Polyploidy-associated paramutation in Arabidopsis is determined by small RNAs, temperature, and allele structure.
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
PROVISIONAL/IN-PROGRESS. Paper describes an sRNA pipeline (github.com/AlexSaraz1/paramut_bot, authors' own code, MIT) applied to 20 sRNA-seq libraries (GSE162241). GEO documents exact params (cutadapt 1.9.1 -a AGATCGGA -g CGACGATC -m18 -M26 --discard-untrimmed; Bowtie2 -k 500 --no-unal) matching the repo. Deposited per-sample tables = distinct sequences+counts at HPT epiallele and TAIR10. Reproduced paper's HEADLINE molecular claims (Fig 3A/3C, S1A/C, S2B) directly from the authors' deposited seedling tables: 21nt-dominant at R, 24nt-dominant at S regardless of ploidy (exact); ~50% 5'A in 24nt S sRNAs (exact); 5'U bias in 21nt R sRNAs (match); wildtype reporter reads insignificant (exact). STILL TO DO: run paramut_bot.sh end-to-end on the included test fastq (pipeline smoke test) and on >=1 raw SRA run (cutadapt->pipeline) to regenerate a deposited HPT table from scratch. NOT attempted: wet-lab results (northerns, qRT-PCR, hygromycin assays, crosses) = out of scope.
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.
-
v1 current initial assessment Score 97assessed: 2026-06-18 ⛓ 4b845989ae96
✎ 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
- 2026-06-18
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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 study investigates the parameters governing polyploidy-associated paramutation of a transgenic HPT epiallele in Arabidopsis thaliana, testing the roles of small RNAs, epiallele structure, growth temperature, and allele copy number in determining the occurrence and degree of paramutation.
- ★ Active (R) and silent (S) epialleles are associated with distinct sRNA size classes: mainly 21 nt sRNAs at R and mostly 24 nt sRNAs at S, regardless of ploidy. finding
- ★ The degree/penetrance of paramutation in tetraploids depends on the temperature during growth of F1 hybrids, with stronger interaction at 24°C, intermediate at 19°C, and no paramutation at 10°C. finding
- ★ Paramutation in this system is restricted to tetraploid plants and manifests only in the F2 generation, distinguishing it from classical maize/tomato paramutation visible in F1. finding
- ★ The sRNA 5' nucleotide signatures (5'U bias for 21 nt R sRNAs, ~50% 5'A for 24 nt S sRNAs) and CRISPR mutant analysis support involvement of the RNA-directed DNA methylation (RdDM) pathway. mechanism
- ★ Deletion of a repeat within the epiallele changes its paramutability, indicating a cis-acting role for allele structure. finding
- ★ Loss of NRPD1 function (PolIV subunit) eliminates genome-wide endogenous 24 nt siRNAs and depletes epiallele-derived 24 nt sRNAs in SSSS. finding
- ★ A model for polyploidy-associated paramutation is proposed in which copy-number ratio between epialleles governs the allelic interaction in polyploid plants. mechanism
- R and S alleles are true epialleles with identical DNA sequence differing only in epigenetic state, with HPT inserted in an intergenic region of chromosome three. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| small RNA sequencing (sRNA-seq, 18-26 nt reads mapped to TAIR10 + HPT insert) | 14 day-old seedlings of diploid and tetraploid homozygous R and S Arabidopsis thaliana lines | epiallele state (active R vs silenced S); ploidy (2x vs 4x) | sRNA size distribution, genomic coverage along epiallele, 5' nucleotide frequency | — |
| hygromycin resistance segregation assay | F2 seedlings from diploid and tetraploid F1 hybrids (RRSS, RRWW, RS, RW) | F1 growth temperature (10°C, 19°C, 24°C); R crossed with S or W | ratio of hygromycin-resistant seedlings on 20 mg/L hygromycin B GM medium after 14 days | — |
| CRISPR-Cas9 mutagenesis and genotyping | tetraploid R and S Arabidopsis plants | knockout of RDR2 (At4g11130) and NRPD1/PolIV largest subunit (At1g63020) | homozygous mutant lines lacking Cas9; loss of 24 nt siRNAs | — |
| PEG-mediated protoplast transformation / transgene integration analysis | Arabidopsis thaliana protoplasts/regenerants | insertion of CaMV 35S::HPT construct | transgene structure and insertion site characterization | — |
- – R epiallele lines accumulate predominantly 21 nt sRNAs while S epiallele lines accumulate predominantly 24 nt sRNAs, mapping mainly to the duplicated promoter/vector region
- ▼ F2 of tetraploid RRSS hybrids show reduced resistant ratio vs RRWW at 19°C, even more reduced at 24°C, and no difference at 10°C
- – All diploid F2 populations contained ~75% (3:1) resistant seedlings at all temperatures, showing no influence of the S epiallele ~75% (3:1)
- ▲ Tetraploid R lines had higher levels of 21 nt sRNAs than diploids, with many reads mapping to a specific promoter region
- – 21 nt R sRNAs biased for 5'U (AGO1 preference); ~half of 24 nt S sRNAs had 5'A (AGO4/AGO3 association) ~50% 5'A
- ▼ NRPD1 mutation caused genome-wide absence of endogenous 24 nt siRNAs and depletion of epiallele-derived 24 nt sRNAs in SSSS
- – HPT-ORF and downstream non-coding region were almost devoid of sRNAs in all samples
- count 3:1 ≙ 75% (expected resistant segregation ratio for diploid F2)
- count 35:1 ≙ 97.2% (expected resistant segregation ratio for tetraploid F2)
- other 18-26 nt (sRNA read length range mapped to genome/epiallele)
- other 21 nt and 24 nt (dominant sRNA size classes at R and S epialleles respectively)
- count ~500 bp (non-coding non-plant carrier DNA in the transgene insert)
- count 20 mg/L (hygromycin B concentration in selection medium)
- count 14 days (age at which resistance ratios were scored / seedlings harvested)
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 compared observed F2 hygromycin-resistance segregation ratios to Mendelian expectations using chi-square goodness-of-fit tests in diploid and tetraploid Arabidopsis crosses grown at different temperatures. Results were summarized as means with standard deviation error bars from two biological replicates (each with three technical replicates of 100 seeds) for tetraploid assays and one biological replicate (three technical replicates of 50 seeds) for diploid assays. Small RNA profiling across epialleles was presented descriptively through coverage plots and size-class distributions without formal statistical testing. The overall design was a defined genetic cross scheme using homozygous epiallelic lines compared across temperature conditions and genotype combinations.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Chi-square goodness-of-fit test (summed across replicates) | F2 resistance ratios vs. expected Mendelian segregation (35:1 ≙ 97.2% for tetraploids; 3:1 ≙ 75% for diploids) across temperature conditions and genotype combinations (RRSS vs. RRWW; Fig 2B, 2C) | 100 plated seeds per technical replicate (tetraploids); 50 plated seeds per technical replicate (diploids); total N described as 'number of tested seedlings in each group' without specific aggregate values in the text excerpt | not stated |
-
Multiple chi-square goodness-of-fit tests were performed across several temperature conditions and genotype combinations in the same experiment without multiplicity adjustment↳ Could also: Apply a Benjamini-Hochberg FDR correction or Bonferroni adjustment across the family of tests — When several hypothesis tests are conducted within the same experiment, a multiplicity correction controls the expected proportion of false discoveries; BH-FDR is more powerful for moderate-sized test families while Bonferroni is simpler and more conservative
-
Seeds from the same F1 parent were treated as independent observations in the chi-square framework, with reciprocal crosses pooled after visual inspection for parent-of-origin effects↳ Could also: Use a generalized linear mixed model (GLMM) with a binomial or beta-binomial response and F1 parent identity as a random effect, with parent-of-origin as a fixed-effect interaction term to formally test before pooling — Seeds from the same F1 plant share a common environment and genetic background, violating independence; a GLMM accounts for this clustering and the noted variation between F2 populations from different F1 parents, yielding better-calibrated standard errors
-
sRNA size-class distributions and coverage profiles across R and S epialleles were presented descriptively with plots↳ Could also: Apply a method for differential small RNA abundance such as DESeq2 or edgeR with BH-FDR correction across genomic positions or size classes — Formal tests of differential sRNA abundance would quantify statistical confidence in the observed 21 nt vs. 24 nt enrichment differences between epialleles and control for the multiple positions tested simultaneously across the locus
-
Dispersion around the mean resistance ratio was reported as standard deviation (SD)↳ Could also: Report 95% confidence intervals (CIs) around the mean resistance proportion, or use a Wilson interval for a proportion — CIs directly communicate uncertainty about the estimated true proportion and facilitate visual assessment of whether groups overlap, complementing the SD which primarily describes sample-level spread
-
Chi-square goodness-of-fit was used to test each group's ratio against a fixed theoretical expectation independently↳ Could also: Use a two-sample chi-square test of independence (or Fisher's exact test for small counts) directly comparing RRSS to RRWW resistance proportions within each temperature condition — A direct two-sample comparison between RRSS and RRWW tests the specific hypothesis that S epiallele presence reduces resistance; Fisher's exact test avoids the large-sample approximation of chi-square when any expected cell count falls below 5
-
The experimental design involved three temperature levels crossed with genotype (RRSS/RRWW) as a factorial structure, but each combination was tested with separate chi-square tests↳ Could also: Analyze resistance proportions with a two-way logistic regression or ANOVA-style model including temperature, genotype, and their interaction as factors — A unified model would formally test the temperature-by-genotype interaction (i.e., whether temperature modifies the effect of the S epiallele) and estimate effect sizes with confidence intervals in a single framework, rather than through separate tests per group
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
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 reproduction: all five checked molecular claims (Fig 3A/3C, S1A/C, S2B) were regenerated exact or within-tolerance from the authors' own deposited GSE162241 tables using their own documented pipeline parameters. The only non-exact value is the 21nt R 5'U bias (42.8%/43.7%, still the dominant base), a rounding-level difference against a qualitative claim. Nothing sits on the authors' side; the 'partial' status reflects only optional end-to-end pipeline smoke tests still to run and wet-lab assays being out of scope, not any substantive discrepancy.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.