Galbase: a comprehensive repository for integrating chicken multi-omics data
Bibliographic record
Abstract
BACKGROUND: Multi-omics data can provide a stereoscopic view to explore potential causal variations and genes, as well as underlying genetic mechanisms of complex traits. However, for many non-mammalian species, including chickens, these resources are poorly integrated and reused, greatly limiting genetic research and breeding processes of the species. RESULTS: Here, we constructed Galbase, an easily accessible repository that integrates public chicken multi-omics data from 928 re-sequenced genomes, 429 transcriptomes, 379 epigenomes, 15,275 QTL entries, and 7,526 associations. A total of 21.67 million SNPs, 2.71 million InDels, and 488,583 cis-regulatory elements were included. Galbase allows users to retrieve genomic variations in geographical maps, gene expression profiling in heatmaps, and epigenomic signals in peak patterns. It also provides modules for batch annotation of genes, regions, and loci based on multi-layered omics data. Additionally, a series of convenient tools, including the UCSC Genome Browser, WashU Epigenome Browser, BLAT, BLAST, and LiftOver, were also integrated to facilitate search, visualization, and analysis of sequence features. CONCLUSION: Galbase grants new opportunities to research communities to undertake in-depth functional genomic studies on chicken. All features of Galbase make it a useful resource to identify genetic variations responsible for chicken complex traits. Galbase is publicly available at http://animal.nwsuaf.edu.cn/ChickenVar .
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.016 |
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".