De novo mutational signature discovery in tumor genomes using SparseSignatures.
Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporate...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1009119 |
| _version_ | 1819036695644864512 |
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| author | Avantika Lal Keli Liu Robert Tibshirani Arend Sidow Daniele Ramazzotti |
| author_facet | Avantika Lal Keli Liu Robert Tibshirani Arend Sidow Daniele Ramazzotti |
| author_sort | Avantika Lal |
| collection | DOAJ |
| description | Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features. |
| first_indexed | 2024-12-21T08:09:37Z |
| format | Article |
| id | doaj.art-ed06227f6ba944be9baaed2c108694e4 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T08:09:37Z |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ed06227f6ba944be9baaed2c108694e42022-12-21T19:10:42ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-06-01176e100911910.1371/journal.pcbi.1009119De novo mutational signature discovery in tumor genomes using SparseSignatures.Avantika LalKeli LiuRobert TibshiraniArend SidowDaniele RamazzottiCancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features.https://doi.org/10.1371/journal.pcbi.1009119 |
| spellingShingle | Avantika Lal Keli Liu Robert Tibshirani Arend Sidow Daniele Ramazzotti De novo mutational signature discovery in tumor genomes using SparseSignatures. PLoS Computational Biology |
| title | De novo mutational signature discovery in tumor genomes using SparseSignatures. |
| title_full | De novo mutational signature discovery in tumor genomes using SparseSignatures. |
| title_fullStr | De novo mutational signature discovery in tumor genomes using SparseSignatures. |
| title_full_unstemmed | De novo mutational signature discovery in tumor genomes using SparseSignatures. |
| title_short | De novo mutational signature discovery in tumor genomes using SparseSignatures. |
| title_sort | de novo mutational signature discovery in tumor genomes using sparsesignatures |
| url | https://doi.org/10.1371/journal.pcbi.1009119 |
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