Meta-analysis of small RNA-sequencing errors reveals ubiquitous post-transcriptional RNA modifications
Bibliographic record
Abstract
Recent advances in DNA-sequencing technology have made it possible to obtain large datasets of small RNA sequences. Here we demonstrate that not all non-perfectly matched small RNA sequences are simple technological sequencing errors, but many hold valuable biological information. Analysis of three small RNA datasets originating from Oryza sativa and Arabidopsis thaliana small RNA-sequencing projects demonstrates that many single nucleotide substitution errors overlap when aligning homologous non-identical small RNA sequences. Investigating the sites and identities of substitution errors reveal that many potentially originate as a result of post-transcriptional modifications or RNA editing. Modifications include N1-methyl modified purine nucleotides in tRNA, potential deamination or base substitutions in micro RNAs, 3' micro RNA uridine extensions and 5' micro RNA deletions. Additionally, further analysis of large sequencing datasets reveal that the combined effects of 5' deletions and 3' uridine extensions can alter the specificity by which micro RNAs associate with different Argonaute proteins. Hence, we demonstrate that not all sequencing errors in small RNA datasets are technical artifacts, but that these actually often reveal valuable biological insights to the sites of post-transcriptional RNA modifications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".