Insights into genetic architecture and disease associations of genes associated with different human blood group systems using codon usage bias
Bibliographic record
Abstract
The differential use of synonymous codons of an amino acid is an imperative evolutionary phenomenon, termed codon usage bias, that functions across various levels of organisms. It is accustomed to providing an understanding of a gene's differential architecture driven by functional regulation of gene expression. Numerous synonymous mutations are linked to various diseases, demonstrating that silent mutations can be deleterious. We employed bioinformatics methods to examine codon usage trends in 263 coding sequences of 44 blood group systems. The blood group systems were categorized into two groups based on association with a sort of neurodegenerative disorder. We performed a CUB study to investigate how multiple components, such as selection, mutation and biased nucleotide composition are accountable for the evolution of the transcripts of the blood group antigens. The compositional analysis implicated blood group genes were GC-rich. RSCU analysis showed G/C-ending codon choice among synonymous codons. Also, a distinct codon choice was found in both blood groups for serine and proline. Moreover, the leucine-coding CTG codon was found the most overrepresented in all the genes, indicating selectional pressure substantially impacts overall codon usage. This was also supported by biplot analysis. Additionally, CpC and GpG overrepresentation is in concordance with the results concerning neurodegenerative disorders where CpC has been attributed to non-CpG methylation and linked to several neurodegenerative ailments. Both the Z-test analysis and rare codon choice showed a substantial difference in codon usage by the genes of both groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".