MedakaBase as a unified genomic resource platform for medaka fish biology
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
Medaka, a group of small, mostly freshwater fishes in the teleost order Beloniformes, includes the rice fish Oryzias latipes, a useful model organism studied in diverse biological fields. Chromosome-scale genome sequences of the Hd-rR strain of this species were obtained in 2007, and its improved version has facilitated various genome-wide studies. However, despite its widespread utility, omics data for O. latipes are dispersed across various public databases and lack a unified platform. To address this, the medaka section of the National Bioresource Project (NBRP) of Japan established a genome informatics team in 2022 tasked with providing various in silico solutions for bench biologists. This initiative led to the launch of MedakaBase (https://medakabase.nbrp.jp), a web server that enables gene-oriented analysis including exhaustive sequence similarity searches. MedakaBase also provides on-demand browsing of diverse genome-wide datasets, including tissue-specific transcriptomes and intraspecific genomic variations, integrated with gene models from different sources. Additionally, the platform offers gene models optimized for single-cell transcriptome analysis, which often requires coverage of the 3' untranslated region (UTR) of transcripts. Currently, MedakaBase provides genome-wide data for seven Oryzias species, including original data for O. mekongensis and O. luzonensis produced by the NBRP team. This article outlines technical details behind the data provided by MedakaBase.
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 | 0.001 | 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