@article{c5672f0253fc419c888601f28de14941,
title = "Integrated transcriptomic–genomic tool Texomer profiles cancer tissues",
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.",
author = "Fang Wang and Shaojun Zhang and Kim, {Tae Beom} and Lin, {Yu yu} and Ramiz Iqbal and Zixing Wang and Vakul Mohanty and Kanishka Sircar and Karam, {Jose A.} and Wendl, {Michael C.} and Funda Meric-Bernstam and Weinstein, {John N.} and Li Ding and Mills, {Gordon B.} and Ken Chen",
note = "Funding Information: This work was supported in part by the NIH (R01CA172652 to K.C., U01CA217842 to G.B.M., U24CA211006 to L.D., U24CA210950 to R.A.), the CPRIT (RP180248 to K.C.), the MD Anderson Cancer Center Sheikh Khalifa Ben Zayed Al Nahyan Institute of Personalized Cancer Therapy grant and an NCI Cancer Center Support Grant (P30 CA016672 to P.P.). We also thank Y. Chen, T. Hart, B. Lim, G. Lozano, S. Xiong, L. Wang and X. Song for insightful discussions, and X. Zheng for data curation. Publisher Copyright: {\textcopyright} 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.",
year = "2019",
month = may,
day = "1",
doi = "10.1038/s41592-019-0388-9",
language = "English",
volume = "16",
pages = "401--404",
journal = "Nature Methods",
issn = "1548-7091",
number = "5",
}