Background: Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal\nheterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of\nfractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as\ndeletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate\nfor allele fraction estimations. However, no systematic method exists that can support such analysis.\nResults: In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural\nvariants integratively from various forms of alignment signals. We develop a tool, Break Down, to estimate the allele\nfractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications,\nand balanced inversions and translocations.\nConclusions: Evaluation based on both simulated and real data indicates that our method systematically enables\nstructural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically\ninstable tumors.
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