Development of BIATECH‐54 standard mixtures for assessment of protein identification and relative expression
Bibliographic record
Abstract
Mixtures of known proteins have been very useful in the assessment and validation of methods for high-throughput (HTP) MS (MS/MS) proteomics experiments. However, these test mixtures have generally consisted of few proteins at near equal concentration or of a single protein at varied concentrations. Such mixtures are too simple to effectively assess the validity of error rates for protein identification and differential expression in HTP MS/MS studies. This work aimed at overcoming these limitations and simulating studies of complex biological samples. We introduced a pair of 54-protein standard mixtures of variable concentrations with up to a 1000-fold dynamic range in concentration and up to ten-fold expression ratios with additional negative controls (infinite expression ratios). These test mixtures comprised 16 off-the-shelf Sigma-Aldrich proteins and 38 Shewanella oneidensis proteins produced in-house. The standard proteins were systematically distributed into three main concentration groups (high, medium, and low) and then the concentrations were varied differently for each mixture within the groups to generate different expression ratios. The mixtures were analyzed with both low mass accuracy LCQ and high mass accuracy FT-LTQ instruments. In addition, these 54 standard proteins closely follow the molecular weight distributions of both bacterial and human proteomes. As a result, these new standard mixtures allow for a much more realistic assessment of approaches for protein identification and label-free differential expression than previous mixtures. Finally, methodology and experimental design developed in this work can be readily applied in future to development of more complex standard mixtures for HTP proteomics studies.
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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 | 0.005 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".