The past, present and future of mitochondrial genomics: have we sequenced enough mtDNAs?
Bibliographic record
Abstract
The year 2014 saw more than a thousand new mitochondrial genome sequences deposited in GenBank-an almost 15% increase from the previous year. Hundreds of peer-reviewed articles accompanied these genomes, making mitochondrial DNAs (mtDNAs) the most sequenced and reported type of eukaryotic chromosome. These mtDNA data have advanced a wide range of scientific fields, from forensics to anthropology to medicine to molecular evolution. But for many biological lineages, mtDNAs are so well sampled that newly published genomes are arguably no longer contributing significantly to the progression of science, and in some cases they are tying up valuable resources, particularly journal editors and referees. Is it time to acknowledge that as a research community we have published enough mitochondrial genome papers? Here, I address this question, exploring the history, milestones and impacts of mitochondrial genomics, the benefits and drawbacks of continuing to publish mtDNAs at a high rate and what the future may hold for such an important and popular genetic marker. I highlight groups for which mtDNAs are still poorly sampled, thus meriting further investigation, and recommend that more energy be spent characterizing aspects of mitochondrial genomes apart from the DNA sequence, such as their chromosomal and transcriptional architectures. Ultimately, one should be mindful before writing a mitochondrial genome paper. Consider perhaps sending the sequence directly to GenBank instead, and be sure to annotate it correctly before submission.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".