SMDesigner: a program to design sequence mutations to assess RNA structure
Bibliographic record
Abstract
The structure of RNA is critical to its function. The advancement of structure prediction algorithms and deep sequencing technology has led to the discovery of numerous conserved RNA structures. However, functional analysis of these sequences is lagging behind the rate of novel RNAs' predictions. Traditionally, mutations are designed to alter the structure of RNA and tested individually to assess function. We developed a program for the large-scale characterization of the structure/function relationship in multiple RNAs. Structure Mutation Designer (SMDesigner) automatically selects both disruptive and compensatory mutations according to inputted structural information. As proof of concept, we designed mutations for riboswitches with SMDesigner and experimentally assessed six of these riboswitches and their mutant sequences using an in-line probing assay to verify the effects on their structure and function. The in-line probing results show expected changes in five of six sequence structure patterns, confirming that SMDesigner can be useful to explore RNA structure and subsequent function. SMDesigner can be download at: https://github.com/lilihou/SMDesigner_0.1/tree/main/dist.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".