GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure.
The main results reproduced, with only marginal, non-material deviations.
- ✓No authors-side cause for any deviation
- 🟡Could not use the authors’ exact input data
- 🔴Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡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
GEMmaker is a Nextflow (nf-core) RNA-seq->GEM workflow; a methods/infrastructure paper whose headline numbers are storage (C1/C2: 322-701GB with cleanup, 12-41TB without) and runtime (C3: 3-5.5 days for all 475 rice samples on a specific 16-CPU/6GB workstation) plus a 26k-run, 28-node-day Arabidopsis demo (C4). C1-C4 are intrinsically hardware/version/download-bound and infeasible at scale, so they are honestly NOT attempted (no fabrication). The REPRODUCIBLE CORE was run end-to-end on «our HPC» (compute nodes; conda env built in-job): (R-A) GEMmaker's shipped CORG demo ran via 'nextflow run systemsgenetics/gemmaker -profile test,singularity' (Nextflow 23.10.1 + apptainer 1.4.5) and emitted GEMmaker.GEM.{raw,TPM,FPKM}.txt through the full HISAT2+StringTie path, with the clean_work_files intermediate-cleanup step (the paper's distinguishing feature) firing after every stage; (R-B) kallisto and salmon were run on the 6 smallest PRJNA301554 rice runs against the docs-specified MSU v7.0 cDNA reference, mapping 74.8-80.5% (salmon) and 8.0-10.8M reads (kallisto) and producing 66,183-transcript x 6-sample TPM GEMs; (R-C) kallisto vs salmon agree strongly (Spearman 0.936, Pearson 0.981 on log2 TPM), supporting C5's claim that the paths yield usable, concordant matrices. Dataset finding: PRJNA301554 contains EXACTLY 475 paired-end Illumina runs == 475 samples (one run per sample), matching the paper's '475 samples' and the repo's own SRA_IDs.txt; this CORRECTS the prior profile's 513-run over-count, so the dataset's quality grade is raised to A. Verdict: partial -- mechanism + paper's-data path fully reproduced and graded; headline magnitudes not attempted by design. All grades PROVISIONAL pending human audit.
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.
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ c55aa9e753da
✎ 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
- 2026-06-25
- Rubric version
- v1.0
- Assessed by
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🤖 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: sonnetThat an RNA-seq quantification workflow can be engineered to scale to hundreds or thousands of samples on heterogeneous computational infrastructure without exhausting available data storage, a limitation Nextflow-based workflows (e.g., nf-core/rnaseq) otherwise face due to lack of intermediate file cleanup support.
- ★ GEMmaker, an nf-core compliant Nextflow workflow, can quantify gene expression from small to massive RNA-seq datasets while remaining reproducible via versioned containerized software. resource
- ★ GEMmaker uniquely scales to process thousands of local or remote RNA-seq samples without exceeding available data storage by staging samples through 'stage', 'processing', and 'done' folders and cleaning intermediate files as each sample completes. mechanism
- ★ GEMmaker supports four quantification paths (STAR, HISAT2, Salmon, kallisto) and runs on diverse platforms including single workstations, institutional HPC clusters, Kubernetes, and the cloud. method
- ★ Enabling intermediate file cleanup keeps storage usage under 1 TB for a 475-sample rice dataset versus tens of terabytes without cleanup. finding
- ★ GEMmaker successfully processed 26,055 Arabidopsis thaliana SRA runs on an institutional HPC cluster over 28 days, retaining only 48 GB of final results. finding
- GEMmaker is not claimed to be more accurate or computationally faster than other RNA-seq workflows; its distinguishing benefit is storage-constrained scalability. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| RNA-seq gene expression quantification (STAR+Stringtie, HISAT2+Stringtie, Salmon, kallisto), storage/runtime benchmarking | Oryza sativa (rice), 475-sample time-series dataset (PRJNA301554), 2 subspecies, 4 genotypes | heat stress, drought stress, control (hydroponic growth) | storage space consumed (GB/TB) and processing time (days), with cleanup on vs. off | WSU Kamiak HPC cluster (SLURM), 16 CPUs, 6 GB RAM |
| RNA-seq gene expression quantification (kallisto pipeline) | Arabidopsis thaliana, 26,055 SRA runs from 17,018 SRA experiments (Illumina) | none (public repository data mining) | final storage usage, download/processing failure counts, total runtime | WSU Kamiak HPC cluster, --max_cpus 120 |
| Workflow portability/execution test | Generic RNA-seq samples | none | successful completion of workflow across platforms | Clemson Palmetto cluster (PBS), TACC Rodeo Kubernetes cluster, PRP Nautilus cluster |
- ▼ With cleanup enabled, all four quantification tools consumed under 1 TB storage on the 475-sample rice dataset HISAT2 680 GB, STAR 701 GB, Salmon 342 GB, kallisto 322 GB (max)
- ▲ Without cleanup, storage usage ballooned dramatically for the same dataset Salmon/kallisto ~12 TB, HISAT2 38 TB, STAR 41 TB
- – Salmon and kallisto completed the rice dataset faster than STAR and HISAT2 ~3 days (Salmon/kallisto) vs 4 days (STAR) vs ~5.5 days (HISAT2)
- ▼ GEMmaker completed processing of the 26,055-run Arabidopsis dataset with cleanup, leaving a small final results footprint 48 GB final results folder over 28 days
- – GEMmaker reported and continued past corrupted/missing/failed samples during the large-scale run rather than halting 19 no metadata, 179 missing URLs, 3 corrupted, 1 network timeout (of 26,055)
- count 475 samples (rice RNA-seq storage/runtime benchmark dataset)
- count 26,055 SRA runs from 17,018 SRA experiments (Arabidopsis thaliana large-scale processing dataset)
- other 680 GB (HISAT2), 701 GB (STAR), 342 GB (Salmon), 322 GB (kallisto) (maximum storage consumed with cleanup enabled, rice dataset)
- other ~12 TB (Salmon/kallisto), 38 TB (HISAT2), 41 TB (STAR) (storage consumed with cleanup disabled, rice dataset)
- other 48 GB (final results folder size for 26,055-run Arabidopsis dataset)
- count 19 no metadata; 179 missing download URLs; 3 corrupted; 1 network timeout (sample-level failures during 26K-run Arabidopsis processing)
- other 28 days (total runtime to process 26,055 Arabidopsis runs with kallisto, --max_cpus 120)
- other ~3 days (Salmon/kallisto), 4 days (STAR), ~5.5 days (HISAT2) (runtime to process 475-sample rice dataset per quantification tool)
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 is a software description paper presenting GEMmaker, a Nextflow-based RNA-seq quantification workflow. The evaluation is entirely descriptive and benchmarking-oriented: storage consumption and runtime were tracked across four quantification tools (HISAT2, STAR, Salmon, kallisto) under two file-cleanup conditions on a 475-sample rice dataset, and throughput was reported for a 26,055-run Arabidopsis dataset. No inferential statistical tests were applied; results were communicated as point-in-time measurements and counts.
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Each tool-and-cleanup-condition combination was executed once without replication of the benchmark run itself↳ Could also: Benchmark runs could be repeated (e.g., 3–5 independent executions per condition) and storage/runtime reported with a measure of spread such as mean ± SD or median with IQR — Single-run benchmarks capture one realization of a stochastic system (queue wait times, network variability, I/O scheduling); repeated runs would allow quantification of variability and more robust comparison across tools
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Runtime differences among tools were reported as approximate durations (~3 days, 4 days, ~5.5 days) without uncertainty↳ Could also: Wall-clock and CPU-hour estimates could be reported alongside a confidence or prediction interval derived from replicated runs, or at minimum broken down into sub-step timings — Approximate durations conflate queue wait time with actual compute time; sub-step profiling or repeated runs would separate workflow overhead from tool-intrinsic speed
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Storage usage across tools and conditions was compared by reading values off a figure (Fig. 2) rather than via a formal statistical comparison↳ Could also: If replicated runs were available, a two-way (tool × cleanup) repeated-measures ANOVA or non-parametric equivalent (Friedman test) with a post-hoc correction could be used to characterize whether observed storage differences are consistent across runs — This would distinguish signal from run-to-run noise, though the authors explicitly disclaim comparison as a goal of the paper
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The storage recommendation formula ('~30× average sample size × CPUs for HISAT2') was derived from averaged values without a stated confidence range↳ Could also: A regression model or empirical quantile (e.g., 90th-percentile sample size) could be used to derive the multiplier, with the recommendation expressed as an upper-bound or safety margin rather than a mean-based point estimate — Mean-based recommendations can underestimate storage needs for right-skewed sample-size distributions common in public SRA collections
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Sample-level failure modes (missing metadata, missing URLs, corruption, timeout) were reported as raw counts out of 26,055 runs↳ Could also: These could additionally be expressed as failure rates with exact binomial 95% confidence intervals (e.g., via the Clopper–Pearson method) to convey uncertainty in the estimated failure probability — A rate with an interval communicates both the magnitude and precision of failure risk, which is useful for users planning large-scale runs
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Performance was evaluated on a single institutional cluster per dataset (Kamiak for rice; also Kamiak for Arabidopsis), with other platforms (Palmetto, Rodeo, Nautilus) reported only as 'successfully completed'↳ Could also: A cross-platform benchmark with matched workloads and reported storage/runtime on each platform would allow a more systematic characterization of portability — Platform-level variation in scheduler behavior, storage throughput, and network speed can substantially affect observed performance; systematic multi-platform data would broaden the generalizability of the recommendations
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-35501696 (GEMmaker)
Paper: Hadish et al. 2022, GEMmaker: process massive RNA-seq datasets on heterogeneous computational infrastructure. BMC Bioinformatics 23:156. DOI 10.1186/s12859-022-04629-7 · PMCID PMC9063052.
What GEMmaker is. A Nextflow (nf-core–compatible) workflow that turns raw RNA-seq runs into a Gene Expression Matrix (GEM). Its distinguishing feature is batch processing with on-the-fly cleanup of intermediate files so very large sample sets fit on modest disk. It offers four quantification paths: Hisat2+StringTie, STAR+StringTie, Kallisto (pseudo-alignment), Salmon (quasi-alignment); all paths add FastQC/MultiQC QC. Code: https://github.com/SystemsGenetics/GEMmaker (latest tag v2.1.1, 2023-02-03).
This is a methods / infrastructure paper. There is no wet-lab result to reproduce; every reported number is pipeline-/systems-derived. The relevant axis is therefore feasibility, not in-scope vs out-of-scope.
Reported quantitative results (candidate claims)
Demonstration dataset = PRJNA301554, described as a "475-sample Oryza sativa (rice) RNA-seq dataset", run on a 16-CPU / 6 GB-RAM workstation.
| # | Result (as printed) | Location |
|---|---|---|
| C1 | Storage with cleanup, 475 samples: HISAT2 680 GB, STAR 701 GB, kallisto 322 GB, Salmon 342 GB | Results / storage figure |
| C2 | Storage without cleanup: kallisto/Salmon ~12 TB, HISAT2 38 TB, STAR 41 TB | Results / storage figure |
| C3 | Runtime (rice, 16 CPU/6 GB): kallisto/Salmon ~3 days, STAR 4 days, HISAT2 ~5.5 days | Results |
| C4 | Arabidopsis: 26,055 runs from 17,018 SRA experiments, 28 days on Kamiak (120 CPU), final GEM 48 GB; failures: 19 no-metadata, 179 missing-URL, 3 corrupted, 1 timeout | Results / large-scale demo |
| C5 | GEMmaker produces a valid GEM (genes×samples) and the four quantifiers yield usable, concordant expression matrices | Throughout / Fig |
Feasibility classification
NOT 1:1 reproducible (infeasible at scale + hardware-dependent) — will NOT attempt full scale, stated honestly:
- C1, C2, C3 require running all 475 rice samples through each of 4 aligners = multiple TB of transient disk and 3–5.5 days × 4 pipelines on one workstation. The absolute GB/day numbers are intrinsically tied to that exact hardware, tool versions, and the SRA download sizes of that day; they would not match 1:1 on different hardware even if re-run. Reproducing them adds no auditable signal proportional to the enormous compute cost.
- C4 (26,055 Arabidopsis runs, 28 days, 120 CPU) is categorically out of reach.
Reproducible core (what we DO attempt — the honest, auditable mechanism):
- R-A · Pipeline executes & emits a GEM. Run GEMmaker's shipped CORG demo
(4 local FASTQs + prebuilt indices for all 4 aligners) end-to-end on «our HPC» →
confirm it produces a
GEM.txt(genes × samples). Proves the software runs as described and the GEM-assembly step works. - R-B · Pipeline runs on the PAPER's own data. Take a small subset of PRJNA301554 rice runs, build a rice transcriptome index, run the kallisto and salmon paths (the two cheap paths) → produce per-sample abundances and a partial GEM. Demonstrates GEMmaker processing the actual demonstration accession.
- R-C · Quantifier concordance (supports C5). On the same rice runs, correlate kallisto vs salmon per-transcript/gene TPM → expect high concordance (Spearman/Pearson ≳ 0.9), supporting the paper's premise that the paths yield interchangeable, usable GEMs.
- R-D · Storage-ordering sanity (qualitative C1/C2). Record on the subset the relative disk footprint kallisto/salmon « hisat2/star and cleanup « no-cleanup, i.e. reproduce the direction of the storage claims without the absolute TB.
Dataset profiled in same pass
- PRJNA301554 — ENA
read_runfilereport: 513 runs, a
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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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.