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RNA sequencing: advances, challenges and opportunities
Open accessAbstract
In the few years since its initial application, massively parallel cDNA sequencing, or RNA-seq, has allowed many advances in the characterization and quantification of transcriptomes. Recently, several developments in RNA-seq methods have provided an even more complete characterization of RNA transcripts. These developments include improvements in transcription start site mapping, strand-specific measurements, gene fusion detection, small RNA characterization and detection of alternative splicing events. Ongoing developments promise further advances in the application of RNA-seq, particularly direct RNA sequencing and approaches that allow RNA quantification from very small amounts of cellular materials.
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“RNA deep-sequencing (RNA-seq) provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays, including an unprecedented capability to discover novel genes, alternative transcript variants, chimeric transcripts, and expressed sequence variants as well as allele-specific expression [8–12].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [10]
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“Moreover, it is established that longer reads will improve performance in several of the noted applications of RNA-seq, such as transcript assembly, transcript quantification, and gene fusion detection [10,11].”
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“RNA-seq also has many advantages, among them, a greater dynamic range, higher sensitivity towards low expression genes, alternative splicing detection, and gene fusion discovery [6].”
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“Particularly, transcriptome analysis can be employed to reveal relationships between plant gene expression and phenotypes [31–33].”
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“Transcriptome sequencing (30) could soon represent an alternative valuable strategy to directly detect mutations, including variants that affect splicing.”
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“Recently, thanks to the advent of Next Generation Sequencing (NGS) technologies, it is possible to care-fully detect and quantify the amount of both known and novel miRNAs in RNA-Seq samples, accounting also for single nucleotide polymorphisms (SNPs) [10–13].”
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Exome versus transcriptome sequencing in identifying coding region variants
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“Direct sequencing NGS methods, such as de-novo genome sequencing (Ellegren 2014), re-sequencing (Stratton 2008) or RNAseq of expressed genes (Ozsolak and Milos 2011; Wickett et al. 2014), can provide whole genome-scale data but may still be limited by high sequencing costs.”
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Transcriptomic analyses of regenerating adult feathers in chicken
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“High-throughput sequencing technologies have been applied to characterize transcriptome architectures [21–26].”
Recent Developments of Genomic Research in Soybean
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“and identification of splicing events and a wide range of non-coding RNA species [23,34].”
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Tamoxifen sensitivity-related microRNA-342 is a useful biomarker for breast cancer survival
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RNA-seq Profiles of Immune Related Genes in the Spleen of Necrotic Enteritis-afflicted Chicken Lines
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“High throughput RNA-Sequencing technology is a recently developed approach to transcriptome profiling using deep-sequencing technologies and generates concomitant gene expression and polymorphism data over the whole transcriptome through a single experiment (Ozsolak and Milos, 2011).”
BackgroundTCF4 (e2‐2; ITF2): A schizophrenia‐associated gene with pleiotropic effects on human disease
Navarrete, K., Pedroso, I., de Jong, S., Stefánsson, H., Steinberg, S., Stefánsson, K., Ophoff, R. A., Schalkwyk, L. C., & Collier, D. A. (2012) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32109 · Cited by 50
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Foodomics: A novel approach for food microbiology
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BASiNET—BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification
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“In addition to the quantification of transcriptomes, these new methods have contributed to a better understanding of the genetic information contained in the RNA sequence, such as those related to non-coding RNAs (3), as well as make possible the sequencing of several species (4).”
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Direct microRNA Sequencing Using Nanopore-Induced Phase-Shift Sequencing
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RNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
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“DETECTION OF TECHNICAL BIASES IN RNA-SEQ DATA Obtaining an accurate assessment of RNA molecules that correspond with disease is not a trivial undertaking and should include a comprehensive evaluation of expression estimates for potential areas of artificial biases (Ozsolak & Milos, 2011).”
Prostate cancer epigenetic biomarkers: next-generation technologies
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GeneFriends: gene co-expression databases and tools for humans and model organisms
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BackgroundMethodRevealing the History and Mystery of RNA-Seq
Gondane, A., & Itkonen, H. M. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45030120 · Cited by 53
“• Masks sample heterogeneity (for example in the case of biopsy [10]).”
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“Recently RNA sequencing (RNA-Seq), referring to the whole-transcriptome shotgun sequencing of fragmented mRNA or cDNA, has emerged as a powerful tool for profiling expressed genes in plants and other organisms [35–41].”
BackgroundNewborn screening for primary immunodeficiencies: beyond SCID and XLA
Borte, S., Wang, N., Óskarsdóttir, S., von Döbeln, U., & Hammarström, L. L. G. (2011) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2011.06350.x · Cited by 50
Characterizing and annotating the genome using RNA-seq data
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Intriguing Interaction of Bacteriophage-Host Association: An Understanding in the Era of Omics
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“For amplification, RNA polymerase (Ozsolak and Milos, 2011) or MDA (Gonzalez et al., 2005) or emulsion PCR is/are performed (Tang et al., 2009).”
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“RNA sequencing (RNA-seq), a high-throughput sequencing based transcriptome profiling, has been proven to provide extensive quantitative and qualitative information on the expression of genes in both prokaryotes and eukaryotes [14, 15] and their potential changes under different conditions.”
BackgroundMethodFreixas, J. A., Munholland, S., Silva, A., et al. (2017) · BMC Plant Biology · doi:10.1186/s12870-017-1037-z · Cited by 49
“, microarrays) as it is capable of detecting low-abundance transcripts [38].”
BackgroundTranscriptome Analysis of Rice Seedling Roots in Response to Potassium Deficiency
Zhang, X., Jiang, H. L., Wang, H., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-05887-9 · Cited by 49
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Is it useful to use several “omics” for obtaining valuable results?
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“Next generation RNA sequencing (RNA-seq) (2) has quickly developed to become a powerful method to measure the expression of small RNAs.”
MethodXiong, J.-Q., Cui, P., Ru, S., Kurade, M. B., Patil, S. M., Yadav, K. K., Fallatah, A. M., Cabral-Pinto, M. M. S., & Jeon, B. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131121 · Cited by 49
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“We have employed next-generation RNA sequencing technology as it offers extensive coverage, precise quantification of transcripts and a large dynamic range [12–14].”
MethodCasey, M. E., Meade, K. G., Nalpas, N., Taraktsoglou, M., Browne, J. A., Killick, K. E., Park, S. D. E., Gormley, E. P., Hokamp, K., Magee, D. A., et al. (2015) · Frontiers in Immunology · doi:10.3389/fimmu.2015.00023 · Cited by 48
“Also, where appropriate, RNA-seq data can be used to quantify alternatively spliced gene variants; identify novel transcribed genes; and study antisense transcription (28, 29, 62, 63).”
MethodAspergilli: Systems biology and industrial applications
Knuf, C., & Nielsen, J. (2012) · Biotechnology Journal · doi:10.1002/biot.201200169 · Cited by 49
“Next-generation sequencing techniques are not only used for sequencing genomes, but also for transcriptome analysis [19].”
MethodRNA-Skim: a rapid method for RNA-Seq quantification at transcript level
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“RNA-Seq technique has been demonstrated as a revolutionary means for examining transcriptome because it provides incomparable deep coverage and base pair-level resolution (Ozsolak and Milos, 2010).”
MethodGenome-wide alternative polyadenylation in animals: insights from high-throughput technologies
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“Based on the second-generation sequencing technologies, RNA-seq was developed for the quantification of genes expression and AS, the discovery of new fusion genes, the improvement of genome assembly, and the identification of the start and end of a transcript (Cloonan et al., 2008; Mortazavi et al., 2008; Wang et al.,…”
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“such as information on low-abundance transcripts, therefore providing results with higher resolution and sensitivity (Ozsolak and Milos, 2011).”
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“The search for methods to speed up transcript expression analysis has evolved from differential display [5], to sequential analysis of gene expression (SAGE) [6], to gene microarrays [7], and to RNA sequencing [8], all of which have contributed to the identification and categorization of GENs.”
MethodNalpas, N., Magee, D. A., Conlon, K. M., Browne, J. A., Healy, C. M., McLoughlin, K. E., Rue-Albrecht, K., McGettigan, P., Killick, K. E., Gormley, E. P., et al. (2015) · Scientific Reports · doi:10.1038/srep13629 · Cited by 50
Modeling Keratoconus Using Induced Pluripotent Stem Cells
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“scriptome profiling by next-generation sequencing (RNA-Seq) has significantly improved the study of gene expression.(56)”
BackgroundGrewe, F., Huang, J.-P., Leavitt, S. D., & Lumbsch, H. T. (2017) · Scientific Reports · doi:10.1038/s41598-017-09906-7 · Cited by 47
TransLiG: a de novo transcriptome assembler that uses line graph iteration
Liu, J., Yu, T., Mu, Z., & Li, G. (2019) · Genome biology · doi:10.1186/s13059-019-1690-7 · Cited by 44
Candidate RNA biomarkers in biofluids for early diagnosis of ovarian cancer: A systematic review
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“Short transcripts of less than 200 nucleotides require specialized techniques for processing and sequencing using SR-based protocols (66).”
MethodA Global View of Transcriptome Dynamics During Male Floral Bud Development in Populus tomentosa
Chen, Z., Rao, P., Yang, X., Su, X., Zhao, T., Gao, K., Yang, X., & An, X. (2018) · Scientific Reports · doi:10.1038/s41598-017-18084-5 · Cited by 46
An, Y., Zhai, Z., Wang, X., Ding, Y., He, L., Li, L., Mo, Q., Mu, C., Xie, R., Liu, T., et al. (2023) · Journal of Advanced Research · doi:10.1016/j.jare.2023.08.008 · Cited by 46
Transcriptional profiling of predator-induced phenotypic plasticity in Daphnia pulex
Rozenberg, A., Parida, M., Leese, F., Weiss, L. C., Tollrian, R., & Manak, J. R. (2015) · Frontiers in Zoology · doi:10.1186/s12983-015-0109-x · Cited by 46
“Currently, the technique of choice suitable for addressing response patterns in gene expression at a genomewide level with potentially unlimited depth of coverage is RNA-Seq [39–41].”
MethodEvolving techniques for gene fusion detection in soft tissue tumours
Mertens, F., & Tayebwa, J. (2013) · Histopathology · doi:10.1111/his.12272 · Cited by 47
Zhu, G., Zhong, D., Cao, J., Zhou, H., Li, J., Liu, Y., Bai, L., Xu, S., Wang, M., Zhou, G., et al. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-448 · Cited by 46
“The recent development of the RNA-seq method enables us to determine gene expression patterns while providing de novo sequencing, assembly and annotation of expressed genes [22].”
BackgroundMethodPandey, R. V., Franssen, S. U., Futschik, A., & Schlötterer, C. (2013) · Molecular Ecology Resources · doi:10.1111/1755-0998.12110 · Cited by 46
“After microarrays revolutionized, the analysis of gene expression (Schena et al. 1995), 2nd-generation-sequencing-based transcriptome profiling has become the method of choice (Garber et al. 2011; Ozsolak & Milos 2011).”
MethodcDNA Hybrid Capture Improves Transcriptome Analysis on Low-Input and Archived Samples
Cabanski, C. R., Magrini, V. J., Griffith, M., Griffith, O. L., McGrath, S. D., Zhang, J., Walker, J., Ly, A., Demeter, R., Fulton, R. S., et al. (2014) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2014.03.004 · Cited by 48
Ibrahim, F., Maragkakis, M., Alexiou, P., Maronski, M., Dichter, M. A., & Mourelatos, Z. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.01.021 · Cited by 47
“However, we note that the impact of TAF15 in splicing is likely more widespread than the one uncovered by the RNA-seq experiments because of the stringent criteria that we used to define changed exons and limitations of RNA-seq (Ozsolak and Milos, 2011).”
BackgroundInfluentialTranscriptional analysis of endocrine disruption using zebrafish and massively parallel sequencing
Baker, M. E., & Hardiman, G. (2014) · Journal of Molecular Endocrinology · doi:10.1530/jme-13-0219 · Cited by 47
“RNA-Seq is rapidly becoming the method of choice for detecting and quantifying all the genes expressed in a cell (Maher, et al. 2009; Ozsolak and Milos 2011; Wang, et al. 2009).”
BackgroundMethodDeep homology in the age of next-generation sequencing
Tschopp, P., & Tabin, C. J. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0475 · Cited by 45
“Massively paralleled sequencing of cDNAs, known as RNA-seq, now allows for genome-wide interrogation of expression status, as well as the global description of transcript structures [38].”
BackgroundYang, X., Li, J., Wu, Y., Ni, B., & Zhang, B. (2019) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365513.2019.1674004 · Cited by 46
“Thus, RNA-seq provides a powerful tool to decipher global gene expression patterns far beyond the limitations of microarrays [30,31].”
MethodImpact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction
Li, T., Wu, P.-Y., Phan, J. H., Hassazadeh, H., Jones, W. D., Shi, L. M., Fischer, M., Mason, C. E., Li, S., Xu, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-74567-y · Cited by 46
Dark Matter: Are Mice the Solution to Missing Heritability?
Parker, C. C., & Palmer, A. A. (2011) · Frontiers in Genetics · doi:10.3389/fgene.2011.00032 · Cited by 45
“Finally, multiple tools are available for altering the mouse genome; permitting the efficient and precise manipulation of genes and allowing for direct experimental testing of observed statistical associations.”
BackgroundMethodDukler, N., Booth, G. T., Huang, Y.-F., Tippens, N. D., Waters, C. T., Danko, C. G., Lis, J. T., & Siepel, A. C. (2017) · Genome Research · doi:10.1101/gr.222935.117 · Cited by 43
Lee, J.-R., Kwon, C. H., Choi, Y., Park, H. J., Kim, H. S., Jo, H.-J., Oh, N., & Park, D. Y. (2016) · BMC Cancer · doi:10.1186/s12885-016-2596-3 · Cited by 46
“Massively parallel RNA sequencing (RNA-seq) is a useful method for annotation of the cancer transcriptome with great efficiency and high resolution [8] RNA-seq has enabled a comprehensive understanding of the complexity of the cancer transcriptome, via genome-wide expression profiling and identification of novel and f…”
Advances in cerebral palsy biomarkers
Savasan, Z. A., Kim, S. K., Oh, K. J., & Graham, S. F. (2020) · Advances in clinical chemistry · doi:10.1016/bs.acc.2020.04.006 · Cited by 47
Genomic reaction norms inform predictions of plastic and adaptive responses to climate change
Oomen, R. A., & Hutchings, J. A. (2022) · Journal of Animal Ecology · doi:10.1111/1365-2656.13707 · Cited by 43
“While the need for a more holistic perspective on environmental responses has been realized for some time, a lack of sufficient data and analytical tools has, to some extent, hindered progress in this regard (reviewed by Pacifici et al., 2015, albeit without consideration for the role of genomics).”
BackgroundMa, X., Shao, C., Jin, Y., et al. (2014) · RNA Biology · doi:10.4161/rna.28725 · Cited by 45
Global estimation of the 3′ untranslated region landscape using RNA sequencing
Kim, M.-H., Yu, B., & Nam, J. (2015) · Methods · doi:10.1016/j.ymeth.2015.04.011 · Cited by 47
Yang, Y., Li, X., Kong, X., Ma, L., Hu, X., & Yang, Y. (2014) · Functional & Integrative Genomics · doi:10.1007/s10142-014-0419-7 · Cited by 45
Right Ventricular Long Noncoding RNA Expression in Human Heart Failure
Salvo, T. G. D., Guo, Y. P., Su, Y. R., Clark, T. A., Brittain, E. L., Absi, T., Maltais, S., & Hemnes, A. R. (2015) · Pulmonary Circulation · doi:10.1086/679721 · Cited by 45
Transcriptome profiling of the rat retina after optic nerve transection
Yasuda, M., Tanaka, Y., Omodaka, K., Nishiguchi, K. M., Nakamura, O., Tsuda, S., & Nakazawa, T. (2016) · Scientific Reports · doi:10.1038/srep28736 · Cited by 46
Low, T. Y., Mohtar, M. A., Ang, M. Y., & Jamal, R. (2018) · PROTEOMICS · doi:10.1002/pmic.201800235 · Cited by 45
Omics-based biomarkers in the diagnosis of diabetes
Gan, W., Ramachandran, V., Lim, C. S. Y., & Koh, R. Y. (2019) · Journal of Basic and Clinical Physiology and Pharmacology · doi:10.1515/jbcpp-2019-0120 · Cited by 51
Imaging Transcription: Past, Present, and FuturePreprint
Coleman, R. A., Liu, Z. J., Darzacq, X., Tjian, R., Singer, R. H., & Lionnet, T. (2015) · Cold Spring Harbor Symposia on Quantitative Biology · doi:10.1101/sqb.2015.80.027201 · Cited by 41
Isensee, J., Carsten, W., Buschow, R., Weißmann, R., Kuss, A. W., & Hucho, T. B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115731 · Cited by 44
“Transcriptome analysis of TRPV1(+) neurons with RNA-Seq We also applied RNA sequencing (RNA-seq), which provides advantages over microarrays such as a larger dynamic range of detection, the number of reads mapping to a gene is proportional to the transcript abundance, and it reveals alternatively spliced transcript va…”
MethodZhang, Y., Jiang, R., Wu, H., Liu, P., Xie, J., He, Y., & Pang, H. (2012) · Genomics · doi:10.1016/j.ygeno.2012.05.002 · Cited by 46
A multidimensional platform for the purification of non-coding RNA species
Chionh, Y. H., Ho, C.-H., Pruksakorn, D., Babu, I. R., Ng, C. S., Hia, F., McBee, M. E., Su, D., Pang, Y. L. J., Gu, C., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt668 · Cited by 41
“Along with such comparative analysis, contamination of small RNA fractions with fragments from larger RNA species can be assessed by RNA sequencing methods (55,56) and can potentially be removed by repurification of RNA fractions by an orthogonal chromatographic system that provides another basis for separating the co…”
BackgroundComparison of microarray and RNA-Seq analysis of mRNA expression in dermal mesenchymal stem cells
Li, J., Hou, R., Niu, X., Liu, R., Wang, Q., Wang, C., Li, X., Hao, Z., Yin, G., & Zhang, K. (2015) · Biotechnology Letters · doi:10.1007/s10529-015-1963-5 · Cited by 47
Kadioglu, O., Cao, J., Kosyakova, N., Mrasek, K., Liehr, T., & Efferth, T. (2016) · Scientific Reports · doi:10.1038/srep36754 · Cited by 44
“RNA sequencing represents a powerful and sensitive method for gene expression profiling 19–21 .”
Metadata
- Authors
- Ozsolak, F., & Milos, P. M.
- Affiliations
- Helicos Biosciences (United States) (US)
- Corresponding author
- Ozsolak, Fatih
- Milos, Patrice M.
- Venue
- Nature Reviews Genetics
- Published
- 2010-12-30
- Volume
- 12
- Issue
- 2
- Pages
- 87-98
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC3031867
- OpenAlex
- W2060842569
- Semantic Scholar
- cf2841f590819568ab156862c2506ab633493747
- Cited by
- 2,135
- References
- 105
- Citations by year
- 2026: 52 · 2025: 108 · 2024: 113 · 2023: 98 · 2022: 131 · 2021: 152 · 2020: 146 · 2019: 134 · 2018: 141 · 2017: 173 · 2016: 163 · 2015: 169 · 2014: 169 · 2013: 168 · 2012: 152
- FWCI
- 10.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (R01HG005230, R44HG005279)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Keywords
- RNA sequencing, transcriptome, direct RNA sequencing, RNA quantification, strand-specific RNA-seq, fusion gene detection, small RNA, alternative splicing
- MeSH terms
- Animals, Humans, RNA, Transcription, Genetic, Alternative Splicing, Sequence Analysis, RNA
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