# Oncodrive3D
**Oncodrive3D** is a fast and accurate computational method designed to analyze patterns of somatic mutation across tumors, with the goal of identifying **three-dimensional (3D) clusters** of missense mutations and detecting genes under **positive selection**.
The method leverages **AlphaFold 2-predicted protein structures** and Predicted Aligned Error (PAE) to define residue contacts within the protein's 3D space. When available, it integrates **mutational profiles** to build an accurate background model of neutral mutagenesis. By applying a novel **rank-based statistical approach**, Oncodrive3D scores potential 3D clusters and computes empirical p-values.
[](https://www.gnu.org/licenses/agpl-3.0)
[](https://hub.docker.com/r/bbglab/oncodrive3d)
[](https://pypi.org/project/Oncodrive3D/)
---
## Requirements
Before you begin, ensure **Python 3.10 or later** is installed on your system.
Additionally, you may need to install additional development tools. Depending on your environment, you can choose one of the following methods:
- If you have sudo privileges:
```bash
sudo apt install built-essential
```
- For HPC cluster environment, it is recommended to use [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) (or [Mamba](https://mamba.readthedocs.io/en/latest/)):
```bash
conda create -n o3d python=3.10.0
conda activate o3d
conda install -c conda-forge gxx gcc libxcrypt clang zlib
```
## Installation
- Install via PyPI:
```bash
pip install oncodrive3d
```
- Alternatively, you can obtain the latest code from the repository and install it for development with pip:
```bash
git clone https://github.com/bbglab/oncodrive3d.git
cd oncodrive3d
pip install -e .
oncodrive3d --help
```
- Or you can use a modern build tool like [uv](https://github.com/astral-sh/uv):
```bash
git clone https://github.com/bbglab/oncodrive3d.git
cd oncodrive3d
uv run oncodrive3d --help
```
## Building Datasets
This step build the datasets necessary for Oncodrive3D to run the 3D clustering analysis. It is required once after installation or whenever you need to generate datasets for a different organism or apply a specific threshold to define amino acid contacts.
> [!WARNING]
> This step is highly time- and resource-intensive, requiring a significant amount of free disk space and computational power. It will download and process a large amount of data. Ensure sufficient resources are available before proceeding, as insufficient capacity may result in extended runtimes or processing failures.
> [!NOTE]
> The first time that you run Oncodrive3D building dataset step with a given reference genome, it will download it from our servers. By default the downloaded datasets go to`~/.bgdata`. If you want to move these datasets to another folder you have to define the system environment variable `BGDATA_LOCAL` with an export command.
```
Usage: oncodrive3d build-datasets [OPTIONS]
Examples:
Basic build:
oncodrive3d build-datasets -o <build_folder>
Build with MANE Select transcripts:
oncodrive3d build-datasets -o <build_folder> --mane
Options:
-o, --output_dir PATH Path to the directory where the output files will be saved.
Default: ./datasets/
-s, --organism PATH Specifies the organism (`human` or `mouse`).
Default: human
-m, --mane Use structures predicted from MANE Select transcripts
(applicable to Homo sapiens only).
-M, --mane_only Use only structures predicted from MANE Select transcripts
(applicable to Homo sapiens only).
-C, --custom_mane_pdb_dir Path to directory containing custom MANE PDB structures.
Default: None
-f, --custom_mane_metadata_path Path to a dataframe (typically a samplesheet.csv) including
Ensembl IDs and sequences of the custom pdbs.
-d, --distance_threshold INT Distance threshold (Å) for defining residues contacts.
Default: 10
-c, --cores INT Number of CPU cores for computation.
Default: All available CPU cores
-v, --verbose Enables verbose output.
-h, --help Show this message and exit.
```
For more information on the output of this step, please refer to the [Building Datasets Output Documentation](https://github.com/bbglab/oncodrive3d/tree/master/docs/build_output.md).
> [!TIP]
> ### Increasing MANE Structural Coverage
> To maximize structural coverage of **MANE Select transcripts**, you can predict missing structures locally and integrate them into Oncodrive3D using:
>
> - `tools/preprocessing/prepare_samplesheet.py`: a standalone utility that:
> - Retrieve the full MANE entries from NCBI.
> - Identifies proteins missing from the AlphaFold MANE dataset.
> - Generates:
> - A `samplesheet.csv` with Ensembl protein IDs, FASTA paths, and optional sequences.
> - Individual FASTA files for each missing protein.
>
> - `--custom_mane_pdb_dir`: use this to provide your own predicted PDB structures (e.g., from [nf-core/proteinfold](https://nf-co.re/proteinfold/1.0.0/)).
>
> - `--custom_mane_metadata_path`: path to the corresponding `samplesheet.csv`, which must include:
> - `sequence`: Ensembl protein ID (required)
> - `refseq`: amino acid sequence (used to inject sequence into PDB if missing)
>
## Running 3D clustering Analysis
For in depth information on how to obtain the required input data and for comprehensive information about the output, please refer to the [Input and Output Documentation](https://github.com/bbglab/oncodrive3d/tree/master/docs/run_input_output.md) of the 3D clustering analysis.
### Input
- **Mutations file** (`required`): It can be either:
- **<input_maf>**: A Mutation Annotation Format (MAF) file annotated with consequences (e.g., by using [Ensembl Variant Effect Predictor (VEP)](https://www.ensembl.org/info/docs/tools/vep/index.html)).
- **<input_vep>**: The unfiltered output of VEP including annotations for all possible transcripts.
- **<mut_profile>** (`optional`): Dictionary including the normalized frequencies of mutations (*values*) in every possible trinucleotide context (*keys*), such as 'ACA>A', 'ACC>A', and so on.
---
> [!NOTE]
> Examples of the input files are available in the [Test Input Folder](https://github.com/bbglab/oncodrive3d/tree/master/test/input).
Please refer to these examples to understand the expected format and structure of the input files.
---
---
> [!NOTE]
> Oncodrive3D uses the mutational profile of the cohort to build an accurate background model. However, it’s not strictly required. If the mutational profile is not provided, the tool will use a simple uniform distribution as the background model for simulating mutations and scoring potential 3D clusters.
---
### Main Output
- **Gene-level output**: CSV file (`\<cohort>.3d_clustering_genes.csv`) containing the results of the analysis at the gene level. Each row represents a gene, sorted from the most significant to the least significant based on the 3D clustering analysis. The table also includes genes that were not analyzed, with the reason for exclusion provided in the `status` column.
- **Residue-level output**: CSV file (`<cohort>.3d_clustering_pos.csv`) containing the results of the analysis at the level of mutated residues. Each row corresponds to a mutated position within a gene and includes detailed information for each potential mutational cluster.
### Usage
```
Usage: oncodrive3d run [OPTIONS]
Examples:
Basic run:
oncodrive3d run -i <input_maf> -p <mut_profile> -d <build_folder> -C <cohort_name>
Example of run using VEP output as input and MANE Select transcripts:
oncodrive3d run -i <input_vep> -p <mut_profile> -d <build_folder> -C <cohort_name> \
--o3d_transcripts --use_input_symbols --mane
Options:
-i, --input_path PATH Path to the input file (MAF or VEP output) containing the
annotated mutations for the cohort. [required]
-p, --mut_profile_path PATH Path to the JSON file specifying the cohort's mutational
profile (192 key-value pairs).
-o, --output_dir PATH Path to the output directory for results.
Default: ./output/
-d, --data_dir PATH Path to the directory containing the datasets built in the
building datasets step.
Default: ./datasets/
-c, --cores INT Number of CPU cores to use.
Default: All available CPU cores
-s, --seed INT Random seed for reproducibility.
-v, --verbose Enables verbose output.
-t, --cancer_type STR Cancer type to include as metadata in the output file.
-C, --cohort STR Cohort name for metadata and output file naming.
-P, --cmap_prob_thr FLOAT Threshold for defining residues contacts based on distance
on predicted structure and predicted aligned error (PAE).
Default: 0.5
--mane Prioritizes MANE Select transcripts when multiple
structures map to the same gene symbol.
--o3d_transcripts Filters mutations including only transcripts in Oncodrive3D
built datasets (requires VEP output as input file).
--use_input_symbols Update HUGO symbols in Oncodrive3D built datasets using the
input file's entries (requires VEP output as input file).
-h, --help Show this message and exit.
```
---
> [!NOTE]
> To maximize the number of matching transcripts between the input mutations and the AlphaFold predicted structures used by Oncodrive3D, it is recommended to use the unfiltered output of VEP (including all possible transcripts) as input, along with the flags `--o3d_transcripts` `--use_input_symbols` in the `oncodrive3d run` command.
---
### Running With Singularity
```
singularity pull oncodrive3d.sif docker://bbglab/oncodrive3d:latest
singularity exec oncodrive3d.sif oncodrive3d run -i <input_maf> -p <mut_profile> \
-d <build_folder> -C <cohort_name>
```
### Testing
To verify that Oncodrive3D is installed and configured correctly, you can perform a test run using the provided test input files:
```
oncodrive3d run -d <build_folder> \
-i ./test/input/maf/TCGA_WXS_ACC.in.maf \
-p ./test/input/mut_profile/TCGA_WXS_ACC.sig.json \
-o ./test/output/ -C TCGA_WXS_ACC
```
Check the output in the `test/output/` directory to ensure the analysis completes successfully.
## Parallel Processing on Multiple Cohorts
This repository provides a [Nextflow](https://www.nextflow.io/) pipeline to run Oncodrive3D in parallel across multiple cohorts, enabling efficient, reproducible and scalable analysis across datasets.
For more information, refer to the [Oncodrive3D Pipeline](https://github.com/bbglab/oncodrive3d/tree/master/oncodrive3d_pipeline/) documentation.
### Usage
---
> [!WARNING]
> When using the Nextflow script, ensure that your input files are organized in the following directory structure (you only need either the `maf/` or `vep/` directory):
>
> ```plaintext
> input/
> ├── maf/
> │ └── <cohort>.in.maf
> ├── vep/
> │ └── <cohort>.vep.tsv.gz
> └── mut_profile/
> └── <cohort>.sig.json
> ```
>
> - `maf/`: Contains mutation files with the `.in.maf` extension.
> - `vep/`: Contains VEP annotation files with the `.vep.tsv.gz` extension, which include annotated mutations with all possible transcripts.
> - `mut_profile/`: Contains mutational profile files with the `.sig.json` extension.
---
```
Usage: nextflow run main.nf [OPTIONS]
Example of run using VEP output as input and MANE Select transcripts:
nextflow run main.nf -profile container --data_dir <build_folder> --indir <input> \
--vep_input true --mane true
Options:
--indir PATH Path to the input directory including the subdirectories
`maf/` or `vep/` and `mut_profile/`.
--outdir PATH Path to the output directory.
Default: run_<timestamp>/
--cohort_pattern STR Pattern expression to filter specific files within the
input directory (e.g., 'TCGA*' select only TCGA cohorts).
Default: *
--data_dir PATH Path to the Oncodrive3D datasets directory, which includes
the files compiled during the building datasets step.
Default: ${baseDir}/datasets/
--max_running INT Maximum number of cohorts to process in parallel.
Default: 5
--cores INT Number of CPU cores used to process each cohort.
Default: 10
--memory STR Amount of memory allocated for processing each cohort.
Default: 70GB
--vep_input BOOL Use `vep/` subdir as input and select transcripts matching
the Ensembl transcript IDs in Oncodrive3D built datasets.
Default: false
--mane BOOL Prioritize structures corresponding to MANE transcrips if
multiple structures are associated to the same gene.
Default: false
--seed INT: Seed value for reproducibility.
Default: 128
```
## License
Oncodrive3D is available to the general public subject to certain conditions described in its [LICENSE](LICENSE).
## Citation
If you use Oncodrive3D in your research, please cite the original paper: [Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selection](https://www.biorxiv.org/content/10.1101/2025.01.11.632354v3).
Raw data
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"description": "# Oncodrive3D\n\n**Oncodrive3D** is a fast and accurate computational method designed to analyze patterns of somatic mutation across tumors, with the goal of identifying **three-dimensional (3D) clusters** of missense mutations and detecting genes under **positive selection**. \n\nThe method leverages **AlphaFold 2-predicted protein structures** and Predicted Aligned Error (PAE) to define residue contacts within the protein's 3D space. When available, it integrates **mutational profiles** to build an accurate background model of neutral mutagenesis. By applying a novel **rank-based statistical approach**, Oncodrive3D scores potential 3D clusters and computes empirical p-values.\n\n[](https://www.gnu.org/licenses/agpl-3.0)\n[](https://hub.docker.com/r/bbglab/oncodrive3d)\n[](https://pypi.org/project/Oncodrive3D/)\n\n![Graphical abstract of Oncodrive3D](docs/images/graphical_abstract.png \"Oncodrive3D\")\n\n---\n\n## Requirements\n\nBefore you begin, ensure **Python 3.10 or later** is installed on your system. \nAdditionally, you may need to install additional development tools. Depending on your environment, you can choose one of the following methods:\n\n- If you have sudo privileges:\n\n ```bash\n sudo apt install built-essential\n ```\n\n- For HPC cluster environment, it is recommended to use [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) (or [Mamba](https://mamba.readthedocs.io/en/latest/)):\n\n ```bash\n conda create -n o3d python=3.10.0\n conda activate o3d\n conda install -c conda-forge gxx gcc libxcrypt clang zlib\n ```\n\n\n## Installation\n\n- Install via PyPI:\n\n ```bash\n pip install oncodrive3d\n ```\n\n- Alternatively, you can obtain the latest code from the repository and install it for development with pip:\n\n ```bash\n git clone https://github.com/bbglab/oncodrive3d.git\n cd oncodrive3d\n pip install -e .\n oncodrive3d --help\n ```\n\n- Or you can use a modern build tool like [uv](https://github.com/astral-sh/uv):\n\n ```bash\n git clone https://github.com/bbglab/oncodrive3d.git\n cd oncodrive3d\n uv run oncodrive3d --help\n ```\n\n## Building Datasets\n\nThis step build the datasets necessary for Oncodrive3D to run the 3D clustering analysis. It is required once after installation or whenever you need to generate datasets for a different organism or apply a specific threshold to define amino acid contacts.\n\n> [!WARNING]\n> This step is highly time- and resource-intensive, requiring a significant amount of free disk space and computational power. It will download and process a large amount of data. Ensure sufficient resources are available before proceeding, as insufficient capacity may result in extended runtimes or processing failures.\n\n> [!NOTE]\n> The first time that you run Oncodrive3D building dataset step with a given reference genome, it will download it from our servers. By default the downloaded datasets go to`~/.bgdata`. If you want to move these datasets to another folder you have to define the system environment variable `BGDATA_LOCAL` with an export command.\n\n```\nUsage: oncodrive3d build-datasets [OPTIONS]\n\nExamples:\n Basic build:\n oncodrive3d build-datasets -o <build_folder>\n \n Build with MANE Select transcripts:\n oncodrive3d build-datasets -o <build_folder> --mane\n\nOptions:\n -o, --output_dir PATH Path to the directory where the output files will be saved. \n Default: ./datasets/\n -s, --organism PATH Specifies the organism (`human` or `mouse`). \n Default: human\n -m, --mane Use structures predicted from MANE Select transcripts \n (applicable to Homo sapiens only).\n -M, --mane_only Use only structures predicted from MANE Select transcripts\n (applicable to Homo sapiens only).\n -C, --custom_mane_pdb_dir Path to directory containing custom MANE PDB structures.\n Default: None\n -f, --custom_mane_metadata_path Path to a dataframe (typically a samplesheet.csv) including \n Ensembl IDs and sequences of the custom pdbs.\n -d, --distance_threshold INT Distance threshold (\u00c5) for defining residues contacts. \n Default: 10\n -c, --cores INT Number of CPU cores for computation. \n Default: All available CPU cores\n -v, --verbose Enables verbose output.\n -h, --help Show this message and exit. \n```\n\nFor more information on the output of this step, please refer to the [Building Datasets Output Documentation](https://github.com/bbglab/oncodrive3d/tree/master/docs/build_output.md).\n\n> [!TIP]\n> ### Increasing MANE Structural Coverage\n> To maximize structural coverage of **MANE Select transcripts**, you can predict missing structures locally and integrate them into Oncodrive3D using:\n>\n> - `tools/preprocessing/prepare_samplesheet.py`: a standalone utility that:\n> - Retrieve the full MANE entries from NCBI.\n> - Identifies proteins missing from the AlphaFold MANE dataset.\n> - Generates:\n> - A `samplesheet.csv` with Ensembl protein IDs, FASTA paths, and optional sequences.\n> - Individual FASTA files for each missing protein.\n>\n> - `--custom_mane_pdb_dir`: use this to provide your own predicted PDB structures (e.g., from [nf-core/proteinfold](https://nf-co.re/proteinfold/1.0.0/)).\n>\n> - `--custom_mane_metadata_path`: path to the corresponding `samplesheet.csv`, which must include:\n> - `sequence`: Ensembl protein ID (required)\n> - `refseq`: amino acid sequence (used to inject sequence into PDB if missing)\n>\n\n\n\n## Running 3D clustering Analysis\n\nFor in depth information on how to obtain the required input data and for comprehensive information about the output, please refer to the [Input and Output Documentation](https://github.com/bbglab/oncodrive3d/tree/master/docs/run_input_output.md) of the 3D clustering analysis. \n\n### Input\n\n- **Mutations file** (`required`): It can be either:\n - **<input_maf>**: A Mutation Annotation Format (MAF) file annotated with consequences (e.g., by using [Ensembl Variant Effect Predictor (VEP)](https://www.ensembl.org/info/docs/tools/vep/index.html)).\n - **<input_vep>**: The unfiltered output of VEP including annotations for all possible transcripts.\n\n- **<mut_profile>** (`optional`): Dictionary including the normalized frequencies of mutations (*values*) in every possible trinucleotide context (*keys*), such as 'ACA>A', 'ACC>A', and so on.\n\n---\n\n> [!NOTE] \n> Examples of the input files are available in the [Test Input Folder](https://github.com/bbglab/oncodrive3d/tree/master/test/input). \nPlease refer to these examples to understand the expected format and structure of the input files.\n\n---\n\n---\n\n> [!NOTE]\n> Oncodrive3D uses the mutational profile of the cohort to build an accurate background model. However, it\u2019s not strictly required. If the mutational profile is not provided, the tool will use a simple uniform distribution as the background model for simulating mutations and scoring potential 3D clusters.\n\n---\n\n### Main Output\n\n- **Gene-level output**: CSV file (`\\<cohort>.3d_clustering_genes.csv`) containing the results of the analysis at the gene level. Each row represents a gene, sorted from the most significant to the least significant based on the 3D clustering analysis. The table also includes genes that were not analyzed, with the reason for exclusion provided in the `status` column.\n \n- **Residue-level output**: CSV file (`<cohort>.3d_clustering_pos.csv`) containing the results of the analysis at the level of mutated residues. Each row corresponds to a mutated position within a gene and includes detailed information for each potential mutational cluster.\n\n\n### Usage\n\n```\nUsage: oncodrive3d run [OPTIONS]\n\nExamples:\n Basic run:\n oncodrive3d run -i <input_maf> -p <mut_profile> -d <build_folder> -C <cohort_name>\n \n Example of run using VEP output as input and MANE Select transcripts:\n oncodrive3d run -i <input_vep> -p <mut_profile> -d <build_folder> -C <cohort_name> \\\n --o3d_transcripts --use_input_symbols --mane\n\nOptions:\n -i, --input_path PATH Path to the input file (MAF or VEP output) containing the \n annotated mutations for the cohort. [required]\n -p, --mut_profile_path PATH Path to the JSON file specifying the cohort's mutational \n profile (192 key-value pairs).\n -o, --output_dir PATH Path to the output directory for results. \n Default: ./output/\n -d, --data_dir PATH Path to the directory containing the datasets built in the \n building datasets step. \n Default: ./datasets/\n -c, --cores INT Number of CPU cores to use. \n Default: All available CPU cores\n -s, --seed INT Random seed for reproducibility.\n -v, --verbose Enables verbose output.\n -t, --cancer_type STR Cancer type to include as metadata in the output file.\n -C, --cohort STR Cohort name for metadata and output file naming. \n -P, --cmap_prob_thr FLOAT Threshold for defining residues contacts based on distance \n on predicted structure and predicted aligned error (PAE). \n Default: 0.5\n --mane Prioritizes MANE Select transcripts when multiple \n structures map to the same gene symbol.\n --o3d_transcripts Filters mutations including only transcripts in Oncodrive3D \n built datasets (requires VEP output as input file).\n --use_input_symbols Update HUGO symbols in Oncodrive3D built datasets using the \n input file's entries (requires VEP output as input file).\n -h, --help Show this message and exit. \n```\n\n\n---\n\n> [!NOTE]\n> To maximize the number of matching transcripts between the input mutations and the AlphaFold predicted structures used by Oncodrive3D, it is recommended to use the unfiltered output of VEP (including all possible transcripts) as input, along with the flags `--o3d_transcripts` `--use_input_symbols` in the `oncodrive3d run` command.\n\n---\n\n### Running With Singularity\n\n```\nsingularity pull oncodrive3d.sif docker://bbglab/oncodrive3d:latest\nsingularity exec oncodrive3d.sif oncodrive3d run -i <input_maf> -p <mut_profile> \\ \n -d <build_folder> -C <cohort_name>\n```\n\n\n### Testing\n\nTo verify that Oncodrive3D is installed and configured correctly, you can perform a test run using the provided test input files: \n\n```\noncodrive3d run -d <build_folder> \\\n -i ./test/input/maf/TCGA_WXS_ACC.in.maf \\ \n -p ./test/input/mut_profile/TCGA_WXS_ACC.sig.json \\\n -o ./test/output/ -C TCGA_WXS_ACC\n```\n\nCheck the output in the `test/output/` directory to ensure the analysis completes successfully.\n\n\n## Parallel Processing on Multiple Cohorts\n\nThis repository provides a [Nextflow](https://www.nextflow.io/) pipeline to run Oncodrive3D in parallel across multiple cohorts, enabling efficient, reproducible and scalable analysis across datasets. \n\nFor more information, refer to the [Oncodrive3D Pipeline](https://github.com/bbglab/oncodrive3d/tree/master/oncodrive3d_pipeline/) documentation.\n\n### Usage\n\n---\n\n> [!WARNING]\n> When using the Nextflow script, ensure that your input files are organized in the following directory structure (you only need either the `maf/` or `vep/` directory):\n> \n> ```plaintext\n> input/\n> \u251c\u2500\u2500 maf/\n> \u2502 \u2514\u2500\u2500 <cohort>.in.maf\n> \u251c\u2500\u2500 vep/\n> \u2502 \u2514\u2500\u2500 <cohort>.vep.tsv.gz\n> \u2514\u2500\u2500 mut_profile/\n> \u2514\u2500\u2500 <cohort>.sig.json\n> ```\n> \n> - `maf/`: Contains mutation files with the `.in.maf` extension.\n> - `vep/`: Contains VEP annotation files with the `.vep.tsv.gz` extension, which include annotated mutations with all possible transcripts.\n> - `mut_profile/`: Contains mutational profile files with the `.sig.json` extension.\n\n---\n\n```\nUsage: nextflow run main.nf [OPTIONS]\n\nExample of run using VEP output as input and MANE Select transcripts:\n nextflow run main.nf -profile container --data_dir <build_folder> --indir <input> \\\n --vep_input true --mane true\n \nOptions:\n --indir PATH Path to the input directory including the subdirectories \n `maf/` or `vep/` and `mut_profile/`. \n --outdir PATH Path to the output directory. \n Default: run_<timestamp>/\n --cohort_pattern STR Pattern expression to filter specific files within the \n input directory (e.g., 'TCGA*' select only TCGA cohorts). \n Default: *\n --data_dir PATH Path to the Oncodrive3D datasets directory, which includes \n the files compiled during the building datasets step.\n Default: ${baseDir}/datasets/\n --max_running INT Maximum number of cohorts to process in parallel.\n Default: 5\n --cores INT Number of CPU cores used to process each cohort. \n Default: 10\n --memory STR Amount of memory allocated for processing each cohort. \n Default: 70GB\n --vep_input BOOL Use `vep/` subdir as input and select transcripts matching \n the Ensembl transcript IDs in Oncodrive3D built datasets. \n Default: false\n --mane BOOL Prioritize structures corresponding to MANE transcrips if \n multiple structures are associated to the same gene.\n Default: false\n --seed INT: Seed value for reproducibility.\n Default: 128\n```\n\n\n## License\n\nOncodrive3D is available to the general public subject to certain conditions described in its [LICENSE](LICENSE).\n\n\n## Citation \nIf you use Oncodrive3D in your research, please cite the original paper: [Oncodrive3D: Fast and accurate detection of structural clusters of somatic mutations under positive selection](https://www.biorxiv.org/content/10.1101/2025.01.11.632354v3).\n",
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"summary": "Oncodrive3D is a method designed to analyse patterns of somatic mutations across tumors to identify three-dimensional (3D) clusters of missense mutations and detect genes that are under positive selection.",
"version": "1.0.6",
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