Year: 2013
Type: article
Abstract: Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.
Source: Genome biology
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Funders: National Science Foundation, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Child Health and Human Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: iDASH: Integrating Data for Analysis, Anonymization, and Sharing, Graduate Training Program in Bioinformatics, Algorithmic strategies for detecting structural variation in genomes, Developmental Mechanisms of Human Structural Brain Defects, AF: Small: Algorithms for Genetics: Epistatic Interactions, Haplotype Assembly, and Selection Signatures
OpenAlex ID: W2166493347