ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation
© 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous can...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Oxford University Press (OUP)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135850 |
| _version_ | 1811077605192892416 |
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| author | Juul, Malene Madsen, Tobias Guo, Qianyun Bertl, Johanna Hobolth, Asger Kellis, Manolis Pedersen, Jakob Skou |
| author_facet | Juul, Malene Madsen, Tobias Guo, Qianyun Bertl, Johanna Hobolth, Asger Kellis, Manolis Pedersen, Jakob Skou |
| author_sort | Juul, Malene |
| collection | MIT |
| description | © 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values. Availability and implementation ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2 Supplementary informationSupplementary dataare available at Bioinformatics online. |
| first_indexed | 2024-09-23T10:45:40Z |
| format | Article |
| id | mit-1721.1/135850 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:45:40Z |
| publishDate | 2021 |
| publisher | Oxford University Press (OUP) |
| record_format | dspace |
| spelling | mit-1721.1/1358502021-10-28T04:26:57Z ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation Juul, Malene Madsen, Tobias Guo, Qianyun Bertl, Johanna Hobolth, Asger Kellis, Manolis Pedersen, Jakob Skou © 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources. Results Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values. Availability and implementation ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2 Supplementary informationSupplementary dataare available at Bioinformatics online. 2021-10-27T20:29:37Z 2021-10-27T20:29:37Z 2019 2019-06-07T14:45:15Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135850 en 10.1093/BIOINFORMATICS/BTY511 Bioinformatics Creative Commons Attribution NonCommercial License 4.0 https://creativecommons.org/licenses/by-nc/4.0/ application/pdf Oxford University Press (OUP) Oxford University Press |
| spellingShingle | Juul, Malene Madsen, Tobias Guo, Qianyun Bertl, Johanna Hobolth, Asger Kellis, Manolis Pedersen, Jakob Skou ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation |
| title | ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation |
| title_full | ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation |
| title_fullStr | ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation |
| title_full_unstemmed | ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation |
| title_short | ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation |
| title_sort | ncddetect2 improved models of the site specific mutation rate in cancer and driver detection with robust significance evaluation |
| url | https://hdl.handle.net/1721.1/135850 |
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