title: cisASE: a likelihood-based method for detecting putative cis-regulated allele-specific expression in RNA sequencing data.
abstract: 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.
  
  
  
  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.
  
  
  
  cisASE is freely available at http://lifecenter.sgst.cn/cisASE CONTACT: biosinodx@gmail.com or yxli@sibs.ac.cnSupplementary information: Supplementary data are available at Bioinformatics online.
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Tuantuan
authors.initials: T
authors.lastname: Gui
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Hong
authors.initials: H
authors.lastname: Li
authors.affiliation: Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
authors.forename: Yunhe
authors.initials: Y
authors.lastname: Fu
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Dong
authors.affiliation: Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China School of Life Science and Technology, Shanghai Jiaotong University, Shanghai 200240, China Shanghai Center for Bioinformation Technology, Shanghai Industrial Technology Institute, Shanghai 201203, China and Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200438, China.
authors.forename: Yixue
authors.initials: Y
authors.lastname: Li
mesh_terms: D000483:Alleles; D056915:DNA Copy Number Variations; D006801:Humans; D016013:Likelihood Functions; D017423:Sequence Analysis, RNA