Rearrangement Phylogeny of Genomes in Contig Form
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
There has been a trend in increasing the phylogenetic scope of genome sequencing while decreasing the quality of the published sequence for each genome. With reduced finishing effort, there is an increasing number of genomes being published in contig form. Rearrangement algorithms, including gene order-based phylogenetic tools, require whole genome data on gene order, segment order, or some other marker order. Items whose chromosomal location is unknown cannot be part of the input. The question we address here is, for gene order-based phylogenetic analysis, how can we use rearrangement algorithms to handle genomes available in contig form only? Our suggestion is to use the contigs directly in the rearrangement algorithms as if they were chromosomes, while making a number of corrections, e.g., we correct for the number of extra fusion/fission operations required to make contigs comparable to full assemblies. We model the relationship between contig number and genomic distance, and estimate the parameters of this model using insect genome data. With this model, we use distance matrix methods to reconstruct the phylogeny based on genomic distance and numbers of contigs. We compare this with methods to reconstruct ancestral gene orders using uncorrected contig data.
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) | 0.000 | 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 | 0.000 | 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