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

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. AVAILABILITY AND IMPLEMENTATION 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.

[1]  Melanie A. Huntley,et al.  Recurrent R-spondin fusions in colon cancer , 2012, Nature.

[2]  Jehyuk Lee,et al.  Digital RNA Allelotyping Reveals Tissue-specific and Allele-specific Gene Expression in Human , 2009, Nature Methods.

[3]  Jerzy K. Kulski,et al.  The HLA genomic loci map: expression, interaction, diversity and disease , 2009, Journal of Human Genetics.

[4]  Daniel A. Skelly,et al.  A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. , 2011, Genome research.

[5]  R. Guigó,et al.  Transcriptome genetics using second generation sequencing in a Caucasian population , 2010, Nature.

[6]  Huayong Xu,et al.  Comparing Computational Methods for Identification of Allele‐Specific Expression based on Next Generation Sequencing Data , 2014, Genetic epidemiology.

[7]  T. Pastinen Genome-wide allele-specific analysis: insights into regulatory variation , 2010, Nature Reviews Genetics.

[8]  Mathieu Blanchette,et al.  Global patterns of cis variation in human cells revealed by high-density allelic expression analysis , 2009, Nature Genetics.

[9]  Z. Werb,et al.  The extracellular matrix: A dynamic niche in cancer progression , 2012, The Journal of cell biology.

[10]  Oleg Mayba,et al.  MBASED: allele-specific expression detection in cancer tissues and cell lines , 2014, Genome Biology.

[11]  M. Gerstein,et al.  AlleleSeq: analysis of allele-specific expression and binding in a network framework , 2011, Molecular systems biology.

[12]  D. Clayton,et al.  Genome-wide analysis of allelic expression imbalance in human primary cells by high-throughput transcriptome resequencing , 2009, Human molecular genetics.

[13]  Craig B. Thompson,et al.  Hierarchical Control of Lymphocyte Survival , 1996, Science.

[14]  David I. Smith,et al.  Tumor Transcriptome Sequencing Reveals Allelic Expression Imbalances Associated with Copy Number Alterations , 2010, PloS one.

[15]  Jian Jin,et al.  Topoisomerase inhibitors unsilence the dormant allele of Ube3a in neurons , 2011, Nature.

[16]  Özlem Türeci,et al.  Immunomic, genomic and transcriptomic characterization of CT26 colorectal carcinoma , 2013, BMC Genomics.

[17]  Wolfgang Sadee,et al.  Whole transcriptome RNA-Seq allelic expression in human brain , 2013, BMC Genomics.

[18]  Wang Dan,et al.  Transcriptome profiling of the cancer and adjacent nontumor tissues from cervical squamous cell carcinoma patients by RNA sequencing , 2015, Tumor Biology.

[19]  A. Shlien,et al.  Copy number variations and cancer , 2009, Genome Medicine.

[20]  Robert A Sikes,et al.  Changes in extracellular matrix (ECM) and ECM-associated proteins in the metastatic progression of prostate cancer , 2004, Reproductive biology and endocrinology : RB&E.

[21]  Christian Schlötterer,et al.  Allelic imbalance metre (Allim), a new tool for measuring allele-specific gene expression with RNA-seq data , 2013, Molecular ecology resources.

[22]  Jong-Keuk Lee,et al.  Large-scale profiling and identification of potential regulatory mechanisms for allelic gene expression in colorectal cancer cells. , 2013, Gene.

[23]  C. Karapetis,et al.  Immunomodulation therapy in colorectal carcinoma. , 2000, Cancer treatment reviews.

[24]  T. Ohta,et al.  Population Biology of Antigen Presentation by MHC Class I Molecules , 1996, Science.

[25]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[26]  Daisuke Hoshino,et al.  Turnover of Focal Adhesions and Cancer Cell Migration , 2012, International journal of cell biology.

[27]  B. Browning,et al.  Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. , 2007, American journal of human genetics.

[28]  K. Hemminki,et al.  Consensus Pathways Implicated in Prognosis of Colorectal Cancer Identified Through Systematic Enrichment Analysis of Gene Expression Profiling Studies , 2011, PloS one.

[29]  P. Donnelly,et al.  A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies , 2009, PLoS genetics.

[30]  John C. Marioni,et al.  Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data , 2009, Bioinform..

[31]  A. Sivachenko,et al.  Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.