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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High-throughput transcriptome sequencing and digital gene expression (DGE) tag profiling are efficient and economic choices for characterizing non-model organisms without a reference genome.
“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]
The ‘RNA-Seq technology and benefits’ section says RNA-Seq is attractive for non-model organisms whose genomes have not been sequenced, permits de novo assembly, and can cost less than other transcriptome methods. But the paper’s introductory discussion says tag-based methods, while high-throughput and capable of digital expression measurement, are often expensive and have short-tag mapping limitations. It does not support calling DGE tag profiling an economical choice for organisms without a reference genome. (LLM, conf=0.89)
Experimental — accuracy not yet measuredRNA-Seq provides comprehensive information on mRNA abundance, alternative splicing, nucleotide variation, and structure alteration.
“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
mNGS of RNA libraries for pathogen detection incidentally yields host gene expression data for transcriptome analysis.
“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]
Most transcriptomic clock 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 sequencing.
“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]
Development of new experimental techniques such as RNA-Seq provides new types of data for in vitro assessment of toxicities that can be used for QSAR modeling.
“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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Self-citation
- 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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