The identification of nuclear mitochondrial pseudogenes using motif statistics
Bibliographic record
Abstract
The analysis and classification of biological sequences is a large field with a great deal of application. Sequence classification is difficult because of the many to one relationship of sequence composition to protein structure and function. Pseudogenes are non-functional genes that arise through point mutations and translocation events. Nuclear Mitochondrial (NUMT) pseudogenes or fragments, are a type of pseudogene which result from failed integrations of mitochondrial genes into the nucleus. NUMTs are characterized by premature stop codons and mutations that compromise the final protein structure, rendering them functionally inactive. DNA barcoding is a recent initiative where organisms are identified and classified based on a particular gene. Cytochrome oxidase subunit I (COI) is a common selection because of its universality and sequence conservation. COI is known to have NUMT copies for various species. NUMT's interfere with DNA barcoding because of the similarity to their mitochondrial parents. NUMT contamination can result in incorrect species classification and spurious species relationships. In our study we demonstrate the effectiveness of Motif Probability Profiling, which uses the normal distribution of motif frequencies to identify NUMTs. We compare and contrast our method with two others; Profile Hidden Markov Model, and Context Comparison Analysis. We also provide the ground work for the existence and further exploration of shared motif patterns in NUMTs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".