HDgraphiX: A web-based tool for visualization of hydrogen deuterium exchange mass spectrometry data
Bibliographic record
Abstract
Abstract Summary Hydrogen deuterium exchange mass spectrometry (HDX-MS) investigates protein structural changes by measuring deuterium incorporation into the protein amide backbone. Due to richness of information provided on protein conformational dynamics, HDX-MS data can be challenging to visualize effectively. To address this, we have developed HDgraphiX, a web-based tool that visualizes HDX data by processing outputs from two popular analysis software packages, DynamX (Waters Corp.) and HDExaminer (Sierra Analytics Inc). HDgraphiX performs statistical analyses, filters data based on statistical significance and presents the results in several forms of user-friendly publication-quality heatmaps (Chiclet plots). Unique features of this tool include the generation of Woods plots, volcano plots, and PyMOL colouring scripts, which are used to map deuterium uptake differences onto protein structures. Additionally, HDgraphiX offers numerous advanced options for customizing data processing and plotting without the need for manual data editing. Availability and Implementation HDgraphiX is available free of charge for all users at https://hdgraphix.net , the Python script and HTML template are deposited at https://github.com/KentV-UofG/HDgraphiX .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 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".