RNA sequencing: advances, challenges and opportunities
Open accessAbstract
In the few years since its initial application, massively parallel cDNA sequencing, or RNA-seq, has allowed many advances in the characterization and quantification of transcriptomes. Recently, several developments in RNA-seq methods have provided an even more complete characterization of RNA transcripts. These developments include improvements in transcription start site mapping, strand-specific measurements, gene fusion detection, small RNA characterization and detection of alternative splicing events. Ongoing developments promise further advances in the application of RNA-seq, particularly direct RNA sequencing and approaches that allow RNA quantification from very small amounts of cellular materials.
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RNA-seq provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays.
“RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific expression [8–12].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [10]
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BackgroundFidelity of Two Retroviral Reverse Transcriptases during DNA-Dependent DNA Synthesis In Vitro
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Quantification of the yeast transcriptome by single-molecule sequencing
Lipson, D., Raz, T., Kieu, A., Jones, D. R., Giladi, E., Thayer, E. C., Thompson, J. F., Letovsky, S., Milos, P. M., & Causey, M. (2009) · Nature Biotechnology · doi:10.1038/nbt.1551 · Cited by 187 · cited 1× · see citing works
“A second approach is the direct sequencing of the first-strand cDNA products that are generated, either in solutio”
BackgroundSelf-citationDetection of single nucleotide variations in expressed exons of the human genome using RNA-Seq
Chepelev, I., Wei, G., Tang, Q., & Zhao, K. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp507 · Cited by 182 · cited 1× · see citing works
“The potential suitability of mRNA-seq data for the identification of nucleotide variations has been demonstrated recently by several studie”
MethodDigital transcriptome profiling using selective hexamer priming for cDNA synthesis
Armour, C. D., Castle, J. C., Chen, R., Babak, T., Loerch, P., Jackson, S., Shah, J. K., Dey, J., Rohl, C. A., Johnson, J. M., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1360 · Cited by 174 · cited 1× · see citing works
“First, the RNA-seq signal across transcripts tends to show non-uniformity of coverage, which may be a result of biases introduced during various steps, such as priming with random hexamer”
MethodDNA-directed DNA Polymerase Activity in Oncogenic RNA Viruses
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“In addition, during first-strand cDNA synthesis, spurious second-strand cDNA artefacts can be introduced, owing to the DNA-dependent DNA polymerase (DDDP) activities of reverse transcriptase”
BackgroundReplication of individual DNA molecules under electronic control using a protein nanopore
Olasagasti, F., Lieberman, K. R., Benner, S., Cherf, G. M., Dahl, J. M., Deamer, D. W., & Akeson, M. A. (2010) · Nature Nanotechnology · doi:10.1038/nnano.2010.177 · Cited by 167 · cited 1× · see citing works
“Examination of splicing patterns and transcript connectivity in an unbiased and genome-wide manner requires full-length transcript sequences to be obtained, which may be enabled in the future by emerging technologie”
A paired-end sequencing strategy to map the complex landscape of transcription initiation
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Prediction of alternative isoforms from exon expression levels in RNA-Seq experiments
Richard, H., Schulz, M. H., Sultan, M., Nürnberger, A., Schrinner, S., Balzereit, D., Dagand, E., Rasche, A., Lehrach, H., Vingron, M., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq041 · Cited by 160 · cited 1× · see citing works
“By counting the number of reads mapping to each exon and spanning each splice junction, these approaches also allowed the splice efficiency of each junction to be determined and the levels of distinct isoforms to be quantifie”
Digital RNA allelotyping reveals tissue-specific and allele-specific gene expression in human
Zhang, K., Li, J. B., Gao, Y., Egli, D. M., Xie, B., Deng, J., Li, Z., Lee, J., Aach, J., LeProust, E. M., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1357 · Cited by 156 · cited 1× · see citing works
“Targeted RNA-seq approaches have been used to detect fusion transcripts, allele-specific expression, mutations and RNA-editing events in a subset of transcript”
MethodLinking promoters to functional transcripts in small samples with nanoCAGE and CAGEscan
Plessy, C., Bertin, N., Takahashi, H., Simone, R., Salimullah, M., Lassmann, T., Vitezic, M., Severin, J., Olivarius, S., Lazarević, D., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1470 · Cited by 154 · see citing works
Screening the human exome: a comparison of whole genome and whole transcriptome sequencing
Cirulli, E. T., Singh, A., Shianna, K. V., Ge, D., Smith, J. P., Maia, J. M., Heinzen, E. L., Goedert, J. J., & Goldstein, D. B. (2010) · Genome biology · doi:10.1186/gb-2010-11-5-r57 · Cited by 151 · cited 1× · see citing works
“The potential suitability of mRNA-seq data for the identification of nucleotide variations has been demonstrated recently by several studie”
Virtual terminator nucleotides for next-generation DNA sequencing
Bowers, J., Mitchell, J., Beer, E., Buzby, P. R., Causey, M., Efcavitch, J. W., Jarosz, M. V., Krzymańska-Olejnik, E., Kung, L. A., Lipson, D., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1354 · Cited by 139 · see citing works
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGEPreprint
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FRT-seq: amplification-free, strand-specific transcriptome sequencing
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“The first involves the ligation of adaptors in a predetermined orientation to the ends of RNAs or to first-strand cDNA molecule”
BackgroundReplicase activity of purified recombinant protein P2 of double‐stranded RNA bacteriophage φ6
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Linear mRNA Amplification from as Little as 5 ng Total RNA for Global gene Expression Analysis
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“However, RNA-seq may be preferable for identifying events that produce aberrant RNA species and therefore have a higher likelihood of being functional or causal in biological or disease setting”
BackgroundChromatin profiling by directly sequencing small quantities of immunoprecipitated DNA
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“Third, the in-solution or on-surface amplification step included in some of these approaches may introduce additional artefacts — for example, in the form of GC biases and duplicate read”
BackgroundSelf-citationGlobal analysis of trans -splicing in Drosophila
McManus, C. J., Duff, M. O., Eipper-Mains, J. E., & Graveley, B. R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1007586107 · Cited by 111 · cited 1× · see citing works
“Furthermore, RNA-seq data include chimeric cDNA artefacts that are generated by template switching during reverse transcription and amplificatio”
MethodPolyadenylation and degradation of incomplete RNA polymerase I transcripts in mammalian cells
Shcherbik, N., Wang, M., Lapik, Y. R., Srivastava, L., & Pestov, D. G. (2010) · EMBO Reports · doi:10.1038/embor.2009.271 · Cited by 109 · cited 1× · see citing works
“Polyadenylation of transcripts also takes place during transcript degradation steps, and thus poly(A) + -enrichment steps may also enrich for RNA degradation products of RNA polymerase I transcripts and other RNA”
BackgroundCharacterizing the mouse ES cell transcriptome with Illumina sequencing
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“Because NGS sample preparation strategies for 'longer' RNAs (>200 nucleotides) are not suitable for sRNAs, such as reverse transcription with random priming (because this way of priming cDNA synthesis from short RNA species yields even shorter cDNA species that are not long enough for efficient alignment), modified pr…”
Self-citationGlobal and unbiased detection of splice junctions from RNA-seq data
Ameur, A., Wetterbom, A., Feuk, L., & Gyllensten, U. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r34 · Cited by 98 · cited 1× · see citing works
“As a result, it is now possible to search for splicing patterns without a requirement for prior knowledge of transcript annotation”
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“It has recently become clear that, although the NGS-based sRNA-profiling approaches can be used for differential expression analyses, the number of reads obtained per sRNA does not necessarily correlate with their actual abundanc”
BackgroundEnhanced Generation of Induced Pluripotent Stem Cells from a Subpopulation of Human Fibroblasts
Byrne, J., Nguyen, H. N., & Pera, R. A. R. (2009) · PLoS ONE · doi:10.1371/journal.pone.0007118 · Cited by 75 · cited 1× · see citing works
“This approach may be useful for the determination of the expression levels of a subset of transcripts across cells of interes”
Background[35] Second-strand cDNA synthesis: mRNA fragments as primers
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“, which can confound sense versus antisense transcript determinatio”
BackgroundWhen good transcripts go bad: artifactual RT‐PCR ‘splicing’ and genome analysis
Roy, S. W., & Irimia, M. (2008) · BioEssays · doi:10.1002/bies.20749 · Cited by 60 · cited 1× · see citing works
“Another limitation imposed by cDNA synthesis is template switchin”
BackgroundDigital transcriptome profiling from attomole-level RNA samplesPreprint
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“A second approach is the direct sequencing of the first-strand cDNA products that are generated, either in solutio”
BackgroundSelf-citationAmplification-free digital gene expression profiling from minute cell quantities
Ozsolak, F., Ting, D. T., Wittner, B. S., Brannigan, B. W., Paul, S., Bardeesy, N. M., Ramaswamy, S., Milos, P. M., & Haber, D. A. (2010) · Nature Methods · doi:10.1038/nmeth.1480 · Cited by 59 · see citing works
Self-citationReverse Transcriptase Adds Nontemplated Nucleotides to cDNAs During 5′-RACE and Primer Extension
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“This approach may be useful for the determination of the expression levels of a subset of transcripts across cells of interes”
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“Second, ligation tends to have sequence preference”
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“Another limitation imposed by cDNA synthesis is template switchin”
BackgroundIs sequencing enlightenment ending the dark age of the transcriptome?
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“Target-enrichment strategies were originally developed for genomic DNA resequencin”
BackgroundFidelity of Enzymatic Ligation for DNA Computing
Faulhammer, D., Lipton, R. J., & Landweber, L. F. (2000) · Journal of Computational Biology · doi:10.1089/10665270050514963 · Cited by 16 · cited 1× · see citing works
“Second, ligation tends to have sequence preference”
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Multi-omics approaches to disease
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“Thus, transcriptomics, employing either expression arrays or RNA-Seq (Box 1), has proven to be particularly useful for identifying causal genes at GWAS loci [16, 22–24].”
BackgroundInfluentialGenome-wide genetic marker discovery and genotyping using next-generation sequencing
Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J., & Blaxter, M. (2011) · Nature Reviews Genetics · doi:10.1038/nrg3012 · Cited by 2,501 · see citing works
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“There are various approaches to solve that problem (Jiang and Wong 2009; Wang et al. 2009; Ozsolak and Milos 2011).”
BackgroundA census of human RNA-binding proteins
Gerstberger, S., Hafner, M., & Tuschl, T. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3813 · Cited by 2,032 · see citing works
“, 2014), small and long RNA-sequencing (Jan et al., 2011; Ozsolak and Milos, 2011; Wang et al., 2009), or 3’-end sequencing methods profiling of alternative polyadenylation sites and poly(A) tail lengths (Chang et al.”
BackgroundAmir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550 · see citing works
The microglial sensome revealed by direct RNA sequencing
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“Expression profiles are both useful complements to already characterized gene functions and also potential means of initially characterizing the function of novel genes (4,5).”
BackgroundNext-generation transcriptome assembly
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“where the indicator function, I, represents whether (1) or not (0) C i (the percentage of”
BackgroundLowe, R. G. T., Shirley, N. J., Bleackley, M. R., Dolan, S., & Shafee, T. M. A. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005457 · Cited by 1,186 · see citing works
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BackgroundMethodInfluentialRNA therapeutics: beyond RNA interference and antisense oligonucleotides
Kole, R., Krainer, A. R., & Altman, S. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3625 · Cited by 1,152 · see citing works
Counting absolute numbers of molecules using unique molecular identifiers
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Methods of integrating data to uncover genotype-phenotype interactions
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085 · see citing works
PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regionsPreprint
Lin, M. F., Jungreis, I., & Kellis, M. (2010) · Nature Precedings · doi:10.1093/bioinformatics/btr209 · Cited by 1,008 · see citing works
“Major challenges remain in integrating these methods into coherent pipelines, harmonizing the results with existing genome annotation databases, and coping with the uneven coverage and relatively high error rate of current high-throughput sequencing technologies (Ozsolak and Milos, 2011).”
BackgroundRecurrent R-spondin fusions in colon cancer
Seshagiri, S., Stawiski, E. W., Durinck, S., Modrusan, Z., Storm, E. E., Conboy, C. B., Chaudhuri, S., Guan, Y., Janakiraman, V., Jaiswal, B. S., et al. (2012) · Nature · doi:10.1038/nature11282 · Cited by 960 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
“In particular, the development of RNA sequencing (RNA-seq) has enabled unprecedented insight into the dynamics of gene expression [2].”
BackgroundSequencing technologies and genome sequencing
Pareek, C. S., Smoczynski, R., & Tretyn, A. (2011) · Journal of Applied Genetics · doi:10.1007/s13353-011-0057-x · Cited by 868 · see citing works
“In the first automated fluorescent DNA sequencing equipment, a complete gene locus for the hypoxanthineguanine phosphoribosyltransferase (HPRT) gene was sequenced, using for the first time the paired-end sequencing approach (Edwards et al. 1990).”
MethodClassification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693 · see citing works
“Over the last 15 years, transcriptome-wide profiling has been a powerful element of the modern biological researcher’s toolkit [1, 2].”
BackgroundThe emerging complexity of gene fusions in cancer
Mertens, F., Johansson, B., Fioretos, T., & Mitelman, F. (2015) · Nature reviews. Cancer · doi:10.1038/nrc3947 · Cited by 675 · see citing works
Corset: enabling differential gene expression analysis for
Davidson, N., & Oshlack, A. (2014) · Genome biology · doi:10.1186/s13059-014-0410-6 · Cited by 677 · see citing works
“Background Next-generation sequencing of RNA, RNA-seq, is a powerful technology for studying various aspects of the transcriptome; it has a broad range of applications, including gene discovery, detection of alternative splicing events, differential expression analysis, fusion detection and identification of variants…”
BackgroundMedulloblastomics: the end of the beginning
Northcott, P. A., Jones, D., Kool, M., Robinson, G. W., Gilbertson, R. J., Cho, Y., Pomeroy, S. L., Korshunov, A., Lichter, P., Taylor, M. D., et al. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3410 · Cited by 661 · see citing works
Revisiting Global Gene Expression Analysis
Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012) · Cell · doi:10.1016/j.cell.2012.10.012 · Cited by 616 · see citing works
“Global gene expression analysis uses DNA microarrays, RNA-Seq, and other methods to measure the levels of RNA species in biological systems (Geiss et al., 2008; Heller, 2002; Lockhart and Winzeler, 2000; Ozsolak and Milos, 2011; Schena et al., 1998; Wang et al., 2009).”
From bulk, single-cell to spatial RNA sequencing
Li, X., & Wang, C.-Y. (2021) · International Journal of Oral Science · doi:10.1038/s41368-021-00146-0 · Cited by 605 · see citing works
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“RNA-seq now allows actual counting of RNA numbers, offering unbiased genome-wide information on average cellular RNA concentrations in cell populations (Ozsolak and Milos, 2011).”
BackgroundThe integrin adhesome: from genes and proteins to human disease
Winograd-Katz, S. E., Fässler, R., Geiger, B., & Legate, K. R. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3769 · Cited by 608 · see citing works
Analysis of RNA-Seq Data Using TopHat and Cufflinks
Ghosh, S., & Chan, C. K. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3167-5_18 · Cited by 596 · see citing works
Multi-omic and multi-view clustering algorithms: review and cancer benchmark
Rappoport, N., & Ron Shamir, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky889 · Cited by 549 · see citing works
“The measured parameters include DNA genome sequence (1), RNA expression (2,3), DNA methylation (4) etc.”
BackgroundIn silico prediction of splice-altering single nucleotide variants in the human genome
Jian, X., Boerwinkle, E., & Liu, X.-M. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1206 · Cited by 505 · see citing works
“A possible solution is to take advantage of the ability of whole transcriptome shotgun sequencing (RNA-seq) to detect alternative splicing (51,52) to recruit variants that do not cause any alternative splicing.”
BackgroundRNA-seq differential expression studies: more sequence or more replication?
Liu, Y., Zhou, J., & White, K. P. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt688 · Cited by 497 · see citing works
“RNA-seq has been widely used for differential expression studies (Oshlack et al., 2010; Ozsolak and Milos, 2011).”
MethodHigh-Throughput Genomic Data in Systematics and Phylogenetics
Lemmon, E. M., & Lemmon, A. R. (2013) · Annual Review of Ecology Evolution and Systematics · doi:10.1146/annurev-ecolsys-110512-135822 · Cited by 506 · see citing works
Sun, Y. V., & Hu, Y.-J. (2016) · Advances in Genetics · doi:10.1016/bs.adgen.2015.11.004 · Cited by 459 · see citing works
“The newest transcriptomics approaches are based on the simultaneous deep sequencing of all RNA transcripts expression (i.e., RNA-Seq) in biological samples across the genome (Ozsolak & Milos, 2011; Z. Wang, Gerstein, & Snyder, 2009).”
BackgroundGenome-wide Association Studies in Maize: Praise and Stargaze
Xiao, Y., Liu, H., Wu, L., Warburton, M. L., & Yan, J. (2016) · Molecular Plant · doi:10.1016/j.molp.2016.12.008 · Cited by 429 · see citing works
Optochemical Control of Biological Processes in Cells and Animals
Ankenbruck, N., Courtney, T. M., Naro, Y., & Deiters, A. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201700171 · Cited by 415 · see citing works
Li, B., Tsoi, L. C., Swindell, W. R., Gudjonsson, J. E., Tejasvi, T., Johnston, A. W. B., Ding, J., Stuart, P. E., Xing, X., Kochkodan, J. J., et al. (2014) · Journal of Investigative Dermatology · doi:10.1038/jid.2014.28 · Cited by 393 · see citing works
“Recently, RNA-seq has emerged as a powerful alternative to microarrays (Garber et al., 2011; Liu et al., 2011; Metzker, 2010; Ozsolak and Milos, 2011; Roy et al., 2011; Wang et al., 2009) that provides increased accuracy and precision (Roy et al., 2011), identifies unmapped transcripts and…”
BackgroundFuentes‐Pardo, A. P., & Ruzzante, D. E. (2017) · Molecular Ecology · doi:10.1111/mec.14264 · Cited by 396 · see citing works
“These techniques can be classified in three major groups: (i) Restriction site Associate DNA sequencing (RAD-seq; Andrews et al., 2016), (ii) Sequencing of cDNA obtained from mRNA (RNAseq; Ozsolak & Milos, 2011) and (iii) Whole-exome sequencing (WES; Warr et al., 2015).”
BackgroundMethodInfluentialComparison of RNA-seq and microarray-based models for clinical endpoint prediction
Zhang, W., Yu, Y., Hertwig, F., Thierry-Mieg, J., Zhang, W., Thierry-Mieg, D., Wang, J., Furlanello, C., Devanarayan, V., Cheng, J., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0694-1 · Cited by 393 · see citing works
“RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific…”
BackgroundThe transcriptional landscape and small RNAs of Salmonella enterica serovar Typhimurium
Kröger, C., Dillon, S. C., Cameron, A. D. S., Papenfort, K., Sivasankaran, S. K., Hokamp, K., Chao, Y., Sittka, A., Hébrard, M., Händler, K., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201061109 · Cited by 384 · see citing works
“Typhimurium SL1344 genome sequence, using Artemis release 11 (13).”
MethodIntegrated genome browser: visual analytics platform for genomics
Freese, N. H., Norris, D. C., & Loraine, A. E. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw069 · Cited by 372 · see citing works
“As ultra high-throughput sequencing of cDNA (RNA-Seq) has supplanted microarrays for surveying gene expression (Ozsolak and Milos, 2011), we added new features to IGB to support visualization of RNA-Seq data.”
Ren, S., Peng, Z., Mao, J., Yu, Y., Yin, C., Gao, X., Cui, Z., Zhang, J., Kang, Y., Xu, W., et al. (2012) · Cell Research · doi:10.1038/cr.2012.30 · Cited by 362 · see citing works
Cell Fate Decision as High-Dimensional Critical State Transition
Mojtahedi, M., Skupin, A., Zhou, J., Castaño, I. G., Leong-Quong, R. Y. Y., Chang, H. H., Trachana, K., Giuliani, A., & Huang, S. (2016) · PLoS Biology · doi:10.1371/journal.pbio.2000640 · Cited by 346 · see citing works
“IC is particularly useful for single-cell resolution snapshots of molecular profiles, as provided by RNA-seq [34] and CyTOF [35], of statistical ensembles of cells (=cell populations) taken at multiple time intervals during a biological time course.”
MethodEnsemble deep learning in bioinformatics
Cao, Y., Geddes, T. A., Yang, J. Y. H., & Yang, P. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0217-y · Cited by 357 · see citing works
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Falco: high-speed FastQC emulation for quality control of sequencing data
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“In addition, global mRNA expression is also profiled with deep sequencing approach (RNA-seq)(84) with many cell types including T cells (85, 86).”
MethodExtensive terminal and asymmetric processing of small RNAs from rRNAs, snoRNAs, snRNAs, and tRNAs
Li, Z., Ender, C., Meister, G., Moore, P. S., Chang, Y., & John, B. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks307 · Cited by 340 · see citing works
“It is likely that emerging third generation sequencing technologies and other microfluidic technologies, combined with multiplexing, will provide methods for cheaper and more robust detection of these difficult-to-study small RNAs that are typically smaller than even miRNAs (59).”
BackgroundGenomics-Driven Oncology: Framework for an Emerging Paradigm
Garraway, L. A. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.46.8934 · Cited by 339 · see citing works
“MPS may also be applied to tumor RNA for systematic profiling of gene fusions, splice isoforms, and differentially expressed genes.(67) Despite these advances, a complete clinical description of the tumor genome and transcriptome still”
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Fusions in solid tumours: diagnostic strategies, targeted therapy, and acquired resistance
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A multivariate approach to the integration of multi-omics datasets
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“Advances in RNA-sequencing and mass spectrometry (MS) based proteomics have dramatically improved coverage and quality of genomic, transcriptomic and proteomic profiling [1-4].”
BackgroundBulked sample analysis in genetics, genomics and crop improvement
Zou, C., Wang, P., & Xu, Y. (2016) · Plant Biotechnology Journal · doi:10.1111/pbi.12559 · Cited by 323 · see citing works
“Compared to other technologies such as microarrays, RNA-seq technology offers the following benefits (Ozsolak and Milos, 2011; Wang et al., 2009).”
BackgroundInterpretation of differential gene expression results of RNA-seq data: review and integration
McDermaid, A., Monier, B., Zhao, J., Liu, B., & Ma, Q. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby067 · Cited by 324 · see citing works
“Specifically, RNA-sequencing (RNA-seq) procedures provide an abundance of information regarding the gene expression levels of various organisms across multiple conditions at a high resolution [6–8].”
BackgroundStatistical methods for Mendelian randomization in genome-wide association studies: A review
Boehm, F. J., & Zhou, X. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.05.015 · Cited by 328 · see citing works
“With growing interest in modeling counts in biomedical data, where molecular phenotyping technologies now acquire RNA molecule counts and protein abundances, there may be opportunities for methods innovations in a generalized linear models framework for MR studies [132-135].”
BackgroundBetanodavirus and VER Disease: A 30-year Research Review
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–Omic and Electronic Health Record Big Data Analytics for Precision Medicine
Wu, P.-Y., Cheng, C., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2573285 · Cited by 308 · see citing works
“ternative splicing detection [32], [33].”
Integrating omic approaches for abiotic stress tolerance in soybean
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“For instance, RNA-seq is being used for transcription start site mapping, strand-specific measurements, gene fusion detection, small RNA characterization, and detection of alternative splicing events (Ozsolak and Milos, 2010).”
BackgroundMethodGerashchenko, M. V., Lobanov, A. V., & Gladyshev, V. N. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120799109 · Cited by 302 · see citing works
“Recent advances in next-generation sequencing have enhanced data acquisition, improved sensitivity, and made this method superior to microarrays in its throughput (9).”
MethodBridger: a new framework for de novo transcriptome assembly using RNA-seq data
Chang, Z., Li, G., Liu, J., Zhang, Y., Ashby, C., Liu, D., Cramer, C. L., & Huang, X. (2015) · Genome biology · doi:10.1186/s13059-015-0596-2 · Cited by 283 · see citing works
“RNA-seq is a powerful technique for collecting gene-expression data at a whole transcriptome level with unprecedented sensitivity and accuracy [1-4].”
MethodMarine Environmental Epigenetics
Eirin-Lopez, J. M., & Putnam, H. M. (2018) · Annual Review of Marine Science · doi:10.1146/annurev-marine-010318-095114 · Cited by 284 · see citing works
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“However, the attempt to explain the complex relationship between genes is still a challenge [8,9].”
Microfluidic single-cell whole-transcriptome sequencing
Streets, A., Zhang, X., Cao, C., Pang, Y., Wu, X., Xiong, L., Yang, L., Fu, Y., Zhao, L., Tang, F., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402030111 · Cited by 274 · see citing works
“There, inevitably, is bias associated with capture and nonlinear amplification of miniscule amounts of mRNA (25).”
BackgroundTools for mapping high-throughput sequencing data
Fonseca, N. A., Rung, J., Brāzma, A., & Marioni, J. C. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts605 · Cited by 276 · see citing works
“The alignment phase in RNA-seq experiments presents many challenges that arise, in general, from splicing events.”
BackgroundInfluentialRNA-Seq and human complex diseases: recent accomplishments and future perspectives
Costa, V., Aprile, M., Esposito, R., & Ciccodicola, A. (2012) · European Journal of Human Genetics · doi:10.1038/ejhg.2012.129 · Cited by 266 · see citing works
Protein-Based Biological Materials: Molecular Design and Artificial Production
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High-Resolution Transcriptome of Human Macrophages
Beyer, M., Mallmann, M. R., Xue, J., Staratschek‐Jox, A., Vorholt, D., Krebs, W., Sommer, D. D., Sander, J., Mertens, C., Niño‐Castro, A., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045466 · Cited by 255 · see citing works
Superparamagnetic nanoarchitectures for disease-specific biomarker detection
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RNA Sequencing: Platform Selection, Experimental Design, and Data Interpretation
Chu, Y., & Corey, D. R. (2012) · Nucleic Acid Therapeutics · doi:10.1089/nat.2012.0367 · Cited by 256 · see citing works
“…RNA sequencing (RNA-seq) (Nagalakshmi et al., 2008; Wilhelm, et al., 2008), rapid advances in experimental protocol development and data acquisition and analysis have brought comprehensive sequencing of cellular RNA within reach of most laboratories (Martin and Wang, 2011; Ozsolak and Milos, 2011).”
BackgroundMethodReproducibility of high-throughput mRNA and small RNA sequencing across laboratories
The GEUVADIS Consortium, 't Hoen, P. A. C., Friedländer, M. R., Almlöf, J., Sammeth, M., Pulyakhina, I., Anvar, S. Y., Laros, J. F. J., Buermans, H. P. J., Karlberg, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2702 · Cited by 250 · see citing works
“(1) Matches which are unique, and do not have any subdominant match: 251 ≥ MAPQ ≥ 255, XT = U (2) Matches which are unique, and have subdominant matches but a different score: 175 ≥ MAPQ ≥ 181, XT = U (3) Matches which are putatively unique (not unique, but distinguishable by score): 119 ≥ MAPQ ≥127, XT = U (4) Matche…”
BackgroundWeckwerth, W. (2011) · Journal of Proteomics · doi:10.1016/j.jprot.2011.07.010 · Cited by 243 · see citing works
Whole transcriptome analysis with sequencing: methods, challenges and potential solutions
Jiang, Z., Zhou, X., Li, R., Michal, J. J., Zhang, S., Dodson, M. V., Zhang, Z., & Harland, R. M. (2015) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-015-1934-y · Cited by 236 · see citing works
“These methods were previously reviewed by Ozsolak and Milos [92].”
Transcriptomics: Advances and approaches
Dong, Z., & Chen, Y. (2013) · Science China Life Sciences · doi:10.1007/s11427-013-4557-2 · Cited by 234 · see citing works
Third-Generation Sequencing: The Spearhead towards the Radical Transformation of Modern Genomics
Athanasopoulou, K., Boti, M. A., Adamopoulos, P. G., Skourou, P. C., & Scorilas, A. (2021) · Life · doi:10.3390/life12010030 · Cited by 234 · see citing works
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230 · see citing works
The power and promise of RNA ‐seq in ecology and evolution
Todd, E. V., Black, M. A., & Gemmell, N. J. (2016) · Molecular Ecology · doi:10.1111/mec.13526 · Cited by 233 · see citing works
“Quickly surpassing microarrays as the high-throughput method of choice to study differential expression (DE) in nonmodel species, RNA-seq promised unprecedented sensitivity for detecting expression differences among rare transcripts, splice variants and microRNAs (Ozso- lak & Milos 2011).”
BackgroundThe crucial role of multiomic approach in cancer research and clinically relevant outcomes
Lu, M., & Zhan, X. (2018) · The EPMA Journal · doi:10.1007/s13167-018-0128-8 · Cited by 229 · see citing works
“gene structural feature to its functions and easier discover the causal of physiological or pathological conditions [87, 88].”
BackgroundTea plant genomics: achievements, challenges and perspectives
Xia, E., Tong, W., Wu, Q., Wei, S., Zhao, J. H., Zhang, Z., Wei, C., & Wan, X. (2020) · Horticulture Research · doi:10.1038/s41438-019-0225-4 · Cited by 225 · see citing works
From single cells to deep phenotypes in cancer
Bendall, S. C., & Nolan, G. P. (2012) · Nature Biotechnology · doi:10.1038/nbt.2283 · Cited by 222 · see citing works
Vijay, N., Poelstra, J. W., Künstner, A., & Wolf, J. B. W. (2012) · Molecular Ecology · doi:10.1111/mec.12014 · Cited by 225 · see citing works
“Massively parallelized RNA sequencing technology (RNA-seq) has added a valuable tool: millions of short reads are generated from steady-state RNA and concomitantly provide transcriptome sequence information and a digital measure of gene expression (Ozsolak & Milos 2010).”
BackgroundNext-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications
Kulski, J. K. (2016) · InTech eBooks · doi:10.5772/61964 · Cited by 219 · see citing works
“RNA-seq is the NGS method that sequences the transcriptome, that is, all the RNA transcript sets expressed by the genome in cells, tissues, and organs at different stages of an organism’s life cycle [12, 18, 19, 20, 30].”
MethodPrinciples of transcriptome analysis and gene expression quantification: an RNA ‐seq tutorial
Wolf, J. B. W. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12109 · Cited by 219 · see citing works
“…and RNA-seq 1 Principles of high throughput sequencing technology: (Metzker 2010) 2 Principles of RNA-seq: (Wang et al. 2009; Oshlack et al. 2010; Ozsolak & Milos 2011) 3 Principles of transcriptome assembly (Martin & Wang 2011) with particular reference to plants (Jain 2012) Applications in…”
BackgroundThe Strawberry Plant Defense Mechanism: A Molecular Review
Amil‐Ruíz, F., Blanco‐Portales, R., Muñoz‐Blanco, J., & Caballero, J. L. (2011) · Plant and Cell Physiology · doi:10.1093/pcp/pcr136 · Cited by 216 · see citing works
Kotze, A. C., Hunt, P. W., Skuce, P. J., von Samson‐Himmelstjerna, G., Martin, R. J., Sager, H., Krücken, J., Hodgkinson, J. E., Lespine, A., Jex, A. R., et al. (2014) · International Journal for Parasitology Drugs and Drug Resistance · doi:10.1016/j.ijpddr.2014.07.007 · Cited by 211 · see citing works
“These genomic data-sets, and their accompanying transcriptomic data, provide major insights into the biology of parasitic nematodes because they are the basis for characterizing SNPs (e.g., DNA-seq; (Metzker, 2010)), changes in transcription and alternative splicing patterns (i.e., RNA-seq: (Ozsolak and Milos, 2011))…”
BackgroundMethodTranscriptional profiles of bovine in vivo pre-implantation development
Jiang, Z., Sun, J., Dong, H., Luo, O. J., Zheng, X., Obergfell, C. J., Tang, Y., Bi, J., O'Neill, R. J. W., Ruan, Y., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-756 · Cited by 209 · see citing works
“The RNA-seq technology provides unique benefits for studying gene expression with high resolutions and reproducibility, as well as for detecting novel transcripts and alternative splicing events [14,15].”
BackgroundSuravajhala, P., Kogelman, L. J. A., & Kadarmideen, H. N. (2016) · Genetics Selection Evolution · doi:10.1186/s12711-016-0217-x · Cited by 219 · see citing works
“Since the initial application of RNA-Seq, quantifying small RNAs, ascertaining alternate splicing events, transcription start sites (TSS) and mapping strand-specific genes [85] have benefited from such techniques.”
BackgroundCornwell, M., Vangala, M., Taing, L., Herbert, Z. T., Köster, J., Li, B., Sun, H., Li, T., Zhang, J., Qiu, X., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2139-9 · Cited by 203 · see citing works
Bioinformatics and Systems Biology of the Lipidome
Subramaniam, S., Fahy, E., Gupta, S., Sud, M., Byrnes, R. W., Cotter, D., Dinasarapu, A. R., & Maurya, M. R. (2011) · Chemical Reviews · doi:10.1021/cr200295k · Cited by 211 · see citing works
Alternative polyadenylation: New insights from global analyses
Shi, Y. (2012) · RNA · doi:10.1261/rna.035899.112 · Cited by 212 · see citing works
“When compared with the Illumina system, the disadvantages of DRS include higher error rate, shorter read lengths, lower yield (read counts), and the lack of multiplexing capability (Ozsolak and Milos 2011).”
BackgroundMethodInfluentialRNA-Seq-Based Monitoring of Infection-Linked Changes in Vibrio cholerae Gene Expression
Mandlik, A., Livny, J., Robins, W. P., Ritchie, J. M., Mekalanos, J. J., & Waldor, M. K. (2011) · Cell Host & Microbe · doi:10.1016/j.chom.2011.07.007 · Cited by 207 · see citing works
“The development of massively parallel cDNA sequencing (RNA-seq) techniques is enabling deeper and more accurate assessment of transcriptomes from eukaryotes (Ozsolak and Milos, 2011) as well as bacteria (van Vliet, 2010; Sorek and Cossart, 2010).”
Mohr, S., Ghanem, E., Smith, W., Sheeter, D. A., Qin, Y., King, O., Polioudakis, D., Iyer, V. R., Hunicke‐Smith, S., Swamy, S., et al. (2013) · RNA · doi:10.1261/rna.039743.113 · Cited by 206 · see citing works
Heavy metal detoxification mechanisms by microalgae: Insights from transcriptomics analysis
Tripathi, S., & Poluri, K. M. (2021) · Environmental Pollution · doi:10.1016/j.envpol.2021.117443 · Cited by 205 · see citing works
Identification of the zebrafish maternal and paternal transcriptomes
Harvey, S. A., Sealy, I. M., Kettleborough, R. N. W., Fényes, F., White, R. J., Stemple, D. L., & Smith, J. C. (2013) · Development · doi:10.1242/dev.095091 · Cited by 202 · see citing works
“Recently, new sequencing technologies have significantly improved our ability to analyse transcription (Ozsolak and Milos, 2011; Collins et al., 2012; Djebali et al., 2012).”
BackgroundIdentification of microRNA-regulated gene networks by expression analysis of target genesPreprint
Gennarino, V. A., D’Angelo, G., Dharmalingam, G., Fernandez, S., Russolillo, G., Sanges, R., M, M., Belcastro, V., Ballabio, A., VERDE, P., et al. (2012) · Genome Research · doi:10.1101/gr.130435.111 · Cited by 200 · see citing works
“…the expected increase of high-resolution transcriptome data facilitated by the advances and cost reduction of next-generation sequencing approaches (Ozsolak and Milos 2011), the current gap in adequate transcriptome coverage of some human tissues will be filled, and therefore this possible caveat…”
BackgroundAkbari, O. S., Antoshechkin, I. A., Amrhein, H., Williams, B. A., DiLoreto, R., Sandler, J. E., & Hay, B. A. (2013) · G3 Genes Genomes Genetics · doi:10.1534/g3.113.006742 · Cited by 190 · see citing works
“RNA-seq can also be used to study differential splicing and locate precise transcription start and stop sites, all at single-base resolution (Ozsolak and Milos 2011).”
BackgroundIntroduction to Single‐Cell RNA Sequencing
Olsen, T. K., & Baryawno, N. (2018) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.57 · Cited by 192 · see citing works
Application of metatranscriptomics to soil environments
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Comprehensive analysis of RNA-seq data reveals the complexity of the transcriptome in Brassica rapa
Tong, C., Wang, X., Yu, J., Wu, J., Li, W., Huang, J., Dong, C., Hua, W., & Liu, S. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-689 · Cited by 188 · see citing works
“Second, it allows researchers to improve gene annotation by extending transcriptional boundaries of genes and permits the discovery of novel genes or transcripts [9].”
BackgroundMolecular phylogeny of extant Holothuroidea (Echinodermata)
Miller, A. K., Kerr, A. M., Paulay, G., Reich, M., Wilson, N. G., Carvajal, J. I., & Rouse, G. W. (2017) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2017.02.014 · Cited by 190 · see citing works
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“Despite RNA-Seq being less cost-effective for sequencing a low number of targets ( ≤ 20) and more time consuming compared to qPCR, it provides a higher discovery power than qPCR, although both methods are highly effective ways of assessing gene expression [ 93 , 94 ].”
Proteomics in evolutionary ecology: linking the genotype with the phenotype
Diz, Á. P., Martínez‐Fernández, M., & Rolán‐Alvarez, E. (2012) · Molecular Ecology · doi:10.1111/j.1365-294x.2011.05426.x · Cited by 187 · see citing works
Guerette, P. A., Hoon, S. S., Seow, Y., Raida, M., Mašić, A., Wong, F. T., Ho, V. H. B., Kong, K. W., Demirel, M. C., Pena‐Francesch, A., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2671 · Cited by 186 · see citing works
- Authors
- Ozsolak, F., & Milos, P. M.
- Affiliations
- Helicos Biosciences (United States) (US)
- Corresponding author
- Ozsolak, Fatih
- Milos, Patrice M.
- Venue
- Nature Reviews Genetics
- Published
- 2010-12-30
- Volume
- 12
- Issue
- 2
- Pages
- 87-98
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3031867
- OpenAlex
- W2060842569
- Semantic Scholar
- cf2841f590819568ab156862c2506ab633493747
- Cited by
- 2,135
- References
- 105
- Citations by year
- 2026: 52 · 2025: 108 · 2024: 113 · 2023: 98 · 2022: 131 · 2021: 152 · 2020: 146 · 2019: 134 · 2018: 141 · 2017: 173 · 2016: 163 · 2015: 169 · 2014: 169 · 2013: 168 · 2012: 152
- FWCI
- 10.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (R01HG005230, R44HG005279)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- RNA sequencing, transcriptome, direct RNA sequencing, RNA quantification, strand-specific RNA-seq, fusion gene detection, small RNA, alternative splicing
- MeSH terms
- Animals, Humans, RNA, Transcription, Genetic, Alternative Splicing, Sequence Analysis, RNA
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