Proteogenomics: Opportunities and Caveats
Bibliographic record
Abstract
Proteogenomics is a rapidly evolving field at the intersection of genomics, transcriptomics, and proteomics. Whole genome, exome, and RNA sequencing are well-established techniques that can provide information at the DNA and RNA level with excellent sequencing coverage and depth. Although tens of thousands of clinical samples have been sequenced thus far, data integration and interpretation still remain largely incomplete. Recent advances in proteomic technologies have enabled the accurate and almost complete characterization of the proteomes of many tissues and biological fluids. Integration of multiomics data for the accurate annotation and reciprocal refinement of genomic and proteomic models is essentially the goal of proteogenomics. This integrative approach has the potential to provide solid evidence for the translation of previously unknown transcripts. Those transcripts and the respective encoded proteins might be implicated in physiological or pathophysiological processes. Novel reported peptides can represent single amino acid variants, splice variants, gene fusions, RNA editing events, novel open reading frames, translated noncoding RNAs, and pseudogenes, among many others. Proteogenomic platforms can now be used to investigate which of these novel “events” gets translated at the protein level, thereby implicating them as candidate new druggable targets or as new diagnostic or prognostic biomarkers for a wide spectrum of diseases. The potential for such identifications is maximized when both sequencing and raw proteomic data originate from the very same sample under investigation. It is becoming clear that this “sample-specific” approach, and the use of matched customized search databases, is associated with lower false-positive and false-negative identification rates. However, like all areas of active research, proteogenomics in its current state is not free of drawbacks. Major limitations in the field are the sensitivity of the mass spectrometers, the increased false discovery rate for the novel peptide hits, and the inherent biophysical properties that render some peptides undetectable. In …
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".