The pseudo-mitochondrial genome influences mistakes in heteroplasmy interpretation
Bibliographic record
Abstract
BACKGROUND: Nuclear mitochondrial pseudogenes (numts) are a potential source of contamination during mitochondrial DNA PCR amplification. This possibility warrants careful experimental design and cautious interpretation of heteroplasmic results. RESULTS: Here we report the cloning and sequencing of numts loci, amplified from human tissue and rho-zero (rho0) cells (control) with primers known to amplify the mitochondrial genome. This paper is the first to fully sequence 46 paralogous nuclear DNA fragments that represent the entire mitochondrial genome. This is a surprisingly small number due primarily to the primer sets used in this study, because prior to this, BLAST searches have suggested that nuclear DNA harbors between 400 to 1,500 paralogous mitochondrial DNA fragments. Our results indicate that multiple numts were amplified simultaneously with the mitochondrial genome and increased the load of pseudogene signal in PCR reactions. Further, the entire mitochondrial genome was represented by multiple copies of paralogous nuclear sequences. CONCLUSION: These findings suggest that mitochondrial genome disease-associated biomarkers must be rigorously authenticated to preclude any affiliation with paralogous nuclear pseudogenes. Importantly, the common perception that mitochondrial template "swamps" numts loci precluding detectable amplification, depends on the region of the mitochondrial genome targeted by the PCR reaction and the number of pseudogene loci that may co-amplify. Cloning and relevant sequencing data will facilitate the correct interpretation. This is the first complete, wet-lab characterization of numts that represent the entire mitochondrial genome.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".