The past, present and future of mitochondrial genomics: have we sequenced enough mtDNAs?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it