondary).
- ">99% reads mapped to the genome" (mapping QC, easy side-check).
OPTIONAL / HARDER (the ~20%, less precisely specified) — attempt only if R1 lands
R2. RNase-Y-processed mRNA candidates (repo HLShiftFinder.py +
RNaseYProcessedOperonFinder.py). Needs probe-level half-lives
(probehalfLifeWT.csv, probehalfLifeRNY.csv) derived from the GSE40198
microarray rifampicin time-course — half-life fitting is NOT specified at the level
needed for a clean rebuild, and the "segmental stability twofold" thresholds and the
manual triage (80 → 29 → discard 14 → 15 final candidates, Table 2) include
human judgement ("14 discarded as random signal variations"). Reported: 80 segmental
stabilities in WT → 29 altered in Δrny → 15 final candidates (Table 2).
Plan: report as analysed-but-not-fully-reproduced unless half-life inputs prove
trivially regenerable; the manual discard step makes an exact 15 non-deterministic.
OUT OF SCOPE (wet-lab / manual / external — not attempted)
- Northern-blot validation of folC1/speG/ypaA/prtF (wet lab).
- qRT-PCR validation (wet lab), r²=0.996.
- ProOpDB as ground-truth biology (external DB; the 81%/71% benchmark is a secondary check, not the core pipeline output).
Primary reproduction target
R1: 865 operons + the 491/169/102/103 monocistronic/di/tri/≥4 distribution, regenerated end-to-end from SRP108413 reads on the CP000829.1 genome with the authors' own (Python-2, unmodified) operon-calling code.
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 an incomplete reproduction, not a discrepant one: the authors' unmodified Python-2 operon-caller and a deterministic Bowtie2→samtools→bedtools pipeline were built and validated end-to-end (genome length 1,815,785 bp and 1,703 CDS confirmed), but the run (SLURM 2180633) was still executing at finalize, so the R1 numbers (865/491/169/102/103, >99% mapping) are all pending and never compared. The gaps are on our side (code-only repo forced self-assembly of public inputs; the run did not finish) plus one authors'-side ambiguity (the non-deterministic manual triage behind Table 2's 15 candidates, left out of scope). There is no fabrication signal — the figures are in-principle derivable from shipped code + public data — but with zero reproduced values produced, no claim can be confirmed, yielding a uniformly cautious yellow.
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.