The genome of the MZM-0403 strain of the African turquoise killifish, <i>Nothobranchius furzeri</i>
Bibliographic record
Abstract
The African turquoise killifish, Nothobranchius furzeri, is an emerging model for functional genomics research. Interest in N. furzeri stems from its extremely short lifespan, and the breadth of research on this fish is rapidly expanding. All currently available whole-genome assemblies for N. furzeri are based on the GRZ strain. However, N. furzeri shows substantial phenotypic differences among populations. Here, we present a whole-genome assembly of the MZM-0403 strain of N. furzeri, which differs from the GRZ strain with respect to lifespan and male coloration patterns. We used PacBio HiFi sequencing to sequence the genome of an MZM-0403 male to ∼48 × coverage. The PacBio reads were de novo assembled and then scaffolded against an existing N. furzeri genome assembly. This strategy resulted in a chromosome-level assembly. Our MZM-0403 assembly differs from previous N. furzeri assemblies in that it is closer to the expected genome size based on independent estimates (∼1.5 Gb) and it has substantially fewer gaps, particularly in the vicinity of genes and within introns. A repeat analysis shows that about two-thirds of the genome is composed of repetitive elements. In addition, our assembly approach allowed us to recover phased fragments of the X- and Y-chromosomes. Analysis of these regions identifies 20 genes that are likely in the nonrecombining region of the sex chromosomes. Overall, this novel genome assembly will be useful for future functional and comparative genomics studies of fishes in the genus Nothobranchius.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".