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Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery
<span title="">2013</span>
<i title="Mary Ann Liebert Inc">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/id6xbd2o6fhy7pts2djgwhtmvy" style="color: black;">Journal of Computational Biology</a>
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Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single-nucleotide variations (SNVs) in
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1089/cmb.2013.0106">doi:10.1089/cmb.2013.0106</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24195709">pmid:24195709</a>
<a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3822366/">pmcid:PMC3822366</a>
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... , related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple, related samples over the accuracy of GATK's Unified Genotyper, the state-of-theart multisample SNV caller.
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