Review v1 · round 1 · manuscript v1
dnoise: Fast Native Data Reduction for Bruker timsTOF
Panel readout
5 specialists · scored 1–5
Legacy scaled score
Range 4.0–5.0, a spread of 1.0.
- ethics5.0Confidence 5 of 5
- scientific validity4.0Confidence 5 of 5
- data analysis4.0Confidence 4 of 5
- reporting reproducibility4.0Confidence 5 of 5
- contribution context4.0Confidence 4 of 5
Score is the referee's assessment of the work.Confidence is how sure that referee was of its own reading, recorded separately and never combined. The editor's verdict is its own judgment of the reports, not a threshold applied to this mean. The legacy aggregate is the historical panel mean multiplied by 20. It is not comparable to the Editor-in-Chief's publication readiness score.
Abstract
as posted by the authors
Bruker timsTOF acquisitions produce dense native .d files whose storage, transfer, and archival become substantial at high throughput. We present dnoise, an open-source Rust tool that removes points directly from timsTOF frames and writes a native-compatible .d directory. dnoise retains ions that form coherent streaks across the ion-mobility dimension and applies acquisition-aware gates to signal that cannot be selected for fragmentation. On a three-species benchmark spanning ddaPASEF and diaPASEF at 5- and 15-minute gradients, default MS1-only denoising reduced the frame binary by 35 to 53%. Label-free quantification accuracy was preserved in both modes. ddaPASEF peptide-spectrum-match, peptide, and protein-group counts were unchanged, as expected with the searched MS/MS spectra untouched, and diaPASEF precursor and protein-group counts changed only slightly. Every tested processing run completed in 69 seconds or less on the benchmark workstation. Optional MS/MS denoising produced greater reduction but sacrificed several percent of identifications. Thus, a substantial fraction of native timsTOF frame data can be removed with little analytical change.
The review
- SummaryThe panel's assessment in brief.
- Decision letterThe editor's verdict and what it requires.
- Desk screenWhether the submission cleared the bar for full review.
- Advocate / skeptic debateThe case for and against, in full.
- Debate synthesisThe condensed account of the debate the editor read.
- Venue suggestionsWhere this might be submitted.
- Manuscript statisticsDeterministic counts over the text the panel read.
Specialist reports
Editorial audits
Factual checklists, not opinions. They skip the debate and go straight to the editor.
The text the panel read
counted, not judged
Counted at ingest, no model involved. These describe theconverted text the referees read, not your PDF.
Size
- Words
- 5,526
- Main text
- 4,514
- Sentences
- 317
- Display equations
- 0
excluding references
Sentences
- Median sentence
- 16 words
- Longest tenth
- 29 words
- Over 40 words
- 4%
- Passive
- ~0.1924/sentence
regex approximation
Evidence on the page
- Citations
- not countable
- Numbers
- 145.67
- p-values
- 0 exact, 0 threshold
this venue most likely sets them as superscript numerals, which convert to bare digits
per 1000 words
Hedging against amplifying
Per 1000 words. Softening ("may", "suggests") against strengthening ("clearly", "demonstrates"). No referee saw these.
Provenance
PeerReviewAgents 0.5.0 · 1cf57690
Reviewed file
v1 · 3136 KiB
sha256 2021124d8f1c84bb742f887e…
Debate rounds
2
Run cost
$1.42
Manuscript read as
markdown
Converted by rustypaper 0.2.0. Quotations match the manuscript text.
Desk screen
triage gate
Which model wrote which report
| Stage | Model |
|---|---|
| Editorial audits (×2) | claude-haiku-4-5 |
| Advocate / skeptic | claude-sonnet-5 |
| Specialist reviewers (×5) + desk screen | claude-haiku-4-5 |
| Editor, debate synthesizer | claude-opus-5 |
| debate synthesizer (override) | claude-sonnet-5 |
| journal recommender (override) | claude-haiku-4-5 |
The widest fan-out runs on the cheapest model. Only the agents that decide the verdict run on the most capable one.
What the referees looked up (25 searches)
| Agent | Search | Answered by | Hits |
|---|---|---|---|
| reviewer contribution context | find related worktimsTOF denoising data reduction preprocessing | N/A | 5 |
| find related workion mobility mass spectrometry native format point removal | N/A | 5 | |
| find related workPASEF acquisition denoising filtering proteomics | N/A | 5 | |
| search preprintstimsTOF data reduction native format Bruker | N/A | 5 | |
| find related workPNNL PreProcessor ion mobility denoising | N/A | 5 | |
| find related workBruker timsTOF data processing software tools | N/A | 5 | |
| find related workspectral simplification peak detection feature finding mass spectrometry | N/A | 5 | |
| search biomedical literaturetimsTOF PASEF data reduction preprocessing | N/A | 0 | |
| search biomedical literatureion mobility spectrometry denoising filtering proteomics | N/A | 0 | |
| find related workWilding-McBride spectral simplification timsTOF | N/A | 5 | |
| search biomedical literatureWilding-McBride simplifying MS1 MS2 spectra timsTOF | N/A | 1 | |
| find related workBilbao preprocessing ion mobility mass spectrometry PNNL | N/A | 5 | |
| search biomedical literatureBilbao preprocessing tool ion mobility enhanced omics | N/A | 0 | |
| search biomedical literaturePNNL PreProcessor ion mobility denoising | N/A | 0 | |
| find related workMaxQuant ion mobility timsTOF data processing | N/A | 5 | |
| find related workmzML conversion peak detection feature finding LC-MS | N/A | 5 | |
| search biomedical literatureBilbao preprocessing enhanced ion mobility mass spectrometry omics workflows 2022 | N/A | 0 | |
| find related worknative format data reduction mass spectrometry storage | N/A | 5 | |
| search biomedical literaturelabel-free quantification timsTOF PASEF proteomics | N/A | 2 | |
| search biomedical literaturePrianichnikov MaxQuant ion mobility enhanced shotgun proteomics | N/A | 1 | |
| search biomedical literatureDemichev DIA-NN neural networks interference correction deep proteome | N/A | 1 | |
| find related workSage proteomics searching quantification open source | N/A | 5 | |
| find related workdata compression lossless mass spectrometry raw data storage | N/A | 5 | |
| search biomedical literatureLazear Sage open source proteomics searching quantification | N/A | 1 | |
| search biomedical literatureIonQuant label-free quantification match-between-runs | N/A | 5 |
Run against arXiv, Semantic Scholar, PubMed and bioRxiv while the review was being written. A search returning zero hits is kept: it is the evidence behind a referee saying it found no prior art.
What each agent cost
| Agent | USD |
|---|---|
| editor | $0.4748 |
| skeptic | $0.2998 |
| advocate | $0.2310 |
| reviewer contribution context | $0.1409 |
| debate synthesizer | $0.0953 |
| audit citation integrity | $0.0567 |
| desk screen | $0.0341 |
| audit methods completeness | $0.0262 |
| reviewer reporting reproducibility | $0.0157 |
| reviewer data analysis | $0.0155 |
| journal recommender | $0.0131 |
| reviewer scientific validity | $0.0127 |
| reviewer ethics | $0.0056 |
Cite this review
Permanent: this review only
This URL is a permanent link to this specific review, and will not change.
In Silico (2026). Review of "dnoise: Fast Native Data Reduction for Bruker timsTOF". In Silico. https://pgarrett-scripps.github.io/insilico/reviews/2026/dnoise-fast-native-data-reduction-for-bruker-10-64898-2026-08-27-747603/v1/
@misc{insilico-dnoise-fast-native-data-reduction-for-bruker-10-64898-2026-08-27-747603-v1,
title = {Review of {dnoise: Fast Native Data Reduction for Bruker timsTOF}},
author = {{In Silico}},
year = {2026},
howpublished = {In Silico, an AI-refereed overlay journal},
url = {https://pgarrett-scripps.github.io/insilico/reviews/2026/dnoise-fast-native-data-reduction-for-bruker-10-64898-2026-08-27-747603/v1/},
note = {Machine-generated peer review of doi:10.64898/2026.08.27.747603 v1. Produced by PeerReviewAgents 0.5.0. Produced by PeerReviewAgents, doi:10.5281/zenodo.21781895.}
}Please cite the preprint itself as well. This reviews that work, it does not replace it. The review is machine-generated and advisory. If you are citing it as evidence about the paper, say so explicitly.