The critical role of isomiRs in accurate differential expression analysis of miRNA-seq data
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) are crucial for the regulation of gene expression and are promising biomarkers and therapeutic targets. miRNA isoforms (isomiRs) differ in their start/end offsets, which can impact the target gene selection and non-canonical function of the miRNA species. In addition, isomiRs frequently differ in their expression patterns from their parent miRNAs, yet their roles and tissue-specific responses are currently understudied, leading to their typical omission in miRNA research. Here, we evaluate the expression differences of isomiRs across conditions and their impact on standard miRNA-seq quantification results. We analyze 28 public miRNA-seq datasets, showing significant expression pattern differences between the isomiRs and their corresponding reference miRNAs, leading to misinterpretation of differential expression signals for both. As a case study, we generate a new dataset assessing isomiR abundance under hypoxia in human endothelial cells between the nuclear and cytosolic compartments. The results suggest that isomiRs are dramatically altered in their nuclear localization in response to hypoxia, indicating a potential non-canonical effect of the species, which would be missed without isomiR-aware analysis. Our results call for a comprehensive re-evaluation of the miRNA-seq analysis practices.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".