Comparative analysis of the diversity of trinucleotide repeats in bacterial genomes
Bibliographic record
Abstract
The human gut is the most favorable niche for microbial populations, and few studies have explored the possibilities of horizontal gene transfer between host and pathogen. Trinucleotide repeat (TNR) expansion in humans can cause more than 40 neurodegenerative diseases. Further, TNRs are a type of microsatellite that resides on coding regions can contribute to the synthesis of homopolymeric amino acids. Hence, the present study aims to estimate the occurrence and diversity of TNRs in bacterial genomes available in the NCBI Genome database. Genome-wide analyses revealed that several bacterial genomes contain different types of uninterrupted TNRs. It was found that TNRs are abundant in the genomes of Alcaligenes faecalis, Mycoplasma gallisepticum, Mycoplasma genitalium, Sorangium cellulosum, and Thermus thermophilus. Interestingly, the genome of Bacillus thuringiensis strain YBT-1518 contained 169 uninterrupted ATT repeats. The genome of Leclercia adecarboxylata had 46 uninterrupted CAG repeats, which potentially translate into polyglutamine. In some instances, the TNRs were present in genes that potentially encode essential functions. Similar occurrences in human genes are known to cause genetic disorders. Further analysis of the occurrence of TNRs in bacterial genomes is likely to provide a better understanding of mismatch repair, genetic disorders, host-pathogen interaction, and homopolymeric amino acids.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".