| <p align="center"> |
| <picture> |
| <img alt="Gener" src="figures/logo.png" width=50%> |
| </picture> |
| </p> |
| |
| <h1 align="center">GENERanno: A Genomic Foundation Model for Metagenomic Annotation</h1> |
|
|
| ## 📰 News |
|
|
| * 🤗 **[2026-02-10]** Our expert model for eukaryotic genome annotation `GENERanno-eukaryote-1.2b-cds-annotator-preview` is now available on [HuggingFace](https://huggingface.co/GenerTeam/GENERanno-eukaryote-1.2b-cds-annotator-preview)! |
| * 📑 **[2025-06-05]** Our paper is now available on [bioRxiv](https://www.biorxiv.org/content/10.1101/2025.06.04.656517v1)! |
| * 🤗 **[2025-05-10]** Our expert model for metagenomic annotation `GENERanno-prokaryote-0.5b-cds-annotator` is now available on [HuggingFace](https://huggingface.co/GenerTeam/GENERanno-prokaryote-0.5b-cds-annotator)! |
| * 🤗 **[2025-02-11]** Our models `GENERanno-prokaryote-0.5b-base`, |
| `GENERanno-eukaryote-0.5b-base` are now available on [HuggingFace](https://huggingface.co/GenerTeam/)! |
|
|
| ## 🔭 Overview |
|
|
| <div align="center"> |
| <img src="figures/model_overview.png" /> |
| <img src="figures/eukaryotic_annotation.png" width="80%" /> |
| <img src="figures/bacteria_annotation_summary.png" /> |
| </div> |
|
|
| In this repository, we present GENERanno, a genomic foundation model featuring a context length of 8k base pairs and |
| 500M parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. Our evaluations |
| demonstrate that the GENERanno achieves comparable performance |
| with [GENERator](https://huggingface.co/GenerTeam/GENERator-eukaryote-1.2b-base) in benchmark evaluations, |
| including [Genomic Benchmarks](https://huggingface.co/datasets/katielink/genomic-benchmarks/tree/main), [NT tasks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks_revised), |
| and our newly proposed [Gener tasks](https://huggingface.co/GenerTeam), making them the top genomic foundation models in |
| the field (2025-02). |
|
|
| Beyond benchmark performance, the GENERanno model is meticulously designed with its specialization in gene annotation. |
| The model efficiently and accurately identifies gene locations, predicts gene function, and annotates gene structure, |
| highlighting its potential to revolutionize genomic research by significantly enhancing the precision and efficiency of |
| gene annotation processes. |
|
|
| Please note that the GENERanno is currently in the developmental phase. We are actively refining the model and will |
| release more technical details soon. Stay tuned for updates! |
|
|
| In this repository, you will find the following model checkpoints: |
|
|
| | Model Name | Parameters | Data | Category | Status | |
| |----------------------------------|:----------:|:----:|:----------------:|:---------------------------------------------:| |
| | `GENERanno-prokaryote-0.5b-base` | 0.5B | 715B | Prokaryote | [Available](https://huggingface.co/GenerTeam/GENERanno-prokaryote-0.5b-prokaryote-base) | |
| | `GENERanno-prokaryote-0.5b-cds-annotator` | 0.5B | 890B | Prokaryote |[Available](https://huggingface.co/GenerTeam/GENERanno-prokaryote-0.5b-cds-annotator)| |
| | `GENERanno-eukaryote-0.5b-base` | 0.5B | 386B | Eukaryote | [Available](https://huggingface.co/GenerTeam/GENERanno-prokaryote-0.5b-eukaryote-base) | |
| | `GENERanno-eukaryote-1.2b-cds-annotator-preview` | 1.2B | 1T | Eukaryote |[Available](https://huggingface.co/GenerTeam/GENERanno-eukaryote-1.2b-cds-annotator-preview)| |
|
|
| ## 🎯 Quick Start |
|
|
| ### Dependencies |
|
|
| * Clone this repo, cd into it |
|
|
| ```shell |
| git clone https://github.com/GenerTeam/GENERanno.git |
| cd GENERanno |
| ``` |
|
|
| * Install requirements with Python 3.10 |
|
|
| ```shell |
| pip install -r requirements.txt |
| ``` |
|
|
| > If your network cannot access huggingface.co normally, we recommend using the following mirror: |
| > ```shell |
| > export HF_ENDPOINT=https://hf-mirror.com |
| > ``` |
| |
| ### Downstream |
| |
| #### Coding DNA Sequence (CDS) Annotation |
| |
| You can run CDS annotation on the [cds-annotation dataset](https://huggingface.co/datasets/GenerTeam/cds-annotation/) using the unified CLI interface below. |
| |
| ##### Basic usage |
| |
| ```bash |
| # Eukaryotic genome annotation |
| python src/tasks/downstream/cds_annotation.py --organism eukaryote |
|
|
| # Prokaryotic genome annotation |
| python src/tasks/downstream/cds_annotation.py --organism prokaryote |
| |
| # Enable BF16 for faster inference (recommended if supported) |
| python src/tasks/downstream/cds_annotation.py --organism eukaryote --bf16 |
| ``` |
| |
| ##### Custom input |
| |
| By default, each `--organism` preset uses a built-in example input. |
| You can override it with your own FASTA or Parquet file: |
| |
| ```bash |
| # Parquet input |
| python src/tasks/downstream/cds_annotation.py \ |
| --organism eukaryote \ |
| --input hf://datasets/GenerTeam/cds-annotation/examples/fly_GCF_000001215.4.parquet |
| |
| # FASTA input |
| python src/tasks/downstream/cds_annotation.py \ |
| --organism prokaryote \ |
| --input hf://datasets/GenerTeam/cds-annotation/examples/Escherichia_coli_genome.fasta |
| ``` |
| |
| ##### Performance options |
| |
| ```bash |
| # Use all available GPUs (default) |
| python src/tasks/downstream/cds_annotation.py --organism eukaryote |
| |
| # Use a specific number of GPUs |
| python src/tasks/downstream/cds_annotation.py --organism eukaryote --gpu_count ${NUM_GPUS} |
|
|
| # Enable BF16 for faster inference (recommended if supported) |
| python src/tasks/downstream/cds_annotation.py --organism eukaryote --bf16 |
| ``` |
| Note: BF16 improves inference speed on supported hardware (e.g. A100) with minimal impact on accuracy. |
| |
| #### Sequence Understanding (Classification/Regression) |
| |
| To run the sequence understanding task |
| on [Gener Tasks](https://huggingface.co/datasets/GenerTeam/gener-tasks), [Prokaryotic Gener Tasks](https://huggingface.co/datasets/GenerTeam/prokaryotic-gener-tasks), [NT Tasks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks_revised), [Genomic Benchmarks](https://huggingface.co/katarinagresova), [DeepSTARR Enhancer](https://huggingface.co/datasets/GenerTeam/DeepSTARR-enhancer-activity), |
| you can use the following arguments: |
| |
| * Gener Tasks / Prokaryotic Gener Tasks |
| * `--dataset_name GenerTeam/gener-tasks` or `--dataset_name GenerTeam/prokaryotic-gener-tasks` |
| * `--subset_name gene_classification` or `--subset_name taxonomic_classification` or ... |
| * NT Tasks |
| * `--dataset_name InstaDeepAI/nucleotide_transformer_downstream_tasks_revised` |
| * `--subset_name H2AFZ` or `--subset_name H3K27ac` or ... |
| * Genomic Benchmarks |
| * `--dataset_name katarinagresova/Genomic_Benchmarks_demo_human_or_worm` or |
| `--dataset_name katarinagresova/Genomic_Benchmarks_human_ocr_ensembl` or ... |
| * DeepSTARR Enhancer Activity |
| * `--dataset_name GenerTeam/DeepSTARR-enhancer-activity` |
| * `--problem_type regression` |
| |
| on following command: |
|
|
| ```shell |
| # Using single GPU |
| python src/tasks/downstream/sequence_understanding.py \ |
| --model_name GenerTeam/GENERator-eukaryote-1.2b-base \ |
| --dataset_name ${DATASET_NAME} \ |
| --subset_name ${SUBSET_NAME} \ |
| --batch_size ${BATCH_SIZE} \ |
| --problem_type ${PROBLEM_TYPE} \ |
| --main_metrics ${MAIN_METRICS} |
| |
| # Using multiple GPUs on single node (DDP) |
| torchrun --nnodes=1 \ |
| --nproc_per_node=${NUM_GPUS} \ |
| --rdzv_backend=c10d \ |
| src/tasks/downstream/sequence_understanding.py |
| |
| # Using multiple GPUs on multiple nodes (DDP) |
| torchrun --nnodes=${NUM_NODES} \ |
| --nproc_per_node=${NUM_GPUS_PER_NODE} \ |
| --rdzv_backend=c10d \ |
| --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} \ |
| src/tasks/downstream/sequence_understanding.py |
| |
| # Using DeepSpeed or Full Sharded Data Parallel (FSDP) |
| torchrun --nnodes=${NUM_NODES} \ |
| --nproc_per_node=${NUM_GPUS_PER_NODE} \ |
| --rdzv_backend=c10d \ |
| --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} \ |
| src/tasks/downstream/sequence_understanding.py \ |
| --distributed_type deepspeed # or fsdp |
| ``` |
|
|
| ## 📚 Datasets |
|
|
| * [Eukaryotic Gener Tasks](https://huggingface.co/datasets/GenerTeam/gener-tasks) |
| * [Prokaryotic Gener Tasks](https://huggingface.co/datasets/GenerTeam/prokaryotic-gener-tasks) |
| * [CDS Annotation](https://huggingface.co/datasets/GenerTeam/cds-annotation) |
|
|
| ## 📜 Citation |
|
|
| ``` |
| @article{li2025generanno, |
| author = {Li, Qiuyi and Wu, Wei and Zhu, Yiheng and Feng, Fuli and Ye, Jieping and Wang, Zheng}, |
| title = {GENERanno: A Genomic Foundation Model for Metagenomic Annotation}, |
| elocation-id = {2025.06.04.656517}, |
| year = {2025}, |
| doi = {10.1101/2025.06.04.656517}, |
| publisher = {Cold Spring Harbor Laboratory}, |
| URL = {https://www.biorxiv.org/content/early/2025/06/05/2025.06.04.656517}, |
| journal = {bioRxiv} |
| } |
| ``` |
|
|
| ## 📈 Benchmark Performance |
|
|
| ### Sequence Understanding (Classification/Regression) — `GENERanno-prokaryote-0.5b-base` |
|  |
|
|
| ### Sequence Understanding (Classification/Regression) — `GENERanno-eukaryote-0.5b-base` |
|  |
|
|
|
|
