Genes, behavior and next-generation RNA sequencing

Authors

  • R. Hitzemann,

    Corresponding author
    1. Research Service, Veterans Affairs Medical Center, Portland, OR, USA
    • Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
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  • D. Bottomly,

    1. Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA
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  • P. Darakjian,

    1. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
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  • N. Walter,

    1. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
    2. Research Service, Veterans Affairs Medical Center, Portland, OR, USA
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  • O. Iancu,

    1. Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
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  • R. Searles,

    1. Integrated Genomics Laboratory, Oregon Health & Science University, Portland, OR, USA
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  • B. Wilmot,

    1. Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA
    2. Division of Bioinformatics & Computational Biology, Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
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  • S. McWeeney

    1. Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA
    2. Division of Biostatistics, Public Health & Preventative Medicine, Oregon Health & Science University, Portland, OR, USA
    3. Division of Bioinformatics & Computational Biology, Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
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Corresponding author: R. Hitzemann, PhD, 3181 SW Sam Jackson Park Road, Portland, OR, 97239-3098, USA. E-mail: hitzeman@ohsu.edu

Abstract

Advances in next-generation sequencing suggest that RNA-Seq is poised to supplant microarray-based approaches for transcriptome analysis. This article briefly reviews the use of microarrays in the brain-behavior context and then illustrates why RNA-Seq is a superior strategy. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, detects allele specific expression and can be used to extract genotype information, e.g. nonsynonymous coding single nucleotide polymorphisms. Examples of where RNA-Seq has been used to assess brain gene expression are provided. Despite the advantages of RNA-Seq, some disadvantages remain. These include the high cost of RNA-Seq and the computational complexities associated with data analysis. RNA-Seq embraces the complexity of the transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior relationship is substantial.

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