SigProfilerAssignment enables assignment of previously known mutational signatures to individual samples and individual somatic mutations. The tool refits different types of reference mutational signatures, including COSMIC signatures, as well as custom signature databases. Refitting of known mutational signatures is a numerical optimization approach that not only identifies the set of operative mutational signatures in a particular sample, but also quantifies the number of mutations assigned to each signature found in that sample. SigProfilerAssignment makes use of SigProfilerMatrixGenerator and SigProfilerPlotting, seamlessly integrating with other SigProfiler tools.

For users that prefer working in an R environment, a wrapper package is provided and can be found and installed from: https://github.com/AlexandrovLab/SigProfilerAssignmentR. Detailed documentation can be found at: https://osf.io/mz79v/wiki/home/.

• Installation
• Running
  • Main Parameters
  • Signature Subgroups
• Examples
• De novo extraction of mutational signatures downstream analysis
• Citation
• Copyright
• Contact Information

Install the current stable PyPi version of SigProfilerAssignment:

$ pip install SigProfilerAssignment

If mutation calling files (MAF, VCF, or simple text files) are used as input, please install your desired reference genome as follows (available reference genomes are: GRCh37, GRCh38, mm9, mm10, and rn6):

$ python
from SigProfilerMatrixGenerator import install as genInstall
genInstall.install('GRCh37')

Assignment of known mutational signatures to individual samples is performed using the cosmic_fit function. Input samples are provided using the samples parameter in the form of mutation calling files (VCFs, MAFs, or simple text files), segmentation files or mutational matrices. COSMIC mutational signatures v3.4 are used as the default reference signatures, although previous COSMIC versions and custom signature databases are also supported using the cosmic_version and signature_database parameters. Results will be found in the folder specified in the output parameter.

from SigProfilerAssignment import Analyzer as Analyze
Analyze.cosmic_fit(samples, output, input_type="matrix", context_type="96",
                   collapse_to_SBS96=True, cosmic_version=3.4, exome=False,
                   genome_build="GRCh37", signature_database=None,
                   exclude_signature_subgroups=None, export_probabilities=False,
                   export_probabilities_per_mutation=False, make_plots=False,
                   sample_reconstruction_plots=False, verbose=False)

When using COSMIC reference signatures, some subgroups of signatures can be removed to improve the refitting analysis. To use this feature, the exclude_signature_subgroups parameter should be added, following the sintax below:

exclude_signature_subgroups = ['MMR_deficiency_signatures',
                               'POL_deficiency_signatures',
                               'HR_deficiency_signatures' ,
                               'BER_deficiency_signatures',
                               'Chemotherapy_signatures',
                               'Immunosuppressants_signatures'
                               'Treatment_signatures'
                               'APOBEC_signatures',
                               'Tobacco_signatures',
                               'UV_signatures',
                               'AA_signatures',
                               'Colibactin_signatures',
                               'Artifact_signatures',
                               'Lymphoid_signatures']

The full list of signature subgroups is included in the following table:

import SigProfilerAssignment as spa
from SigProfilerAssignment import Analyzer as Analyze

Analyze.cosmic_fit(samples=spa.__path__[0]+"/data/tests/vcf_input", 
                   output="example_vcf",
                   input_type="vcf",
                   context_type="96",
                   genome_build="GRCh37",
                   cosmic_version=3.4)

import SigProfilerAssignment as spa
from SigProfilerAssignment import Analyzer as Analyze

Analyze.cosmic_fit(samples=spa.__path__[0]+"/data/tests/cnv_input/all.breast.ascat.summary.sample.tsv", 
                   output="example_sf",
                   input_type="seg:ASCAT_NGS",
                   cosmic_version=3.4,
                   collapse_to_SBS96=False)

import SigProfilerAssignment as spa
from SigProfilerAssignment import Analyzer as Analyze

Analyze.cosmic_fit(samples=spa.__path__[0]+"/data/tests/txt_input/sample_matrix_SBS.txt", 
                   output="example_mm",
                   input_type="matrix",
                   genome_build="GRCh37",
                   cosmic_version=3.4)

Additional functionalities for downstream analysis of de novo extraction of mutational signatures are also available as part of SigProfilerAssignment, including assignment of de novo extracted mutational signatures and decomposition of de novo signatures using a known set of signatures. More information can be found on the wiki page at https://osf.io/mz79v/wiki/5.%20Advanced%20mode/.

Unit tests can be run with the following commands:

python setup.py sdist
pip install .[tests]
pytest tests

Díaz-Gay, M., Vangara, R., Barnes, M., ... & Alexandrov, L. B. (2023). Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment, Bioinformatics, 2023-07. doi: https://doi.org/10.1093/bioinformatics/btad756

This software and its documentation are copyright 2022 as a part of the SigProfiler project. The SigProfilerAssignment framework is free software and is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Please address any queries or bug reports to Raviteja Vangara at rvangara@health.ucsd.edu or Marcos Díaz-Gay at mdiazgay@health.ucsd.edu.