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https://github.com/helicalAI/helical

A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.
https://github.com/helicalAI/helical

artificial-intelligence bioinformatics biology deep-learning dna-sequences evo2 foundation-models gene-expression geneformer helixmrna pre-trained-model pre-training rna rna-seq rnaseq scgpt transcriptformer transformer uce vcf

Last synced: 17 days ago
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A framework for state-of-the-art pre-trained bio foundation models on genomics and transcriptomics modalities.

Awesome Lists containing this project

README 

          


  


  

    

    

    Helical Logo

  

  





# What is Helical ?



Helical builds the Virtual AI Lab for Biological Discovery.

This open framework provides access to state-of-the-art Bio Foundation Models across genomics, transcriptomics, and single-cell data modalities.



Helical simplifies the entire lifecycle of applying Bio Foundation Models — from model access to fine-tuning and in-silico experimentation. With Helical's open-source framework, you can:

	•	Leverage the latest Bio Foundation Models through a simple Python interface

	•	Run example notebooks for key downstream tasks

	•	Customize models and workflows for your own datasets and experiments

  

This repository is continuously updated with new models, benchmarks, and utilities.

Join us in shaping the next generation of AI-powered biology.



Let’s build the most exciting AI-for-Bio community together!




![Workflow](https://github.com/helicalAI/helical/actions/workflows/release.yml/badge.svg)  

![Workflow](https://github.com/helicalAI/helical/actions/workflows/github-code-scanning/codeql/badge.svg)  

[![Docs](https://img.shields.io/badge/docs-available-brightgreen)](https://helical.readthedocs.io/)  

[![PyPI version](https://badge.fury.io/py/helical.svg)](https://badge.fury.io/py/helical)  

![GitHub contributors](https://img.shields.io/github/contributors/helicalAI/helical)  






## What's new?



### Tahoe-x1

We have integrated the Tahoe-x1 foundation model for single-cell RNA-seq data. This transformer-based model can extract both cell and gene embeddings from raw count data and supports attention weight extraction for interpretability. Try it out with our [comprehensive tutorial notebook](./examples/notebooks/Tahoe-x1-Tutorial.ipynb)!



### Cell2Sentence-Scale

We have integrated the new Cell2Sentence-Scale models which use cell sentences as input and are based on the Gemma language model architecture (2B and 27B models available in quantised versions too). You can use this model for embeddings and perturbation prediction. Follow our notebook tutorial [here](./examples/notebooks/Cell2Sen-Tutorial.ipynb). 



### New Larger Geneformer Models

We have integrated the new Geneformer models which are larger and have been trained on more data. Find out which models have been integrated into the Geneformer suite in the [model card](./helical/models/geneformer/README.md). Check out the our notebook on drug perturbation prediction using different Geneformer scalings [here](./examples/notebooks/Geneformer-Series-Comparison.ipynb).



### TranscriptFormer

We have integrated [TranscriptFormer](https://github.com/czi-ai/transcriptformer) into our helical package and have made a model card for it in our [Transcriptformer model folder](helical/models/transcriptformer/README.md). If you would like to test the model, take a look at our [example notebook](examples/notebooks/Geneformer-vs-TranscriptFormer.ipynb)!



### 🧬 Introducing Helix-mRNA-v0: Unlocking new frontiers & use cases in mRNA therapy 🧬

We’re thrilled to announce the release of our first-ever mRNA Bio Foundation Model, designed to:



1) Be Efficient, handling long sequence lengths effortlessly

2) Balance Diversity & Specificity, leveraging a 2-step pre-training approach

3) Deliver High-Resolution, using single nucleotides as a resolution



Check out our blog post to learn more about our approach and read the model card to get started.



## Installation



We recommend installing Helical within a conda environment with the commands below (run them in your terminal) - this step is optional:

```

conda create --name helical-package python=3.11.13

conda activate helical-package

```



To install the latest pip release of our Helical package, you can run the command below:

```

pip install helical

```



***Note***

Sometimes Torch is not installed as the CUDA compiled version (e.g. on different architectures) which is why you need to manually install Helical with GPU support, run the command below (or install pytorch with cuda first and then install helical):

```

pip install helical --extra-index-url https://download.pytorch.org/whl/cuXXX (replace XXX with your cuda version, e.g. 128 for cuda 12.8)

```



To install the latest Helical package, you can run the command below:

```

pip install --upgrade git+https://github.com/helicalAI/helical.git

```



Alternatively, clone the repo and install it:

```

git clone https://github.com/helicalAI/helical.git

pip install .

```



###Flash Attention Support

To enable Flash Attention (required by some models), run the command below:

```

pip install flash-attn --no-build-isolation

```

**Important** Make sure that your Pytorch CUDA Version matches your system CUDA version, especially when using flash-attn.



###Mamba-SSM Model Installation

[Optional] To install mamba-ssm and causal-conv1d use the command below:

```

pip install helical[mamba-ssm]

```

or in case you're installing from the Helical repo cloned locally:

```

pip install .[mamba-ssm]

```

###Evo2 Model Installation

To install Evo2 Specifically, follow the instructions in the [evo-2 model card](helical/models/evo_2/README.md).



### Tahoe-X1 Model Installation

To install Tahoe-X1 do the following after installing helical:

```

pip install helical[tahoe]

```



## Notes on the installation: 

- Make sure your machine has GPU(s) and Cuda installed. Currently this is a requirement for the packages mamba-ssm and causal-conv1d. 

- The package `causal_conv1d` requires `torch` to be installed already. First installing `helical` separately (without `[mamba-ssm]`) will install `torch` for you. A second installation (with `[mamba-ssm]`), installs the packages correctly.

- If you have problems installing `mamba-ssm`, you can install the package via the provided `.whl` files on their release page [here](https://github.com/state-spaces/mamba/releases/tag/v2.2.4). Choose the package according to your cuda, torch and python version:

```

pip install https://github.com/state-spaces/mamba/releases/download/v2.2.4/mamba_ssm-2.2.4+cu12torch2.3cxx11abiFALSE-cp311-cp311-linux_x86_64.whl

```

- Now continue with `pip install .[mamba-ssm]` to also install the remaining `causal-conv1d`.



### Singularity (Optional)

If you desire to run your code in a singularity file, you can use the [singularity.def](./singularity.def) file and build an apptainer with it:

```

apptainer build --sandbox singularity/helical singularity.def

```



and then shell into the sandbox container (use the --nv flag if you have a GPU available):

```

apptainer shell --nv --fakeroot singularity/helical/

```



### RNA models:

- [Helix-mRNA](https://helical.readthedocs.io/en/latest/model_cards/helix_mrna/)

- [Mamba2-mRNA](https://helical.readthedocs.io/en/latest/model_cards/mamba2_mrna/)

- [Geneformer](https://helical.readthedocs.io/en/latest/model_cards/geneformer/)

- [scGPT](https://helical.readthedocs.io/en/latest/model_cards/scgpt/)

- [Universal Cell Embedding (UCE)](https://helical.readthedocs.io/en/latest/model_cards/uce/)

- [TranscriptFormer](https://helical.readthedocs.io/en/latest/model_cards/transcriptformer/)

- [Tahoe-x1](https://helical.readthedocs.io/en/latest/model_cards/tahoe/)



### DNA models:

- [HyenaDNA](https://helical.readthedocs.io/en/latest/model_cards/hyena_dna/)

- [Caduceus](https://helical.readthedocs.io/en/latest/model_cards/caduceus/)

- [Evo 2](https://helical.readthedocs.io/en/latest/model_cards/evo_2/)



## Demo & Use Cases



To run examples, be sure to have installed the Helical package (see Installation) and that it is up-to-date.



You can look directly into the example folder above and download the script of your choice, look into our [documentation](https://helical.readthedocs.io/) for step-by-step guides or directly clone the repository using:

```

git clone https://github.com/helicalAI/helical.git

```

Within the `examples/notebooks` folder, open the notebook of your choice. We recommend starting with `Quick-Start-Tutorial.ipynb`



### Current Examples:



| Example | Description | Colab |

| ----------- | ----------- |----------- |                                                        

|[Quick-Start-Tutorial.ipynb](./examples/notebooks/Quick-Start-Tutorial.ipynb)| A tutorial to quickly get used to the helical package and environment. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Quick-Start-Tutorial.ipynb)|

|[Helix-mRNA.ipynb](./examples/notebooks/Helix-mRNA.ipynb)|An example of how to use the Helix-mRNA model.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Helix-mRNA.ipynb) |

|[Geneformer-vs-TranscriptFormer.ipynb](./examples/notebooks/Geneformer-vs-TranscriptFormer.ipynb) | Zero-Shot Reference Mapping with Geneformer & TranscriptFormer and compare the outcomes. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Geneformer-vs-TranscriptFormer.ipynb) |

|[Hyena-DNA-Inference.ipynb](./examples/notebooks/Hyena-DNA-Inference.ipynb)|An example how to do probing with HyenaDNA by training a neural network on 18 downstream classification tasks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Hyena-Dna-Inference.ipynb) |

|[Cell-Type-Annotation.ipynb](./examples/notebooks/Cell-Type-Annotation.ipynb)|An example how to do probing with scGPT by training a neural network to predict cell type annotations.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Type-Annotation.ipynb) |

|[Cell-Type-Classification-Fine-Tuning.ipynb](./examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb)|An example how to fine-tune different models on classification tasks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Type-Classification-Fine-Tuning.ipynb) |

|[HyenaDNA-Fine-Tuning.ipynb](./examples/notebooks/HyenaDNA-Fine-Tuning.ipynb)|An example of how to fine-tune the HyenaDNA model on downstream benchmarks.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/HyenaDNA-Fine-Tuning.ipynb) |

|[Cell-Gene-Cls-embedding-generation.ipynb](./examples/notebooks/Cell-Gene-Cls-embedding-generation.ipynb)|A notebook explaining the different embedding modes of single cell RNA models.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell-Gene-Cls-embedding-generation.ipynb) |

|[Geneformer-Series-Comparison.ipynb](./examples/notebooks/Geneformer-Series-Comparison.ipynb)|A zero shot comparison between Geneformer model scaling on drug perturbation prediction|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Geneformer-Series-Comparison.ipynb) |

|[Cell2Sen-Tutorial.ipynb](./examples/notebooks/Cell2Sen-Tutorial.ipynb)|An example tutorial of how to use cell2sen models for embeddings and perturbation predictions.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Cell2Sen-Tutorial.ipynb) |

|[Tahoe-x1-Tutorial.ipynb](./examples/notebooks/Tahoe-x1-Tutorial.ipynb)|A comprehensive tutorial on using the Tahoe-x1 model for extracting cell and gene embeddings, with attention visualization.|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/helicalAI/helical/blob/main/examples/notebooks/Tahoe-x1-Tutorial.ipynb) |



## Stuck somewhere ? Other ideas ?

We are eager to help you and interact with you:

- Join our [Slack channel](https://dk1sxv04.eu1.hubspotlinksfree.com/Ctc/L2+113/dk1sxv04/VWtlqj8M7nFNVf1vhw52bPfMW8wLjj95ptQw7N1k24YY3m2ndW8wLKSR6lZ3ldW7fZmPx5PxJ2lW8mYJtq5xWH5BVsxw821cWpdKW8CYXdj753XHSW8b5vG-7PTQ2LW1zs6x622rZxDW6930hX7RPKh3N5-trBXyRHkwVfJ3Zs3wRQV_N5NbYL3-lm47W1HvYX63pJp9cW6QXY-x6QsWMTW8G5jZh7T4vphN4Qtr7dMCxlJW8rM1-Y42pS-PW5sfJbh4FyRMhW5mHPkD4yCl56W36YW1_4GpPrGW7-sRYG1gXy8hMXqK6Sp5p69W8YTpvd3tC80SW2PTYtr6hP0dxW863B5F4KNCYkVFSWl390bSlQW78rxWn7JbS3LW14ZJ735n7SpFVSVlQr7lm7vwVlWslf6g9JRQf8mBL3b04) where you can discuss applications of bio foundation models.

- You can also open Github issues [here](https://github.com/helicalAI/helical/issues).



## Why should I use Helical & what to expect in the future?

If you are (or plan to) working with bio foundation models s.a. Geneformer or UCE on RNA and DNA data, Helical will be your best buddy! We provide and improve on:

- Up-to-date model library

- A unified API for all models

- User-facing abstractions tailored to computational biologists, researchers & AI developers

- Innovative use case and application examples and ideas

- Efficient data processing & code-base



We will continuously upload the latest model, publish benchmarks and make our code more efficient.



## Contributing



We welcome all kinds of contributions, including code, documentation, bug reports, and feature suggestions. Please read our [Contributing Guidelines](CONTRIBUTING.md) to help us keep the project organized and collaborative.



## Acknowledgements



A lot of our models have been published by talented authors developing these exciting technologies. We sincerely thank the authors of the following open-source projects:



- [scGPT](https://github.com/bowang-lab/scGPT/)

- [Geneformer](https://huggingface.co/ctheodoris/Geneformer)

- [UCE](https://github.com/snap-stanford/UCE)

- [TranscriptFormer](https://github.com/czi-ai/transcriptformer)

- [HyenaDNA](https://github.com/HazyResearch/hyena-dna)

- [Cell2Sen](https://github.com/vandijklab/cell2sentence)

- [Tahoe-X1](https://github.com/tahoebio/tahoe-x1)

- [llm-foundry](https://github.com/mosaicml/llm-foundry)

- [composer](https://github.com/mosaicml/composer)

- [anndata](https://github.com/scverse/anndata)

- [scanpy](https://github.com/scverse/scanpy)

- [transformers](https://github.com/huggingface/transformers)

- [scikit-learn](https://github.com/scikit-learn/scikit-learn)

- [GenePT](https://github.com/yiqunchen/GenePT)

- [Caduceus](https://github.com/kuleshov-group/caduceus)

- [Evo2](https://github.com/ArcInstitute/evo2)

- [torch](https://github.com/pytorch/pytorch/blob/main/LICENSE)

- [torchvision](https://github.com/pytorch/vision/blob/release/0.21/LICENSE)



### Licenses



You can find the Licenses for each model implementation in the model repositories:



- [Helix-mRNA](https://github.com/helicalAI/helical/blob/release/helical/models/helix_mrna/LICENSE)

- [Mamba2-mRNA](https://github.com/helicalAI/helical/blob/release/helical/models/mamba2_mrna/LICENSE)

- [scGPT](https://github.com/helicalAI/helical/blob/release/helical/models/scgpt/LICENSE)

- [Geneformer](https://github.com/helicalAI/helical/blob/release/helical/models/geneformer/LICENSE)

- [UCE](https://github.com/helicalAI/helical/blob/release/helical/models/uce/LICENSE)

- [TranscriptFormer](https://github.com/helicalAI/helical/blob/release/helical/models/transcriptformer/LICENSE.md)

- [HyenaDNA](https://github.com/helicalAI/helical/blob/release/helical/models/hyena_dna/LICENSE)

- [Evo2](https://github.com/helicalAI/helical/blob/release/helical/models/evo_2/LICENSE)

- [Cell2Sen](https://github.com/helicalAI/helical/blob/release/helical/models/c2s/LICENSE)

- [Tahoe-X1](https://github.com/helicalAI/helical/blob/release/helical/models/tahoe/LICENSE)



## Citation



Please use this BibTeX to cite this repository in your publications:



```bibtex

@software{allard_2024_13135902,

  author       = {Helical Team},

  title        = {helicalAI/helical: v1.1.0},

  month        = nov,

  year         = 2024,

  publisher    = {Zenodo},

  version      = {1.1.0},

  doi          = {10.5281/zenodo.13135902},

  url          = {https://doi.org/10.5281/zenodo.13135902}

}

```