Borzoi Human Model Predictor (Linder et al. 2025) using the Genomic API for Model Evaluation (GAME) Framework
Bibliographic record
Abstract
This record provides a Predictor container for the Borzoi human model (Linder et al. 2025, Nature Genetics). Borzoi is a deep learning model that predicts cell-type and tissue-specific RNA-seq coverage directly from DNA sequence, enabling interpretation of genetic variants across multiple layers of gene regulation, including transcription, splicing, and polyadenylation. It is trained on human RNA-seq data from ENCODE (with 866 datasets across diverse biosamples, including cell lines and adult tissues) and Genotype-Tissue Expression (GTEx) data (with 2-3 replicates for each tissue, processed by the recount3 project). The training dataset also includes epigenomic datasets from the Enformer model, such as CAGE, DNase-seq, ATAC-seq, and ChIP-seq tracks. The Predictor container (borzoi_human_predictor.sif) includes: Predictor script for sequence processing and error handling. Integrated Borzoi model with its dependencies and borzoi-gpu conda environment created using borzoi_gpu_environment.yml. Baskerville and Borzoi packages, installed from local source copies of repositories (as of March 2025), which also contain helper scripts and 4 replicate model weights. Support scripts like: api_preprocessing_utils.py, error_message_functions_updated.py, predictor_help_message.json. NOTE: This container requires a GPU for execution because the Borzoi model relies on TensorFlow's GPU-accelerated operations. Running the container: Ensure Apptainer is intalled in the system the container is intended to run. Always run the Predictor first, so it can listen for incoming connections from Evaluators: apptainer run --containall --nv borzoi_human_predictor.sif HOST_IP HOST_PORT MATCHER_IP MATCHER_PORT Additional information about the GAME framework can be found on GitHub: Genomic API for Model Evaluation Borzoi Human Predictor-specific information, along with a sample Evaluator, can be found within the same repository: Borzoi Predictor Code repository for Borzoi models: Borzoi GitHub Paper can be found here: Linder, J., Srivastava, D., Yuan, H. et al. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Nat Genet 57, 949–961 (2025). https://doi.org/10.1038/s41588-024-02053-6
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".