Comparative analysis of organelle genomes, a biologist's view of computational challenges (abstract only)
Bibliographic record
Abstract
With genomic data (generated by classical, functional, structural, proteo- and other `omic' approaches) accumulating at a stupendous rate, there is an ever increasing need for the development of new, more efficient and more sensitive computational methods. To highlight aspects of our computational needs, we will present results that emerged from the comparative genome analysis of mitochondria. Having originated from an alpha-proteobacterial endosymbiont, these eukaryotic organelles contain small and extremely variable genomes, and are thus perfect model systems for the much more complex eubacterial and archaeal genomes. We are currently in vestigating mitochondrial DNAs (mtDNAs) in a lineage of unicellular, primitive protistan eukaryotes, the jakobids, with the aim to understand the evolution of mitochondrial genomes, genes and their regulation. Because these organisms are difficult to grow, biochemical approaches aimed at understanding gene regulation are laborious, thus it is possible to capitalize considerably from predictions on genome and gene organization, and regulatory elements. Contrary to approaches in which molecular data (gene order, sequence similarities) are used to infer the phylogenetic relationships among a group of organism, we know their phylogeny and employ this information to identify and model more or less conserved genetic elements and structural RNA genes that are difficult to spot by conventional methods, in a phylogenetic-comparative approach.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".