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Record W4392171333 · doi:10.5194/egusphere-2023-3148

Results from a Multi-Laboratory Ocean Metaproteomic Intercomparison: Effects of LC-MS Acquisition and Data Analysis Procedures

2024· preprint· en· W4392171333 on OpenAlexafffund
Mak A. Saito, Jaclyn K. Saunders, Matthew R. McIlvin, Erin M. Bertrand, J. A. Breier, Margaret Mars Brisbin, Sophie M. Colston, Jaimee R. Compton, Timothy J. Griffin, W. Judson Hervey, Robert L. Hettich, Pratik Jagtap, Michael G. Janech, Rod Johnson, Rick Keil, Hugo B.C. Kleikamp, Dagmar H. Leary, Lennart Martens, J. Scott P. McCain, Eli K. Moore, Subina Mehta, Dawn M. Moran, Jaqui Neibauer, Benjamin A. Neely, Michael V. Jakuba, Jim Johnson, Megan Duffy, Gerhard J. Herndl, Richard J. Giannone, Ryan Mueller, Brook L. Nunn, Martin Pabst, Samantha L. Peters, Andrew T. Rajczewski, Elden Rowland, Brian C. Searle, Tim Van Den Bossche, Gary J. Vora, Jacob Waldbauer, Haiyan Zheng, Zihao Zhao

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institute of Standards and TechnologyDirectorate for GeosciencesNational Aeronautics and Space AdministrationAustrian Science FundUniversity of MinnesotaNational Institutes of HealthNational Science Foundation
KeywordsReproducibilityMetagenomicsMetaproteomicsComputer scienceStatisticsBiologyMathematics

Abstract

fetched live from OpenAlex

Abstract. Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-Series Study in the North Atlantic Ocean collected by in situ pumps and the AUV Clio were distributed with a paired metagenome, and one-dimensional liquid chromatographic data dependent acquisition mass spectrometry analyses was stipulated. Analysis of mass spectra from seven laboratories through a common informatic pipeline identified a shared set of 1056 proteins from 1395 shared peptides constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values ranging from 0.43 to 0.83, and a Sørensen similarity analysis of the top 1,000 proteins revealed 70–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. An informatic intercomparison study, involving 10 laboratories using 8 software packages successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research including integration into global scale ocean surveys and ocean biogeochemical models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.233

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.055
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.287
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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