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

The DOI matches its Crossref record; 104 of the 105 references it cites were found in a registry or an index.

RNA sequencing: advances, challenges and opportunities

Open access

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

Open this paper in CoChat — free account

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nrg2934

Cites

104 of 104 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · cited 1×
“The reference sequence used for alignment was supplemented with 'artificial' sequences that surround all possible splice junctions between the annotated exons of genes, allowing the reads to be aligne”
Method
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · cited 1×
“This development eliminated several challenges posed by microarray technologies, including the limited dynamic range of detectio”
Background
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,682
Gene ontology analysis for RNA-seq: accounting for selection bias
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-2-r14 · Cited by 7,107
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066 · cited 1×
“The introduction of high-throughput next-generation DNA sequencing (NGS) technologie”
Method
Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project
The ENCODE Project Consortium (2007) · Nature · doi:10.1038/nature05874 · Cited by 5,223
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · cited 1×
“The reference sequence used for alignment was supplemented with 'artificial' sequences that surround all possible splice junctions between the annotated exons of genes, allowing the reads to be aligne”
Method
Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., & Weissman, J. S. (2009) · Science · doi:10.1126/science.1168978 · Cited by 3,853 · cited 1×
“These strategies have already begun to contribute to our understanding of transcriptomes, including mapping of translation states of RNAs (for example, polysome profiling”
Background

Show all 105 references

Sorted by citations

Items 901–1,000 of 1,000

Comment on "A comprehensive overview and evaluation of circular RNA detection tools"
Chen, C.-Y., & Chuang, T. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006158 · Cited by 19
BioMaster: Multi-agent system for automated bioinformatics analysis workflow
Su, H., Feng, J., Lu, Y., Xu, Y., Yang, J., Lu, H., Yang, J., Yang, X., Xie, S., Long, W., et al. (2026) · Patterns · doi:10.1016/j.patter.2026.101611 · Cited by 29
Exhaustive database searching for amino acid mutations in proteomes
Hyatt, D., & Pan, C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts274 · Cited by 18
Assessment of Bioleaching Microbial Community Structure and Function Based on Next-Generation Sequencing Technologies
Zhou, S., Gan, M., Zhu, J., Liu, X., & Qiu, G. (2018) · Minerals · doi:10.3390/min8120596 · Cited by 18
“Inefficient or efficient RNA-RNA or RNA-DNA ligation at some sequences, combined with uneven coverage caused by random priming, may create different bias in the outcome [112,113].”
Background
Transcriptomic Analysis of Skin Color in Anole Lizards
de Mello, P. L. H., Hime, P. M., & Glor, R. E. (2021) · Genome Biology and Evolution · doi:10.1093/gbe/evab110 · Cited by 20
Transcriptomic analysis of the Non-Obstructive Azoospermia (NOA) to address gene expression regulation in human testis
Balagannavar, G. C., Basavaraju, K., Bajpai, A. K., Davuluri, S., Kannan, S., Srini, V. S., Chandrashekar, D. S., Chitturi, N., & Acharya, K. K. (2023) · Systems Biology in Reproductive Medicine · doi:10.1080/19396368.2023.2176268 · Cited by 20
A urine extracellular vesicle lncRNA classifier for high-grade prostate cancer and increased risk of progression: A multi-center study
Tao, W., Wang, B.-Y., Luo, L., Li, Q., Meng, Z., Xia, T., Deng, W., Yang, M. S., Zhou, J., Zhang, X., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101240 · Cited by 18
“41 The expression of lncRNAs was normalized and calculated using fragments per kilobase per million mapped reads, StringTie and Ballgown were applied.”
Method
Classification of RNA-seq Data
Tan, K. M., Petersen, A. J., & Witten, D. (2014) · Springer eBooks · doi:10.1007/978-3-319-07212-8_11 · Cited by 18
Towards next generation CHO cell biology: Bioinformatics methods for RNA‐Seq‐based expression profiling
Monger, C., Kelly, P. S., Gallagher, C., CLYNES, M. M., Barron, N., & Clarke, C. (2015) · Biotechnology Journal · doi:10.1002/biot.201500107 · Cited by 20
“Transcriptomic analysis via NGS has had a dramatic impact on our understanding of the RNA universe through not just the expression levels of individual mRNAs and miRNAs but also the regulation of alternative splicing and the study of noncoding RNAs [85].”
Background
Transcriptional programming of human macrophages: on the way to systems immunology
Schultze, J. L. (2015) · Journal of Molecular Medicine · doi:10.1007/s00109-015-1286-y · Cited by 18
“Therefore, important aspects of transcriptional regulation are missed in all previous microarray studies [65–68].”
Intramolecular circularization increases efficiency of RNA sequencing and enables CLIP-Seq of nuclear RNA from human cells
Chu, Y., Wang, T., Dodd, D., Xie, Y., Janowski, B. A., & Corey, D. R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv213 · Cited by 18
“RNA sequencing (RNA-Seq) has become a widely used tool for investigating gene expression (1).”
Method
Improving the genome annotation of the acarbose producer Actinoplanes sp. SE50/110 by sequencing enriched 5′-ends of primary transcripts
Schwientek, P., Neshat, A., Kalinowski, J., Klein, A., Rückert, C., Schneiker-Bekel, S., Wendler, S., Stoye, J., & Pühler, A. (2014) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2014.03.013 · Cited by 18
SeaBase: A Multispecies Transcriptomic Resource and Platform for Gene Network Inference
Fischer, A., Mozzherin, D., Eren, A. M., Lans, K. D., Wilson, N., Cosentino, C., & Smith, J. (2014) · Integrative and Comparative Biology · doi:10.1093/icb/icu065 · Cited by 19
“RNA-Seq and targeted genetic perturbations provide key tools for interrogating complex systems (Ozsolak and Milos 2011).”
Background
Analyzing the Meiotic Transcriptome Using Isolated Meiocytes of Arabidopsis thaliana
Chen, C., & Retzel, E. F. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-333-6_20 · Cited by 18
A Survey of Software and Hardware Approaches to Performing Read Alignment in Next Generation Sequencing
Kawam, A. A., Khatri, S., & Datta, A. A. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2586070 · Cited by 18
“regulation [34].”
Discovery and expression analysis of novel transcripts of the human SR-related CTD-associated factor 1 (SCAF1) gene in human cancer cells using Next-Generation Sequencing
Adamopoulos, P. G., Raptis, G. D., Kontos, C. K., & Scorilas, A. (2018) · Gene · doi:10.1016/j.gene.2018.05.044 · Cited by 18
GIVE: portable genome browsers for personal websites
Cao, X., Yan, Z., Wu, Q., Zheng, A., & Zhong, S. (2018) · Genome biology · doi:10.1186/s13059-018-1465-6 · Cited by 17
Inheritance patterns of the transcriptome in hybrid chickens and their parents revealed by expression analysis
Gu, H., Qi, X., Jia, Y., Zhang, Z., Nie, C., Li, X., Li, J., Jiang, Z., Wang, Q., & Qu, L. (2019) · Scientific Reports · doi:10.1038/s41598-019-42019-x · Cited by 18
dsRNA-seq Reveals Novel RNA Virus and Virus-Like Putative Complete Genome Sequences from Hymeniacidon sp. Sponge
Urayama, S., Takaki, Y., Hagiwara, D., & Nunoura, T. (2020) · Microbes and Environments · doi:10.1264/jsme2.me19132 · Cited by 18
Novel Gene Fusions in Glioblastoma Tumor Tissue and Matched Patient Plasma
Wang, L., Yekula, A., Muralidharan, K., Small, J. L., Rosh, Z. S., Kang, K. M., Carter, B. S., & Balaj, L. (2020) · Cancers · doi:10.3390/cancers12051219 · Cited by 18
The Known Unknowns of the Immune Response to Coccidioides
Ward, R., Thompson, G. R., Villani, A., Li, B., Mansour, M. K., Wüthrich, M., Tam, J. M., Klein, B. S., & Vyas, J. M. (2021) · Journal of Fungi · doi:10.3390/jof7050377 · Cited by 18
“Of these approaches, bulk RNA sequence is the least powerful, since it primarily reflects the average gene expression across all cells [97].”
Background
[Retracted] Intelligent Environmental Art Design Combining Big Data and Artificial Intelligence
Li, Y. (2021) · Complexity · doi:10.1155/2021/1606262 · Cited by 18
Retracted
Identification of mutations, gene expression changes and fusion transcripts by whole transcriptome RNAseq in docetaxel resistant prostate cancer cells
Ma, Y., Miao, Y., Peng, Z., Sandgren, J., de Ståhl, T. D., Huss, M., Lennartsson, L., Liu, Y., Nistér, M., Nilsson, S. F., et al. (2016) · SpringerPlus · doi:10.1186/s40064-016-3543-0 · Cited by 18
“This study applied next generation RNA sequencing (RNAseq) technology in combination with specific software (Ozsolak and Milos 2011) to determine gene expression changes, mutations and fusions in docetaxel sensitive cell lines versus docetaxel resistant cell lines.”
Method
Transcriptional Variation of Diverse Enteropathogenic Escherichia coli Isolates under Virulence-Inducing Conditions
Hazen, T. H., Daugherty, S. C., Shetty, A. C., Nataro, J. P., & Rasko, D. A. (2017) · mSystems · doi:10.1128/msystems.00024-17 · Cited by 19
Curcumin may serve an anticancer role in human osteosarcoma cell line U-2 OS by targeting ITPR1
Luo, Z., Li, D., Luo, X., et al. (2018) · Oncology Letters · doi:10.3892/ol.2018.8032 · Cited by 18
“Several developments in RNA-seq, including mapping of the transcription start site, characterization of small RNA and detection of strand-specific, gene fusion and alternative splicing events, have been identified (17).”
Transcriptomic Insights into Benzenamine Effects on the Development, Aflatoxin Biosynthesis, and Virulence of Aspergillus flavus
Yang, M., Lu, L., Li, S., Zhang, J., Li, Z., Wu, S., Guo, Q. M., Liu, H., & Wang, C. (2019) · Toxins · doi:10.3390/toxins11020070 · Cited by 19
“High-throughput sequencing technologies are currently revolutionizing the field of biology and RNA sequencing (RNA-Seq) has been applied to study a range of eukaryotic transcriptomes, with less sampling bias, higher resolution, and much broader expression range coverage [20,21].”
Background
Transcriptome Analysis of Clinopodium chinense (Benth.) O. Kuntze and Identification of Genes Involved in Triterpenoid Saponin Biosynthesis
Shi, Y., Zhang, S., Peng, D., et al. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112643 · Cited by 18
BioMaster: Multi-agent System for Automated Bioinformatics Analysis Workflow
Su, H., Long, W., & Zhang, Y. (2025) · doi:10.1101/2025.01.23.634608 · Cited by 18
Single-molecule real-time (SMRT) sequencing facilitates Tachypleus tridentatus genome annotation
Lou, F., Song, N., Han, Z., & Gao, T. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.01.029 · Cited by 17
Gene Expression and Transcriptome Sequencing: Basics, Analysis, Advances
Magar, N. D., Shah, P., Harish, K., Bosamia, T. C., Barbadikar, K. M., Shukla, Y., Phule, A. S., Zala, H. N., Madhav, M. S., Mangrauthia, S. K., et al. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.105929 · Cited by 17
Transcriptomics: illuminating the molecular landscape of vegetable crops: a review
P, P., V, Bidaramali, V., Lata, S., & Yadav, R. K. (2024) · Journal of Plant Biochemistry and Biotechnology · doi:10.1007/s13562-023-00871-z · Cited by 17
“This technology offers several advantages over traditional microarray-based methods, including increased sensitivity, wider dynamic range, and the ability to detect novel transcripts (Ozsolak and Milos 2011).”
Advancements in omics technologies: Molecular mechanisms of acute lung injury and acute respiratory distress syndrome (Review)
Zheng, Z., Qiao, X., Yin, J., Kong, J., Han, W., Qin, J., Meng, F., Tian, G., & Feng, X. (2024) · International Journal of Molecular Medicine · doi:10.3892/ijmm.2024.5479 · Cited by 17
“In ALI/ARdS, transcriptomics helps identify gene expression changes and functional pathways, define gene function and map regulatory networks (186).”
RNA-Seq-Based Metatranscriptomic and Microscopic Investigation Reveals Novel Metalloproteases of Neobodo sp. as Potential Virulence Factors for Soft Tunic Syndrome in Halocynthia roretzi
Jang, H. B., Kim, Y. K., del Castillo, C. S., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052379 · Cited by 17
“RNA-seq (RNA sequencing), which is a massively parallel cDNA sequencing technique, has become the method of choice for monitoring eukaryotic [22] and bacterial [16,17] transcriptomes.”
Method
Computational challenges, tools, and resources for analyzing co‐ and post‐transcriptional events in high throughput
Bahrami‐Samani, E., Vo, D. T., Araujo, P. R. D., Vogel, C., Smith, A. D., Penalva, L. O. F., & Uren, P. J. (2014) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1274 · Cited by 15
“Also in contrast to transcriptomics, where some consensus has been reached in terms of methods and analysis pipelines, 7 – 10 RNA biologists continue to use a range of different experimental and analysis approaches.”
Background
Transcriptome Analysis of the SL221 Cells at the Early Stage during Spodoptera litura Nucleopolyhedrovirus Infection
Qian, Y., Xiong, Y., Liu, J., Wen, D., Wu, X., & Yin, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0147873 · Cited by 17
“RNA-seq is a powerful tool for transcript discovery, genome annotation and expression profiling [24,25].”
Method
Analyses of alternative polyadenylation: from old school biochemistry to high-throughput technologies
Yeh, H.-S., Zhang, W., & Yong, J. (2017) · BMB Reports · doi:10.5483/bmbrep.2017.50.4.019 · Cited by 18
“higher error rate, shorter read lengths, lower throughput, and the lack of multiplexing capability (29, 32).”
BackgroundMethod
RNA-Seq analysis on chicken taste sensory organs: An ideal system to study organogenesis
Cui, X., Marshall, B., Shi, N., Chen, S., Romdhane Rekaya, A., & Liu, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-09299-7 · Cited by 17
CamurWeb: a classification software and a large knowledge base for gene expression data of cancer
Weitschek, E., Lauro, S. D., Cappelli, E., Bertolazzi, P., & Felici, G. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2299-7 · Cited by 16
Salivary Biomarkers in Oral Cancer
Panta, P., & Wong, D. T. (2019) · Springer eBooks · doi:10.1007/978-3-319-61255-3_14 · Cited by 17
A novel method for the capture-based purification of whole viral native RNA genomes
Tan, C. C. S., Maurer‐Stroh, S., Wan, Y., Sessions, O. M., & de Sessions, P. F. (2019) · AMB Express · doi:10.1186/s13568-019-0772-y · Cited by 17
Deep Sequencing of Cardiac MicroRNA-mRNA Interactomes in Clinical and Experimental Cardiomyopathy
Matkovich, S. J., & Dorn, G. W. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2572-8_3 · Cited by 17
“We use next-generation / deep-sequencing methods to obtain quantitative RNA abundance information that cannot be determined using qPCR techniques or microarrays, and which have superior dynamic range and sensitivity compared to these approaches [10,18].”
Method
Time-Course Transcriptome Analysis for Drug Repositioning in Fusobacterium nucleatum-Infected Human Gingival Fibroblasts
Kang, W., Jia, Z., Tang, D., Zhao, X., Shi, J., Jia, Q., He, K., & Feng, Q. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00204 · Cited by 18
Comprehensive transcriptome analysis and tissue-specific profiling of gene expression in jute (Corchorus olitorius L.)
Yang, Z., Wu, Y., Dai, Z., et al. (2020) · Industrial Crops and Products · doi:10.1016/j.indcrop.2020.112101 · Cited by 17
Surfactant and oil formulations for monodisperse droplet emulsion PCR
Zhang, F., Liao, P., Sun, Y. M., Chen, Z., Pang, Y., & Huang, Y. (2020) · Lab on a Chip · doi:10.1039/d0lc00052c · Cited by 19
Translational Research in Retinopathy of Prematurity: From Bedside to Bench and Back Again
Arima, M., Fujii, Y., & Sonoda, K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10020331 · Cited by 16
Comparative transcriptome analysis of roots, stems, and leaves of Pueraria lobata (Willd.) Ohwi: identification of genes involved in isoflavonoid biosynthesis
Wang, C., Xu, N., & Cui, S. (2021) · PeerJ · doi:10.7717/peerj.10885 · Cited by 17
An Interpretable Approach for Lung Cancer Prediction and Subtype Classification using Gene Expression
Ramos, B., Pereira, T., Moranguinho, J., Morgado, J., Costa, J. L., & Oliveira, H. P. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630775 · Cited by 17
“RNA-seq tells us which genes are turned on in a cell, what their level of expression is, and at what times they are activated or shut off [4], which can enable towards a deeper understanding of molecular changes that might lead to disease.”
Background
Characterization of the Cellular Microenvironment and Novel Specific Biomarkers in Pterygia Using RNA Sequencing
Wolf, J., Hajdú, R. I., Boneva, S., Schlecht, A., Lapp, T., Wacker, K., Agostini, H. T., Reinhard, T., Auw‐Haedrich, C., Schlunck, G., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.714458 · Cited by 20
“RNA sequencing technology, in contrast, allows a more accurate and unbiased analysis of gene expression with less technical variation and a lower false positive rate and is additionally able to detect novel and rare transcripts that have previously been missed by conventional microarray technology (11, 12).”
Background
Recent developments in the diagnosis of Marfan syndrome and related disorders
SUMMERS, K. M., West, J. A., Hattam, A., et al. (2012) · The Medical Journal of Australia · doi:10.5694/mja12.10560 · Cited by 17
Explaining a Weighted DAG with Few Paths for Solving Genome-Guided Multi-Assembly
Tomescu, A. I., Gagie, T., Popa, A., Rizzi, R., Kuosmanen, A., & Mäkinen, V. (2015) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2418753 · Cited by 19
What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins
Hutchins, J. R. A. (2014) · Molecular Biology of the Cell · doi:10.1091/mbc.e13-10-0602 · Cited by 17
“These methods, which include the functional identification of genes using traditional genetic and RNA interference (RNAi)-based knockdown screens (Forsburg, 2001; Boutros and Ahringer, 2008), the identification of DNA and RNA populations by microarray analysis or nextgeneration sequencing (Capaldi, 2010; Niedringhaus…”
The challenges of delivering bioinformatics training in the analysis of high-throughput data
Carvalho, B. S., & Rustici, G. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbt018 · Cited by 19
“NGS technologies [1] are now routinely used in many applications including genome sequencing/ re-sequencing, small RNA discovery [2], deep SNP discovery [3], chromatin immunoprecipitation sequencing (ChIP-seq) [4], ribonomics [5], transcriptome analysis for discovery and characterization of alternative splicing [6] an…”
Method
Use of next-generation DNA sequencing to analyze genetic variants in rheumatic disease
Wiley, G. B., Kelly, J. A., & Gaffney, P. M. (2014) · Arthritis Research & Therapy · doi:10.1186/s13075-014-0490-4 · Cited by 19
“Deep sequencing for transcriptomic studies (RNA-seq) generates more detailed data, including specific isoform, exon-specific transcript and allelic expression levels [36-38], mapping of transcription start sites, identification of sense and antisense transcripts, detection of alternative splicing events, and discovery…”
Background
Identification and characterization of toxins in the venom gland of the Chinese bird spider,Haplopelma hainanum, by transcriptomic analysis
Cheng, T., Long, R., Wu, Y., Guo, Y., Liu, D., Peng, L. J., Li, D., Yang, D., Xu, X., Liu, F., et al. (2015) · Insect Science · doi:10.1111/1744-7917.12305 · Cited by 17
“During the past few years, next-generation transcriptome sequencing technology has been widely applied to transcriptomic studies of different organisms because its sensitivity and output have enormously increased (Ozsolak & Milos, 2011).”
Background
Transcriptional regulation of long-term potentiation
Bliim, N., Leshchyns’ka, I., Sytnyk, V., & Janitz, M. (2016) · Neurogenetics · doi:10.1007/s10048-016-0489-x · Cited by 18
Reverse Transcription Errors and RNA–DNA Differences at Short Tandem Repeats
Fungtammasan, A., Tomaszkiewicz, M., Campos-Sánchez, R., Eckert, K. A., DeGiorgio, M., & Makova, K. D. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw139 · Cited by 16
Comparative transcriptome analysis of shortened fruit mutant in woodland strawberry ( Fragaria vesca ) using RNA-Seq
Wang, S., Li, W., Liu, Y., et al. (2017) · Journal of Integrative Agriculture · doi:10.1016/s2095-3119(16)61448-x · Cited by 17
RNA-seq of Rice Yellow Stem BorerScirpophaga incertulasReveals Molecular Insights During Four Larval Developmental Stages
Renuka, P., Madhav, M. S., Padmakumari, A. P., Barbadikar, K. M., Mangrauthia, S. K., Kola, V. S. R., Marla, S. S., & Babu, V. R. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.043737 · Cited by 17
“Transcriptome analysis through RNA-seq can be utilized efficiently to identify the temporal and unique gene expression patterns in an organism (Ozsolak and Milos 2011).”
Method
Whole transcriptome analyis of human lung tissue to identify COPD-associated genes
Zhu, Y., Li, Q., 周爱媛, Racanelli, A. C., Choi, A. M. K., & Yin, Y. (2020) · Genomics · doi:10.1016/j.ygeno.2020.05.025 · Cited by 17
Deep intronic mis-splicing mutation in JAK3 gene underlies T − B + NK − severe combined immunodeficiency phenotype
Stepensky, P., Keller, B., Shamriz, O., et al. (2016) · Clinical Immunology · doi:10.1016/j.clim.2016.01.001 · Cited by 17
Single‐Cell Transcriptomic Analysis
Zheng, Z., Chen, E., Lü, W., Mouradian, G. C., Hodges, M. R., Liang, M., Liu, P., & Lü, Y. (2020) · Comprehensive physiology · doi:10.1002/cphy.c190037 · Cited by 28
Graph-Based Prediction of miRNA-Drug Associations With Multisource Information and Metapath Enhancement Matrices
Wu, M., Hu, P., You, Z., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3558303 · Cited by 16
“For example, RNA sequencing technology is used to compare miRNAs in cell or tissue samples with validated resistance-related miRNAs to identify potential resistance-associated miRNAs [14].”
Potential key genes involved in metabolic resistance to malathion in the southern house mosquito, Culex quinquefasciatus, and functional validation of CYP325BC1 and CYP9M12 as candidate genes using RNA interference
Huang, X., Kaufman, P. E., Athrey, G., Fredregill, C., Alvarez, C., Shetty, V., & Slotman, M. A. (2023) · BMC Genomics · doi:10.1186/s12864-023-09241-4 · Cited by 16
“The development of next-generation sequencing (NGS) techniques, including RNA-Seq, has become a reliable and cost-effective tool for analysis of gene expression patterns [16].”
Method
A deep-learning-based RNA-seq germline variant caller
Cook, D. E., Venkat, A., Yelizarov, D., Pouliot, Y., Chang, P.-C., Carroll, A. W., & Vega, F. M. D. L. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad062 · Cited by 17
From cancer big data to treatment: Artificial intelligence in cancer research
Danishuddin, M., Khan, S., & Kim, J.-J. (2023) · The Journal of Gene Medicine · doi:10.1002/jgm.3629 · Cited by 16
Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction
Kim, W.-J., Choi, B. R., Noh, J. J., et al. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1342021 · Cited by 16
Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry
Shi, W., Zhang, J., Huang, S., Fan, Q., Cao, J., Zeng, J., Wu, L., & Yang, C. J. (2024) · JACS Au · doi:10.1021/jacsau.4c00118 · Cited by 17
Regulation ofDscamexon 17 alternative splicing by steric hindrance in combination with RNA secondary structures
Yue, Y., Li, G., Yang, Y., et al. (2013) · RNA Biology · doi:10.4161/rna.27176 · Cited by 16
“Increasing numbers of novel alternative transcripts have been detected because of advances in high-throughput sequencing technology.(4) In humans, up to 95% of primary transcripts are estimated to undergo alternative splicing.”
Background
The diverse applications of RNA‐seq for functional genomic studies in Aspergillus fumigatus
Rokas, A., Gibbons, J. G., Zhou, X., Beauvais, A., & Latgé, J. (2012) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2012.06755.x · Cited by 17
Analysis of expressed sequence tags derived from a compatiblePlasmodiophora brassicae–canola interaction
Feng, J., Hwang, S., & Strelkov, S. E. (2012) · Canadian Journal of Plant Pathology · doi:10.1080/07060661.2012.722128 · Cited by 16
“…whole transcriptome sequencing using nextgeneration sequencing (NGS) technologies or RNA sequencing (RNA-seq) has started to be utilized to reveal the complex landscape of the transcriptome from various organisms at an unprecedented level of sensitivity and accuracy (Ozsolak & Milos, 2011).”
Background
Transcriptomics: A Step behind the Comprehension of the Polygenic Influence on Oxidative Stress, Immune Deregulation, and Mitochondrial Dysfunction in Chronic Kidney Disease
Granata, S., Gassa, A. D., Bellin, G., Lupo, A., & Zaza, G. (2016) · BioMed Research International · doi:10.1155/2016/9290857 · Cited by 16
Transcriptome Analysis of Induced Pluripotent Stem Cell (iPSC)-derived Pancreatic β-like Cell Differentiation
Huang, Y. H., Wan, J., Guo, Y., Zhu, S., Wang, Y., Wang, L., Guo, Q., Lu, Y., & Wang, Z. (2017) · Cell Transplantation · doi:10.1177/0963689717720281 · Cited by 16
“RNA-seq has considerable advantages for whole-genome transcriptome profiling.(10) RNA-seq directly detects transcripts, avoiding hybridization biases of microarrays and providing more precise information on transcript levels.”
Background
A Path to Implement Precision Child Health Cardiovascular Medicine
Touma, M., Reemtsen, B. L., Halnon, N. J., Alejos, J. C., Finn, J. P., Nelson, S. F., & Wang, Y. (2017) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2017.00036 · Cited by 16
“Further, the transcriptome is tissue/cell specific and may vary with time; hence, the transcriptome of a specific tissue only partially reflects the exome (70).”
Background
Long Non-Coding RNAs as Key Regulators of Cardiovascular Diseases
Ono, K., Kuwabara, Y., Horie, T., & Kimura, T. (2018) · Circulation Journal · doi:10.1253/circj.cj-18-0169 · Cited by 16
Emerging properties of nuclear RNP biogenesis and export
Oeffinger, M., & Montpetit, B. (2015) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2015.04.007 · Cited by 16
Functional genomics applications and therapeutic implications in sarcopenia
Melouane, A., Ghanemi, A., Yoshioka, M., & St‐Amand, J. (2019) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2019.04.003 · Cited by 16
De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments
Woo, J., Winterhoff, B. J. N., Starr, T. K., Aliferis, C., & Wang, J. (2019) · Life Science Alliance · doi:10.26508/lsa.201900443 · Cited by 16
“Recent advances in single-cell transcriptome pro ﬁ ling techniques using RNA-sequencing (RNA-seq; Ozsolak & Milos, 2011; Ziegenhain et al, 2017), together with customized computational methods (Buettner et al, 2015; Bacher & Kendziorski, 2016; Ilicic et al, 2016; Alpert et al, 2018; Edsg¨ard et al,…”
Method
Transcriptomic Profiling Provides Insights into Inbreeding Depression in Yesso Scallop Patinopecten yessoensis
Zhao, L., Li, Y., Lou, J., et al. (2019) · Marine Biotechnology · doi:10.1007/s10126-019-09907-9 · Cited by 16
“RNA sequencing (RNA-seq) is an efficient tool for profiling expressed gene in organisms (Ozsolak and Milos 2011).”
Effectiveness of Conventional Crop Improvement Strategies vs. Omics
Qamar, M. T. U., Faryad, A., Bari, A., Zahid, B., Zhu, X.-T., & Chen, L. (2020) · Springer eBooks · doi:10.1007/978-3-030-49732-3_11 · Cited by 16
Immune-associated biomarkers for early diagnosis of Parkinson’s disease based on hematological lncRNA–mRNA co-expression
Lei, K., Zhang, L., He, Y., Sun, H., Tong, W., Xu, Y., & Jin, L. (2020) · Bioscience Reports · doi:10.1042/bsr20202921 · Cited by 16
Demystifying the Advancements of Big Data Analytics in Medical Diagnosis: An Overview
Naik, N., Rallapalli, Y., Krishna, M., Vellara, A. S., Shetty, D. K., Patil, V., Hameed, B. M. Z., Paul, R., Prabhu, N., Rai, B. P., et al. (2022) · Engineered Science · doi:10.30919/es8d580 · Cited by 18
A Guide to the Chloroplast Transcriptome Analysis Using RNA-Seq
Michel, E. J. S., Hotto, A. M., Strickler, S. R., Stern, D. B., & Castandet, B. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8654-5_20 · Cited by 16
Transcriptional characterisation of the Exaiptasia pallida pedal disc
Davey, P. A., Rodrigues, M., Clarke, J., & Aldred, N. (2019) · BMC Genomics · doi:10.1186/s12864-019-5917-5 · Cited by 18
RNA-seq analysis of synovial fibroblasts in human rheumatoid arthritis
Wang, X., Xia, S., & Fu, B. (2014) · Molecular Medicine Reports · doi:10.3892/mmr.2014.2182 · Cited by 16
“It has fostered numerous advances in the characterization and quantification of transcripts, since it allows a nearly complete characterization of transcriptomes (18).”
Phenomics in Livestock Research: Bottlenecks and Promises of Digital Phenotyping and Other Quantification Techniques on a Global Scale
Upadhyay, V. R., Ramesh, V., Kumar, H., Somagond, Y. M., Priyadarsini, S., Kuniyal, A., Prakash, V., & SAHOO, A. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0109 · Cited by 17
Omics advancements towards exploring arsenic toxicity and tolerance in plants: a review
Hassan, S. H., Simiele, M., Scippa, G. S., Morabito, D., & Trupiano, D. (2025) · Planta · doi:10.1007/s00425-025-04646-9 · Cited by 16
“…in RNA quality or library preparation can introduce biases, making experimental consistency critical, thus, it benefits from being integrated with complementary methods like deep sequencing for a more comprehensive understanding of transcriptomic responses (Stark et al. 2019; Ozsolak et al. 2011).”
Proteomics and transcriptomics jointly identify the key role of oxidative phosphorylation in fluoride-induced myocardial mitochondrial dysfunction in rats
Xie, J., Zhao, Y., Dong, N., Tian, X., Feng, J. Q., Liu, P., Li, M., Wang, M., Ying, X., Yuan, J., et al. (2021) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2021.112271 · Cited by 15
Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis
Cui, W., Xue, H., Geng, Y., Zhang, J., Liang, Y., Tian, X., & Wang, Q. (2021) · Annals of Human Genetics · doi:10.1111/ahg.12441 · Cited by 16
“RNA-seq is currently the most popular technology for cataloging and comparing genome-wide gene expression and has been extensively applied in identifying DEGs under different conditions (Stark et al., 2019, Ozsolak & Milos, 2011, Wang et al., 2009).”
BackgroundMethod
scDLC: a deep learning framework to classify large sample single-cell RNA-seq data
Zhou, Y., Peng, M., Yang, B., Tong, T., Zhang, B., & Tang, N. (2022) · BMC Genomics · doi:10.1186/s12864-022-08715-1 · Cited by 15
“The development of RNA sequencing (RNA-seq) has enabled unprecedented insight into the dynamics of gene expression [1–7].”
Background
Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence
Perez, B. C., Bink, M. C. A. M., Svenson, K. L., Churchill, G. A., & Calus, M. P. L. (2022) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkac258 · Cited by 17
Embryonic transcriptome unravels mechanisms and pathways underlying embryonic development with respect to muscle growth, egg production, and plumage formation in native and broiler chickens
Mogilicherla, K., Ashwini, R., CHATTERJEE, R. N., & Bhattacharya, T. K. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.990849 · Cited by 19
“The identification of genes linked to changes in feather and scale will be aided by transcriptome investigations and bioinformatic analysis (Ozsolak and Milos, 2011; Chang et al., 2015).”
Background
Antifungal mechanism of volatile compounds emitted by Actinomycetota Paenarthrobacter ureafaciens from a disease-suppressive soil on Saccharomyces cerevisiae
Nguyen, T.-P., Meng, D.-R., Chang, C., Su, P., Ou, C.-A., Hou, P.-F., Sung, H., Chou, C., Ohme‐Takagi, M., & Huang, H.-J. (2023) · mSphere · doi:10.1128/msphere.00324-23 · Cited by 15
“The recent development of RNA sequencing (RNA-seq) has also enabled researchers to reveal extensive molecular changes that occur in yeast exposed to environmental stresses (38–41).”
Background
An Integrative ATAC-Seq and RNA-Seq Analysis of the Endometrial Tissues of Meishan and Duroc Pigs
Zhang, H., Liu, Z., Wang, J., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914812 · Cited by 15
“Additionally, RNA-seq can identify differentially expressed genes (DEGs) and, to a certain extent, represent the level of gene expression in tissues at particular time points [20].”
Background
Unlocking melanoma Suppression: Insights from Plasma-Induced potent miRNAs through PI3K-AKT-ZEB1 axis
Bhartiya, P., Jaiswal, A., Negi, M., et al. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.02.022 · Cited by 15
“…novel transcripts, short RNAs, single-nucleotide polymorphisms, alternative splicing products, sense and antisense transcripts, fusion transcripts, and transcription initiation sites than other technologies, such as hybridization-based microarrays and Sanger sequencing-based approaches [39–42].”
PLEKv2: predicting lncRNAs and mRNAs based on intrinsic sequence features and the coding-net model
Li, A., Zhou, H., Xiong, S., Li, J., Mallik, S., Fei, R., Liu, Y., Zhou, H., Wang, X., Hei, X., et al. (2024) · BMC Genomics · doi:10.1186/s12864-024-10662-y · Cited by 17
“As sequencing technology develops, many novel animal and plant transcripts have been sequenced [7].”
Background
Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy
Ren, Y., Yue, Y., Li, X., Weng, S., Xu, H., Liu, L., Cheng, Q., Luo, P., Zhang, T., Liu, Z., et al. (2024) · International Immunopharmacology · doi:10.1016/j.intimp.2024.113147 · Cited by 17
Temperature effects on the fidelity of a thermostable HIV ‐1 reverse transcriptase
Álvarez, M., & Menéndez‐Arias, L. (2013) · FEBS Journal · doi:10.1111/febs.12605 · Cited by 15
Locus-Specific Databases in Cancer: What Future in a Post-Genomic Era? The TP53 LSDB paradigm
Soussi, T. (2014) · Human Mutation · doi:10.1002/humu.22518 · Cited by 16
RNA sequencing of sarcomas with simple karyotypes: identification and enrichment of fusion transcripts
Hofvander, J., Tayebwa, J., Nilsson, J., Magnusson, L., Brosjö, O., Larsson, O., von Steyern, F. V., Domanski, H. A., Mandahl, N., & Mertens, F. (2015) · Laboratory Investigation · doi:10.1038/labinvest.2015.50 · Cited by 16
“8 Myxoid liposarcoma, reclassified as lipoblastoma 46,XX,der(5)t(5;8)(q35;q13)inv(8)(q22q24),der(8)t(5;8) 16 951 072 RNA-sequencing of sarcomas J Hofvander et al”
Background
SAVoR: a server for sequencing annotation and visualization of RNA structures
Li, F., Ryvkin, P., Childress, D. M., Valladares, O., Gregory, B. D., & Wang, L.-S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks310 · Cited by 15

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

RNA sequencing: advances, challenges and opportunities

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks