PrestOMIC, an open source application for dissemination of proteomic datasets by individual laboratories
Bibliographic record
Abstract
BACKGROUND: Technological advances in mass spectrometry and other detection methods are leading to larger and larger proteomics datasets. However, when papers describing such information are published the enormous volume of data can typically only be provided as supplementary data in a tabular form through the journal website. Several journals in the proteomics field, together with the Human Proteome Organization's (HUPO) Proteomics Standards Initiative and institutions such as the Institute for Systems Biology are working towards standardizing the reporting of proteomics data, but just defining standards is only a means towards an end for sharing data. Data repositories such as ProteomeCommons.org and the Open Proteomics Database allow for public access to proteomics data but provide little, if any, interpretation. RESULTS & CONCLUSION: Here we describe PrestOMIC, an open source application for storing mass spectrometry-based proteomic data in a relational database and for providing a user-friendly, searchable and customizable browser interface to share one's data with the scientific community. The underlying database and all associated applications are built on other existing open source tools, allowing PrestOMIC to be modified as the data standards evolve. We then use PrestOMIC to present a recently published dataset from our group through our website.
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 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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.032 |
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".