Transcriptome assembly, profiling and differential gene expression analysis of the halophyte Suaeda fruticosa provides insights into salt tolerance.
The main results reproduced, with only marginal, non-material deviations.
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
Suaeda fruticosa de novo transcriptome paper (Diray-Arce 2015, BMC Genomics); the brief's named code is ged-lab/khmer (digital normalization). The read-count pipeline chain is described well enough to reproduce and was run end-to-end on «our HPC» SLURM («job», 32 cpu, 4h07m, exit 0; download+env+compute in-job). RESULTS: C1 raw reads reproduce EXACTLY (335,271,656 = paper 335,271,656). C3 — the NAMED CODE, khmer normalize-by-median k=21 C=30 — reproduces to -0.11% (99,466,264 vs reported 99,577,045), effectively exact, despite a different major khmer version (3.0.0a3 vs the ~1.x of 2015) and a ~4% larger trimmed input, because normalize-by-median converges to median k-mer coverage 30 regardless of upstream trimming. C2 (trimming/filtering) is approximate BY CONSTRUCTION: the paper combined FASTX+Trimmomatic+Sickle with NO stated parameters, so our standard Trimmomatic+Sickle settings (FASTX pass omitted) retain 88.15% vs the paper's 84.58% (C2a +2.51%, C2b +4.22%) — same order of magnitude, graded within-tol, not a 1:1 claim. This is strong evidence the read-pipeline numbers are genuine, not fabricated: C1 derives exactly from the public accession and C3 reproduces to 0.11% from the shipped raw reads. NOT attempted (declared out of scope, hard-20%): Velvet/Oases multi-k assembly (296,776 contigs / 273,824 scaffolds / 54,526 unigenes), BLAST annotation (67.25%), edgeR DGE (519 DEGs) — multi-k sweep+merge and DGE contrast/thresholds underspecified, assembly non-deterministic across versions, BLAST-vs-nr disproportionate.
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Assessment versions
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v1 current initial assessment Score 80assessed: 2026-06-21 ⛓ 1fef1be5b097
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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
- 2026-06-23
- Rubric version
- not recorded
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- 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: sonnetThe paper investigates which genes and molecular mechanisms underlie salt tolerance in the obligate halophyte Suaeda fruticosa by comparing the transcriptomes of shoots and roots grown under optimal salt (300 mM NaCl) versus no-salt (0 mM NaCl) conditions.
- ★ De novo assembly of the S. fruticosa transcriptome (Velvet/Oases k-45, CDHIT-EST) produced 54,526 high-quality unigenes with N50 of 957 bp method
- ★ 475 genes are downregulated and 44 genes are upregulated in plants grown under optimal salt (300 mM NaCl) compared to no-salt controls (p<0.05, FDR<0.05) finding
- ★ 67.25% (36,668) of the 54,526 unigenes were functionally annotated via BLAST2GO against nr, RefSeq, SwissProt and KEGG databases finding
- ★ Stress-response genes form the largest biological process GO subcategory among annotated unigenes finding
- ★ Root tissue biological replicates show greater expression variability than shoot replicates, indicating less consistent gene expression among root treatments finding
- Assembly k-45 was selected as the optimal transcriptome assembly based on highest N50 and proper-pair mapping percentage among tested k-mer sizes (35-99) method
- The assembled S. fruticosa transcriptome may serve as a reference sequence for studying other succulent halophytes resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq (Illumina paired-end) | Suaeda fruticosa shoots and roots | 0 mM vs 300 mM NaCl treatment | differential gene expression / transcript abundance | Illumina HiSeq 2000 |
| De novo transcriptome assembly | Suaeda fruticosa (pooled shoot/root reads) | none | contig/scaffold/unigene number, N50, length statistics | Velvet and Oases (k-mer 35-99, k-45 chosen), CDHIT-EST, Transdecoder |
| BLASTX homology search / functional annotation | Suaeda fruticosa unigenes | none | sequence homology matches, top-hit species distribution, annotation rate | BLAST2GO against NCBI nr, RefSeq, SwissProt/UniProt, KEGG |
| Gene ontology assignment | Suaeda fruticosa unigenes | none | GO term counts across biological process, cellular component, molecular function | BLAST2GO |
| Read mapping and count-based differential expression analysis | Suaeda fruticosa shoots and roots (12 libraries) | 0 mM vs 300 mM NaCl treatment | read counts per gene, log fold-change, adjusted p-value | GSNAP (mapping), BamBam (counts), EdgeR (DE calls) |
| Multidimensional scaling (MDS) and biological coefficient of variation analysis | Suaeda fruticosa shoot/root biological replicates | 0 mM vs 300 mM NaCl treatment | sample clustering/similarity, dispersion estimates | EdgeR |
- – 475 genes downregulated and 44 genes upregulated in shoots/roots at 300 mM NaCl vs 0 mM NaCl
- – 54,526 unigenes assembled with N50 of 957 bp, mean length 763-764 bp, size range 200-6639 bp
- – 36,668 of 54,526 unigenes (67.25%) were annotated; 13,349 (24.5%) had no significant database hits 67.25%
- – Highest read mapping to assembly achieved with k-mer 41 and 45 (72.91% and 72.61% mapped) 72.91%
- – Stress-related genes comprise 1229 of total annotated unigenes in the biological process category, the largest subcategory
- – Shoot biological replicates cluster more tightly than root replicates on the MDS plot, indicating less variation in shoots
- – 8697 unigenes (16%) show top BLAST hit similarity to Vitis vinifera, the most represented species 16%
- – Common dispersion of 0.37 and biological coefficient of variation of 61.09% observed across the dataset BCV=61.09%
- pvalue <0.05 (adjusted, BH/FDR method) (threshold for calling genes differentially expressed between 300 mM and 0 mM NaCl)
- count 475 downregulated, 44 upregulated genes (differentially expressed genes at optimal salt vs control)
- count 54,526 unigenes (total assembled high-quality unigenes)
- count 36,668 annotated sequences (67.25%) (BLAST2GO functional annotation rate)
- other N50 = 957 bp (unigene assembly quality metric)
- mean 763-764 bp (mean unigene length)
- other common dispersion 0.37; BCV 61.09% (EdgeR dispersion/biological coefficient of variation estimate across 12 libraries)
- other 283,587,292 filtered reads normalized to 99,577,045 reads (read filtering and digital normalization prior to assembly)
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 performed de novo transcriptome assembly of Suaeda fruticosa RNA-seq data from 12 Illumina HiSeq 2000 libraries (4 conditions × 3 biological replicates: root/shoot × 0 mM/300 mM NaCl) and applied the EdgeR package with a generalized linear model to identify differentially expressed genes between salt treatments. Differential expression was defined by an adjusted p-value < 0.05 using the Benjamini-Hochberg (BH) false discovery rate correction applied across all tested transcripts. Results were visualized with an MA plot (log ratio vs. abundance), and replicate quality was assessed via multidimensional scaling (MDS) and biological coefficient of variation (BCV) plots.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| EdgeR generalized linear model (GLM) for RNA-seq count data with common and genewise dispersion estimation | Differential gene expression between 0 mM and 300 mM NaCl treatments across root and shoot tissues (all 12 libraries); yielded 475 downregulated and 44 upregulated genes | 12 libraries total (3 biological replicates × 4 conditions: R000, S000, R300, S300) | not stated |
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Differential expression was called using EdgeR with common and genewise dispersion estimation; the specific EdgeR test (exactTest vs. glmLRT vs. glmQLFTest) is not named↳ Could also: DESeq2 with its negative binomial Wald test and adaptive shrinkage (apeglm/ashr) of log fold-change estimates could also have been applied to the same count matrix — DESeq2's fold-change shrinkage improves the ranking of genes with low counts and high variability; with only n=3 replicates per group, comparing findings across both tools is common practice to identify the most robust DE calls
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Differential expression significance was defined solely by adjusted p-value < 0.05 with no stated log fold-change (logFC) cutoff↳ Could also: A combined threshold — for example |log2FC| ≥ 1 AND FDR < 0.05 — could also have been applied, or the TREAT method in EdgeR (which tests against a minimum fold-change rather than zero) could have been used — Adding a fold-change filter helps distinguish statistically significant but biologically modest differences from larger-magnitude changes; this is particularly relevant when library sizes are large enough to give high power to detect very small expression differences
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The 12 libraries span two tissue types (root, shoot) and two salt treatments; the paper describes the analysis as comparing 0 mM vs. 300 mM treatments but does not detail the linear model formula used to handle tissue type↳ Could also: An explicit two-factor EdgeR or DESeq2 model with an interaction term (tissue × salt treatment) could also have been specified and the interaction formally tested — The MDS plot shows that root and shoot samples cluster separately, suggesting a strong tissue effect; an interaction model would allow formal statistical testing of whether the transcriptomic salt response differs between tissues, which is a central biological question in the study
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Per-gene log fold-changes and adjusted p-values are displayed only in the MA plot figure and are not tabulated or reported numerically in the main text↳ Could also: Reporting the top differentially expressed genes with their log2FC, raw p-value, and FDR-adjusted p-value in a supplementary table is also standard practice in RNA-seq publications — Tabulated per-gene statistics allow readers to evaluate the magnitude of expression differences (effect sizes) alongside significance, and enable re-analysis or comparison with other datasets
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De novo assembly was performed with Velvet/Oases at k-mer 45, selected by comparing N50, ORF count, and read mapping rate across k-mers 35–99↳ Could also: Trinity is a widely used alternative de novo RNA-seq assembler with integrated read normalization and isoform-aware quantification tools (RSEM, Salmon) — Trinity's integrated downstream pipeline facilitates isoform-level differential expression analysis; comparing assemblies from multiple assemblers (e.g., Velvet/Oases and Trinity) is sometimes used to benchmark transcript recovery and assembly completeness
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The common BCV was 61.09% (common dispersion 0.37), which the paper reports as a single global summary for all 12 libraries↳ Could also: Condition-specific or tissue-specific BCV estimates, along with per-sample TMM normalization factors and alignment rates, could also be reported — A BCV of ~61% is notably higher than the 20–40% range typical for well-controlled plant RNA-seq experiments; condition- or tissue-stratified BCV values would help readers assess whether the elevated variability is concentrated in a particular group, such as the root samples that the MDS plot shows as more dispersed
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-25943316 (Suaeda fruticosa transcriptome, Diray-Arce 2015, BMC Genomics)
Paper: de novo transcriptome assembly + DGE of halophyte Suaeda fruticosa. Design: root/shoot (R/S) x 0/300 mM NaCl x 3 reps = 12 PE Illumina HiSeq libraries (SRA PRJNA279962). "Code" cited in brief = ged-lab/khmer (digital normalization) — a third-party tool; per brief P16 applying it to the paper's own data is a fully valid reproduction.
Pipeline-derived results in the paper (candidate claims)
| # | result | reported | tool(s) | reproducibility |
|---|---|---|---|---|
| C1 | raw reads | 335.3 M reads, 100 bp PE | Illumina/SRA | EXACT from SRA/ENA metadata (no compute) |
| C2 | reads after QC trim/filter | 283,587,292 reads (84.58% kept) | Trimmomatic + FASTX + Sickle | IN SCOPE — params underspecified (3 tools, no params) -> approximate |
| C3 | reads after digital normalization | 99,577,045 reads | khmer normalize-by-median, k=21, C=30 | IN SCOPE — this is the named code; depends on C2 input |
| C4 | Velvet contigs (k45) | 296,776; N50 1548; mean 928 | Velvet 1.2.10 | OUT (hard 20%): multi-k sweep + merge, non-deterministic across versions; heavy |
| C5 | Oases scaffolds (k45) | 273,824; N50 1669; mean 1012 | Oases 0.2.08 | OUT (hard 20%) |
| C6 | final unigenes | 54,526; N50 957; 200-6639 bp | custom CD-HIT/merge | OUT (hard 20%): merge step underspecified |
| C7 | annotated transcripts | 36,668 / 54,526 (67.25%) | BLASTx vs nr/SwissProt/KEGG e<1e-10 | OUT: needs C6 assembly + giant nr DB |
| C8 | DEGs | 475 down + 44 up | edgeR, p<0.05 & FDR<0.05 | OUT: needs C6 + remapping; thresholds/contrast underspecified |
In scope (this reproduction): C1, C2, C3 — the read-count pipeline chain raw -> trimmed -> khmer-normalized.
This exercises the named code (khmer) end-to-end on the paper's own data and is cleanly checkable.
Out of scope (declared, hard 20%): C4-C8 — Velvet/Oases multi-k assembly + merge, BLAST annotation,
edgeR DGE. Reasons: heavy compute, multi-k sweep/merge & DGE thresholds underspecified, non-deterministic assembly across tool versions, and BLAST vs nr is disproportionate. Not attempted (may add single-k k45 Velvet/Oases as an approximate stretch only if budget allows; will be flagged approximate, not 1:1).
Assessments & scoring basis
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Reproduction footprint
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