Integrated transcriptomic–genomic tool Texomer profiles cancer tissues

Fang Wang, Shaojun Zhang, Tae Beom Kim, Yu yu Lin, Ramiz Iqbal, Zixing Wang, Vakul Mohanty, Kanishka Sircar, Jose A. Karam, Michael C. Wendl, Funda Meric-Bernstam, John N. Weinstein, Li Ding, Gordon B. Mills, Ken Chen

Research output: Contribution to journalArticle

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)401-404
Number of pages4
JournalNature Methods
Volume16
Issue number5
DOIs
StatePublished - May 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

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  • Cite this

    Wang, F., Zhang, S., Kim, T. B., Lin, Y. Y., Iqbal, R., Wang, Z., Mohanty, V., Sircar, K., Karam, J. A., Wendl, M. C., Meric-Bernstam, F., Weinstein, J. N., Ding, L., Mills, G. B., & Chen, K. (2019). Integrated transcriptomic–genomic tool Texomer profiles cancer tissues. Nature Methods, 16(5), 401-404. https://doi.org/10.1038/s41592-019-0388-9