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RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
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DNA microarrays have been used for over a decade to profile gene expression on a genomic scale. While this technology has advanced our understanding of complex cellular function, the reliance of microarrays on hybridization kinetics results in several technical limitations. For example, knowledge of the sequences being probed is required, distinguishing similar sequences is difficult because of cross-hybridization, and the relatively narrow dynamic range of the signal limits sensitivity. Recently, new technologies have been introduced that are based on novel sequencing methodologies. These next-generation sequencing methods do not have the limitations inherent to microarrays. Next-generation sequencing is unique since it allows the detection of all known and novel RNAs present in biological samples without bias toward known transcripts. In addition, the expression of coding and noncoding RNAs, alternative splicing events, and expressed single nucleotide polymorphisms (SNPs) can be identified in a single experiment. Furthermore, this technology allows for remarkably higher throughput while lowering sequencing costs. This significant shift in throughput and pricing makes low-cost access to whole genomes possible and more importantly expands sequencing applications far beyond traditional uses (Morozova & Marra, 2008) to include sequencing the transcriptome (RNA-Seq), providing detail on gene structure, alternative splicing events, expressed SNPs, and transcript size (Mane et al., 2009; Tang et al., 2009; Walter et al., 2009), in a single experiment, while also quantifying the absolute abundance of genes, all with greater sensitivity and dynamic range than the competing cDNA microarray technology (Mortazavi, Williams, McCue, Schaeffer, & Wold, 2008).
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“Transcriptomes from complex tissues, such as human brain, may be organized into networks of co-expressed genes that better reflect the biological functions and organization of the tissue7–14.”
From Analysis of whole genome-transcriptomic organization in brain to identify genes associated with alcoholism (2019) · cites this paper as [13]
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Self-citation
Metadata
- Authors
- Farris, S. P., & Dayne Mayfield, R.
- Affiliations
- The University of Texas at Austin (US)
- ORCID
- Farris, Sean P.
- Venue
- International Review of Neurobiology
- Publisher
- Elsevier
- Published
- 2014
- Volume
- 116
- Pages
- 275-300
- ISSN
- 0074-7742 (ISSN-L)
- PMC
- PMC4267562
- OpenAlex
- W1867589655
- Cited by
- 49
- References
- 92
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 9 · 2020: 5 · 2019: 5 · 2018: 6 · 2017: 7 · 2016: 3 · 2015: 5
- FWCI
- 6.35
- 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
- National Institute on Alcohol Abuse and Alcoholism (P01AA020683, RC2AA019382, R28AA012725, R01AA012404, T32AA007471, U01AA020926)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- RNA-seq, next-generation sequencing, gene expression profiling, alcoholism, transcriptome, alternative splicing, non-coding RNA, cross-hybridization
- MeSH terms
- Alcoholism, Brain, Humans, Genome, High-Throughput Nucleotide Sequencing, Transcriptome
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