CanDrA: cancer-specific driver missense mutation annotation with optimized features.
Driver mutations are somatic mutations that provide growth advantage to tumor cells, while passenger mutations are those not functionally related to oncogenesis. Distinguishing drivers from passengers is challenging because drivers occur much less frequently than passengers, they tend to have low pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3813554?pdf=render |
| _version_ | 1828485732461707264 |
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| author | Yong Mao Han Chen Han Liang Funda Meric-Bernstam Gordon B Mills Ken Chen |
| author_facet | Yong Mao Han Chen Han Liang Funda Meric-Bernstam Gordon B Mills Ken Chen |
| author_sort | Yong Mao |
| collection | DOAJ |
| description | Driver mutations are somatic mutations that provide growth advantage to tumor cells, while passenger mutations are those not functionally related to oncogenesis. Distinguishing drivers from passengers is challenging because drivers occur much less frequently than passengers, they tend to have low prevalence, their functions are multifactorial and not intuitively obvious. Missense mutations are excellent candidates as drivers, as they occur more frequently and are potentially easier to identify than other types of mutations. Although several methods have been developed for predicting the functional impact of missense mutations, only a few have been specifically designed for identifying driver mutations. As more mutations are being discovered, more accurate predictive models can be developed using machine learning approaches that systematically characterize the commonality and peculiarity of missense mutations under the background of specific cancer types. Here, we present a cancer driver annotation (CanDrA) tool that predicts missense driver mutations based on a set of 95 structural and evolutionary features computed by over 10 functional prediction algorithms such as CHASM, SIFT, and MutationAssessor. Through feature optimization and supervised training, CanDrA outperforms existing tools in analyzing the glioblastoma multiforme and ovarian carcinoma data sets in The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia project. |
| first_indexed | 2024-12-11T09:20:03Z |
| format | Article |
| id | doaj.art-83a55f6fef694fd390a27d1b86f36297 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T09:20:03Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-83a55f6fef694fd390a27d1b86f362972022-12-22T01:13:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01810e7794510.1371/journal.pone.0077945CanDrA: cancer-specific driver missense mutation annotation with optimized features.Yong MaoHan ChenHan LiangFunda Meric-BernstamGordon B MillsKen ChenDriver mutations are somatic mutations that provide growth advantage to tumor cells, while passenger mutations are those not functionally related to oncogenesis. Distinguishing drivers from passengers is challenging because drivers occur much less frequently than passengers, they tend to have low prevalence, their functions are multifactorial and not intuitively obvious. Missense mutations are excellent candidates as drivers, as they occur more frequently and are potentially easier to identify than other types of mutations. Although several methods have been developed for predicting the functional impact of missense mutations, only a few have been specifically designed for identifying driver mutations. As more mutations are being discovered, more accurate predictive models can be developed using machine learning approaches that systematically characterize the commonality and peculiarity of missense mutations under the background of specific cancer types. Here, we present a cancer driver annotation (CanDrA) tool that predicts missense driver mutations based on a set of 95 structural and evolutionary features computed by over 10 functional prediction algorithms such as CHASM, SIFT, and MutationAssessor. Through feature optimization and supervised training, CanDrA outperforms existing tools in analyzing the glioblastoma multiforme and ovarian carcinoma data sets in The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia project.http://europepmc.org/articles/PMC3813554?pdf=render |
| spellingShingle | Yong Mao Han Chen Han Liang Funda Meric-Bernstam Gordon B Mills Ken Chen CanDrA: cancer-specific driver missense mutation annotation with optimized features. PLoS ONE |
| title | CanDrA: cancer-specific driver missense mutation annotation with optimized features. |
| title_full | CanDrA: cancer-specific driver missense mutation annotation with optimized features. |
| title_fullStr | CanDrA: cancer-specific driver missense mutation annotation with optimized features. |
| title_full_unstemmed | CanDrA: cancer-specific driver missense mutation annotation with optimized features. |
| title_short | CanDrA: cancer-specific driver missense mutation annotation with optimized features. |
| title_sort | candra cancer specific driver missense mutation annotation with optimized features |
| url | http://europepmc.org/articles/PMC3813554?pdf=render |
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