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    Recommendations for Accurate Resolution of Gene and Isoform Allele-Specific Expression in RNA-Seq Data

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    12
    Author
    Wood, DLA; Nones, K; Steptoe, A; Christ, A; Harliwong, I; Newell, F; Bruxner, TJC; Miller, D; Cloonan, N; Grimmond, SM
    Date
    2015-05-12
    Source Title
    PLoS One
    Publisher
    PUBLIC LIBRARY SCIENCE
    University of Melbourne Author/s
    Grimmond, Sean
    Affiliation
    Centre for Cancer Research
    Metadata
    Show full item record
    Document Type
    Journal Article
    Citations
    Wood, D. L. A., Nones, K., Steptoe, A., Christ, A., Harliwong, I., Newell, F., Bruxner, T. J. C., Miller, D., Cloonan, N. & Grimmond, S. M. (2015). Recommendations for Accurate Resolution of Gene and Isoform Allele-Specific Expression in RNA-Seq Data. PLOS ONE, 10 (5), https://doi.org/10.1371/journal.pone.0126911.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/260124
    DOI
    10.1371/journal.pone.0126911
    Abstract
    Genetic variation modulates gene expression transcriptionally or post-transcriptionally, and can profoundly alter an individual's phenotype. Measuring allelic differential expression at heterozygous loci within an individual, a phenomenon called allele-specific expression (ASE), can assist in identifying such factors. Massively parallel DNA and RNA sequencing and advances in bioinformatic methodologies provide an outstanding opportunity to measure ASE genome-wide. In this study, matched DNA and RNA sequencing, genotyping arrays and computationally phased haplotypes were integrated to comprehensively and conservatively quantify ASE in a single human brain and liver tissue sample. We describe a methodological evaluation and assessment of common bioinformatic steps for ASE quantification, and recommend a robust approach to accurately measure SNP, gene and isoform ASE through the use of personalized haplotype genome alignment, strict alignment quality control and intragenic SNP aggregation. Our results indicate that accurate ASE quantification requires careful bioinformatic analyses and is adversely affected by sample specific alignment confounders and random sampling even at moderate sequence depths. We identified multiple known and several novel ASE genes in liver, including WDR72, DSP and UBD, as well as genes that contained ASE SNPs with imbalance direction discordant with haplotype phase, explainable by annotated transcript structure, suggesting isoform derived ASE. The methods evaluated in this study will be of use to researchers performing highly conservative quantification of ASE, and the genes and isoforms identified as ASE of interest to researchers studying those loci.

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