Cited by 2,135Genomics and Phylogenetic StudiesSingle-cell and spatial transcriptomicsRNA Research and Splicing

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RNA sequencing: advances, challenges and opportunities

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Ozsolak, Fatih, Milos, Patrice M.·2011·Nature Reviews Genetics

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Abstract

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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Method
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Method
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Background
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Background
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Background
Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis
Carrara, M., Lum, J., Cordero, F., Beccuti, M., Poidinger, M., Donatelli, S., Calogero, R., & Zolezzi, F. (2015) · BMC Bioinformatics · doi:10.1186/1471-2105-16-s9-s2 · Cited by 23
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Background
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Yalamanchili, H. K., Li, Z., Wang, P., Wong, M. P., Yao, J., & Wang, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku577 · Cited by 23
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Background
Transcriptomic analysis of Casuarina equisetifolia L. in responses to cold stress
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Background
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Background
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Background
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Background
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Background
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“, solid-state arrays), which have been reviewed elsewhere (44, 54, 73).”
Background
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Method
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Background
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Background
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Background
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BackgroundMethodInfluential
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“massive scale compared to traditional cloning methods [37].”
Result
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Background
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Background
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“In the alignment procedures, the mapping read length is critical for alignment accuracy and precision [31].”
A new approach to determining whole viral genomic sequences including termini using a single deep sequencing run
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BackgroundResult
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Background
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“To examine gene expression, the RNA sequencing method is convenient [18].”
Method
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Background
Connectivity Mapping for Candidate Therapeutics Identification Using Next Generation Sequencing RNA-Seq Data
McArt, D. G., Dunne, P. D., Blayney, J. K., Salto‐Tellez, M., Schaeybroeck, S. V., Hamilton, P. W., & Zhang, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066902 · Cited by 20
“NGS offers such advantages over the older constrained microarray approach in increased sensitivity, not suffering from cross hybridisation and the fact that no dependence on any prior knowledge is necessary, as have been discussed in other articles [1–7].”
Background
Applications of systems biology in cancer immunotherapy: from target discovery to biomarkers of clinical outcome
GuhaThakurta, D., Sheikh, N. A., Meagher, T. C., Letarte, S., & Trager, J. B. (2013) · Expert Review of Clinical Pharmacology · doi:10.1586/17512433.2013.811814 · Cited by 20
Transcriptome Profiling of Pediatric Core Binding Factor AML
Hsu, C.-H., Nguyen, C. D., Yan, C., Ries, R. E., Chen, Q.-R., Hu, Y., Ostronoff, F., Stirewalt, D. L., Komatsoulis, G. A., Levy, S., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138782 · Cited by 20
“RNA-seq for whole-transcriptome sequencing has become a powerful approach for studying mRNA transcripts[9, 10].”
Background
Methods to Study Long Noncoding RNA Biology in Cancer
Luo, M. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-981-10-1498-7_3 · Cited by 20
Transcriptome analysis reveals the genetic basis underlying the seasonal development of keratinized nuptial spines in Leptobrachium boringii
Zhang, W., Guo, Y., Li, J., et al. (2016) · BMC Genomics · doi:10.1186/s12864-016-3295-9 · Cited by 20
“Differential gene expression analysis allows studying the genetic basis of context-specific tissue development without the need for a reference genome [33].”
Background
Oregano dietary supplementation modifies the liver transcriptome profile in broilers: RNASeq analysis
Sabino, M., Capomaccio, S., Cappelli, K., Supplizi, A. V., Bomba, L., Ajmone‐Marsan, P., Cobellis, G., Olivieri, O., Pieramati, C., & Trabalza‐Marinucci, M. (2017) · Research in Veterinary Science · doi:10.1016/j.rvsc.2017.11.009 · Cited by 20
Expression of cyp1a induced by benzo(A)pyrene and related biochemical and genotoxic biomarkers in the neotropical freshwater fish Prochilodus lineatus
Santos, C., de Oliveira, M. T., Cólus, I. M. D. S., et al. (2018) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2018.05.008 · Cited by 20
An Optimised Protocol Harnessing Laser Capture Microdissection for Transcriptomic Analysis on Matched Primary and Metastatic Colorectal Tumours
Ong, C. J., Tan, Q. X., Lim, H. J., Shannon, N. B., Lim, W. K., Hendrikson, J., Ng, W. H., Tan, J. W., Koh, K. K. N., Wasudevan, S. D., et al. (2020) · Scientific Reports · doi:10.1038/s41598-019-55146-2 · Cited by 21
The developmental transcriptome landscape of receptive endometrium during embryo implantation in dairy goats
Zhang, L., Liu, X., Liu, J., Ma, L., Zhou, Z.-Q., Song, Y., & Cao, B. (2017) · Gene · doi:10.1016/j.gene.2017.08.026 · Cited by 20
NFATC3–PLA2G15 Fusion Transcript Identified by RNA Sequencing Promotes Tumor Invasion and Proliferation in Colorectal Cancer Cell Lines
Jang, J.-E., Kim, H., Han, S., Jang, H. S., Lee, S.-H., Song, S., Bang, D., & Kim, T. (2018) · Cancer Research and Treatment · doi:10.4143/crt.2018.103 · Cited by 20
High Impact Gene Discovery: Simple Strand-Specific mRNA Library Construction and Differential Regulatory Analysis Based on Gene Co-Expression Network
Ichihashi, Y., Fukushima, A., Shibata, A., et al. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8657-6_11 · Cited by 20
De novo transcriptome based on next-generation sequencing reveals candidate genes with sex-specific expression in Arapaima gigas (Schinz, 1822), an ancient Amazonian freshwater fish
Watanabe, L. A., Gomes, F., Júnior, J. L. D. S. G. V., Nunes, M. R. T., Cardoso, J. F., de Lima, C. P. S., Schneider, H., & Sampaio, I. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206379 · Cited by 20
SuperTranscript: a data driven reference for analysis and visualisation of transcriptomesPreprint
Davidson, N. M., Hawkins, A. D., & Oshlack, A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/077750 · Cited by 20
Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era
Xiong, X., Wang, X., Liu, C., Shao, Z.-M., & Yu, K. (2024) · Biomarker Research · doi:10.1186/s40364-024-00654-1 · Cited by 21
Transcriptome analysis of Oncidium petals provides new insights into the initiation of petal senescence
Yang, C., Xia, Z., Jin, H., Zhuang, Y.-F., Pan, Y.-W., & Liu, J. (2018) · The Journal of Horticultural Science and Biotechnology · doi:10.1080/14620316.2018.1432297 · Cited by 19
Transcriptome and gene expression analysis ofRhynchophorus ferrugineus(Coleoptera: Curculionidae) during developmental stages
Yang, H., Xu, D., Zhuo, Z., Hu, J., & Lu, B. (2020) · PeerJ · doi:10.7717/peerj.10223 · Cited by 19
Development and Validation of an RNA Sequencing Assay for Gene Fusion Detection in Formalin-Fixed, Paraffin-Embedded Tumors
Peng, H., Huang, R., Wang, K., Wang, C., Li, B., Guo, Y., Li, M., Zhang, D., Hua, D., Chen, H., et al. (2020) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2020.11.005 · Cited by 19
Profiling MicroRNAs with Associated Spatial Dynamics in Acute Tissue Slices
Xie, K., Wang, Z., Lin, Q., Zhao, X., Wang, Y., Jin, Q., Xu, P., Huang, L., Zhang, W., Yang, Y., et al. (2021) · ACS Nano · doi:10.1021/acsnano.0c09676 · Cited by 18
Cultivating Lentinula edodes on Substrate Containing Composted Sawdust Affects the Expression of Carbohydrate and Aromatic Amino Acid Metabolism-Related Genes
Huang, X., Zhang, R., Yang, Q., et al. (2022) · mSystems · doi:10.1128/msystems.00827-21 · Cited by 19
“Briefly, mRNA was purified from three mg RNA using poly-T oligo-attached magnetic beads (43).”
Method
Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code
Deng, L., Kumar, J., Rose, R. E., McIntyre, W. D., & Fabris, D. (2022) · Mass Spectrometry Reviews · doi:10.1002/mas.21798 · Cited by 19
Endophytic fungi as a potential source of anti-cancer drug
Sonowal, S., Gogoi, U., Buragohain, K., & Nath, R. (2024) · Archives of Microbiology · doi:10.1007/s00203-024-03829-4 · Cited by 19
“RNA‑sequencing RNA-Seq involves the integration of high-throughput sequencing techniques with computational approaches for capturing and quantifying transcripts within an RNA sample (Ozsolak and Milos 2011).”
ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets
Samanta, P., Cooke, S. F., McNulty, R., Hormoz, S., & Rosenthal, A. (2024) · Nature Protocols · doi:10.1038/s41596-024-01002-1 · Cited by 19
Application of the microarray technology to the transcriptional analysis of muscle phenotypes in pigs
Pena, R. N., Quintanilla, R., Manunza, A., Gallardo, D., Casellas, J., & Amills, M. (2014) · Animal Genetics · doi:10.1111/age.12146 · Cited by 19
Accurate Diagnostics for Bovine tuberculosis Based on High-Throughput Sequencing
Churbanov, A., & Milligan, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050147 · Cited by 17
“It can easily handle the uneven coverage biases of modern sequencers such as Illumina and SOLID [34], and different lengths of underlying loci.”
Background
Construction of gene regulatory networks mediated by vegetative and reproductive stage‐specific small RNA s in rice ( O ryza sativa)
Meng, Y., Shao, C., Wang, H., et al. (2012) · New Phytologist · doi:10.1111/nph.12018 · Cited by 19
Systems Biology Approach for New Target and Biomarker Identification
Wang, I., Stone, D. J., Nickle, D. C., Loboda, A. P., Puig, Ó., & Roberts, C. (2012) · Current topics in microbiology and immunology · doi:10.1007/82_2012_252 · Cited by 19
“2011), whole transcriptome (Ozsolak and Milos 2011) analysis of human patient cohorts which have already impacted medical research in an unprecedented way (Chin et al.”
The Molecular Revolution in Cutaneous Biology: Era of Next-Generation Sequencing
Sarig, O., & Sprecher, E. (2017) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2016.02.818 · Cited by 19
“NGS-based approaches (e.g., RNA sequencing) have also recently replaced microarrays as a means of quantitatively ascertaining gene expression (Ozsolak and Milos, 2011) and are used for genome-wide mapping of protein-DNA interactions (Mundade et al., 2014) and of epigenetic modifications (Ku et al.,…”
Method
Leveraging Genomic Data in Smoking Cessation Trials in the Era of Precision Medicine: Why and How
Chen, L., Zawertailo, L. A., Piasecki, T. M., Kaprio, J. A., Foreman, M. G., Elliott, H. R., David, S. P., Bergen, A. W., Baurley, J. W., Tyndale, R. F., et al. (2017) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntx097 · Cited by 20
Transcriptome profiling of cancer and normal tissues from cervical squamous cancer patients by deep sequencing
Lin, W., Feng, M., Li, X., Zhong, P., Guo, A., Chen, G., Xu, Q., & Ye, Y. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.6855 · Cited by 19
“With the development of next generation sequencing technologies, RNA sequencing (RNA-Seq) has become a powerful tool for analyzing cancer transcriptomes, detecting such items as alternative splicing, isoform usage, gene fusions and novel transcripts with greater accuracy and higher efficiency (5,6).”
Quantum Point Contact Single-Nucleotide Conductance for DNA and RNA Sequence Identification
Afsari, S., Korshoj, L. E., Abel, G. R., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b05500 · Cited by 19
Review of GPU implementation to process of RNA sequence on cancer
Rahman, M. A., & Muniyandi, R. C. (2017) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2017.10.008 · Cited by 20
Comparative transcriptome profiling of healthy and diseased Chowghat Green Dwarf coconut palms from root (wilt) disease hot spots
Rajesh, M. K., Rachana, K. E., Kulkarni, K., et al. (2017) · European Journal of Plant Pathology · doi:10.1007/s10658-017-1365-8 · Cited by 19
“With focus only on the transcribed portion of the genome, RNA-Seq circumvents repetitive and noncoding sequences, which constitute the bulk of eukaryotic genomes (Ozsolak and Milos 2011).”
Background
Applying functional genomics to the study of lamprey development and sea lamprey population control
York, J. R., Thresher, R. E., & McCauley, D. W. (2020) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2020.03.010 · Cited by 19
Host–Pathogen Interactions between Xanthomonas fragariae and Its Host Fragaria × ananassa Investigated with a Dual RNA-Seq Analysis
Gétaz, M., Puławska, J., Smits, T. H. M., & Pothier, J. F. (2020) · Microorganisms · doi:10.3390/microorganisms8081253 · Cited by 22
“With the advent of next-generation sequencing, high-throughput mRNA sequencing (RNA-seq) has become the major method for transcriptomic analysis, which can quantify genomewide expression in a single assay with higher resolution and better dynamic range of detection [37].”
Method
Differential network analysis of bovine muscle reveals changes in gene coexpression patterns in response to changes in maternal nutrition
Liu, L., Amorín, R., Moriel, P., DiLorenzo, N., Lancaster, P. A., & Peñagaricano, F. (2020) · BMC Genomics · doi:10.1186/s12864-020-07068-x · Cited by 21
Universal Count Correction for High-Throughput Sequencing
Hashimoto, T., Edwards, M. D., & Gifford, D. K. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003494 · Cited by 19
“RNA-seq [3,4], used to study gene expression, requires isolating the RNA content of a sample,”
Method
IRcall and IRclassifier: two methods for flexible detection of intron retention events from RNA-Seq data
Bai, Y., Ji, S., & Wang, Y. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s2-s9 · Cited by 19
“With the development of next-generation sequencing technology, RNA-Seq, which extends the analysis of previous unidentified genes and splicing variants, is rapidly outperforming microarrays for genomewide studies [6,7].”
Background
RNA sequence analysis of rat acute experimental pancreatitis with and without fatty liver: a gene expression profiling comparative study
Wang, Q., Yan, H., Wang, G., Qiu, Z., Bai, B., Wang, S., Yu, P., Feng, Q., Zhao, Q., He, X., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-00821-5 · Cited by 19
Identification of potentially relevant genes for myocardial infarction using RNA sequencing data analysis
Zhao, Q., Wu, K., Li, N., et al. (2017) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2017.5580 · Cited by 19
“RNA sequencing (RNA-seq) is a progressive method for transcription profiling that provides more precise information about gene expression (14).”

Authors: Ozsolak, F., & Milos, P. M.
Affiliations: Helicos Biosciences (United States) (US)
Corresponding author: Ozsolak, Fatih
Milos, Patrice M.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2010-12-30
Volume: 12
Issue: 2
Pages: 87-98
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg2934
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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