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Tumor genomes are often highly heterogeneous, consisting of genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor genome analysis. With the advancement of next-generation sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from cancer genome sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy.
Pubmed ID: 25707430 RIS Download
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Software for deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity. The model resolves the identifiability problem by integrating two types of sequencing information - somatic copy number alterations and loss of heterozygosity - within an unified probabilistic framework.
View all literature mentionsSoftware that implements a probabilistic graphical model to analyze sequence data from tumor / normal pairs. The model draws statistical strength by analysing both genome jointly to more accurately classify germline and somatic mutations. It effectively reduces false positive somatic mutation predictions in tumour-normal pair sequencing data. It is highly recommended to post-process results with mutationSeq in order to filter technical artifacts.
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