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RNA sequencing: advances, challenges and opportunities
Open accessAbstract
In the few years since its initial application, massively parallel cDNA sequencing, or RNA-seq, has allowed many advances in the characterization and quantification of transcriptomes. Recently, several developments in RNA-seq methods have provided an even more complete characterization of RNA transcripts. These developments include improvements in transcription start site mapping, strand-specific measurements, gene fusion detection, small RNA characterization and detection of alternative splicing events. Ongoing developments promise further advances in the application of RNA-seq, particularly direct RNA sequencing and approaches that allow RNA quantification from very small amounts of cellular materials.
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“RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific expression [8–12].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [10]
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“RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific…”
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“Recent advances in next-generation sequencing have enhanced data acquisition, improved sensitivity, and made this method superior to microarrays in its throughput (9).”
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“…RNA sequencing (RNA-seq) (Nagalakshmi et al., 2008; Wilhelm, et al., 2008), rapid advances in experimental protocol development and data acquisition and analysis have brought comprehensive sequencing of cellular RNA within reach of most laboratories (Martin and Wang, 2011; Ozsolak and Milos, 2011).”
BackgroundMethodReproducibility of high-throughput mRNA and small RNA sequencing across laboratories
The GEUVADIS Consortium, 't Hoen, P. A. C., Friedländer, M. R., Almlöf, J., Sammeth, M., Pulyakhina, I., Anvar, S. Y., Laros, J. F. J., Buermans, H. P. J., Karlberg, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2702 · Cited by 250
“(1) Matches which are unique, and do not have any subdominant match: 251 ≥ MAPQ ≥ 255, XT = U (2) Matches which are unique, and have subdominant matches but a different score: 175 ≥ MAPQ ≥ 181, XT = U (3) Matches which are putatively unique (not unique, but distinguishable by score): 119 ≥ MAPQ ≥127, XT = U (4) Matche…”
BackgroundWeckwerth, W. (2011) · Journal of Proteomics · doi:10.1016/j.jprot.2011.07.010 · Cited by 244
Whole transcriptome analysis with sequencing: methods, challenges and potential solutions
Jiang, Z., Zhou, X., Li, R., Michal, J. J., Zhang, S., Dodson, M. V., Zhang, Z., & Harland, R. M. (2015) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-015-1934-y · Cited by 249
“These methods were previously reviewed by Ozsolak and Milos [92].”
Transcriptomics: Advances and approaches
Dong, Z., & Chen, Y. (2013) · Science China Life Sciences · doi:10.1007/s11427-013-4557-2 · Cited by 251
Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics
Athanasopoulou, K., Boti, M. A., Adamopoulos, P. G., Skourou, P. C., & Scorilas, A. (2021) · Life · doi:10.3390/life12010030 · Cited by 253
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230
The power and promise of RNA ‐seq in ecology and evolution
Todd, E. V., Black, M. A., & Gemmell, N. J. (2016) · Molecular Ecology · doi:10.1111/mec.13526 · Cited by 229
“Quickly surpassing microarrays as the high-throughput method of choice to study differential expression (DE) in nonmodel species, RNA-seq promised unprecedented sensitivity for detecting expression differences among rare transcripts, splice variants and microRNAs (Ozso- lak & Milos 2011).”
BackgroundThe crucial role of multiomic approach in cancer research and clinically relevant outcomes
Lu, M., & Zhan, X. (2018) · The EPMA Journal · doi:10.1007/s13167-018-0128-8 · Cited by 235
“gene structural feature to its functions and easier discover the causal of physiological or pathological conditions [87, 88].”
BackgroundTea plant genomics: achievements, challenges and perspectives
Xia, E., Tong, W., Wu, Q., Wei, S., Zhao, J. H., Zhang, Z., Wei, C., & Wan, X. (2020) · Horticulture Research · doi:10.1038/s41438-019-0225-4 · Cited by 232
From single cells to deep phenotypes in cancer
Bendall, S. C., & Nolan, G. P. (2012) · Nature Biotechnology · doi:10.1038/nbt.2283 · Cited by 225
Vijay, N., Poelstra, J. W., Künstner, A., & Wolf, J. B. W. (2012) · Molecular Ecology · doi:10.1111/mec.12014 · Cited by 230
“Massively parallelized RNA sequencing technology (RNA-seq) has added a valuable tool: millions of short reads are generated from steady-state RNA and concomitantly provide transcriptome sequence information and a digital measure of gene expression (Ozsolak & Milos 2010).”
BackgroundNext-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications
Kulski, J. K. (2016) · InTech eBooks · doi:10.5772/61964 · Cited by 248
“RNA-seq is the NGS method that sequences the transcriptome, that is, all the RNA transcript sets expressed by the genome in cells, tissues, and organs at different stages of an organism’s life cycle [12, 18, 19, 20, 30].”
MethodPrinciples of transcriptome analysis and gene expression quantification: an RNA ‐seq tutorial
Wolf, J. B. W. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12109 · Cited by 235
“…and RNA-seq 1 Principles of high throughput sequencing technology: (Metzker 2010) 2 Principles of RNA-seq: (Wang et al. 2009; Oshlack et al. 2010; Ozsolak & Milos 2011) 3 Principles of transcriptome assembly (Martin & Wang 2011) with particular reference to plants (Jain 2012) Applications in…”
BackgroundThe Strawberry Plant Defense Mechanism: A Molecular Review
Amil‐Ruíz, F., Blanco‐Portales, R., Muñoz‐Blanco, J., & Caballero, J. L. (2011) · Plant and Cell Physiology · doi:10.1093/pcp/pcr136 · Cited by 228
Kotze, A. C., Hunt, P. W., Skuce, P. J., von Samson‐Himmelstjerna, G., Martin, R. J., Sager, H., Krücken, J., Hodgkinson, J. E., Lespine, A., Jex, A. R., et al. (2014) · International Journal for Parasitology Drugs and Drug Resistance · doi:10.1016/j.ijpddr.2014.07.007 · Cited by 223
“These genomic data-sets, and their accompanying transcriptomic data, provide major insights into the biology of parasitic nematodes because they are the basis for characterizing SNPs (e.g., DNA-seq; (Metzker, 2010)), changes in transcription and alternative splicing patterns (i.e., RNA-seq: (Ozsolak and Milos, 2011))…”
BackgroundMethodTranscriptional profiles of bovine in vivo pre-implantation development
Jiang, Z., Sun, J., Dong, H., Luo, O. J., Zheng, X., Obergfell, C. J., Tang, Y., Bi, J., O'Neill, R. J. W., Ruan, Y., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-756 · Cited by 215
“The RNA-seq technology provides unique benefits for studying gene expression with high resolutions and reproducibility, as well as for detecting novel transcripts and alternative splicing events [14,15].”
BackgroundSuravajhala, P., Kogelman, L. J. A., & Kadarmideen, H. N. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0217-x · Cited by 232
“Since the initial application of RNA-Seq, quantifying small RNAs, ascertaining alternate splicing events, transcription start sites (TSS) and mapping strand-specific genes [85] have benefited from such techniques.”
BackgroundCornwell, M., Vangala, M., Taing, L., Herbert, Z. T., Köster, J., Li, B., Sun, H., Li, T., Zhang, J., Qiu, X., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2139-9 · Cited by 207
Bioinformatics and Systems Biology of the Lipidome
Subramaniam, S., Fahy, E., Gupta, S., Sud, M., Byrnes, R. W., Cotter, D., Dinasarapu, A. R., & Maurya, M. R. (2011) · Chemical Reviews · doi:10.1021/cr200295k · Cited by 218
Alternative polyadenylation: New insights from global analyses
Shi, Y. (2012) · RNA · doi:10.1261/rna.035899.112 · Cited by 212
“When compared with the Illumina system, the disadvantages of DRS include higher error rate, shorter read lengths, lower yield (read counts), and the lack of multiplexing capability (Ozsolak and Milos 2011).”
BackgroundMethodInfluentialRNA-Seq-Based Monitoring of Infection-Linked Changes in Vibrio cholerae Gene Expression
Mandlik, A., Livny, J., Robins, W. P., Ritchie, J. M., Mekalanos, J. J., & Waldor, M. K. (2011) · Cell Host & Microbe · doi:10.1016/j.chom.2011.07.007 · Cited by 207
“The development of massively parallel cDNA sequencing (RNA-seq) techniques is enabling deeper and more accurate assessment of transcriptomes from eukaryotes (Ozsolak and Milos, 2011) as well as bacteria (van Vliet, 2010; Sorek and Cossart, 2010).”
Mohr, S., Ghanem, E., Smith, W., Sheeter, D. A., Qin, Y., King, O., Polioudakis, D., Iyer, V. R., Hunicke‐Smith, S., Swamy, S., et al. (2013) · RNA · doi:10.1261/rna.039743.113 · Cited by 209
Heavy metal detoxification mechanisms by microalgae: Insights from transcriptomics analysis
Tripathi, S., & Poluri, K. M. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.117443 · Cited by 208
Identification of the zebrafish maternal and paternal transcriptomes
Harvey, S. A., Sealy, I. M., Kettleborough, R. N. W., Fényes, F., White, R. J., Stemple, D. L., & Smith, J. C. (2013) · Development · doi:10.1242/dev.095091 · Cited by 209
“Recently, new sequencing technologies have significantly improved our ability to analyse transcription (Ozsolak and Milos, 2011; Collins et al., 2012; Djebali et al., 2012).”
BackgroundIdentification of microRNA-regulated gene networks by expression analysis of target genesPreprint
Gennarino, V. A., D’Angelo, G., Dharmalingam, G., Fernandez, S., Russolillo, G., Sanges, R., M, M., Belcastro, V., Ballabio, A., VERDE, P., et al. (2012) · Genome Research · doi:10.1101/gr.130435.111 · Cited by 201
“…the expected increase of high-resolution transcriptome data facilitated by the advances and cost reduction of next-generation sequencing approaches (Ozsolak and Milos 2011), the current gap in adequate transcriptome coverage of some human tissues will be filled, and therefore this possible caveat…”
BackgroundAkbari, O. S., Antoshechkin, I. A., Amrhein, H., Williams, B. A., DiLoreto, R., Sandler, J. E., & Hay, B. A. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.006742 · Cited by 201
“RNA-seq can also be used to study differential splicing and locate precise transcription start and stop sites, all at single-base resolution (Ozsolak and Milos 2011).”
BackgroundIntroduction to Single‐Cell RNA Sequencing
Olsen, T. K., & Baryawno, N. (2018) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.57 · Cited by 201
Application of metatranscriptomics to soil environments
Carvalhais, L. C., Dennis, P. G., Tyson, G. W., & Schenk, P. M. (2012) · Journal of Microbiological Methods · doi:10.1016/j.mimet.2012.08.011 · Cited by 200
Comprehensive analysis of RNA-seq data reveals the complexity of the transcriptome in Brassica rapa
Tong, C., Wang, X., Yu, J., Wu, J., Li, W., Huang, J., Dong, C., Hua, W., & Liu, S. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-689 · Cited by 190
“Second, it allows researchers to improve gene annotation by extending transcriptional boundaries of genes and permits the discovery of novel genes or transcripts [9].”
BackgroundMolecular phylogeny of extant Holothuroidea (Echinodermata)
Miller, A. K., Kerr, A. M., Paulay, G., Reich, M., Wilson, N. G., Carvajal, J. I., & Rouse, G. W. (2017) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2017.02.014 · Cited by 190
Salthouse, D., Novakovic, K., Hilkens, C. M. U., & Ferreira, A. M. (2022) · Acta Biomaterialia · doi:10.1016/j.actbio.2022.11.003 · Cited by 196
“Despite RNA-Seq being less cost-effective for sequencing a low number of targets ( ≤ 20) and more time consuming compared to qPCR, it provides a higher discovery power than qPCR, although both methods are highly effective ways of assessing gene expression [ 93 , 94 ].”
Proteomics in evolutionary ecology: linking the genotype with the phenotype
Diz, Á. P., Martínez‐Fernández, M., & Rolán‐Alvarez, E. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2011.05426.x · Cited by 195
Guerette, P. A., Hoon, S. S., Seow, Y., Raida, M., Mašić, A., Wong, F. T., Ho, V. H. B., Kong, K. W., Demirel, M. C., Pena‐Francesch, A., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2671 · Cited by 186
Metadata
- Authors
- Ozsolak, F., & Milos, P. M.
- Affiliations
- Helicos Biosciences (United States) (US)
- Corresponding author
- Ozsolak, Fatih
- Milos, Patrice M.
- Venue
- Nature Reviews Genetics
- Published
- 2010-12-30
- Volume
- 12
- Issue
- 2
- Pages
- 87-98
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3031867
- OpenAlex
- W2060842569
- Semantic Scholar
- cf2841f590819568ab156862c2506ab633493747
- Cited by
- 2,135
- References
- 105
- Citations by year
- 2026: 52 · 2025: 108 · 2024: 113 · 2023: 98 · 2022: 131 · 2021: 152 · 2020: 146 · 2019: 134 · 2018: 141 · 2017: 173 · 2016: 163 · 2015: 169 · 2014: 169 · 2013: 168 · 2012: 152
- FWCI
- 10.78
- 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 (R01HG005230, R44HG005279)
- 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 sequencing, transcriptome, direct RNA sequencing, RNA quantification, strand-specific RNA-seq, fusion gene detection, small RNA, alternative splicing
- MeSH terms
- Animals, Humans, RNA, Transcription, Genetic, Alternative Splicing, Sequence Analysis, RNA
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