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GFMBench-API offers a unified middleware that decouples genomic foundation model architectures from diverse tasks, standardizes data streams and metrics, & enables users to submit models through a single interface for reproducible evaluation and reporting

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# GFMBench-API

GFMBench-API is an extensible benchmarking suite for assessing genomic foundation models (GFMs) across a diverse set of downstream tasks, including classification, variant effect prediction, and zero-shot evaluation.

This repository accompanies the paper [**“GFMBench-API: A Standardized Interface for Benchmarking Genomic Foundation Models”**](https://doi.org/10.64898/2026.02.19.706811) (bioRxiv, 2026). If you use GFMBench-API in your research, please cite the paper:

```bibtex
@article{larey2026gfmbench,
  title   = {GFMBench-API: A Standardized Interface for Benchmarking Genomic Foundation Models},
  author  = {Larey, Ariel and Dahan, Elay and Bleiweiss, Amit and Kellerman, Raizy and Leib, Guy and Nayshool, Omri and Ofer, Dan and Zinger, Tal and Dominissini, Dan and Rechavi, Gideon and Bussola, Nicole and Lee, Simon and O'Connell, Shane and Hoang, Dung and Wirth, Marissa and Charney, Alexander W. and Shavit, Yoli and Daniel, Nati},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.02.19.706811}
}
```

## Quick Start

### Installation

GFMBench-API separates **package dependencies** from **model dependencies**. Model runtimes (Evo2, Nucleotide Transformer, DNABERT2, etc.) are maintained by their own projects and are not bundled into the core package.

1. Create a virtual environment (choose one option):

**Option A: Using pip (venv)**
   ```bash
   python -m venv gfmbench_env
   source gfmbench_env/bin/activate  # On Windows: gfmbench_env\Scripts\activate
   ```

**Option B: Using conda**
   ```bash
   conda create -n gfmbench_env python=3.11
   conda activate gfmbench_env
   ```

2. Install dependencies for **your model** first, following that model's own environment setup. Examples:

| Model | Dependency source |
   |-------|-------------------|
   | Evo2 | [evo2 `pyproject.toml`](https://github.com/ArcInstitute/evo2/blob/main/pyproject.toml) |
   | Nucleotide Transformer (NTv3) | [nucleotide-transformer `setup.py`](https://github.com/instadeepai/nucleotide-transformer/blob/main/setup.py) |
   | DNABERT2 | [DNABERT_2 `requirements.txt`](https://github.com/MAGICS-LAB/DNABERT_2/blob/main/requirements.txt) |

3. Install GFMBench-API core dependencies on top of your model environment:
   ```bash
   pip install -r basic_requirements.txt
   ```

`basic_requirements.txt` contains only what the `gfmbench_api` package needs (tasks, metrics, data I/O, etc.) — no model-specific libraries.

---

## What’s Included?

GFMBench-API provides:

- A **core API package** for unified GFM evaluation (`gfmbench_api/`).
- A **suite of standard benchmark tasks** covering supervised and zero-shot scenarios.
- Consistent interfaces for models and tasks, enabling both out-of-the-box use and customized evaluation pipelines.
- Example scripts and templates (`usage_examples/`) to get started quickly or for rapid prototyping.

---

## Repository Organization

```
gfmbench_api_rep/
├── gfmbench_api/              # Main API package
│   ├── benchmark_report/      # CSV report utilities
│   ├── metrics/               # Built-in metrics (AUROC, AUPRC, etc.)
│   ├── tasks/                 # Task definitions
│   │   ├── base/              # Base task/model classes
│   │   └── concrete/          # 20+ ready-to-use tasks
│   └── utils/                 # Misc utilities (data I/O, download helpers, inference cache)
├── usage_examples/            # Getting started scripts and toy models
│   ├── run_benchmark.py
│   ├── trainers/
│   └── sanity_models/
├── logs/                      # Logs (autocreated)
└── basic_requirements.txt     # GFMBench-API core only (model-agnostic)
```

---

## Supported Benchmarks & Tasks

GFMBench-API supports evaluation on **20 unique tasks**, grouped as:

### Supervised Classification & Variant Prediction

| Task Class                       | Description                             |
| --------------------------------- | --------------------------------------- |
| GuePromoterAllTask                | Binary classification of promoter vs non-promoter DNA sequences. |
| GueSpliceSiteTask                 | Three-class classification of splice sites as donor, acceptor, or non-splice. |
| GueTranscriptionFactorTask        | Binary classification of transcription factor binding sites from ChIP-seq data. |
| VariantBenchmarksCodingTask          | Binary classification of coding variants as benign or pathogenic. |
| VariantBenchmarksNonCodingTask       | Binary classification of non-coding variants as benign or pathogenic. |
| VariantBenchmarksExpressionTask      | Binary classification of variants affecting gene expression. |
| VariantBenchmarksCommonVsRareTask    | Binary classification distinguishing common variants from synthetic rare controls. |
| VariantBenchmarksMEQTLTask           | Binary classification of variants affecting DNA methylation rates. |
| VariantBenchmarksSQTLTask            | Binary classification of variants affecting alternative splicing. |
| LRBCausalEqtlTask                    | Binary classification of variants causally influencing gene expression with tissue context. |

### Zero-Shot Variant Effect Prediction

| Task Class                          | Description                                |
| -------------------------------------|--------------------------------------------|
| VepevalClinvarTask                   | Zero-shot pathogenicity prediction for ClinVar SNVs using embedding-distance scoring. |
| IndelClinvarTask                     | Zero-shot pathogenicity prediction for ClinVar insertions and deletions. |
| BendVEPExpression                    | Zero-shot prediction of expression effects for non-coding variants. |
| BendVEPDisease                       | Zero-shot prediction of disease effects for non-coding variants. |
| SonglabClinvarTask                | Zero-shot pathogenicity prediction for ClinVar SNVs using likelihood-based scoring. |
| BRCA1Task                         | Zero-shot prediction of functional impact for BRCA1 variants (LOF, intermediate, functional). |
| TraitGymComplexTask               | Zero-shot prediction of complex trait-associated variants. |
| TraitGymMendelianTask             | Zero-shot prediction of Mendelian disease-associated variants. |
| LrbVariantEffectPathogenicOmimTask   | Zero-shot prediction of pathogenic variants associated with Mendelian diseases. |
| LoleveCausalEqtlTask                 | Zero-shot prediction of causal expression-modulating variants (indels) in promoters. |

Several zero-shot variant effect prediction tasks repeat the same reference sequence across variants. For tasks where that pattern is common, reference sequences are cached during evaluation to avoid redundant forward passes and improve efficiency. Caching is applied only where it offers a meaningful memory and latency tradeoff. To disable caching, set `"cache_size": 0` in `task_config`. To cap RAM usage, set `"cache_size"` to a positive value in GB.

---

## How Model & Task Interfaces Work

### Task API

All task classes expose a consistent interface (see `gfmbench_api/tasks/base/`):

- `get_task_name()`
- `get_task_attributes()` &mdash; metadata (e.g. number of labels, dataset splits)
- `get_finetune_dataset()`
- `eval_test_set(model)`
- `eval_validation_set(model)`
- `eval_cross_validation_fold(model, train_indices)`

### Model Integration

Simply implement the methods below in your model class; inheritance is **not** required. The API uses duck typing and will only call methods needed by the specific tasks you run.

| Method Signature                                                                     | Description                            |
|:-------------------------------------------------------------------------------------|:------------------------------------------|
| `infer_sequence_to_labels_probs(sequences, ...)`                                     | Returns single-label, binary multi-label, or multi-class multi-label probabilities for sequences. |
| `infer_variant_ref_sequences_to_labels_probs(variant_sequences, ref_sequences, ...)`  | Returns the same classification probability layouts for variant/reference sequence pairs. |
| `infer_sequence_to_sequence(sequences, ...)`                                         | Takes a list of DNA sequences and returns per-nucleotide probabilities, per-position embeddings, and a single sequence-level embedding. |
| `sequence_pos_to_prob_pos(sequences, pos)`                                           | Takes a list of DNA sequences and a position index, returns the corresponding output position indices accounting for tokenization differences. |
| `infer_masked_sequence_to_token_probs(sequences, variant_pos, variant_letters, reference_letters, ...)` | Takes sequences and masks the variant position, returns probabilities for the variant and reference nucleotides at the masked position. |

Classification probabilities use shape `[batch, classes]` for single-label tasks,
`[batch, labels]` for binary multi-label tasks, and `[batch, labels, classes]`
for multi-class multi-label tasks.

- Any not-implemented methods can simply return `None` and metrics depending on them will be skipped.
- See `gfmbench_api/tasks/base/base_gfm_model.py` for detailed docstrings.

---

## Example: Running Benchmarks

### The `usage_examples/run_benchmark.py` script

This reference script demonstrates a standard workflow:
- Loading a model (built-in or your own)
- Running a configurable set of tasks
- Supervised fine-tuning or zero-shot evaluation
- Auto-generating benchmark CSVs

#### Common CLI Arguments

| Argument             | Type    | Default | Notes                                             |
|----------------------|---------|---------|---------------------------------------------------|
| `--model`            | str     | DNABERT2| Model string key (see `MODEL_REGISTRY`)           |
| `--checkpoint_path`  | str     | None    | Path to model checkpoint                          |
| `--mlm_head_path`    | str     | None    | Optional: path to MLM head checkpoint             |
| `--report_algo_name` | str     | temp    | Column name for this model in benchmark CSV       |
| `--csv_path`         | str     | ...     | Where benchmark report is saved                   |
| `--linear_prob`      | flag    | False   | If set: train only projection layer               |
| `--epochs`           | int     | 3       | Num epochs for fine-tuning                        |
| `--disable_safe_model_call` | flag | False | Bypass try/except if set                          |
| `--cache_size`       | float   | None    | Cache RAM limit in GB; None = no limit; 0 disables caching |

#### Key Variables to Adjust in `run_benchmark.py`

- **Root data directory**  
  ⚠️ Update around line 164:  
  ```python
  root_data_dir_path = "/path/to/your/data/"
  ```
- **Sanity check mode** (100-sample subsets)  
  ```python
  sanity_check_mode = True  # Set to False for full eval
  ```
- **Task configuration**  
  Edit parameters like `max_sequence_length`, `batch_size`, or `cache_size`.
- **Training parameters**  
  Change `num_epochs`, `learning_rate`, etc. in the training_params dict.
- **Reproducibility:** Using `num_workers > 0` may cause non-deterministic results on several tasks; for full reproducibility in `usage_examples/run_benchmark.py`, keep the default `num_workers = 0`.
- **Task list**  
  Edit/add the tasks in the `tasks = [...]` list.
- **Model registry**  
  Add your models to the `MODEL_REGISTRY` dict.

#### Example usage

Default (no checkpoint):  
```bash
python usage_examples/run_benchmark.py \
    --model DNABERT2 \
    --report_algo_name dna_bert2_baseline \
    --csv_path results/baseline_results.csv
```

With a custom model checkpoint:  
```bash
python usage_examples/run_benchmark.py \
    --model DNABERT2 \
    --checkpoint_path /path/to/model.pt \
    --report_algo_name my_custom_model \
    --csv_path results/my_results.csv \
    --epochs 5
```

Linear probe (freeze backbone, train only projection):  
```bash
python usage_examples/run_benchmark.py \
    --model DNABERT2 \
    --linear_prob \
    --report_algo_name linear_probe \
    --csv_path results/linear_probe_results.csv
```

Disable inference cache:  
```bash
python usage_examples/run_benchmark.py \
    --model DNABERT2 \
    --cache_size 0 \
    --report_algo_name no_cache \
    --csv_path results/no_cache_results.csv
```

Cap inference cache RAM (200 GB):  
```bash
python usage_examples/run_benchmark.py \
    --model DNABERT2 \
    --cache_size 200 \
    --report_algo_name capped_cache \
    --csv_path results/capped_cache_results.csv
```

#### Inference caching

Selected zero-shot variant effect prediction tasks cache repeated reference sequences during evaluation (see above). Set `"cache_size": 0` in `task_config` to turn caching off, or a positive GB value to cap cache RAM.

On top of that, `usage_examples/run_benchmark.py` uses the same caching utility (`gfmbench_api/utils/caching_utils.py`) in two other places:

- **Linear probing (`--linear_prob`):** caches frozen backbone forwards when only the projection layer is trained.
- **Supervised variant effect prediction:** caches reference sequences during evaluation.

When omitted, `--cache_size` is unlimited (a warning is logged at startup). When a cap is reached, existing entries are still served but new entries are not stored until the cache is cleared. Pass `--cache_size 0` to disable all caching.

---

## Data Preparation

### Root Data Directory

All task data is stored under a single root directory (customize the path!):

```python
root_data_dir_path = "/path/to/your/data/"
```

### Task Data Structure

Each task creates a subdirectory, e.g.:

```
<root_data_dir_path>/
├── gue_promoter_all/
│   ├── train.csv
│   ├── dev.csv
│   └── test.csv
├── clinvar_vepeval/
│   └── ...
└── ...
```

- Task subdirectory names correspond to class names.
- If files are missing, they will be automatically downloaded on first use.

#### Dataset Format

| Task Type        | Key columns                          | Example          |
|------------------|--------------------------------------|------------------|
| Classification   | `sequence`, `label`                  | `ATCCGA...`, 1   |
| Variant effect   | `ref_sequence`, `alt_sequence`, label| `A...T...`, 0    |

##### Sequence Symbol Conventions

GFMBench-API uses the following standard symbols for DNA sequences in datasets:

| Symbol | Meaning | Usage |
|--------|---------|-------|
| `A`, `T`, `C`, `G` | Standard DNA nucleotides | Used to represent the four DNA bases (adenine, thymine, cytosine, guanine) |
| `N` | Unknown/ambiguous nucleotide | Used when the nucleotide at a position is unknown or ambiguous |
| `P` | Padding character | Used to pad sequences when extracting windows near chromosome boundaries |

**Notes:**
- All sequences are case-insensitive (automatically converted to uppercase)
- The `P` padding character should be added when handling boundary cases
- Unknown nucleotides (`N`) may appear in reference genome data or when sequence information is incomplete
- When creating custom tasks, ensure your sequences use only these accepted symbols

---

## Benchmark Output

Results are auto-saved in a single CSV with this schema:

| task             | metric       | model1 | model2 | ... |
|------------------|-------------|--------|--------|-----|
| gue_promoter_all | accuracy    | 0.85   | 0.82   | ... |
| gue_promoter_all | auroc       | 0.91   | 0.88   | ... |
| ...              | ...         | ...    | ...    | ... |

- Each (task, metric) is a row.
- Each model name (as passed to `--report_algo_name`) is a column.
- If a result is missing, its cell is `NO_RESULTS`.
- Incremental saving ensures safe restarts/interruption recovery.

Logs are written to `logs/` for all major steps (run progress, model errors, fine-tune loss curves, auto-downloading messages, etc).

---

## Customizing

### Example: Minimal Custom Pipeline

```python
from gfmbench_api.tasks.concrete.gue_promoter_all_task import GuePromoterAllTask
from gfmbench_api.benchmark_report import BenchmarkReport

task = GuePromoterAllTask(
    root_data_dir_path="/your/data/path",
    task_config={"max_sequence_length": 2500, "batch_size": 16},
)

my_model = MyCustomModel(device="cuda")  # Implements the required inference methods

# Optionally, supervised fine-tuning:
if task.get_task_attributes()["has_finetuning_data"]:
    dataset = task.get_finetune_dataset()
    # ... Your custom training code ...
    # After fine-tuning, wrap model with projection layer for classification (my_model = wrapped_model)

# Evaluate
my_model.eval()
results = task.eval_test_set(my_model)
print(results)

# Save results
report = BenchmarkReport(csv_path="results.csv")
report.add_scores("gue_promoter_all", "my_model", results)
report.save_csv()
```

---

## Adding Tasks

To add a new concrete task, inherit from the appropriate base class based on your task type and implement the required methods.

**Note:** If your task has unique functionality that doesn't fit the standard task types, you can inherit directly from `BaseGFMTask` and implement all required methods from scratch.

### Task Type Hierarchy

Choose the appropriate base class based on your task:

- **Single-sequence classification**: Inherit from `BaseGFMSupervisedSingleSeqTask`
- **Variant-effect classification**: Inherit from `BaseGFMSupervisedVariantEffectTask`
- Classification mode is derived from `_get_num_labels()`: one label is
  single-label classification; multiple labels are independent classification targets.

- **Supervised Regression**: Inherit from `BaseGFMSupervisedRegressionTask`
  - Implement `is_spatial_binned()` to return `True` for binned outputs or
    `False` for sequence-level outputs
  
- **Zero-Shot SNV Variant Effect**: Inherit from `BaseGFMZeroShotSNVTask`
  - For zero-shot evaluation of single-nucleotide variants (SNVs) with equal-length reference and variant sequences
  
- **Zero-Shot General Indel**: Inherit from `BaseGFMZeroShotGeneralIndelTask`
  - For zero-shot evaluation of insertions/deletions with variable-length sequences

### Required Methods to Implement

All concrete tasks must implement:

1. **`get_task_name() -> str`**: Return the task name (must match the data directory name)
2. **`_get_default_max_seq_len() -> int`**: Return the default maximum sequence length
3. **`_create_datasets()` or `_create_test_dataset()`**: Create and return datasets
   - Supervised tasks: Return `Tuple[Optional[Dataset], Optional[Dataset], Dataset]` (train, validation, test)
   - Zero-shot tasks: Return `Dataset` (test only)
   - **Important:** When creating datasets, you must account for:
     - `self.max_sequence_length`: Truncate sequences if they exceed this length (use `truncate_sequence_from_ends()` from `gfmbench_api.utils.preprocutils`)
     - `self.max_num_samples`: Limit the number of samples per split if specified (for sanity testing with smaller datasets)
4. **`get_conditional_input_meta_data_frame() -> Optional[pd.DataFrame]`**: Return metadata DataFrame if task uses conditional inputs, otherwise return `None`

Additional methods for supervised classification tasks:

5. **`_get_num_labels() -> int`**: Return the number of predicted labels
6. **`_get_num_classes() -> int`**: Return the number of classes per label

Additional methods for supervised regression tasks:

5. **`_get_num_labels() -> int`**: Return the number of continuous labels per sequence or bin
6. **`is_spatial_binned() -> bool`**: Return whether outputs include a spatial bin dimension

Additional methods for zero-shot tasks:

5. **`use_reference_cache() -> bool`**: Return `True` if repeated reference sequences make eval-time caching worthwhile; otherwise `False`.
### Example: Supervised Single-Sequence Task

```python
from gfmbench_api.tasks.base import (
    BaseGFMSupervisedSingleSeqTask,
)
import os
import pandas as pd
import torch
import numpy as np

class MyCustomTask(BaseGFMSupervisedSingleSeqTask):
    def get_task_name(self) -> str:
        return "my_custom_task"
    
    def _get_default_max_seq_len(self) -> int:
        return 512
    
    def _get_num_classes(self) -> int:
        return 2  # Binary classification

def _get_num_labels(self) -> int:
        return 1
    
    def _create_datasets(self):
        data_dir = os.path.join(self.root_data_dir_path, self.get_task_name())
        train_df = pd.read_csv(os.path.join(data_dir, "train.csv"))
        val_df = pd.read_csv(os.path.join(data_dir, "dev.csv")) if os.path.exists(os.path.join(data_dir, "dev.csv")) else None
        test_df = pd.read_csv(os.path.join(data_dir, "test.csv"))
        
        # Data process: Account for self.max_num_samples and self.max_sequence_length
        train_dataset = [(seq, label, np.array([])) for seq, label in zip(train_df['sequence'], train_df['label'])]
        validation_dataset = [(seq, label, np.array([])) for seq, label in zip(val_df['sequence'], val_df['label'])] if val_df is not None else None
        test_dataset = [(seq, label, np.array([])) for seq, label in zip(test_df['sequence'], test_df['label'])]
        
        return train_dataset, validation_dataset, test_dataset
    
    def get_conditional_input_meta_data_frame(self):
        return None
```

### Example: Zero-Shot SNV Task

```python
from gfmbench_api.tasks.base.base_gfm_zeroshot_snv_task import BaseGFMZeroShotSNVTask
import os
import pandas as pd
import numpy as np

class MyZeroShotTask(BaseGFMZeroShotSNVTask):
    def get_task_name(self) -> str:
        return "my_zero_shot_task"
    
    def _get_default_max_seq_len(self) -> int:
        return 1024
    
    def _create_test_dataset(self):
        data_dir = os.path.join(self.root_data_dir_path, self.get_task_name())
        test_df = pd.read_csv(os.path.join(data_dir, "test.csv"))
        
        # Data process: Account for self.max_num_samples and self.max_sequence_length
        test_dataset = [
            (var_seq, ref_seq, label, np.array([]))
            for var_seq, ref_seq, label in zip(test_df['variant_sequence'], test_df['reference_sequence'], test_df['label'])
        ]
        
        return test_dataset
    
    def _get_variant_position_in_sequence(self) -> int:
        return self.max_sequence_length // 2
    
    def get_conditional_input_meta_data_frame(self):
        return None

def use_reference_cache(self) -> bool:
        return True  # Set False if references are mostly unique per variant
```

## NOTICE

This project will download and install additional third-party open source software projects, including datasets licensed with non-commercial terms. Review the license terms of these open source projects before use.

**Third-party components and attributions:** see [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) (Python dependencies, Hugging Face assets, reference genomes, and other data URLs). Example: LicenseRef-UCSC-Genome-Browser — https://genome.ucsc.edu/license/

## License

NVIDIA-authored code in this repository is licensed under the **Apache License, Version 2.0**. The full text is in [LICENSE](LICENSE).

Each contributed `.py` file includes NVIDIA `SPDX-FileCopyrightText`, `SPDX-License-Identifier: Apache-2.0`, and the **short Apache-2.0 notice** (through “limitations under the License”), then **third-party URL** notices scoped to that file (or the line stating that the module does not embed third-party data download URLs). Python package attributions remain in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md). The full Apache-2.0 text is in [LICENSE](LICENSE).

## Citation
@article{larey2026gfmbench,
  title={GFMBench-API: A Standardized Interface for Benchmarking Genomic Foundation Models},
  author={Ariel Larey, Elay Dahan, Amit Bleiweiss, Raizy Kellerman, Guy Leib, Omri Nayshool, Dan Ofer, Tal Zinger, Dan Dominissini,  Gideon Rechavi,  Nicole Bussola,  Simon Lee,  Shane O’Connell,  Dung Hoang,  Marissa Wirth,  Alexander W. Charney,  Yoli Shavit,  Nati Daniel},
  journal={bioRxiv},
  pages={2026--02},
  year={2026},
  publisher={Cold Spring Harbor Laboratory}
}

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