MetaProClust-MS1: A tool for clustering metaproteomes using rapid MS1 profiling
Bibliographic record
Abstract
Summary Metaproteomics is used to explore the composition, dynamics and function of microbial communities. How-ever, acquiring data by tandem mass spectrometry is time consuming and resource intensive. To mediate this challenge, we present MetaProClust-MS1, a computational framework for microbiome screening developed to reduce the time required for data acquisition by mass spectrometry. In this proof-of-concept study, we tested MetaProClust-MS1 on data acquired using short 15 minute MS1-only mass spectrometry gradients and compared the results to those produced using data acquired by a traditional tandem mass spectrometry approach. MetaProClust-MS1 identified robust microbiome shifts caused by xenobiotics in both datasets. Cluster topologies were also significantly correlated. We demonstrate that MetaProClust-MS1 is able to rapidly screen microbiomes using only short MS1 profiles. This approach can be used to prioritize samples for deep metaproteomic analysis and will be especially useful in large-scale metaproteomic screens or in clinical settings where rapid results are required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".