Detailed modeling of positive selection improves detection of cancer driver genes
Finding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets.
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2019-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-11284-9 |
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doaj-e2cfc4a4bb3344c3b518ce9ae9dea3be2021-05-11T12:34:20ZengNature Publishing GroupNature Communications2041-17232019-07-0110111310.1038/s41467-019-11284-9Detailed modeling of positive selection improves detection of cancer driver genesSiming Zhao0Jun Liu1Pranav Nanga2Yuwen Liu3A. Ercument Cicek4Nicholas Knoblauch5Chuan He6Matthew Stephens7Xin He8Department of Human Genetics, University of ChicagoDepartment of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, Howard Hughes Medical Institute, University of ChicagoDepartment of Computer Science, University of ChicagoDepartment of Human Genetics, University of ChicagoComputer Engineering Department, Bilkent UniversityDepartment of Human Genetics, University of ChicagoDepartment of Chemistry, Department of Biochemistry and Molecular Biology, Institute for Biophysical Dynamics, Howard Hughes Medical Institute, University of ChicagoDepartment of Human Genetics, University of ChicagoDepartment of Human Genetics, University of ChicagoFinding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets.https://doi.org/10.1038/s41467-019-11284-9 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Siming Zhao Jun Liu Pranav Nanga Yuwen Liu A. Ercument Cicek Nicholas Knoblauch Chuan He Matthew Stephens Xin He |
| spellingShingle |
Siming Zhao Jun Liu Pranav Nanga Yuwen Liu A. Ercument Cicek Nicholas Knoblauch Chuan He Matthew Stephens Xin He Detailed modeling of positive selection improves detection of cancer driver genes Nature Communications |
| author_facet |
Siming Zhao Jun Liu Pranav Nanga Yuwen Liu A. Ercument Cicek Nicholas Knoblauch Chuan He Matthew Stephens Xin He |
| author_sort |
Siming Zhao |
| title |
Detailed modeling of positive selection improves detection of cancer driver genes |
| title_short |
Detailed modeling of positive selection improves detection of cancer driver genes |
| title_full |
Detailed modeling of positive selection improves detection of cancer driver genes |
| title_fullStr |
Detailed modeling of positive selection improves detection of cancer driver genes |
| title_full_unstemmed |
Detailed modeling of positive selection improves detection of cancer driver genes |
| title_sort |
detailed modeling of positive selection improves detection of cancer driver genes |
| publisher |
Nature Publishing Group |
| series |
Nature Communications |
| issn |
2041-1723 |
| publishDate |
2019-07-01 |
| description |
Finding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets. |
| url |
https://doi.org/10.1038/s41467-019-11284-9 |
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