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Introduction to Sequencing the Brain Transcriptome
Open accessAbstract
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.
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“These studies are probably underpowered to detect subtle effects that might be expected to arise as a result of this complex disease and within tightly regulated brain tissue9, among other limitations of existing microarray-based gene expression studies10.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [10]
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“These studies are probably underpowered to detect subtle effects that might be expected to arise as a result of this complex disease and within tightly regulated brain tissue9, among other limitations of existing microarray-based gene expression studies10.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [10]
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Metadata
- Authors
- Hitzemann, R., Darakjian, P., Walter, N., Dan Iancu, O., Searles, R., & McWeeney, S.
- Affiliations
- Oregon Health & Science University (US)
- Corresponding author
- Hitzemann, Robert
- Venue
- International Review of Neurobiology
- Publisher
- Elsevier
- Published
- 2014
- Volume
- 116
- Pages
- 1-19
- ISSN
- 0074-7742 (ISSN-L)
- PMC
- PMC4773050
- OpenAlex
- W2399509814
- Cited by
- 20
- References
- 94
- Citations by year
- 2024: 1 · 2022: 2 · 2021: 2 · 2020: 4 · 2019: 1 · 2018: 4 · 2017: 3 · 2016: 1 · 2015: 2
- FWCI
- 2.53
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International review of neurobiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Veterans Affairs
- National Institute on Alcohol Abuse and Alcoholism (R01AA011034, AA 13484, U01AA013484, AA11034, P60AA010760)
- National Institute of Mental Health (MH 51372, R01MH051372)
- National Institute on Drug Abuse (P50DA018165)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- brain transcriptome, RNA-seq, microarray, gene expression profiling, alternative splicing, non-coding RNA, gene regulatory networks, single nucleotide polymorphisms, mouse brain, schizophrenia, drug abuse, brain development
- MeSH terms
- Animals, Brain, Humans, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Gene Regulatory Networks, High-Throughput Nucleotide Sequencing, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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