The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
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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Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Trapnell, C., Roberts, A., Goff, L. A., Pertea, G. M., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.016 · Cited by 12,145
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Genome sequencing in microfabricated high-density picolitre reactors
Margulies, M., Egholm, M., Altman, W. E., Attiya, S., Bader, J. S., Bemben, L. A., Berka, J., Braverman, M. S., Chen, Y.-J., Chen, Z., et al. (2005) · Nature · doi:10.1038/nature03959 · Cited by 7,693
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
Base-Calling of Automated Sequencer Traces Using Phred. I. Accuracy AssessmentPreprint
Ewing, B., Hillier, L., Wendl, M. C., & Green, P. (1998) · Genome Research · doi:10.1101/gr.8.3.175 · Cited by 6,993
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951
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Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091
Brain Transcriptome Databases: A User's Guide
Keil, J. M., Qalieh, A., & Kwan, K. Y. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.1930-17.2018 · Cited by 79
Mills, J. D., Iyer, A. M., van Scheppingen, J., Bongaarts, A., Anink, J. J., Janssen, B. J., Zimmer, T. S., Spliet, W. G. M., van Rijen, P. C., Jansen, F. E., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-06145-8 · Cited by 63
Johnson, K. R., Hindmarch, C. C. T., Salinas, Y. D., Shi, Y., Greenwood, M. P., Hoe, S.-Z., Murphy, D., & Gainer, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124523 · Cited by 32
Chen, J., Yin, D., He, X., Gao, M., Choi, Y., Luo, G., Wang, H., & Qu, X. (2020) · International Journal of Cardiology · doi:10.1016/j.ijcard.2020.01.035 · Cited by 27
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
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- Hitzemann, R., Darakjian, P., Walter, N., Dan Iancu, O., Searles, R., & McWeeney, S. (2014). Introduction to Sequencing the Brain Transcriptome. International Review of Neurobiology. https://doi.org/10.1016/b978-0-12-801105-8.00001-1
