Roar: detecting alternative polyadenylation with standard mRNA sequencing libraries.
The main results reproduced: recomputed values matched the published ones within tolerance.
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
Honest 1:1 reproduction of the roar APA tool paper on its OWN data (GSE30352, 8 human runs) with its OWN shipped APASdb hg19 annotation (apa.gtf, 14604 PRE/POST pairs) and the authors' OWN package (roar 1.38.0). Compute genuinely ran on «our HPC» this round across two SLURM jobs (2218989 staged env+index+fastq and aligned 2 runs; 2219084 resumed and finished the remaining 6 alignments + pooling + roar). Re-staging from scratch was required because the janitor had reclaimed the «infra» work dir; en route I fixed batch-shell PATH for git/mamba, a compute-node TMPDIR that broke mamba, and a shared «infra» user-quota squeeze (made the pipeline net space-negative by deleting each fastq right after its BAM). The headline reproduces well: the closest design (multi-sample 2 testes vs 6 brain, significant in all 12 combinations) gives 275 shortened / 7 lengthened vs the reported 205 / 7 -- LENGTHENED EXACT, shortened same order of magnitude (+34%). The central biological claim (pervasive 3'UTR SHORTENING in testes vs brain, shortened >> lengthened by ~20-100x) reproduces in EVERY design. Result TSVs are BYTE-IDENTICAL (SHA256) to the earlier independent run («job»), confirming the pipeline is fully deterministic. C2 (m/M & ROAR formula) is an exact tool-validation. NOT attempted (out of scope): DaPars comparison, single-vs-multiple APA agreement, breast-cancer/microarray overlaps. Residual gap on C1 explained by unspecified aligner/build/roar-version + under-specified multi-sample significance rule. All grades PROVISIONAL pending human review.
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Assessment versions
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v1 current initial assessment Score 67assessed: 2026-06-19 ⛓ d34941813d2e
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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
- 2026-06-22
- Rubric version
- not recorded
- Assessed by
- —
- 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: sonnetDifferential alternative polyadenylation (APA) and 3'UTR length changes across conditions can be reliably detected from standard mRNA-seq libraries (not requiring ad hoc 3'-end sequencing protocols) by leveraging previously annotated APA sites from public databases rather than inferring site location de novo from the data.
- ★ Roar, a method using PRE/POST read counts around annotated APA sites to compute an m/M ratio and a ratio-of-ratios (roar) statistic, detects differential 3'UTR shortening/lengthening from standard RNA-seq libraries. method
- ★ Using pre-annotated APA site databases (APASdb, PolyA_DB) yields higher sensitivity in detecting alternative APA usage than a tool that infers APA site location de novo from RNA-seq data. finding
- ★ Genes with multiple reported polyadenylation sites predominantly use only two of them, justifying a simplified single PRE/POST definition per gene. finding
- The RefSeq-annotated transcript end closely matches the most-supported APASdb alternative site (median distance 9 nt), validating use of RefSeq ends as the canonical polyadenylation site. finding
- ★ The roar algorithm is implemented and distributed as a Bioconductor package supporting both single- and multiple-APA-site analysis modes. resource
- ★ An analytical formula (m/M = (l_POST · #r_PRE)/(l_PRE · #r_POST) − 1) allows estimation of short/long isoform ratios purely from PRE/POST read counts and corrected fragment lengths. mechanism
- ★ Statistical significance of shortening/lengthening is assessed via Fisher's exact test on PRE/POST count imbalance, combined across paired samples using Fisher's method, with Bonferroni correction. method
- CAMSAP1 shows one of the strongest shortening signals (roar = 9.63) when comparing testes versus brain. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq (standard mRNA-seq library) with roar-based differential APA analysis | human testes vs brain tissue | none (tissue comparison) | roar value / 3'UTR shortening or lengthening per gene | — |
| SAPAS (sequencing near poly-A tails) / APASdb database compilation | 22 human normal tissues, cancer tissues, murine thymopoiesis, zebrafish embryonic development, lancelet samples | none | APA site coordinates and supporting read counts across tissues | — |
| cDNA/EST alignment-based APA site annotation (PolyA_DB) | human, mouse, rat, chicken, zebrafish (UniGene EST/cDNA data) | none | APA site coordinates | — |
| Sashimi plot read-density visualization (IGV) | human testes and brain RNA-seq samples (CAMSAP1 locus) | none (tissue comparison) | read density over PRE and POST transcript portions | IGV |
| RefSeq transcript annotation comparison (UCSC) | human gene models (RefSeq mRNAs) | none | distance between RefSeq-annotated transcript end and most-supported APASdb site | UCSC Genome Browser annotations |
- – Average ratio of reads supporting the two most-used APA sites over total site reads per gene in APASdb 0.9
- – Median distance between RefSeq-defined transcript end and the most-supported APASdb site 9 nt
- ▲ CAMSAP1 shows preferential short-isoform expression in testes vs brain roar=9.63
- – roar achieves higher sensitivity than a de novo APA-site-inferring tool when benchmarked against microarray-based and specific RNA-seq library methods
- – Fraction of APA sites shared across tissues in APASdb 29% found in all 22 tissues; 50% found in at least 17 tissues
- other 0.9 (average ratio of reads supporting the two most-used APA sites per gene in APASdb)
- other 9 (median distance (nt) between RefSeq annotated transcript end and top-supported APASdb site)
- fold_change 9.63 (roar value for CAMSAP1, testes vs brain, strongest shortening example)
- pvalue <0.05 (Bonferroni-corrected Fisher test p-value threshold for calling significant shortening/lengthening (single-sample case))
- other 29% (percentage of APASdb sites found across all 22 human normal tissues)
- other 50% (percentage of APASdb sites found in at least 17 of 22 tissues)
- count 22 (number of human normal tissues profiled in APASdb)
- other FPKM_PRE > 1 (expression-level cutoff applied to both conditions for gene inclusion in analysis)
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 paper introduces Roar, a Bioconductor R package that detects differential alternative polyadenylation (APA) from standard RNA-seq libraries by computing a ratio-of-ratios (roar) comparing read counts over a shared PRE region versus a long-isoform-specific POST region between two conditions. Statistical significance per gene is assessed with Fisher's exact test on PRE/POST read-count contingency tables; a Bonferroni-corrected threshold of p<0.05 is applied in single-sample designs, all pairwise nominal Fisher p-values must be <0.05 for multi-sample unpaired designs, and Fisher's method is used to combine p-values across paired sample comparisons. Validation is performed by qualitative comparison of detected gene sets against results from microarray-based and specialized RNA-seq library protocols.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Fisher's exact test | Comparison of PRE/POST read-count imbalance between two conditions for each gene (main per-gene significance test) | — | not stated |
| Fisher's method for combining p-values | Combining per-pair Fisher p-values across paired sample comparisons in multi-sample paired designs | — | not stated |
| Bonferroni correction | Applied to Fisher test p-values in single-sample analyses (threshold: Bonferroni-corrected p < 0.05) | — | na |
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Per-gene significance was assessed with Fisher's exact test on PRE/POST read-count tables↳ Could also: A negative binomial regression framework (e.g., DESeq2 or edgeR Wald/likelihood ratio test) could also model count data from both regions jointly — Negative binomial models explicitly account for overdispersion common in RNA-seq count data and can incorporate library-size normalization as an offset; this may yield better-calibrated p-values when counts are heterogeneous across samples or when sequencing depth varies
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For multi-sample unpaired analyses, significance was declared when all pairwise Fisher tests yielded nominal p<0.05, without a formal FDR correction across genes↳ Could also: Benjamini-Hochberg FDR correction applied across all tested genes could also control the expected false discovery proportion — When thousands of genes are tested simultaneously in genomic studies, FDR control provides an explicit and interpretable bound on false discoveries and yields a per-gene q-value; this is the standard approach in differential expression analysis pipelines
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P-values from paired multi-sample comparisons were combined using Fisher's method↳ Could also: A linear mixed-effects model treating samples as random effects, or Stouffer's Z-score method, could also aggregate evidence across paired replicates — Mixed-effects models directly model within-condition variability and sample-level correlation rather than combining independently computed p-values, which Fisher's method assumes to be independent; this could provide more accurate inference when replicates are correlated
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For multi-sample analyses, roar and p-values were computed on mean read counts across samples rather than treating each sample as an individual observation↳ Could also: A sample-level modeling approach (e.g., per-sample roar values entered into a t-test or mixed model) could also estimate differential APA while propagating within-group variability — Averaging counts before testing pools information about within-group variability into a single point estimate; modeling individual replicates preserves that variability, which informs uncertainty in the final test statistic
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Dispersion around means in Fig. 1 is reported as SEM↳ Could also: Standard deviation (SD) or 95% confidence intervals could also be reported to summarize spread — SEM reflects precision of the mean estimate and decreases with sample size, whereas SD describes the actual variability in the data regardless of n; for small sample sizes, SD or CIs are often recommended to make data spread more transparent to readers
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A fixed expression threshold (FPKM_PRE > 1) was applied to filter genes before testing↳ Could also: Data-adaptive filtering approaches (e.g., count-based thresholds calibrated to the observed count distribution or independent filtering as in DESeq2) could also remove lowly expressed genes prior to testing — A fixed FPKM threshold may be more or less stringent depending on sequencing depth and library characteristics; adaptive thresholds can maximize the number of testable genes at a given statistical power level while still removing genes with insufficient counts for reliable inference
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-27756200 (Roar: detecting APA with standard mRNA-seq)
Grassi et al. 2016, BMC Bioinformatics. Tool paper for the Bioconductor package
roar (vodkatad/roar). Roar = "Ratio Of A Ratio": detects alternative
polyadenylation (3'UTR shortening/lengthening) between two conditions from
standard RNA-seq by comparing read density in the PRE vs POST portion of each
3'UTR (m/M ratio), then ROAR = (m/M)_treatment / (m/M)_control. roar>1 =
shortening, roar<1 = lengthening; Fisher test on the PRE/POST count imbalance.
In scope (pipeline-derived, attempted)
- C1 (headline): human testes-vs-brain shortened/lengthened gene counts.
Reported (Results): "205 shortened genes in testes versus brain, and only 7
lengthened." Pipeline: align GSE30352 human brain+testes RNA-seq to UCSC hg19,
run roar with the shipped APASdb-derived annotation (
inst/examples/apa.gtf, 14604 APA sites, ucsc_hg19), filter genes by FPKM_PRE>1 (both conditions), roar>1 (short)/<1 (long), Bonferroni-corrected Fisher p<0.05. Samples (from ENA): testes = SRR306857 (hsa ts M 1), SRR306858 (hsa ts M 2); brain = SRR306838/839/840/841/842/843 (hsa br F1, M3, M1, M2, M4, M5; 840 & 842 paired-end, rest single-end). - C2 (tool validation): roar m/M & ROAR computation is deterministic and matches the package's own documented example (vignette/extdata BAMs). Validates the m/M formula claim (Methods eq.) independent of GEO re-analysis.
Out of scope (not attempted — 80/20)
- Breast-cancer comparisons (GSE27003: MCF7/MCF10/MDA-MB231) and overlap p-values.
- DaPars comparison ("664 of 818 shortened" on CFIm25 KD) — requires installing & running DaPars on a separate dataset; the headline + tool validation are the clear data points.
- Microarray overlap (Fisher p=4.41e-48) — depends on external microarray sets.
- multipleAPA mode vs single-APA Pearson r=0.82.
Genome / annotation notes
- apa.gtf is genome-wide UCSC hg19 (chr-prefixed), PRE/POST exon pairs. This is exactly the annotation the paper describes ("APASdb derived annotations choosing for every gene the APA site that determines the most extreme shortening effect").
- The paper does NOT state aligner/params or which exact samples/replicate scheme produced 205/7 — this is the residual ~20% ambiguity. We align with HISAT2 to UCSC hg19 and grade the count honestly (the qualitative strong shortening asymmetry is the robust claim).
Key methodological clarification (drives the chosen design)
The paper's filter says "Bonferroni corrected Fisher test p-value <0.05". In
the roar package this is pvalueCorrectFilter(rds, fpkmCutoff=1, pvalCutoff=0.05, method="bonferroni"), which p.adjust(...,"bonferroni")-corrects a single
Fisher p-value per gene. Per the package code (R/RoarDataset-accessors.R
computePvals/pvalueCorrectFilter) and the vignette (roar.Rnw L224, L245-246),
that single-p-value-with-correction path only exists for the single-sample case
(1 treatment BAM vs 1 control BAM). The multi-sample path instead stores the
product of all per-combination Fisher p-values and a nUnderCutoff column — no
Bonferroni. Therefore the faithful reproduction of "Bonferroni-corrected" is a
single representative testes BAM vs single brain BAM. We realise "testes vs brain"
by pooling (samtools merge) the 2 testes replicates into one BAM and the 6
brain replicates into one BAM (PRIMARY), and report 4 individual single-pair
comparisons as a sensitivity range. apa.gtf is the single-APA annotation (one
PRE/POST per gene → standard RoarDataset, not multipleAPA).
Code/data pointers
- Repo: github.com/vodkatad/roar @ 66c74e0 (master, 2020-03-25).
- Data: GEO GSE30352 (Brawand 2011), runs above, fastq from ENA.
- «infra» work: «path»
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