OpenMS 3 enables reproducible analysis of large-scale mass spectrometry data
Bibliographic record
Abstract
Mass spectrometry has become an indispensable tool in the life sciences.The new major version 3 of the computational framework OpenMS provides signi cant advancements regarding open, scalable, and reproducible high-throughput work ows for proteomics, metabolomics, and oligonucleotide mass spectrometry.OpenMS makes analyses from emerging elds available to experimentalists, enhances computational work ows, and provides a reworked Python interface to facilitate access for bioinformaticians and data scientists. MainMass spectrometry (MS) is an analytical technique with a wide range of biological and medical applications, which can produce data on the terabyte scale demanding large-scale high-throughput analysis.Computational mass spectrometry seeks to distill MS data into insights that advance understanding in the life sciences.Here we proudly present OpenMS 3, which is a major leap forward in support for omics data beyond proteomics (e.g., metabolomics and RNA/DNA), support for work ow systems, and an improved user experience.OpenMS is an actively maintained, open-source software project that has aided mass spectrometrists with their data processing since 2002.It started as a C++ library and has expanded to include applications, Python libraries, work ows, and documentation repositories.Since the release of OpenMS 2.0 in 2015, more than 20,000 Git commits were created by more than 150 developers contributing code.Here, we discuss major new features and highlight how developers and experimentalists can bene t from OpenMS 3.The OpenMS framework provides users and developers with a large collection of MS-related algorithms, tools, and work ows.The OpenMS tools 1 can be combined into powerful analysis work ows.The
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".