Introduction to sequencing the brain transcriptome.

Autor:	Hitzemann R; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA; Research Service, Veterans Affairs Medical Center, Portland, Oregon, USA. Electronic address: hitzeman@ohsu.edu., Darakjian P; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA., Walter N; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA; Research Service, Veterans Affairs Medical Center, Portland, Oregon, USA., Iancu OD; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA., Searles R; Integrative Genomics Laboratory, Oregon Health & Science University, Portland, Oregon, USA., McWeeney S; Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, Oregon, USA; Division of Biostatistics, Public Health & Preventative Medicine, Oregon Health & Science University, Portland, Oregon, USA.
Jazyk:	angličtina
Zdroj:	International review of neurobiology [Int Rev Neurobiol] 2014; Vol. 116, pp. 1-19.
DOI:	10.1016/B978-0-12-801105-8.00001-1
Abstrakt:	High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. 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, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior-disease relationships is substantial. (© 2014 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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