Editorial: Coding and non-coding RNA-based complexes in organismal development and disease pathogenesis
Bibliographic record
Abstract
Editorial on the Research Topic Coding and non-coding RNA-based complexes in organismal development and disease pathogenesis Progress in "omics" technologies indicate that most of the human genome is transcribed at some point during development.While the importance of proteincoding mRNAs remains of primary interest, the majority of the cellular RNAs appear to be non-coding and subject to regulation (ENCODE Project Consortium, 2012;Hon et al., 2017).Non-coding RNAs could potentially have direct regulatory functions or affect gene expression through "noise".One open question remains how to connect the wealth of molecular detail with phenotypic effects such as changes in physiological function and adaptation.The articles included in this Research Topic explore fundamental, developmental and pathological processes that involve coding and non-coding RNAs.This series of articles constitute the second volume of a Research Topic on RNA in Development and Disease previously published in Frontiers in Genetics in 2020 (Chartrand et al., 2020).While the first volume focused on mechanisms of posttranscriptional gene regulation such as mRNA export, translational control and mRNA localization, this second volume covers new ground as it includes non-coding RNAs and mechanisms not previously discussed, such as alternative splicing and mRNA decay.A first group of articles focus on the evolution and function of non-coding RNAs.On the origin and evolution of non-coding RNAs, Palazzo and Kejiou offer a global view on non-darwinian evolution and describe how sloppy and inefficient processes within cells may explain how junk RNA can evolve toward functional non-coding RNA.Long noncoding RNAs have emerged as key regulators of gene expression, particularly due to their capacity to bind both protein cofactors (transcriptional regulators or DNA modifiers) and interact with DNA with high sequence specificity.In their article Alecki and Vera review the different non-canonical nucleic acid structures formed by non-coding RNA and DNA,
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".