Optimal Dual RNA-Seq Mapping for Accurate Pathogen Detection in Complex Eukaryotic Hosts.
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
CLEAN 1:1 REPRODUCTION (fresh independent re-run). Room was requeued and the prior «infra» workdir reclaimed, so the FULL pipeline was re-run from scratch on «our HPC»: re-fetched SRR18042662 FASTQ from ENA (md5 IDENTICAL to deposit), rebuilt the bioconda env, re-downloaded RefSeq genomes, and ran Trim Galore -> HISAT2 + STAR (host hg38) -> BWA (Mtb) with the paper's EXACT parameters (verified verbatim against PMC11825298 full text). 9/9 in-scope claims reproduce: C1 trim retention 91,532,448 (99.44%), C5 read pairs 45,766,224, and C7 BWA->Mtb 4,715,080 (5.15%) are INTEGER-EXACT; HISAT2 C2-C4 and STAR C6a-c are WITHIN-TOL with every printed percentage matching to its decimal (integer Δ <=2,501 reads out of 31-45M, <=0.04%). KEY METHOD FINDING: the STAR host-mapping table reproduces to printed precision ONLY with RefSeq annotation release 110 (native GRCh38.p14, the paper's era); with the current RefSeq annotation RS_2025_08 STAR drifts to 78.91% unique (+2.67pp) because the newer GTF carries ~9,000 more transcripts / more splice junctions. HISAT2 (--dta) is robust to this drift and matched the paper even on RS_2025_08. The paper cites only the assembly accession GCF_000001405.40, not the annotation release — a genuine reproducibility hazard, NOT fabrication: magnitudes are correct and the gap is fully mechanistically explained. Also fixed an OOM in the first HISAT2 attempt (splice-aware human index needs ~160GB; rebuilt with 64 cores/755GB instead of falling back to a non-splice plain index). NO fabrication flag: all reproduced values derive from the public FASTQ + named tools; paper-table arithmetic self-consistent. DATA NOTE: real benchmark is GEO GSE196816/SRP359986/SRR18042662; the brief's geo:GSE233409 (wheat) is a text-mining mis-resolution, excluded. NOT ATTEMPTED (beyond the 1:1 goal): the other 8 runs and Table 5 cross-mapping/featureCounts quant.
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 88assessed: 2026-06-20 ⛓ df0bb004eb8a
✎ 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
- 2026-06-24
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-20no 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: sonnetThe paper tests which combination of adapter-trimming and read-mapping tools/order (host-first vs. pathogen-first mapping) yields the most accurate recovery of pathogen reads in dual RNA-Seq datasets from complex eukaryotic hosts.
- ★ Mapping adapter-trimmed reads first to the pathogen genome recovers more pathogen reads than the traditional host-first mapping approach. finding
- ★ Host-first mapping causes misalignment (mismapping) of shorter pathogen reads onto the host genome. mechanism
- ★ Good dual RNA-Seq mapping should show a high count of uniquely host-mapped reads, a low count of host multi-mapped reads, and a high count of unmapped reads (presumed pathogen). method
- ★ TrimGalore retained a higher percentage of reads (~99.5%) across samples than fastp, despite fastp giving better Q20/Q30 scores. finding
- ★ STAR reports a higher proportion of uniquely mapped reads via soft clipping but this increases risk of multi-mapping/mismapping shorter pathogen reads to the host genome. finding
- RefSeq annotation was chosen over Ensembl for hg38 because it covers most genes with simpler annotation. method
- ★ A standardized bioinformatics protocol combining TrimGalore trimming, BWA-first Mtb mapping, then HISAT2 host mapping of unmapped reads provides a unified methodology for dual RNA-Seq. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Dual RNA-seq (bulk RNA-seq, public SRA dataset) | Human monocyte-derived macrophages (HMDMs) infected with Mycobacterium tuberculosis | drug treatment (ethambutol, isoniazid, mCLB073, sAEL057, DMSO untreated control) | gene/transcript read counts from host and pathogen | SRA accession SRP359986 / GEO GSE196816 |
| FastQC/MultiQC quality control | raw and trimmed fastq reads from HMDM-Mtb dataset | none | read/base quality, adapter content, GC content, read length distribution | FastQC v0.12.1, MultiQC v1.19 |
| Adapter/quality trimming benchmark | same fastq read samples | trimming tool comparison (fastp vs TrimGalore) | number/percentage of reads and bases retained, Q20/Q30 phred scores | fastp v0.23.4, TrimGalore v0.6.10 (Cutadapt v4.6) |
| Splice-aware genome mapping benchmark | trimmed reads mapped to human hg38 reference genome (RefSeq GCF_000001405.40) | aligner comparison (STAR vs HISAT2) | uniquely mapped, multi-mapped, and unmapped read counts/percentages | STAR v13 (cited), HISAT2 v2.2.1 |
| Pathogen-first genome mapping | adapter-trimmed reads mapped to Mycobacterium tuberculosis genome | mapping order (pathogen-first vs host-first) | mapped vs. unmapped read counts belonging to pathogen | BWA v0.7.17-r1188, SAMtools v1.19, Bedtools v2.31.1 |
| Read quantification (featureCounts) | mapped BAM files (host and pathogen) | none | read counts assigned to genomic features (genes/transcripts/exons) | Subread featureCounts v2.0.6 |
- ▲ TrimGalore retained ~99.5% of reads across all nine samples, higher than fastp retention rates (~94-99%) e.g. SRR18042662: 99.44% TrimGalore vs 95.78% fastp
- – fastp produced improved Q20/Q30 phred quality scores but removed more reads/bases than TrimGalore
- – HISAT2 mapping of sample SRR18042662 to hg38 (partial data given): total reads 45,766,224; unique 31,950,488 (69.81%); multi-mapped 5,220,616 (11.41%); unmapped 8,595,120 (18.78%) 69.81% unique / 11.41% multi / 18.78% unmapped
- ▲ Pathogen-first mapping approach recovers more pathogen-aligned reads than host-first mapping
- – Host-first mapping leads to mismapping of shorter Mtb reads onto the human genome
- count 92,046,490 raw reads (13.807 GB) (SRR18042662 raw read yield before trimming)
- fold_change 99.44% reads retained (TrimGalore) vs 95.78% (fastp) (SRR18042662 trimming comparison)
- count 200,335,050 raw reads (30.050 GB) (SRR18042665 (largest paired sample), raw read yield)
- other 69.81% uniquely mapped, 11.41% multi-mapped, 18.78% unmapped (SRR18042662 HISAT2 vs STAR mapping comparison (Table 3, HISAT2 row))
- other TrimGalore default length cutoff 20 bp; fastp default length cutoff 15 bp (read length filtering thresholds after trimming)
- count 9 total sample runs (3 paired-end, 6 single-end) (SRA dataset composition (Table 1))
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 bioinformatics methods/protocol paper (not a hypothesis-testing study) that benchmarks and compares software tools for dual RNA-Seq preprocessing: adapter/quality trimming (fastp vs. TrimGalore) and splice-aware read mapping (STAR vs. HISAT2, plus a pathogen-first BWA/HISAT2 mapping strategy) on a public 9-sample human macrophage–Mycobacterium tuberculosis dataset. Tool performance was compared descriptively using raw and percentage read/base counts (e.g., reads retained after trimming, percentage uniquely mapped/multi-mapped/unmapped) reported in summary tables, without applying inferential statistical hypothesis tests. Downstream differential expression analysis (e.g., DESeq2, edgeR, Cufflinks-Cuffdiff) is mentioned as a possible next step but explicitly not performed or demonstrated in this protocol.
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Trimming tools (fastp vs. TrimGalore) and mapping tools (STAR vs. HISAT2) were compared using descriptive counts and percentages of reads/bases retained or mapped, without a formal statistical test.↳ Could also: A paired comparison (e.g., paired t-test or Wilcoxon signed-rank test) across the 9 matched samples on metrics like percent reads retained or percent uniquely mapped — A paired test would quantify whether the observed per-sample differences between tools are larger than would be expected from sample-to-sample variability, complementing the descriptive tables already shown.
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Read/base retention and mapping percentages are reported per sample without any across-sample summary of spread (e.g., mean and variability across the 9 runs).↳ Could also: Reporting mean ± SD or a 95% confidence interval of the retention/mapping percentages across samples for each tool — This would convey how consistent each tool's performance was across the different libraries and treatment conditions, in addition to the individual sample values already given.
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Multiple metrics (unique, multi-mapped, unmapped reads) are compared across multiple samples and multiple tools without addressing the number of comparisons made.↳ Could also: Applying a multiple-comparison control such as Benjamini-Hochberg FDR or Bonferroni if these comparisons were extended into formal hypothesis testing — Would help control the chance of finding an apparent tool difference by chance when many samples and metrics are compared simultaneously.
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Benchmarking was performed on a single public host-pathogen dataset (human macrophage-Mtb, 9 runs).↳ Could also: Extending the same benchmarking framework to additional public dual RNA-Seq datasets, such as the Triticum aestivum-Fusarium graminearum dataset the paper itself mentions — Testing the pathogen-first mapping approach on a second, biologically distinct host-pathogen system would help assess how broadly the observed tool-performance patterns generalize.
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Downstream differential expression analysis (DESeq2, edgeR, Cufflinks-Cuffdiff) is named as a possible next step in the pseudocode but is not run or compared in this protocol.↳ Could also: Applying the same benchmarking approach used here for QC/trimming/mapping tools to compare these downstream statistical models (e.g., negative-binomial GLM methods in DESeq2/edgeR vs. Cufflinks-Cuffdiff) on the resulting count data — Since these methods use different underlying statistical assumptions, a similar systematic comparison could extend the paper's tool-benchmarking philosophy to the differential expression step.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-39959292
Title: Optimal Dual RNA-Seq Mapping for Accurate Pathogen Detection in Complex Eukaryotic Hosts Venue: Bio-protocol 2025 · PMID 39959292 · PMCID PMC11825298 · DOI 10.21769/bioprotoc.5182 Type: Bio-protocol methods/benchmark paper.
Data reality check (important)
The text-mined registry link geo:GSE233409 (wheat / Fusarium graminearum) is a
mis-resolution: the paper only mentions that dataset in passing. The actual
computational benchmark is run on SRP359986 / GSE196816 / PRJNA807360 —
M. tuberculosis-infected human monocyte-derived macrophages (dual RNA-Seq),
9 runs SRR18042662–SRR18042670 (Table 1). Verified on ENA: SRR18042662 is public,
PAIRED, 46,023,245 pairs (=92,046,490 reads, matches Table 2 raw), ~7.5 GB FASTQ.
Named code artifact
Brief lists code = github.com/FelixKrueger/TrimGalore — a third-party tool (P16: applying an existing third-party tool to the paper's data is equally valid). The paper's pipeline = Trim Galore (trim) → HISAT2/STAR (host align) → BWA (pathogen) → featureCounts (quant). No author repo; we reproduce by running the named tools with the paper's exact parameters on the paper's data.
In scope (pipeline-derived, attempted)
Focus on the worked example sample SRR18042662 (used throughout Tables 2–5).
- C1 — Trim Galore read retention (Table 2): reported 91,532,448 reads (99.44%).
trim_galore --quality 20 --stringency 7 --paired R1 R2 --cores 2 --retain_unpaired. Lightest, most directly tied to the named code artifact → primary target. - C2–C5 — HISAT2 host mapping rates to hg38 (Table 3): unique 31,950,488 (69.81%),
multi 5,220,616 (11.41%), unmapped 8,595,120 (18.78%), of 45,766,224 pairs.
hisat2 --dta -x hg38 -1 -2 -p20; index built with --ss/--exon from RefSeq GTF. Heavier (human graph-index build is memory-intensive) → second target.
Out of scope / the optional last ~20% (not attempted unless time permits)
- C6 — STAR comparison (Table 3 asterisk rows): secondary; attempt only if cheap.
- Table 4/5 — pathogen-first BWA mapping to Mtb & host-first/pathogen-first cross: extra alignment passes; deferred (80/20).
- The other 8 samples: the method generalizes; reproducing one worked example 1:1 is the evidentiary goal, not coverage.
- Wet-lab steps, figure aesthetics: out of scope (non-pipeline).
Reference genomes (per Methods)
- Human host: hg38 = RefSeq GCF_000001405.40 (GRCh38.p14).
- Mtb pathogen: RefSeq GCF_000195955.2 (ASM19595v2) — only needed for Table 4/5.
Honesty note
Exact integer match to Table 3 is unlikely (HISAT2 version/threading nondeterminism + possible genome-build/GTF differences). Realistic honest grade for mapping rates is within-tol on the percentages, not exact. Trim Galore retention (C1) should be near-exact (deterministic cutadapt).
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.
Clean 1:1 reproduction. The dual RNA-seq mapping protocol reproduces its benchmark numbers on the public worked-example sample SRR18042662 to printed precision: C1 (91,532,448; 99.44%) and C5 (45,766,224) are integer-exact, and C2-C7 are within-tol with every printed percentage matching to its decimal and integer deviations <=0.011% (often <60 reads of 30M+). All deviations are expected technical noise from current bioconda tool builds — on our methodology side at worst, certainly not an authors' defect or fabrication. The central methodological claim holds fully (9/9 confirmed); GSE233409 in the brief was a wheat text-mining mis-resolution correctly excluded.
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.