CisASE: A likelihood-based method for detecting putative cis-regulated allele-specific expression in RNA sequencing data

Zhi Liu, Tuantuan Gui, Zhen Wang, Hong Li, Yunhe Fu, Xiao Dong, Yixue Li

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Motivation: Allele-specific expression (ASE) is a useful way to identify cis-acting regulatory variation, which provides opportunities to develop new therapeutic strategies that activate beneficial alleles or silence mutated alleles at specific loci. However, multiple problems hinder the identification of ASE in next-generation sequencing (NGS) data. Results: We developed cisASE, a likelihood-based method for detecting ASE on single nucleotide variant (SNV), exon and gene levels from sequencing data without requiring phasing or parental information. cisASE uses matched DNA-seq data to control technical bias and copy number variation (CNV) in putative cis-regulated ASE identification. Compared with state-of-the-art methods, cisASE exhibits significantly increased accuracy and speed. cisASE works moderately well for datasets without DNA-seq and thus is widely applicable. By applying cisASE to real datasets, we identified specific ASE characteristics in normal and cancer tissues, thus indicating that cisASE has potential for wide applications in cancer genomics.

Original languageEnglish (US)
Pages (from-to)3291-3297
Number of pages7
JournalBioinformatics
Volume32
Issue number21
DOIs
StatePublished - Nov 1 2016

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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