Figure S3 from Exact Bayesian inference for the detection of graft-mobile transcripts from sequencing data
Bibliographic record
Abstract
The long-distance transport of messenger RNAs (mRNAs) has been shown to be important for several developmental processes in plants. A popular method for identifying travelling mRNAs is to perform RNA-Seq on grafted plants. This approach depends on the ability to correctly assign sequenced mRNAs to the genetic background from which they originated. The assignment is often based on the identification of single-nucleotide polymorphisms (SNPs) between otherwise identical sequences. A major challenge is therefore to distinguish SNPs from sequencing errors. Here, we show how Bayes factors can be computed analytically using RNA-Seq data over all the SNPs in an mRNA. We used simulations to evaluate the performance of the proposed framework and demonstrate how Bayes factors accurately identify graft-mobile transcripts. The comparison with other detection methods using simulated data shows how not taking the variability in read depth, error rates and multiple SNPs per transcript into account can lead to incorrect classification. Our results suggest experimental design criteria for successful graft-mobile mRNA detection and show the pitfalls of filtering for sequencing errors or focussing on single SNPs within an mRNA.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.224 | 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".