Hydra: software for tailored processing of H/D exchange data from MS or tandem MS analyses
Bibliographic record
Abstract
Background Hydrogen/deuterium exchange mass spectrometry (H/DX-MS) experiments implemented to characterize protein interaction and protein folding generate large quantities of data. Organizing, processing and visualizing data requires an automated solution, particularly when accommodating new tandem mass spectrometry modes for H/DX measurement. We sought to develop software that offers flexibility in defining workflows so as to support exploratory treatments of H/DX-MS data, with a particular focus on the analysis of very large protein systems and the mining of tandem mass spectrometry data. Results We present a software package ("Hydra") that supports both traditional and exploratory treatments of H/DX-MS data. Hydra's software architecture tolerates flexible data analysis procedures by allowing the addition of new algorithms without significant change to the underlying code base. Convenient user interfaces ease the organization of raw data files and input of peptide data. After executing a user-defined workflow, extracted deuterium incorporation values can be visualized in tabular and graphical formats. Hydra also automates the extraction and visualization of deuterium distribution values. Manual validation and assessment of results is aided by an interface that aligns extracted ion chromatograms and mass spectra, while providing a means of rapidly reprocessing the data following manual adjustment. A unique feature of Hydra is the automated processing of tandem mass spectrometry data, demonstrated on a large test data set in which 40,000 deuterium incorporation values were extracted from replicate analysis of approximately 1000 fragment ions in one hour using a typical PC. Conclusion The customizable workflows and user-friendly interfaces of Hydra removes a significant bottleneck in processing and visualizing H/DX-MS data and helps the researcher spend more time executing new experiments and interpreting results. This increased efficiency will encourage the analysis of larger protein systems. The ability to accommodate the tandem MS dimension supports alternative data collection and analysis strategies, as well as higher resolution localization of deuteration where permitted by the fragmentation mechanism.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.021 |
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".