title: Analysis considerations for utilizing RNA-Seq to characterize the brain transcriptome.
abstract: RNA-Seq allows one to examine only gene expression as well as expression of noncoding RNAs, alternative splicing, and allele-specific expression. With this increased sensitivity and dynamic range, there are computational and statistical considerations that need to be contemplated, which are highly dependent on the biological question being asked. We highlight these to provide an overview of their importance and the impact they can have on downstream interpretation of the brain transcriptome.
authors.affiliation: Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA; Knight Cancer Institute, Oregon Health, Oregon Health and Science University, Portland, Oregon, USA. Electronic address: zheng@ohsu.edu.
authors.forename: Christina L
authors.initials: CL
authors.lastname: Zheng
authors.affiliation: Clinical & Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.
authors.forename: Sunita
authors.initials: S
authors.lastname: Kawane
authors.affiliation: Clinical & Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.
authors.forename: Daniel
authors.initials: D
authors.lastname: Bottomly
authors.affiliation: Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA; Clinical & Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.
authors.forename: Beth
authors.initials: B
authors.lastname: Wilmot
mesh_terms: D000818:Animals; D001921:Brain / Q000378:metabolism*; D001330:Electronic Data Processing; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D012313:RNA / Q000235:genetics* / Q000378:metabolism; D059467:Transcriptome / Q000235:genetics*
chemical_list: D012313:RNA
keywords: Alignment; Computational processing; Differential expression; Functional annotation; Mapping; RNA-Seq; Statistical modeling; Transcriptomics