Widespread and extensive lengthening of 3′ UTRs in the mammalian brain
Bibliographic record
Abstract
Remarkable advances in techniques for gene expression profiling have radically changed our knowledge of the transcriptome. Recently, the mammalian brain was reported to express many long intergenic noncoding (lincRNAs) from loci downstream from protein-coding genes. Our experimental tests failed to validate specific accumulation of lincRNA transcripts, and instead revealed strongly distal 3' UTRs generated by alternative cleavage and polyadenylation (APA). With this perspective in mind, we analyzed deep mammalian RNA-seq data using conservative criteria, and identified 2035 mouse and 1847 human genes that utilize substantially distal novel 3' UTRs. Each of these extends at least 500 bases past the most distal 3' termini available in Ensembl v65, and collectively they add 6.6 Mb and 5.1 Mb to the mRNA space of mouse and human, respectively. Extensive Northern analyses validated stable accumulation of distal APA isoforms, including transcripts bearing exceptionally long 3' UTRs (many >10 kb and some >18 kb in length). The Northern data further illustrate that the extensions we annotated were not due to unprocessed transcriptional run-off events. Global tissue comparisons revealed that APA events yielding these extensions were most prevalent in the mouse and human brain. Finally, these extensions collectively contain thousands of conserved miRNA binding sites, and these are strongly enriched for many well-studied neural miRNAs. Altogether, these new 3' UTR annotations greatly expand the scope of post-transcriptional regulatory networks in mammals, and have particular impact on the central nervous system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".