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RNA-Seq: a revolutionary tool for transcriptomics
Open accessAbstract
RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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In the citing paper
“High-throughput transcriptome sequencing and digital gene expression (DGE) tag profiling are efficient and economic choice for characterizing non-model organisms without a reference genome [9], [10].”
From Digital Gene Expression Analysis Based on Integrated De Novo Transcriptome Assembly of Sweet Potato [Ipomoea batatas (L.) Lam.] (2012) · cites this paper as [10]
Experimental — accuracy not yet measuredIn the citing paper
“RNA-Seq provides comprehensive information on mRNA abundance, alternative splicing, nucleotide variation, and structure alteration.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Z. Wang et al., 2009
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“mNGS of RNA libraries used for the detection of pathogens such as RNA viruses in clinical samples incidentally produces host gene expression data for transcriptome (RNA-seq) analyses66.”
From Clinical metagenomics (2019) · cites this paper as [66]
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“Most studies have been conducted on small sample sizes and not tested on independent cohorts, or used older microarray technology, which is less accurate and reproducible than modern RNA sequencing85,86.”
From Measuring biological age using omics data (2022) · cites this paper as [85]
In the citing paper
“It should be emphasized that development of new experimental techniques such as deep-sequencing RNA-Seq,90 provides new types of data for in vitro assessment of toxicities that can also be used for QSAR modeling.91”
From QSAR without borders (2020) · cites this paper as [90]
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Metadata
- Authors
- Wang, Z., Gerstein, M., & Snyder, M.
- Affiliations
- Yale University (US)
- Corresponding author
- Snyder, Michael
- Venue
- Nature Reviews Genetics
- Published
- 2009-01
- Volume
- 10
- Issue
- 1
- Pages
- 57-63
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC2949280
- OpenAlex
- W1981509058
- Cited by
- 13,174
- References
- 44
- Citations by year
- 2026: 518 · 2025: 745 · 2024: 728 · 2023: 662 · 2022: 856 · 2021: 974 · 2020: 916 · 2019: 862 · 2018: 911 · 2017: 867 · 2016: 901 · 2015: 960 · 2014: 900 · 2013: 815 · 2012: 662
- FWCI
- 22.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (P50HG002357)
- National Cancer Institute (R01CA077808)
- 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 › RNA Research and Splicing
- Keywords
- RNA-seq, transcriptomics, gene expression profiling, deep sequencing, mRNA isoforms, transcript quantification, next-generation sequencing
- MeSH terms
- Animals, Base Sequence, Chromosome Mapping, Exons, Humans, Models, Genetic, Molecular Sequence Data, RNA, Transcription, Genetic, Sequence Analysis, RNA, Gene Expression Profiling
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