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

Flagged first, then by citations

Show all 105 references

1 of the 105 references couldn’t be matched to a known paper.

Sorted by citations

Items 501–600 of 1,000

Alternative splicing interference by xenobiotics
Zaharieva, E. E., Chipman, J. K., & Soller, M. (2012) · Toxicology · doi:10.1016/j.tox.2012.01.014 · Cited by 46
Precision medicine journey through omics approach
Hasanzad, M., Sarhangi, N., Chimeh, S. E., Ayati, N., Afzali, M., Khatami, F., Nikfar, S., & Meybodi, H. R. A. (2021) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-021-00913-0 · Cited by 44
Single-cell RNA sequencing in skeletal muscle developmental biology
Cai, C., Yue, Y., & Yue, B. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114631 · Cited by 45
Long noncoding RNA MALAT1 is dynamically regulated in leader cells during collective cancer invasion
Zhu, N., Ahmed, M. A., Li, Y., Liao, J. C., & Wong, P. K. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305410120 · Cited by 43
Alternative adaptive immunity strategies: coelacanth, cod and shark immunity
Buonocore, F., & Gerdol, M. (2015) · Molecular Immunology · doi:10.1016/j.molimm.2015.09.003 · Cited by 43
Expression profile of COL2A1 and the pseudogene SLC6A10P predicts tumor recurrence in high‐grade serous ovarian cancer
Ganapathi, M. K., Jones, W. D., Sehouli, J., Michener, C. M., Braicu, E. I., Norris, E. J., Biscotti, C. V., Vaziri, S. A. J., & Ganapathi, R. N. (2015) · International Journal of Cancer · doi:10.1002/ijc.29815 · Cited by 43
Computational Identification of Novel Genes: Current and Future Perspectives
Klasberg, S., Bitard‐Feildel, T., & Mallet, L. (2016) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s39950 · Cited by 44
“43.”
Extraction of high-quality RNA from human articular cartilage
Bleu, H. K. L., Kamal, F., Kelly, M., Ketz, J. P., Zuscik, M. J., & Elbarbary, R. A. (2016) · Analytical Biochemistry · doi:10.1016/j.ab.2016.11.018 · Cited by 43
Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction
Cappelli, E., Felici, G., & Weitschek, E. (2018) · BioData Mining · doi:10.1186/s13040-018-0184-6 · Cited by 41
Transcriptome Sequencing and Analysis of Changes Associated with Insecticide Resistance in the Dengue Mosquito (Aedes aegypti) in Vietnam
Liên, N. T. K., Ngọc, N. T. H., Lan, N. N., Hien, N. T., Tung, N. V., Ngân, N. T. T., Hoàng, N. H., & Binh, N. T. H. (2019) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.18-0607 · Cited by 47
Fusion detection and quantification by pseudoalignmentPreprint
Melsted, P., Hateley, S. L., Joseph, I., Pimentel, H., Bray, N., & Pachter, L. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166322 · Cited by 45
“Currently, most fusion detection methods rely upon RNA sequencing 10 (RNA-Seq) because it is cost-effective, covers all transcribed regions of the genome, and is highly interpretable.”
Background
An Overview of the Genetics and Genomics of the Urochloa Species Most Commonly Used in Pastures
Ferreira, R. C. U., Moraes, A. D. C. L., Chiari, L., Simeão, R. M., Vigna, B. B. Z., & de Souza, A. P. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.770461 · Cited by 42
“The transcriptome is the set of RNA transcripts contained in a cell or tissue at a specific stage of development and/or under specific physiological conditions (Ozsolak and Milos, 2010).”
Postoperative Prognosis Prediction of Pancreatic Cancer With Seven MicroRNAs
Lee, K. H., Lee, J. K., Choi, D. W., Do, I., Sohn, I., Jang, K., Jung, S., Heo, J. S., Choi, S., & Lee, K. T. (2015) · Pancreas · doi:10.1097/mpa.0000000000000346 · Cited by 42
A quality control system for profiles obtained by ChIP sequencing
Mendoza-Parra, M. A., Gool, W. V., Saleem, M.-A. M., Ceschin, D. G., & Gronemeyer, H. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt829 · Cited by 42
“Moreover, the mapping of transcriptomes by RNA-seq (10–13), global nascent RNA sequencing or global run on sequencing (GRO-seq) (14) or ribosome-associated (‘ribosome footprinting’) RNAs (15), and technologies revealing chromatin conformation are also based on massive parallel sequencing (16–18).”
Background
Transcriptome Profiling of a Multiple Recurrent Muscle-Invasive Urothelial Carcinoma of the Bladder by Deep Sequencing
Zhang, S., Liu, Y., Liu, Z., Zhang, C., Cao, H., Ye, Y., Wang, S., Zhang, Y., Xiao, S., Peng, Y., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091466 · Cited by 42
“The advantage of RNA-seq is that this technique makes feasible the study of the cancer transcriptome complexity, including alternative splicing, isoform usage, gene fusions and novel transcripts (reviewed in [20,21].”
BackgroundMethod
Root Systems Biology: Integrative Modeling across Scales, from Gene Regulatory Networks to the Rhizosphere
Hill, K., Porco, S., Lobet, G., Zappala, S., Mooney, S. J., Draye, X., & Bennett, M. J. (2013) · PLANT PHYSIOLOGY · doi:10.1104/pp.113.227215 · Cited by 42
“The majority of root transcriptome data sets contain microarray data, but many future data sets will contain next-generation sequencing data, from RNA analysis through complementary DNA sequencing at a massive scale (RNA-seq; Ozsolak and Milos, 2011).”
BackgroundInfluential
CRISPLD2 (LGL1) inhibits proinflammatory mediators in human fetal, adult, and COPD lung fibroblasts and epithelial cells
Zhang, H., Kho, A. T., Wu, Q. H., et al. (2016) · Physiological Reports · doi:10.14814/phy2.12942 · Cited by 42
Prognostic gene expression profiling in esophageal cancer: a systematic review
Visser, E., Franken, I. A., Brosens, L. A. A., et al. (2016) · Oncotarget · doi:10.18632/oncotarget.13328 · Cited by 42
“This exciting emerging technique can provide additional information on gene fusions and alternative splicing with high accuracy and sensitivity [52, 53].”
Background
Microfluidics for genome-wide studies involving next generation sequencing
Ma, S., Murphy, T. W., & Lu, C. (2017) · Biomicrofluidics · doi:10.1063/1.4978426 · Cited by 42
An Integrated Analysis of Cashmere Fineness lncRNAs in Cashmere Goats
Zheng, Y. Y., Sheng, S. D., Hui, T. Y., et al. (2019) · Genes · doi:10.3390/genes10040266 · Cited by 42
“RNA-seq allows large-scale data production via high throughput sequencing, which has helped us to detect transcripts with low abundances, identify novel transcript units, and investigate their differential expression between diverse samples [28,29].”
Background
Biomarkers of Infection: Are They Useful in the ICU?
Heilmann, E., Gregoriano, C., & Schüetz, P. (2019) · Seminars in Respiratory and Critical Care Medicine · doi:10.1055/s-0039-1696689 · Cited by 45
“13, 14 Pattern-recognition receptors on immune cells are activated by different pathogen-derived ligands, which results in the initiation of distinct sets of transcriptional programs.”
Background
Deep sequencing of non-enzymatic RNA primer extension
Duzdevich, D., Carr, C. E., & Szostak, J. W. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa400 · Cited by 38
“As a preliminary test of whether a hairpin construct could be effectively prepared for deep-sequencing (33–35), we adapted a standard RNA-Seq protocol in which the RT Handle is a 5′-adenylated (App) DNA oligo ligated by a derivative of T4 RNA Ligase 2, and the 5′ Handle is ligated by T4 RNA Ligase 1 (36,37).”
Method
Virulence factors of the genus Fusarium with targets in plants
Gutiérrez-Sánchez, A., Plasencia, J., Monribot‐Villanueva, J. L., Rodríguez‐Haas, B., Ruíz-May, E., Guerrero‐Analco, J. A., & Sánchez-Rangel, D. (2023) · Microbiological Research · doi:10.1016/j.micres.2023.127506 · Cited by 48
LGR4 and Its Role in Intestinal Protection and Energy Metabolism
Li, Z., Zhang, W., & Mulholland, M. W. (2015) · Frontiers in Endocrinology · doi:10.3389/fendo.2015.00131 · Cited by 41
The Impacts of Read Length and Transcriptome Complexity for De Novo Assembly: A Simulation Study
Chang, Z., Wang, Z., & Li, G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094825 · Cited by 43
“RNA-seq provides a powerful tool to reveal the complex landscape and dynamics of transcriptomes at an unprecedented level of sensitivity and accuracy [1–4].”
Method
Transcriptome Analysis of Cytokinin Response in Tomato Leaves
Shi, X., Gupta, S., Lindquist, I. E., Cameron, C. T., Mudge, J., & Rashotte, A. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055090 · Cited by 41
“RNA-sequencing (RNA-Seq) has been proven to be a simpler and more powerful approach to quantifying expression at a transcriptome level, especially in species like tomato where existing microarrays cover only about a third of all genes in the genome [27–29].”
BackgroundMethod
Deep Sequencing Reveals Differences in the Transcriptional Landscapes of Fibers from Two Cultivated Species of Cotton
Lacape, J.-M., Claverie, M., Vidal, R. O., Carazzolle, M. F., Pereira, G., Ruíz, M., Pré, M., Llewellyn, D. J., Al-Ghazi, Y., Jacobs, J. J. M. R., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048855 · Cited by 41
“The increasing throughput of NGS technologies, in particular, shows great potential for cost–effective large-scale generation of ESTs and has already been used in several plant species [21,22].”
Background
Bacterial small RNAs in the Genus Rickettsia
Schroeder, C. L. C., Narra, H. P., Rojas, M., Sahni, A., Patel, J. G., Khanipov, K., Wood, T. G., Fofanov, Y., & Sahni, S. K. (2015) · BMC Genomics · doi:10.1186/s12864-015-2293-7 · Cited by 40
“Because RNA sequencing is a novel and robust methodology, which provides valuable insights into the global transcriptome [76], we subjected total RNA from R.”
Avian transcriptomics: opportunities and challenges
Jax, E., Wink, M., & Kraus, R. H. (2018) · Journal für Ornithologie · doi:10.1007/s10336-018-1532-5 · Cited by 44
“Furthermore, a number of errors, such as self-priming, template switching, and reverse transcriptase inaccuracies can occur during cDNA synthesis (Ozsolak and Milos 2011; Wang et al. 2009).”
Background
Targeted RNA‐seq successfully identifies normal and pathogenic splicing events in breast/ovarian cancer susceptibility and Lynch syndrome genes
Brandão, R. D., Mensaert, K., López‐Perolio, I., Tserpelis, D., Xenakis, M. N., Lattimore, V., Walker, L. C., Kvist, A., Vega, A. I., Gutiérrez‐Enríquez, S., et al. (2019) · International Journal of Cancer · doi:10.1002/ijc.32114 · Cited by 43
“In CE tests, which were performed for multiple samples (average of 8 [2-32] samples), interindividual variability was observed for 54% of the splicing events.”
Background
Transcriptomic Characterization of Human Choroidal Neovascular Membranes Identifies Calprotectin as a Novel Biomarker for Patients with Age-Related Macular Degeneration
Schlecht, A., Boneva, S., Gruber, M., Zhang, P., Horres, R., Bucher, F., Auw‐Haedrich, C., Hansen, L. L., Stahl, A., Hilgendorf, I., et al. (2020) · American Journal Of Pathology · doi:10.1016/j.ajpath.2020.04.004 · Cited by 41
Deep Sequencing-Based Transcriptome Analysis of Chicken Spleen in Response to Avian Pathogenic Escherichia coli (APEC) Infection
Nie, Q., Sandford, E., Zhang, X., Nolan, L. K., & Lamont, S. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041645 · Cited by 43
“With emergence of second generation sequencing, RNA-seq is a more powerful approach for transcriptome analysis [13]; however, such investigations in chickens and other birds are very limited.”
Background
Pathogenesis and precision medicine for predicting response in inflammatory bowel disease: advances and future directions
Little, R. D., Jayawardana, T. C., Koentgen, S., Zhang, F., Connor, S. J., Boussioutas, A., Ward, M. G., Gibson, P. R., Sparrow, M., & Hold, G. L. (2024) · eGastroenterology · doi:10.1136/egastro-2023-100006 · Cited by 42
“136 More recently, RNA-seq has been performed at the single-cell level (scRNA-seq). scRNA-seq allows identification and comparison of the transcriptomes of individual cells within a heterogeneous sample.”
Background
Quantitative Transcriptomics using Designed Primer-based Amplification
Bhargava, V., Ko, P., Willems, E., Mercola, M. A., & Subramaniam, S. (2013) · Scientific Reports · doi:10.1038/srep01740 · Cited by 40
RNA‐sequencing reveals oligodendrocyte and neuronal transcripts in microglia relevant to central nervous system disease
Solga, A. C., Pong, W. W., Walker, J., Wylie, T., Magrini, V. J., Apicelli, A. J., Griffith, M., Griffith, O. L., Kohsaka, S., Wu, G. F., et al. (2014) · Glia · doi:10.1002/glia.22754 · Cited by 43
“GLIA 2015;63:531–548 Key words: laser-capture microdissection, fluorescence-activated cell sorting, macrophage Introduction Advances in RNA-sequencing methodologies now enableresearchers to study the individual contributions of select cell types to disease (Ozsolak and Milos, 2011).”
De novo root transcriptome of a medicinally important rare tree Oroxylum indicum for characterization of the flavonoid biosynthesis pathway
Deshmukh, A. B., Datir, S. S., Bhonde, Y., et al. (2018) · Phytochemistry · doi:10.1016/j.phytochem.2018.09.013 · Cited by 40
Comparative study on seasonal hair follicle cycling by analysis of the transcriptomes from cashmere and milk goats
Zhang, Y., Wu, K., Wang, L., Wang, Z., Han, W., Chen, D., Wei, Y., Su, R., Wang, R., Liu, Z., et al. (2019) · Genomics · doi:10.1016/j.ygeno.2019.02.013 · Cited by 41
Selection of Suitable Reference Genes for RT-qPCR Gene Expression Analysis in Siberian Wild Rye (Elymus sibiricus) under Different Experimental Conditions
Zhang, J., Xie, W., Yu, X., et al. (2019) · Genes · doi:10.3390/genes10060451 · Cited by 40
“Currently, transcriptome sequencing has been widely used in plant biological research, which greatly facilitates the understanding of important molecular mechanisms in plants [15,16].”
Background
Human papillomavirus 16E6/E7 activates autophagy via Atg9B and LAMP1 in cervical cancer cells
Tingting, C., Yang, S., Zhang, S., et al. (2019) · Cancer Medicine · doi:10.1002/cam4.2351 · Cited by 40
Identification of Hypoxia-Specific Biomarkers in Salmonids Using RNA-Sequencing and Validation Using High-Throughput qPCR
Akbarzadeh, A., Houde, A. L. S., Sutherland, B. J. G., Günther, O. P., & Miller, K. M. (2020) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.120.401487 · Cited by 40
“To this end, we applied RNA-seq 145 (Wang et al. 2009; Ozsolak and Milos 2011) with best practices for analysis (Conesa et al.”
Genes, pathways and networks responding to drought stress in oil palm roots
Wang, L., Lee, M., Ye, B., & Yue, G. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-78297-z · Cited by 42
Pomc Plays an Important Role in Sexual Size Dimorphism in Tilapia
Wan, Z., Lin, V. C. L., & Yue, G. H. (2021) · Marine Biotechnology · doi:10.1007/s10126-020-10015-2 · Cited by 40
A Comparative analysis study of lung cancer detection and relapse prediction using XGBoost classifier
Abdu-Aljabar, R. D., & Awad, O. A. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1076/1/012048 · Cited by 40
“This kind of sequence (NGS) provides a gene expression with more precise and sensitive measurement levels than microarrays in the analysis of many samples [30].”
Background
Genome-wide transcriptome analysis in murine neural retina using high-throughput RNA sequencing
Uzun, E. D. G., Ouyang, Q., Schmidt, M., Nagpal, S., & Morrow, E. M. (2011) · Genomics · doi:10.1016/j.ygeno.2011.09.003 · Cited by 39
“With microarray technologies, transcripts will only be detected based on prior knowledge as required for probe placement [16].”
Background
Host genetic studies in adult pulmonary tuberculosis
Meyer, C. G., & Thye, T. (2014) · Seminars in Immunology · doi:10.1016/j.smim.2014.09.005 · Cited by 39
Transcriptome Analysis of Blunt Snout Bream (Megalobrama amblycephala) Reveals Putative Differential Expression Genes Related to Growth and Hypoxia
Li, F.-G., Chen, J., Jiang, X., & Zou, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142801 · Cited by 39
Transcriptome profiling analysis of senescent gingival fibroblasts in response to Fusobacterium nucleatum infection
Ahn, S. H., Chun, S., Park, C., Lee, J., Lee, S.-W., & Lee, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188755 · Cited by 39
A Guide for Designing and Analyzing RNA-Seq Data
Chatterjee, A., Ahn, A., Rodger, E. J., Stockwell, P. A., & Eccles, M. R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7834-2_3 · Cited by 42
Evaluating the genome and resistome of extensively drug-resistant Klebsiella pneumoniae using native DNA and RNA Nanopore sequencing
Pitt, M. E., Nguyen, S. H., Duarte, T., Teng, H., Blaskovich, M. A. T., Cooper, M. A., & Coin, L. (2020) · GigaScience · doi:10.1093/gigascience/giaa002 · Cited by 37
“Other sequencing technologies rely on fragmentation, complementary DNA (cDNA) conversion, and PCR steps that create experimental bias and hinder the accuracy of determining gene expression [19, 20].”
Integration of transcriptomics, proteomics and metabolomics identifies biomarkers for pulmonary injury by polyhexamethylene guanidine phosphate (PHMG-p), a humidifier disinfectant, in rats
Lee, J. D., Kim, H. Y., Kang, K., Jeong, H. G., Song, M., Tae, I. H., Lee, S. H., Kim, H.-R., Lee, K., Chae, S., et al. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02657-x · Cited by 40
Comparison of Growth Performance and Meat Quality Traits of Commercial Cross-Bred Pigs versus the Large Black Pig Breed
Wang, Y., Thakali, K. M., Morse, P. D., Shelby, S., Chen, J., APPLE, J. K., & Huang, Y. (2021) · Animals · doi:10.3390/ani11010200 · Cited by 39
“Based on RNA-seq results, functional analysis of gene ontology (GO) and biological processes were used to highlight enriched relevant biological pathways [9,10].”
Method
Disease ecology of wild fish: opportunities and challenges for linking infection metrics with behaviour, condition, and survival
Chapman, J. M., Kelly, L. A., Teffer, A. K., et al. (2021) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2020-0315 · Cited by 39
“RNA sequencing is another application of NGS technologies and is a useful molecular tool for viral discovery (e.g., Mordecai et al. 2019, 2021) and the study of pathogens and host–pathogen interactions focused on transcriptome profiling (Ozsolak and Milos 2011).”
Method
Translational control of gene function through optically regulated nucleic acids
Darrah, K. E., & Deiters, A. (2021) · Chemical Society Reviews · doi:10.1039/d1cs00257k · Cited by 40
Overcoming Barriers in Cancer Biology Research: Current Limitations and Solutions
(2025) · doi:10.3390/cancers17132102 · Cited by 44
“RNA sequencing (RNA-Seq) [195]: A measure of the expression of all genes in a sample (tumor or normal).”
Gene expression sequences clustering based on the internal and external clustering quality criteria
Babichev, S. A., Krejčí, J., Bicanek, J., & Lytvynenko, V. I. (2017) · IEEE International Conference on Computer Sciences and Information Technologies (CSIT) · doi:10.1109/stc-csit.2017.8098744 · Cited by 41
Expression profiling of leukemia patients: Key lessons and future directions
Shivarov, V. S., & Bullinger, L. (2014) · Experimental Hematology · doi:10.1016/j.exphem.2014.04.006 · Cited by 40
DeepSAGE Reveals Genetic Variants Associated with Alternative Polyadenylation and Expression of Coding and Non-coding Transcripts
Zhernakova, D. V., de Klerk, E., Westra, H.-J., Mastrokolias, A., Amini, S., Ariyürek, Y., Jansen, R., Penninx, B. W. J. H., Hottenga, J., Willemsen, G. A. H. M., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003594 · Cited by 38
“But recently, high-throughput next generation sequencing (NGS) has become available, which allows quantification of expression levels by counting mRNA fragments (RNA-seq) or sequence tags (including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequ…”
Background
Transcription factor MrpC binds to promoter regions of hundreds of developmentally-regulated genes in Myxococcus xanthus
Robinson, M., Son, B., Kroos, D., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-1123 · Cited by 38
“This type of further work would be most useful if done in a way that identified transcript 5’ ends (potential transcription start sites) genome-wide, which is possible using RNA-seq approaches [122].”
Background
An Integrated Proteomics/Transcriptomics Approach Points to Oxygen as the Main Electron Sink for Methanol Metabolism in Methylotenera mobilis
Beck, D. A. C., Hendrickson, E. L., Vorobev, A. V., et al. (2011) · Journal of Bacteriology · doi:10.1128/jb.05375-11 · Cited by 38
“As the RNA-seq method results in data sets of significant size for each sample, typically, complete genome coverage is achieved (9, 25).”
Method
De novo Transcriptome Sequencing and Development of Abscission Zone-Specific Microarray as a New Molecular Tool for Analysis of Tomato Organ Abscission
Sundaresan, S., Philosoph‐Hadas, S., Riov, J., Mugasimangalam, R. C., Kuravadi, N. A., Kochanek, B., Salim, S., Tucker, M. L., & Meir, S. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2015.01258 · Cited by 38
Screening and Validation of Housekeeping Genes of the Root and Cotyledon of Cunninghamia lanceolata under Abiotic Stresses by Using Quantitative Real-Time PCR
Bao, W., Qu, Y., Shan, X., & Wan, Y. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17081198 · Cited by 38
Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding
Fu, Y., Yang, M.-H., Zeng, F., & Biligetu, B. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01182 · Cited by 40
“…profiling, can be applied to study each transcript of genes affecting a trait at a developmental stage, and has opened many avenues to develop informative markers associated with genes controlling genetically complex traits of agronomical importance (Wang et al., 2009; Ozsolak and Milos, 2011).”
Background
Genomic Tools for the Enhancement of Vegetable Crops: A Case in Eggplant
Gramazio, P., Prohens, J., Plazas, M., Mangino, G., Herraiz, F., García‐Fortea, E., & Vilanova, S. (2017) · Notulae Botanicae Horti Agrobotanici Cluj-Napoca · doi:10.15835/nbha46110936 · Cited by 38
Integrated Analysis of Proteomic and Transcriptomic Data Highlights Late Fetal Muscle Maturation Process
Voillet, V., Cristobal, M. S., Pére, M.-C., Billon, Y., Canario, L., Liaubet, L., & Lefaucheur, L. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.066357 · Cited by 40
“(CC), (2) subnetwork 2 consisted in 24 nodes and 65 edges FIG.”
Background
Transcriptional inaccuracy threshold attenuates differences in RNA-dependent DNA synthesis fidelity between retroviral reverse transcriptases
Sebastián-Martín, A., Barrioluengo, V., & Menéndez‐Arias, L. (2018) · Scientific Reports · doi:10.1038/s41598-017-18974-8 · Cited by 36
How Omics Data Can Be Used in Nephrology
Rhee, E. P. (2018) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2017.12.008 · Cited by 38
Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective
Zhang, S., Zhang, L., Tai, Y., Wang, X., Ho, C., & Wan, X. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.00480 · Cited by 40
“With the advent of NGS technology (specifically RNA-seq), the gene decoding has made great achievements in the determination of gene structures and expression profiles relying on its high throughout and coverage superiority (Ozsolak and Milos, 2011).”
Method
Skin transcriptome reveals the periodic changes in genes underlying cashmere (ground hair) follicle transition in cashmere goats
Liu, Z., Yang, F., Zhao, M., Mu, Q., Che, T., Xie, Y., Ma, L., Mi, L., Su, R., Zhao, Y., et al. (2020) · BMC Genomics · doi:10.1186/s12864-020-06779-5 · Cited by 39
Genome-wide co-expression analysis predicts protein kinases as important regulators of phosphate deficiency-induced root hair remodeling in Arabidopsis
Lan, P., Li, W., & Schmidt, W. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-210 · Cited by 38
“The RNA-seq technology has proven to provide precise digital information on gene expression, and is able to discriminate genes of high sequence identity [26].”
Background
Benchmarking of methods for DNA methylome deconvolution
Ridder, K. D., Che, H., Leroy, K., & Thienpont, B. (2024) · Nature Communications · doi:10.1038/s41467-024-48466-z · Cited by 37
Mimicking and analyzing the tumor microenvironment
Crouigneau, R., Li, Y., Auxillos, J., Gonçalves‐Alves, E., Marie, R., Sandelin, A., & Pedersen, S. F. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100866 · Cited by 45
Is this the real time for genomics?
Guarnaccia, M., Gentile, G., Alessi, E., et al. (2014) · Genomics · doi:10.1016/j.ygeno.2014.02.003 · Cited by 37
“Due to cost reduction, the latter application may soon re-placeDNAmicroarrays in transcriptomepro ﬁ linganalysis [9].”
Background
Genome-Wide Gene Expression Profiling of Nucleus Accumbens Neurons Projecting to Ventral Pallidum Using both Microarray and Transcriptome Sequencing
Chen, H., Liu, Z., Gong, S., Wu, X.-J., Taylor, W. L., Williams, R. W., Matta, S. G., & Sharp, B. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00098 · Cited by 36
Single-tube linear DNA amplification for genome-wide studies using a few thousand cells
Shankaranarayanan, P., Mendoza-Parra, M. A., Gool, W. V., Trindade, L. M., & Gronemeyer, H. (2012) · Nature Protocols · doi:10.1038/nprot.2011.447 · Cited by 37
Advances in the genetic dissection of plant cell walls: tools and resources available in Miscanthus
Slavov, G., Allison, G. G., & Bosch, M. (2013) · Frontiers in Plant Science · doi:10.3389/fpls.2013.00217 · Cited by 39
“An important implication of this result is that RNA-seq approaches, while potentially very informative for a range of biological questions (Ozsolak and Milos, 2011), may not be the most appropriate genotyping tool for GWASs.”
Background
High Through-Put Sequencing of the Parhyale hawaiensis mRNAs and microRNAs to Aid Comparative Developmental Studies
Blythe, M. J., Malla, S., Everall, R., Shih, Y.-H., Lemay, V., Moreton, J., Wilson, R., & Aboobaker, A. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033784 · Cited by 34
“Alternatively, massively parallel short tag sequencing offers an even cheaper per base pair alternative [12,13].”
Background
Comprehensive processing of high-throughput small RNA sequencing data including quality checking, normalization, and differential expression analysis using the UEA sRNA Workbench
Beckers, M., Mohorianu, I. I., Stocks, M. B., Applegate, C., Dalmay, T., & Moulton, V. (2017) · RNA · doi:10.1261/rna.059360.116 · Cited by 36
Transcriptome sequencing of an Antarctic microalga, Chlorella sp. (Trebouxiophyceae, Chlorophyta) subjected to short-term ultraviolet radiation stress
Poong, S., Lim, P., Phang, S., Wong, C.-Y., Pai, T., Chen, C., Yang, C.-H., & Liu, C.-C. (2017) · Journal of Applied Phycology · doi:10.1007/s10811-017-1124-4 · Cited by 37
Drought-Tolerant Brassica rapa Shows Rapid Expression of Gene Networks for General Stress Responses and Programmed Cell Death Under Simulated Drought Stress
Guo, Y. M., Samans, B., Chen, S., Kibret, K. B., Hatzig, S., Turner, N. C., Nelson, M. N., Cowling, W. A., & Snowdon, R. J. (2017) · Plant Molecular Biology Reporter · doi:10.1007/s11105-017-1032-4 · Cited by 41
Unraveling long non-coding RNAs through analysis of high-throughput RNA-sequencing data
Tripathi, R., Chakraborty, P., & Varadwaj, P. K. (2017) · Non-coding RNA Research · doi:10.1016/j.ncrna.2017.06.003 · Cited by 36
Fingerprints of Modified RNA Bases from Deep Sequencing Profiles
Kietrys, A. M., Velema, W. A., & Kool, E. T. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.7b07914 · Cited by 37
A deeper meaning for shallow‐level phylogenomic studies: nested anchored hybrid enrichment offers great promise for resolving the tiger moth tree of life (Lepidoptera: Erebidae: Arctiinae)
Dowdy, N. J., Keating, S. E., Lemmon, A. R., Lemmon, E. M., Conner, W. E., Chialvo, C. H. S., Weller, S. J., Simmons, R. B., Sisson, M. S., & Zaspel, J. M. (2020) · Systematic Entomology · doi:10.1111/syen.12433 · Cited by 36
Quercetin Improving Lipid Metabolism by Regulating Lipid Metabolism Pathway of Ileum Mucosa in Broilers
Wang, M., Mao, Y., Wang, B., Wang, S., Han, L., Ying, L., & Li, Y. (2020) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2020/8686248 · Cited by 38
“Recently, the RNA-seq technique has been a powerful and revolutionary approach to quantify gene expression levels and survey detailed transcriptome profiles at unprecedented resolution and sensitivity [38, 39].”
Method
Evaluating statistical analysis models for RNA sequencing experiments
Reeb, P., & Steibel, J. P. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00178 · Cited by 36
“RNA sequencing (RNA-seq) technology is being rapidly adopted as the platform of choice for high-throughput gene expression analysis (Ozsolak and Milos, 2011).”
Method
Transcriptome-wide Profiling and Posttranscriptional Analysis of Hematopoietic Stem/Progenitor Cell Differentiation toward Myeloid Commitment
Klimmeck, D., Cabezas‐Wallscheid, N., Reyes, A., von Paleske, L., Renders, S., Hansson, J., Krijgsveld, J., Huber, W. K., & Trumpp, A. (2014) · Stem Cell Reports · doi:10.1016/j.stemcr.2014.08.012 · Cited by 33
“, RNA sequencing [RNA-seq]) has significantly extended the possibilities to study gene expression (Ozsolak and Milos, 2011), which was also used to investigate young versus aged HSCs (Sun et al.”
Background
Optimal assembly strategies of transcriptome related to ploidies of eukaryotic organisms
He, B., Zhao, S., Chen, Y., Cao, Q., Wei, C., Cheng, X., & Zhang, Y. (2015) · BMC Genomics · doi:10.1186/s12864-014-1192-7 · Cited by 34
“assembly tools designed for Sanger sequencing, as they are incapable of handling the millions to billions of short reads (35–400 bp each) generated by next-generation sequencing platforms [1, 8].”
Background
Cardiovascular RNA Interference Therapy
Poller, W. C., Tank, J., Skurk, C., & Gast, M. (2013) · Circulation Research · doi:10.1161/circresaha.113.301056 · Cited by 37
Peregrine
Langevin, S. A., Bent, Z., Solberg, O. D., Curtis, D. J., Lane, P. D., Williams, K. P., Schoeniger, J. S., Sinha, A., Lane, T. W., & Branda, S. S. (2013) · RNA Biology · doi:10.4161/rna.24284 · Cited by 36
“RNASeq enables measurement of relative abundances of transcripts, discovery of novel coding and non-coding transcripts, identification of splice junction sites and alternative splicing events and identification of sequence polymorphisms that differentiate RNA samples.(3,4) The utility of RNA-Seq depends upon preparati…”
Background
Power, resolution and bias: recent advances in insect phylogeny driven by the genomic revolution
Yeates, D. K., Meusemann, K., Trautwein, M., Wiegmann, B. M., & Zwick, A. (2015) · Current Opinion in Insect Science · doi:10.1016/j.cois.2015.10.007 · Cited by 40
Single-Cell Transcriptome Study as Big Data
Yu, P., & Lin, W. (2016) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2016.01.005 · Cited by 36
“Besides including external molecule controls, improved single-cell chemistry and physics [8,9,55], as well as incorporation of molecular barcoding system [56], have signiﬁcantly reduced the noise level within each study.”
Background
Transcriptome analysis of Ophiocordyceps sinensis before and after infection of Thitarodes larvae
Zhong, X., Gu, L., Li, S.-S., Kan, X.-T., Zhang, G., & Liu, X. (2016) · Fungal Biology · doi:10.1016/j.funbio.2016.02.003 · Cited by 36
Identifying the cellular targets of natural products using T7 phage display
Piggott, A. M., & Karuso, P. (2016) · Natural Product Reports · doi:10.1039/c5np00128e · Cited by 36
RNA sequencing reveals resistance of TLR4 ligand-activated microglial cells to inflammation mediated by the selective jumonji H3K27 demethylase inhibitor
Das, A., Arifuzzaman, S., Yoon, T., Kim, S. H., Chai, J. C., Lee, Y. S., Jung, K. H., & Chai, Y. G. (2017) · Scientific Reports · doi:10.1038/s41598-017-06914-5 · Cited by 36
High throughput transcriptome analysis of coffee reveals prehaustorial resistance in response to Hemileia vastatrix infection
Florez, J. C., Mofatto, L. S., Freitas-Lopes, R. D. L., et al. (2017) · Plant Molecular Biology · doi:10.1007/s11103-017-0676-7 · Cited by 36
“Transcriptome profiling studies are essential to fully understand the biological pathways that are activated in various physiological conditions or stages of an organism development (Wang et al. 2009; Ozsolak and Milos 2010).”
Background
Expression of MicroRNA-9 is Associated With Overall Survival in Breast Cancer Patients
Sporn, J. C., Katsuta, E., Yan, L., et al. (2018) · Journal of Surgical Research · doi:10.1016/j.jss.2018.08.020 · Cited by 36
Bioinformatics applied to biotechnology: A review towards bioenergy research
de Carvalho, L. M., Borelli, G., Camargo, A. P., de Assis, M. A., de Ferraz, S. M. F., Fiamenghi, M. B., José, J., Mofatto, L. S., Nagamatsu, S. T., Persinoti, G. F., et al. (2019) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2019.02.016 · Cited by 37
MicroRNA Gene Regulation in Extremely Young and Parallel Adaptive Radiations of Crater Lake Cichlid Fish
Franchini, P., Xiong, P., Fruciano, C., Schneider, R., Woltering, J. M., Hulsey, C. D., & Meyer, A. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz168 · Cited by 36
New insights on the effects of ionic liquid structural changes at the gene expression level: Molecular mechanisms of toxicity in Daphnia magna
Jeremias, G., Jesus, F. T., Ventura, S. P. M., Gonçalves, F. J. M., Asselman, J., & Pereira, J. L. (2020) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2020.124517 · Cited by 36
Liquid–liquid phase separation of proteins and peptides derived from biological materials: Discovery, protein engineering, and emerging applications
Sun, Y., Lim, Z. W., Guo, Q., Yu, J., & Miserez, A. (2020) · MRS Bulletin · doi:10.1557/mrs.2020.301 · Cited by 36
Identification and characterization of large DNA deletions affecting oil quality traits in soybean seeds through transcriptome sequencing analysis
Goettel, W., Ramírez, M. E., Upchurch, R. G., et al. (2016) · Theoretical and Applied Genetics · doi:10.1007/s00122-016-2725-z · Cited by 35
“Availability of next-generation sequencing technologies enables us to sequence transcriptomes of soybean seeds, which simultaneously reveals two functional attributes of expressed genes, transcript sequence and accumulation levels (Goettel et al. 2014; Ozsolak and Milos 2011).”
Background

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