<i>anota</i>: analysis of differential translation in genome-wide studies
Bibliographic record
Abstract
UNLABELLED: Translational control of gene expression has emerged as a major mechanism that regulates many biological processes and shows dysregulation in human diseases including cancer. When studying differential translation, levels of both actively translating mRNAs and total cytosolic mRNAs are obtained where the latter is used to correct for a possible contribution of differential cytosolic mRNA levels to the observed differential levels of actively translated mRNAs. We have recently shown that analysis of partial variance (APV) corrects for cytosolic mRNA levels more effectively than the commonly applied log ratio approach. APV provides a high degree of specificity and sensitivity for detecting biologically meaningful translation changes, especially when combined with a variance shrinkage method for estimating random error. Here we describe the anota (analysis of translational activity) R-package which implements APV, allows scrutiny of associated statistical assumptions and provides biologically motivated filters for analysis of genome wide datasets. Although the package was developed for analysis of differential translation in polysome microarray or ribosome-profiling datasets, any high-dimensional data that result in paired controls, such as RNP immunoprecipitation-microarray (RIP-CHIP) datasets, can be successfully analyzed with anota. AVAILABILITY: The anota Bioconductor package, www.bioconductor.org.
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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) | 0.000 | 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 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".