In silico Mining of Protein-coding and Non-coding RNA (ncRNA) Specific Genes in Exotic versus Indigenous Gaddi Dogs
Bibliographic record
Abstract
Background: Comparative functional genomics will aid in the molecular identification of diverse dog breeds. Methods: The current proposal aimed at conducting a differential study between the genomes of exotic canines (Labrador, Basenji, Tasha-Boxer breed, Mischka breed German Shepherd, Zoey breed Great Dane) and indigenous (Gaddi) breeds through whole genome annotation. Results: The prediction analysis by GeneMark tool yielded an average of 46484 transcripts, in Gaddi dogs and exotic breeds ranging from 29669 to 30956. A total of 57 miRNAs were discovered in exotic breeds and 22 miRNAs in Gaddi dogs, 18 are common in both, while 4 were unique to Gaddi dogs. lncRNA was predicted using the PLEK, CPAT, and LGC tools, resulting in 3201, 396, and 4188 noncoding sequences in exotic breeds, respectively. Approximately, 31 thousand lncRNA transcripts were identified in the Gaddi dog genome. Microsatellites were found to be distributed through approximately 0.3% of both genomes. The average island length of CpG ranged between 24246.48 to 28080.66 in exotic breeds at chromosome level assembly and 697.15 in indigenous Gaddi dogs at contig level assembly. The predicted protein-coding genes were subjected to pathway analysis by DAVID and PANTHER. Five genes that are expressed in the blood (INSL3, CLDN3, MYH1, CLN5, and GALC) were selected for validation through qPCR. The results indicated that the genes were expressed in both groups. Conclusion: The study is the maiden report on the comparative genome analysis between indigenous Gaddi dogs and exotic dog breeds. The findings set the stage for further research into the known and novel genes, which might be employed as biomarkers for disease diagnosis and to investigate their regulatory role.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".