| Literature DB >> 30988467 |
Fang Wang1, Shaojun Zhang2, Tae-Beom Kim1, Yu-Yu Lin1, Ramiz Iqbal1, Zixing Wang1, Vakul Mohanty1, Kanishka Sircar3, Jose A Karam4, Michael C Wendl5, Funda Meric-Bernstam6, John N Weinstein1,7, Li Ding5, Gordon B Mills7, Ken Chen8.
Abstract
Profiling of both the genome and the transcriptome promises a comprehensive, functional readout of a tissue sample, yet analytical approaches are required to translate the increased data dimensionality, heterogeneity and complexity into patient benefits. We developed a statistical approach called Texomer ( https://github.com/KChen-lab/Texomer ) that performs allele-specific, tumor-deconvoluted transcriptome-exome integration of autologous bulk whole-exome and transcriptome sequencing data. Texomer results in substantially improved accuracy in sample categorization and functional variant prioritization.Entities:
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Year: 2019 PMID： 30988467 PMCID： PMC7337246 DOI： 10.1038/s41592-019-0388-9
Source DB: PubMed Journal: Nat Methods ISSN： 1548-7091 Impact factor: 28.547