OGRe: a relational database for comparative analysis of mitochondrial genomes
Bibliographic record
Abstract
Organellar Genome Retrieval (OGRe) is a relational database of complete mitochondrial genome sequences for over 250 Metazoan species. OGRe provides a resource for the comparative analysis of mitochondrial genomes at several levels. At the sequence level, OGRe allows the retrieval of any selected set of mitochondrial genes from any selected set of species. Species are classified using a taxonomic system that allows easy selection of related groups of species. Sequence alignments are also available for some species. At the level of individual nucleotides, the system contains information on base frequencies and codon usage frequencies that can be compared between organisms. At the level of whole genomes, OGRe provides several ways of visualizing information on gene order. Diagrams illustrating the genome arrangement can be generated for any selected set of species automatically from the information in the database. Searches can be done based on gene arrangement to find sets of species that have the same order as one another. Diagrams for pairwise comparison of species can be produced that show the positions of break-points in the gene order and use colour to highlight the sections of the genome that have moved. OGRe is available from http://www.bioinf.man.ac.uk/ogre.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.020 |
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".