Comparative transcriptome analysis of tomato (Solanum lycopersicum) in response to exogenous abscisic acid.
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.
- ✓Same input data as the authors
- 🟡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
Pipeline-derived reproduction of pmid-24289302 (tomato ABA transcriptome, PMC4046761): raw read counts for both SRA accessions (SRR926182=a1d, SRR926185=c1d) exactly match the paper's Table 1, resolving their condition mapping with high confidence. Adapter trimming (SeqPrep, the paper's own tool), genome alignment (HISAT2 substituting TopHat/SOAPaligner), and transcript assembly (StringTie substituting Cufflinks+Trinity) were all executed on the «our HPC» HPC cluster and produced results in the same qualitative range as the paper (high alignment rates, similar order-of-magnitude transcript/gene counts) but not numerically identical, for well-understood methodological reasons documented per-claim. Differential expression (edgeR) and functional annotation (Blast2GO/STRING/KEGG) were not attempted: the former due to a genuine R/Bioconductor unavailability on the cluster, the latter because those tools are proprietary/GUI-only and not batch-automatable. No claim is asserted as a perfect match; all comparisons are left for human review.
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- Reproduced
- 2026-07-30
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-07-31no human curator yet
- Last updated
- 2026-07-31
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: opusThe study asks how exogenous abscisic acid (ABA) reshapes genome-wide gene expression in tomato (Solanum lycopersicum) leaves, testing whether ABA-regulated transcripts reveal mechanisms by which ABA could enhance pathogen resistance and abiotic stress tolerance in this model fruit crop, for which little genome-wide ABA-response information existed.
- ★ Exogenous ABA alters the expression of a majority (54.73%) of expressed tomato leaf transcripts, with 2,787 significantly differentially expressed genes, predominantly up-regulated. finding
- ★ Exogenous ABA acts through the canonical ABA signaling pathway centered on PYR/PYL/RCARs-PP2Cs-SnRK2s(-ABFs), affecting members of all four component families. mechanism
- ★ The tomato transcriptome contains 18 PYL receptor genes, more than the 14 members reported in Arabidopsis thaliana, enriching the PYL receptor gene family. finding
- ★ ABA up-regulates large numbers of genes for transcription factors, heat shock proteins, pathogen resistance, and the salicylic acid, jasmonic acid and ethylene signaling pathways, indicating ABA has potential to improve pathogen resistance and abiotic stress tolerance in tomato. finding
- PP2Cs behave as negative regulators of ABA signaling, consistent with three PP2C transcripts being significantly repressed by ABA treatment. mechanism
- Only a small fraction of SnRK2s participate in signaling under exogenous ABA; the single induced SnRK2 (SAPK8-like) belongs to ABA-strongly-activated subclass III. mechanism
- ABFs act mainly as positive regulators of the ABA response (7 of 18 up-regulated vs 3 down-regulated), though expressed at low levels in both libraries. finding
- ★ The work provides an annotated tomato leaf transcriptome resource (50,770 transcripts with GO, COG and KEGG annotation) as a database for investigating ABA-induced gene function. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Illumina RNA-sequencing (paired-end, 101 bp reads) | Tomato (Solanum lycopersicum L.) seedlings, young third leaves | Foliar spray with 7.58 μmol L-1 ABA solution, leaves collected 24 h later (sample a1d) | Transcript abundance (FPKM), differential expression vs control | Illumina HiSeq 2000; Solexa paired-end sequencing |
| Illumina RNA-sequencing (paired-end, 101 bp reads) | Tomato (Solanum lycopersicum L.) seedlings, young third leaves | none (untreated control, sample c1d, collected at 1 d) | Transcript abundance (FPKM), baseline expression | Illumina HiSeq 2000; Solexa paired-end sequencing |
| Read alignment and transcript assembly/merging (bioinformatic) | Tomato reference genome | none | Mapping rate, number of genes and transcripts, novel isoforms/intergenic transcripts | SOAPaligner/soap2 (two base mismatches allowed); Cuffmerge |
| Functional annotation and Gene Ontology classification (bioinformatic) | Assembled tomato leaf transcripts | none | Number of transcripts annotated and assigned to GO functional groups | Blast2GO version 2.3.5 |
| Orthology-based functional classification (bioinformatic) | Assembled tomato leaf transcripts | none | Transcripts assigned to COG functional categories | STRING 9.0 / COG database |
| Pathway annotation (bioinformatic) | Assembled tomato leaf transcripts | none | Transcripts assigned to KEGG pathways | KEGG database (sequence similarity) |
| Open reading frame prediction and homology search (bioinformatic) | Assembled tomato leaf transcripts | none | Transcripts with predicted ORFs; homologs in NCBI non-redundant protein database | NCBI NR database; NCBI blastx |
- – Of 39,671 expressed transcripts, 21,712 (54.73%) changed expression after exogenous ABA (14,559 up, 7,153 down); 2,787 were significant DEGs (1,952 up, 835 down) 54.73% altered; 2,787 DEGs (12.84% of 21,712)
- – Sequencing produced 266.98 million raw reads (26.96 Gb) and 212.78 million clean reads (20.95 Gb); 81.97% of clean reads mapped to the tomato genome (36.53% unique, 45.44% multi-position) 173,589,477 total alignments; 81.97%
- – Transcriptome assembly and merging yielded 37,633 genes and 51,606 transcripts; 50,770 transcripts (≥150 bp) retained, of which 47,877 described in the tomato genome, 42,583 with NR homologs, 45,704 (90.02%) with ORFs 50,770 transcripts; 90.02% with ORFs
- – 31,107 transcripts assigned to 57 GO functional groups, 18,885 to 25 COG categories, and 14,371 to 310 KEGG pathways
- – ABA signaling components identified: 18 PYLs, 23 PP2Cs, 12 SnRK2s, 18 ABFs; PYL expression changed only slightly (0.55- to 1.43-fold, 5 up / 5 down, 8 unchanged) 0.55–1.43 fold for PYLs
- – Three PP2C transcripts were significantly down-regulated by ABA and one lowly expressed PP2C was up-regulated 2.05-fold down 2.42-, 3.92- and 7.22-fold; up 2.05-fold
- – Among 12 SnRK2 transcripts, one subclass III SAPK8-like transcript was induced (4.41→5.34 FPKM) and four were repressed 4.41 → 5.34 FPKM (induced)
- – 1,682 transcripts were expressed only in the ABA-treated library and 1,045 only in the control, with 36,944 shared
- count 266,982,856 raw reads (26,955,268,456 bp); 211,779,690 clean reads (20,953,102,579 bp) (Sequencing output across c1d control and a1d ABA-treated libraries)
- other 81.97% total alignment (36.53% unique, 45.44% multi-position, 18.03% unmatched) (Mapping of clean reads to the tomato reference genome)
- count 2,787 DEGs (1,952 up-regulated, 835 down-regulated) at |log2FC| ≥ 1 and FDR < 0.05 (Significantly differentially expressed transcripts, ABA vs control)
- other 21,712 of 39,671 = 54.73% of expressed transcripts altered (|log2FC| ≥ 0.25) (Overall transcriptional response to exogenous ABA)
- fold_change 7.22-fold decrease (TCONS_00030068, 2.12 → 0.29 FPKM); 3.92-fold (TCONS_00012476, 16.86 → 4.30 FPKM); 2.42-fold (TCONS_00014686, 13.79 → 5.70 FPKM) (Down-regulated PP2C transcripts under ABA treatment)
- fold_change 2.05-fold increase (TCONS_00014920, 0.37 → 0.74 FPKM) (Up-regulated PP2C transcript under ABA treatment)
- count 18 PYLs, 23 PP2Cs, 12 SnRK2s, 18 ABFs; TF families included 193 MYB, 135 bHLH, 107 bZIP, 81 WRKY, 63 NAC, 61 MADS-box, 55 AP2/ERF, 41 HSF (ABA signaling and transcription factor genes detected in the tomato transcriptome)
- other FPKM distribution: <1 = 28.87%, 1–10 = 44.64%, 10–100 = 23.49%, 100–1000 = 2.73%, >1000 = 0.26% (Expression level distribution of all detected transcripts)
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 used Illumina RNA-sequencing to generate a comparative transcriptome of tomato leaves 24 hours after exogenous ABA treatment versus control, with one sequencing library per condition (c1d control, a1d ABA-treated). Reads were mapped with SOAPaligner/soap2 and transcripts assembled with Cufflinks/Cuffmerge; differentially expressed genes (DEGs) were called using a fold-change threshold (|log2 fold-change| ≥ 1) combined with a false discovery rate (FDR) cutoff (<0.05). Functional characterization used Blast2GO, STRING/COG, and KEGG pathway annotation, and results were reported primarily as FPKM values, fold-changes, and DEG counts rather than with variability statistics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Fold-change (|log2FC| ≥ 1) combined with false discovery rate (FDR < 0.05) thresholding to call differentially expressed genes; the specific underlying statistical/count model is not stated in the provided text | Genome-wide comparison of transcript expression between control (c1d) and ABA-treated (a1d) leaf transcriptomes, and for individual gene families (e.g., PYL, PP2C, SnRK2, ABF) | One RNA-seq library per condition (c1d and a1d), each representing pooled leaves from randomly-selected plants | not stated |
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Differential expression was assessed from a single RNA-seq library per condition (one control, one ABA-treated).↳ Could also: A design with multiple independent biological replicates per condition, analyzed with count-based tools such as DESeq2 or edgeR — would let the model estimate between-replicate biological variance directly and apply a variance-aware significance test (e.g., a Wald or likelihood-ratio test) rather than relying on a fold-change/FDR filter from single libraries
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DEGs were defined using a fixed fold-change cutoff (|log2FC| ≥ 1) combined with an FDR threshold.↳ Could also: A generalized linear model approach (e.g., negative binomial models as used in DESeq2/edgeR) that incorporates count variance and library-size normalization into the significance test itself — can provide a probabilistic significance measure tied to estimated variance for each transcript, which is informative when comparing genes across a wide range of expression magnitudes
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No measures of variability (e.g., SD, SEM, CI) were reported alongside FPKM expression values or fold-changes.↳ Could also: Reporting confidence intervals or standard errors for expression/fold-change estimates, typically derived from replicate-based dispersion estimates — would convey the precision of expression differences in addition to the point estimates already reported
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Significance was reported as an FDR threshold (<0.05) rather than as exact (adjusted) p-values for individual transcripts.↳ Could also: Reporting exact adjusted p-values alongside the significance threshold — allows readers to gauge the relative strength of evidence across genes rather than only a binary significant/non-significant classification
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The comparison captured a single time point (24 h after ABA treatment) with one library per condition.↳ Could also: A time-course design with replicate libraries at multiple time points, analyzed with time-series-aware differential expression methods (e.g., maSigPro, ImpulseDE2) — could characterize the dynamics of the ABA transcriptional response over time, complementing the single-time-point snapshot presented here
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Key gene-family expression changes (e.g., PYL, PP2C, SnRK2, ABF) were interpreted from RNA-seq FPKM fold-changes without a stated independent validation step.↳ Could also: Independent validation of selected differentially expressed genes using qRT-PCR — provides an orthogonal confirmation of RNA-seq-derived expression differences for genes highlighted as biologically important
What was reproduced
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Data identity is exemplary: direct gunzip+line-count of SRR926182/SRR926185 reproduced the paper's Table 1 raw counts exactly (140,109,218 / 126,873,638 / 266,982,856) and even resolved the accession→condition mapping the paper leaves implicit. From there the reproduction diverges for reasons that sit mostly on our side: SeqPrep run in merge mode, HISAT2 substituted for SOAPaligner/TopHat (96.16% vs 81.92% alignment), StringTie substituted for Cufflinks+Cuffmerge+Trinity (47,355 vs 50,770 transcripts) — all order-of-magnitude consistent but never numerically comparable. One genuine authors'-side gap exists: the unreleased in-house Q25 Perl filter means the clean-read counts (111,323,699 / 100,455,991) are not derivable, since SeqPrep alone discards <0.11% of pairs. Critically, the paper's actual findings were never tested — edgeR (2,787 DEGs) failed on a cluster environment gap and Blast2GO/STRING/KEGG are proprietary/GUI-only — so this is a well-documented partial reproduction of the upstream pipeline, not evidence for or against the paper's biology.
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