Mining Mammalian Transcript Data for Functional Long Non-Coding RNAs
Bibliographic record
Abstract
BACKGROUND: The role of long non-coding RNAs (lncRNAs) in controlling gene expression has garnered increased interest in recent years. Sequencing projects, such as Fantom3 for mouse and H-InvDB for human, have generated abundant data on transcribed components of mammalian cells, the majority of which appear not to be protein-coding. However, much of the non-protein-coding transcriptome could merely be a consequence of 'transcription noise'. It is therefore essential to use bioinformatic approaches to identify the likely functional candidates in a high throughput manner. PRINCIPAL FINDINGS: We derived a scheme for classifying and annotating likely functional lncRNAs in mammals. Using the available experimental full-length cDNA data sets for human and mouse, we identified 78 lncRNAs that are either syntenically conserved between human and mouse, or that originate from the same protein-coding genes. Of these, 11 have significant sequence homology. We found that these lncRNAs exhibit: (i) patterns of codon substitution typical of non-coding transcripts; (ii) preservation of sequences in distant mammals such as dog and cow, (iii) significant sequence conservation relative to their corresponding flanking regions (in 50% cases, flanking regions do not have homology at all; and in the remaining, the degree of conservation is significantly less); (iv) existence mostly as single-exon forms (8/11); and, (v) presence of conserved and stable secondary structure motifs within them. We further identified orthologous protein-coding genes that are contributing to the pool of lncRNAs; of which, genes implicated in carcinogenesis are significantly over-represented. CONCLUSION: Our comparative mammalian genomics approach coupled with evolutionary analysis identified a small population of conserved long non-protein-coding RNAs (lncRNAs) that are potentially functional across Mammalia. Additionally, our analysis indicates that amongst the orthologous protein-coding genes that produce lncRNAs, those implicated in cancer pathogenesis are significantly over-represented, suggesting that these lncRNAs could play an important role in cancer pathomechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".