From Understudied to Understood: The MiniENCODE Framework for Multi-Omics Analysis in Diverse Species
Bibliographic record
Abstract
Abstract Building multi-omics resources for understudied organisms requires assay selection, public-data curation, gene identifier handling, portal deployment, and visualization, yet these tasks are rarely packaged into a reusable framework. To fill this gap, we developed the mini Omics Data Portal (miniODP), which combines species pages, gene-centric modules, genome browsing, and sequence search with configuration files, species onboarding workflows, and demonstration data for self-deployment. Alongside the software, we propose miniENCODE core assays: a reduced RNA-seq, ATAC-seq, and H3K27ac profiling set for regulatory analysis. Current miniODP includes seven species, covering 3,568 bulk runs, 3.61 million cells, and 1,865 genome-browser tracks. Matched core-assay datasets support regulatory outputs. As a zebrafish benchmark, using datasets from three core assays and 17 samples, we identified 52,350 enhancer-like signatures (ELSs) and constructed ELS-to-gene linkages and gene regulatory networks (GRNs). Genes near H3K27ac-supported ELSs were expressed at higher levels than those near ATAC-only distal peaks. Literature curation of top TFs from zebrafish and cattle GRNs found 19 direct, 15 indirect, and six unsupported cases among 40 candidates. Datasets lacking matched core assays still provide browsing, visualization, and search functions. miniODP serves as a reusable framework for constructing and extending multi-omics resources for understudied organisms. Graphical abstract
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".