RNA Visualization: Relevance and the Current State-of-the-Art Focusing on Pseudoknots
Bibliographic record
Abstract
RNA visualization is crucial in order to understand the relationship that exists between RNA structure and its function, as well as the development of better RNA structure prediction algorithms. However, in the context of RNA visualization, one key structure remains difficult to visualize: Pseudoknots. Pseudoknots occur in RNA folding when two secondary structural components form base-pairs between them. The three-dimensional nature of these components makes them challenging to visualize in two-dimensional media, such as print media or screens. In this review, we focus on the advancements that have been made in the field of RNA visualization in two-dimensional media in the past two decades. The review aims at presenting all relevant aspects of pseudoknot visualization. We start with an overview of several pseudoknotted structures and their relevance in RNA function. Next, we discuss the theoretical basis for RNA structural topology classification and present RNA classification systems for both pseudoknotted and non-pseudoknotted RNAs. Each description of RNA classification system is followed by a discussion of the software tools and algorithms developed to date to visualize RNA, comparing the different tools' strengths and shortcomings.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".