Telencephalic Embryonic Subtractive Sequences: A Unique Collection of Neurodevelopmental Genes
Bibliographic record
Abstract
The vertebrate telencephalon is composed of many architectonically and functionally distinct areas and structures, with billions of neurons that are precisely connected. This complexity is fine-tuned during development by numerous genes. To identify genes involved in the regulation of telencephalic development, a specific subset of differentially expressed genes was characterized. Here, we describe a set of cDNAs encoded by genes preferentially expressed during development of the mouse telencephalon that was identified through a functional genomics approach. Of 832 distinct transcripts found, 223 (27%) are known genes. Of the remaining, 228 (27%) correspond to expressed sequence tags of unknown function, 58 (7%) are homologs or orthologs of known genes, and 323 (39%) correspond to novel rare transcripts, including 48 (14%) new putative noncoding RNAs. As an example of this latter group of novel precursor transcripts of micro-RNAs, telencephalic embryonic subtractive sequence (TESS) 24.E3 was functionally characterized, and one of its targets was identified: the zinc finger transcription factor ZFP9. The TESS transcriptome has been annotated, mapped for chromosome loci, and arrayed for its gene expression profiles during neural development and differentiation (in Neuro2a and neural stem cells). Within this collection, 188 genes were also characterized on embryonic and postnatal tissue by in situ hybridization, demonstrating that most are specifically expressed in the embryonic CNS. The full information has been organized into a searchable database linked to other genomic resources, allowing easy access to those who are interested in the dissection of the molecular basis of telencephalic development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".