How to Illuminate the Dark Proteome Using the Multi‐omic OpenProt Resource
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ten of thousands of open reading frames (ORFs) are hidden within genomes. These alternative ORFs, or small ORFs, have eluded annotations because they are either small or within unsuspected locations. They are found in untranslated regions or overlap a known coding sequence in messenger RNA and anywhere in a "non-coding" RNA. Serendipitous discoveries have highlighted these ORFs' importance in biological functions and pathways. With their discovery came the need for deeper ORF annotation and large-scale mining of public repositories to gather supporting experimental evidence. OpenProt, accessible at https://openprot.org/, is the first proteogenomic resource enforcing a polycistronic model of annotation across an exhaustive transcriptome for 10 species. Moreover, OpenProt reports experimental evidence cumulated across a re-analysis of 114 mass spectrometry and 87 ribosome profiling datasets. The multi-omics OpenProt resource also includes the identification of predicted functional domains and evaluation of conservation for all predicted ORFs. The OpenProt web server provides two query interfaces and one genome browser. The query interfaces allow for exploration of the coding potential of genes or transcripts of interest as well as custom downloads of all information contained in OpenProt. © 2020 The Authors. Basic Protocol 1: Using the Search interface Basic Protocol 2: Using the Downloads interface.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it