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Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
Open accessAbstract
High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
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“transcripts of between 8000 and 16 000 protein-coding genes expressed from a single cell type can be detected”
From Deep proteome and transcriptome mapping of a human cancer cell line (2011) · cites this paper as Trapnell et al, 2010
In the citing paper
“assembled into 49 000 unique transcripts (Trapnell et al, 2010) that mapped to 16 554 different protein-coding genes”
From Deep proteome and transcriptome mapping of a human cancer cell line (2011) · cites this paper as Trapnell et al, 2010
In the citing paper
“Cufflinks [39] estimates transcript expression from a mapping to the genome obtained from mappers such as TopHat using an expectation-maximization approach that estimates transcript abundances.”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [39]
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The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Self-citationMapping 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
Self-citationTopHat: 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
Self-citationAlternative 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
Chromatin signature reveals over a thousand highly conserved large non-coding RNAs in mammals
Guttman, M., Amit, I., Garber, M., French, C., Lin, M. F., Feldser, D., Huarte, M., Zuk, O., Carey, B. W., Cassady, J. P., et al. (2009) · Nature · doi:10.1038/nature07672 · Cited by 4,098
Genome-Wide Mapping of in Vivo Protein-DNA Interactions
Johnson, D. S., Mortazavi, A., Myers, R. M., & Wold, B. (2007) · Science · doi:10.1126/science.1141319 · Cited by 2,927
Self-citationRNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008) · Genome Research · doi:10.1101/gr.079558.108 · Cited by 2,771
The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing
Nagalakshmi, U., Wang, Z., Waern, K., Shou, C., Raha, D., Gerstein, M., & Snyder, M. (2008) · Science · doi:10.1126/science.1158441 · Cited by 2,479
Improving the Arabidopsis genome annotation using maximal transcript alignment assemblies
Haas, B. J. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg770 · Cited by 2,316
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Research works printed but not linked: 2
- Mortazavi, A., Williams, B., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008). · DOI · Paper page
- Fuglede, B. & Topsøe, F. in Proceedings of the IEEE International Symposium on Information Theory, 31 (2004).
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Timp, W., & Timp, G. L. (2020) · Science Advances · doi:10.1126/sciadv.aax8978 · Cited by 312
The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets
Medico, E., Russo, M., Picco, G., Cancelliere, C., Valtorta, E., Corti, G., Buscarino, M., Isella, C., Lamba, S., Martinoglio, B., et al. (2015) · Nature Communications · doi:10.1038/ncomms8002 · Cited by 302
Escape from X Inactivation Varies in Mouse Tissues
Berletch, J. B., Ma, W., Yang, F., Shendure, J., Noble, W. S., Distèche, C. M., & Deng, X. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1005079 · Cited by 307
Expression Profile of Ectopic Olfactory Receptors Determined by Deep Sequencing
Flegel, C., Manteniotis, S., Osthold, S., Hatt, H. H., & Gisselmann, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055368 · Cited by 306
Benner, C., van der Meulen, T., Cacéres, E., Tigyi, K., Donaldson, C. J., & Huising, M. O. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-620 · Cited by 302
Maternal imprinting at the H19-Igf2 locus maintains adult haematopoietic stem cell quiescence
Venkatraman, A., He, X. C., Thorvaldsen, J. L., Sugimura, R., Perry, J. M., Tao, F., Zhao, M., Christenson, M. K., Sanchez, R., Yu, J. Y., et al. (2013) · Nature · doi:10.1038/nature12303 · Cited by 296
Differentiated human stem cells resemble fetal, not adult, β cells
Hrvatin, S., O’Donnell, C. W., Deng, F., Millman, J. R., Pagliuca, F. W., DiIorio, P., Rezania, A., Gifford, D. K., & Melton, D. A. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400709111 · Cited by 302
Landscape and Regulation of m6A and m6Am Methylome across Human and Mouse Tissues
Liu, J., Li, K., Cai, J., Zhang, M., Zhang, X., Xiong, X., Meng, H., Xu, X., Huang, Z., Peng, J., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.09.032 · Cited by 291
Aanes, H., Winata, C. L., Lin, C. H., Chen, J. P., Srinivasan, K. G., Lee, S. G. P., Lim, A. Y. M., Hajan, H. S., Collas, P., Bourque, G., et al. (2011) · Genome Research · doi:10.1101/gr.116012.110 · Cited by 285
Hong, S., Zhang, Z., Liu, H., Tian, M., Zhu, X., Zhang, Z., Wang, W., Zhou, X., Zhang, F., Ge, Q., et al. (2018) · Immunity · doi:10.1016/j.immuni.2018.08.012 · Cited by 298
Foxman, E. F., Storer, J. A., Fitzgerald, M. E., Wasik, B. R., Hou, L., Zhao, H., Turner, P. E., Pyle, A. M., & Iwasaki, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1411030112 · Cited by 302
Extrachromosomal circular DNA is common in yeast
Møller, H. D., Parsons, L. R., Jørgensen, T. S., Botstein, D., & Regenberg, B. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508825112 · Cited by 296
5-Methylcytosine RNA Methylation in Arabidopsis Thaliana
Cui, X., Liang, Z., Shen, L., Zhang, Q., Bao, S., Geng, Y., Zhang, B., Leo, V. I., Vardy, L. A., Lu, T., et al. (2017) · Molecular Plant · doi:10.1016/j.molp.2017.09.013 · Cited by 299
Genetic Background Drives Transcriptional Variation in Human Induced Pluripotent Stem Cells
Rouhani, F., Kumasaka, N., de Brito, M. C., Bradley, A., Vallier, L., & Gaffney, D. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004432 · Cited by 294
Noncanonical Wnt Signaling Maintains Hematopoietic Stem Cells in the Niche
Sugimura, R., He, X., Venkatraman, A., Arai, F., Box, A., Semerad, C. L., Haug, J. S., Peng, L., Zhong, X.-B., Suda, T., et al. (2012) · Cell · doi:10.1016/j.cell.2012.05.041 · Cited by 306
Genome-wide analysis of alternative splicing of pre-mRNA under salt stress in Arabidopsis
Ding, F. H., Cui, P., Wang, Z., Zhang, S., Ali, S., & Xiong, L. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-431 · Cited by 294
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Zhan, S., Zhang, W., Niitepõld, K., Hsu, J. L., Haeger, J. F., Zalucki, M. P., Altizer, S., de Roode, J. C., Reppert, S. M., & Kronforst, M. R. (2014) · Nature · doi:10.1038/nature13812 · Cited by 295
Methanogenesis in oxygenated soils is a substantial fraction of wetland methane emissions
Angle, J., Morin, T., Solden, L., Narrowe, A. B., Smith, G., Borton, M., Rey-Sánchez, C., Daly, R., Mirfenderesgi, G., Hoyt, D., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01753-4 · Cited by 272
High-quality genome assembly of the silkworm, Bombyx mori
Kawamoto, M., Jouraku, A., Toyoda, A., Yokoi, K., Minakuchi, Y., Katsuma, S., Fujiyama, A., Kiuchi, T., Yamamoto, K., & Shimada, T. (2019) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2019.02.002 · Cited by 293
Regulation of RNA polymerase II activation by histone acetylation in single living cells
Stasevich, T. J., Hayashi‐Takanaka, Y., Sato, Y., Maehara, K., Ohkawa, Y., Sakata-Sogawa, K., Tokunaga, M., Nagase, T., Nozaki, N., McNally, J. G., et al. (2014) · Nature · doi:10.1038/nature13714 · Cited by 294
Twine, N. A., Janitz, K., Wilkins, M. R., & Janitz, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016266 · Cited by 309
Bell, R. D., Long, X., Lin, M., Bergmann, J. H., Nanda, V., Cowan, S. L., Zhou, Q., Han, Y., Spector, D. L., Zheng, D., et al. (2014) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.114.303240 · Cited by 294
R-Loop Mediated trans Action of the APOLO Long Noncoding RNA
Ariel, F., Lucero, L. E., Christ, A., Mammarella, M. F., Jégu, T., Veluchamy, A., Mariappan, K. G., Latrasse, D., Blein, T., Liu, C., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.12.015 · Cited by 291
Transcriptome analysis of human tissues and cell lines reveals one dominant transcript per gene
Gonzàlez-Porta, M., Frankish, A., Rung, J., Harrow, J., & Brazma, A. (2013) · Genome Biology · doi:10.1186/gb-2013-14-7-r70 · Cited by 266
Zhang, R., Calixto, C. P. G., Márquez, Y., Venhuizen, P., Τzioutziou, N. A., Guo, W., Spensley, M., Entizne, J. C., Lewandowska, D., ten Have, S., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx267 · Cited by 290
Global analyses of UPF1 binding and function reveal expanded scope of nonsense-mediated mRNA decayPreprint
Hurt, J. A., Robertson, A. D., & Burge, C. B. (2013) · Genome Research · doi:10.1101/gr.157354.113 · Cited by 288
Bridger: 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 292
Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas
Zhao, Z., Meng, F., Wang, W., Zheng, W., Zhang, C., & Jiang, T. (2017) · Scientific Data · doi:10.1038/sdata.2017.24 · Cited by 281
Agrochemical control of plant water use using engineered abscisic acid receptors
Park, S., Peterson, F. C., Mosquna, A., Yao, J., Volkman, B. F., & Cutler, S. R. (2015) · Nature · doi:10.1038/nature14123 · Cited by 287
Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads
Turro, E., Su, S.-Y., Gonçalves, Â., Coin, L. J., Richardson, S., & Lewin, A. (2011) · Genome Biology · doi:10.1186/gb-2011-12-2-r13 · Cited by 264
Minke whale genome and aquatic adaptation in cetaceans
Yim, H., Cho, Y. S., Guang, X., Kang, S. G., Jeong, J.-Y., Cha, S., Oh, H., Lee, J., Yang, E. C., Kwon, K. K., et al. (2013) · Nature Genetics · doi:10.1038/ng.2835 · Cited by 287
Deng, X., Hiatt, J. B., Nguyen, D. K., Ercan, S., Sturgill, D. M., Hillier, L. W., Schlesinger, F. J., Davis, C., Reinke, V., Gingeras, T., et al. (2011) · Nature Genetics · doi:10.1038/ng.948 · Cited by 283
The histone H3.3K36M mutation reprograms the epigenome of chondroblastomas
Fang, D., Gan, H., Lee, J., Han, J., Wang, Z., Riester, S. M., Jin, L. G., Chen, J., Zhou, H., Wang, J., et al. (2016) · Science · doi:10.1126/science.aae0065 · Cited by 285
Convergent regulatory evolution and loss of flight in paleognathous birds
Sackton, T. B., Grayson, P., Cloutier, A., Hu, Z., Liu, J. S., Wheeler, N. E., Gardner, P. P., Clarke, J. A., Baker, A. J., Clamp, M., et al. (2019) · Science · doi:10.1126/science.aat7244 · Cited by 291
Genomic-scale exchange of mRNA between a parasitic plant and its hosts
Kim, G., LeBlanc, M. L., Wafula, E. K., dePamphilis, C. W., & Westwood, J. H. (2014) · Science · doi:10.1126/science.1253122 · Cited by 284
Chen, Y., Zhao, Y., Chen, J., Peng, C., Zhang, Y., Tong, R., Cheng, Q., Yang, B., Feng, X., Lu, Y., et al. (2020) · Molecular Cancer · doi:10.1186/s12943-020-01239-w · Cited by 283
Schartl, M., Walter, R. B., Shen, Y., Garcia, T., Catchen, J., Amores, A., Braasch, I., Chalopin, D., Volff, J., Lesch, K., et al. (2013) · Nature Genetics · doi:10.1038/ng.2604 · Cited by 284
Zhu, Y., Gujar, A. D., Wong, C., Tjong, H., Ngan, C. Y., Gong, L., Chen, Y., Kim, H., Liu, J., Li, M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.03.006 · Cited by 275
Modelling and simulating generic RNA-Seq experiments with the flux simulator
Griebel, T., Zacher, B., Ribeca, P., Raineri, E., Lacroix, V., Guigó, R., & Sammeth, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks666 · Cited by 290
Global Dissection of Alternative Splicing in Paleopolyploid Soybean
Shen, Y., Zhou, Z., Wang, Z., Li, W., Fang, C. Q., Wu, M., Ma, Y., Liu, T., Kong, L., Peng, D., et al. (2014) · The Plant Cell · doi:10.1105/tpc.114.122739 · Cited by 282
Advanced Applications of RNA Sequencing and Challenges
Han, Y., Gao, S., Muegge, K., Zhang, W., & Zhou, B. (2015) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s28991 · Cited by 303
Brookes, E., de Santiago, I., Hebenstreit, D., Morris, K. J., Carroll, T., Xie, S. Q., Stock, J. K., Heidemann, M., Eick, D., Nozaki, N., et al. (2012) · Cell stem cell · doi:10.1016/j.stem.2011.12.017 · Cited by 290
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Belgard, T. G., Marques, A. C., Oliver, P. L., Abaan, H. Ö., Sirey, T., Hoerder‐Suabedissen, A., García‐Moreno, F., Molnár, Z., Margulies, E. H., & Ponting, C. P. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.039 · Cited by 277
Simultaneous RNA-Seq Analysis of a Mixed Transcriptome of Rice and Blast Fungus Interaction
Kawahara, Y., Oono, Y., Kanamori, H., Matsumoto, T., Itoh, T., & Minami, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049423 · Cited by 273
Tyner, C., Barber, G. P., Casper, J., Clawson, H., Diekhans, M., Eisenhart, C., Fischer, C. M., Gibson, D., Gonzalez, J. N., Guruvadoo, L., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1134 · Cited by 299
Banaszynski, L. A., Wen, D., Dewell, S. B., Whitcomb, S. J., Lin, M., Diaz, N. S., Elsässer, S. J., Chapgier, A. L. A., Goldberg, A. D., Canaani, E., et al. (2013) · Cell · doi:10.1016/j.cell.2013.08.061 · Cited by 294
Tunable protein synthesis by transcript isoforms in human cells
Floor, S. N., & Doudna, J. A. (2016) · eLife · doi:10.7554/elife.10921 · Cited by 265
Allele-Specific Chromatin Recruitment and Therapeutic Vulnerabilities of ESR1 Activating Mutations
Jeselsohn, R. M., Bergholz, J. S., Pun, M. J., Cornwell, M., Liu, W., Nardone, A., Xiao, T., Li, W., Qiu, X., Buchwalter, G., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.01.004 · Cited by 281
Amphioxus functional genomics and the origins of vertebrate gene regulation
Marlétaz, F., Firbas, P., Maeso, I., Tena, J. J., Bogdanović, O., Perry, M., Wyatt, C. D. R., de la Calle‐Mustienes, E., Bertrand, S., Burguera, D., et al. (2018) · Nature · doi:10.1038/s41586-018-0734-6 · Cited by 281
Jex, A. R., Liu, S., Li, B., Young, N. D., Hall, R. S., Li, Y., Yang, L., Zeng, N., Xu, X., Xiong, Z., et al. (2011) · Nature · doi:10.1038/nature10553 · Cited by 276
Alternative splicing: Enhancing ability to cope with stress via transcriptome plasticity
Mastrangelo, A. M., Marone, D., Laidò, G., Leonardis, A. M. D., & Vita, P. D. (2011) · Plant Science · doi:10.1016/j.plantsci.2011.09.006 · Cited by 277
Leveraging transcript quantification for fast computation of alternative splicing profiles
Alamancos, G. P., Pagès, A., Trincado, J. L., Bellora, N., & Eyras, E. (2015) · RNA · doi:10.1261/rna.051557.115 · Cited by 285
Frisvad, J. C., Grijseels, S., Prigent, S., Ji, B., Dainat, J., Nielsen, K. F., Frisvad, J. C., Workman, M., & Nielsen, J. (2017) · Nature Microbiology · doi:10.1038/nmicrobiol.2017.44 · Cited by 283
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Cuomo, C. A., Desjardins, C. A., Bakowski, M. A., Goldberg, J. M., Ma, A. T., Becnel, J. J., Didier, E. S., Fan, L., Heiman, D. I., Levin, J. Z., et al. (2012) · Genome Research · doi:10.1101/gr.142802.112 · Cited by 279
Flockhart, R. J., Webster, D. E., Qu, K., Mascarenhas, N., Kovalski, J., Kretz, M., & Khavari, P. A. (2012) · Genome Research · doi:10.1101/gr.140061.112 · Cited by 277
Xenome—a tool for classifying reads from xenograft samples
Conway, T., Wazny, J., Bromage, A., Tymms, M., Sooraj, D., Williams, E. D., & Beresford-Smith, B. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts236 · Cited by 276
Image-based transcriptomics in thousands of single human cells at single-molecule resolution
Battich, N., Stoeger, T., & Pelkmans, L. (2013) · Nature Methods · doi:10.1038/nmeth.2657 · Cited by 269
Zhan, J., Thakare, D., Ma, C., Lloyd, A., Nixon, N. M., Arakaki, A. M., Burnett, W. J., Logan, K. O., Wang, D., Wang, X., et al. (2015) · The Plant Cell · doi:10.1105/tpc.114.135657 · Cited by 277
CREBBP Inactivation Promotes the Development of HDAC3-Dependent Lymphomas
Jiang, Y., Ortega-Molina, A., Geng, H., Ying, H.-Y., Hatzi, K., Parsa, S., McNally, D., Wang, L., Doane, A. S., Agirre, X., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-16-0975 · Cited by 278
Microbiota-Induced Type I Interferons Instruct a Poised Basal State of Dendritic Cells
Schaupp, L., Muth, S., Rogell, L., Kofoed-Branzk, M., Melchior, F., Lienenklaus, S., Ganal-Vonarburg, S. C., Klein, M., Guendel, F., Hain, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.022 · Cited by 263
Cassava genome from a wild ancestor to cultivated varieties
Wang, W., Feng, B., Xiao, J., Xia, Z., Zhou, X., Li, P., Zhang, W., Wang, Y., Møller, B. L., Zhang, P., et al. (2014) · Nature Communications · doi:10.1038/ncomms6110 · Cited by 271
N6-Methyladenosine Guides mRNA Alternative Translation during Integrated Stress Response
Zhou, J. L., Wan, J., Shu, X. E., Mao, Y., Liu, X., Yuan, X. H., Zhang, X., Hess, M. E., Brüning, J. C., & Qian, S. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.01.019 · Cited by 276
Wang, Y., Xie, Z., Kutschera, E., Adams, J. I., Kadash-Edmondson, K. E., & Xing, Y. (2024) · Nature Protocols · doi:10.1038/s41596-023-00944-2 · Cited by 296
WormBase in 2022—data, processes, and tools for analyzing Caenorhabditis elegans
Davis, P., Zarowiecki, M., Arnaboldi, V., Becerra, A., Cain, S., Chan, J., Chen, W. J., Cho, J., Beltrame, E. D. V., Diamantakis, S., et al. (2022) · Genetics · doi:10.1093/genetics/iyac003 · Cited by 261
Intragenic Enhancers Act as Alternative Promoters
Kowalczyk, M. S., Hughes, J. R., Garrick, D., Lynch, M. D., Sharpe, J. A., Sloane-Stanley, J. A., McGowan, S. J., De Gobbi, M., Hosseini, M., Vernimmen, D., et al. (2012) · Molecular Cell · doi:10.1016/j.molcel.2011.12.021 · Cited by 267
Czech, B., Preall, J. B., McGinn, J., & Hannon, G. J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.04.007 · Cited by 269
Molina, J., Hazzouri, K. M., Nickrent, D., Geisler, M., Meyer, R. S., Pentony, M. M., Flowers, J. M., Pelser, P., Barcelona, J., Inovejas, S. A., et al. (2014) · Molecular Biology and Evolution · doi:10.1093/molbev/msu051 · Cited by 269
Laumont, C. M., Daouda, T., Laverdure, J., Bonneil, É., Caron-Lizotte, O., Hardy, M., Granados, D. P., Durette, C., Lemieux, S., Thibault, P., et al. (2016) · Nature Communications · doi:10.1038/ncomms10238 · Cited by 279
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. L., & van Veen, J. A. (2016) · The ISME Journal · doi:10.1038/ismej.2016.108 · Cited by 266
Cancer transcriptome profiling at the juncture of clinical translation
Cieslik, M. P., & Chinnaiyan, A. M. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.96 · Cited by 266
Local lung hypoxia determines epithelial fate decisions during alveolar regeneration
Xi, Y., Kim, T., Brumwell, A. N., Driver, I. H., Wei, Y., Tan, V. M., Jackson, J., Xu, J., Lee, D.-K., Gotts, J. E., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3580 · Cited by 268
TPMCalculator: one-step software to quantify mRNA abundance of genomic features
Alvarez, R. V., Pongor, L. S., Mariño‐Ramírez, L., & Landsman, D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty896 · Cited by 259
Regulski, M., Lu, Z., Kendall, J., Donoghue, M. T. A., Reinders, J., Llaca, V., Deschamps, S., Smith, A. D., Levy, D., McCombie, W. R., et al. (2013) · Genome Research · doi:10.1101/gr.153510.112 · Cited by 270
Wu, J., Bao, J., Kim, M. K., Yuan, S., Tang, C., Zheng, H., Mastick, G. S., Xu, C., & Yan, W. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407777111 · Cited by 265
High-throughput annotation of full-length long noncoding RNAs with capture long-read sequencing
Lagarde, J., Uszczyńska-Ratajczak, B., Carbonell, S., Pérez-Lluch, S., Abad, A., Davis, C., Gingeras, T., Frankish, A., Harrow, J., Guigó, R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3988 · Cited by 265
Defining a personal, allele-specific, and single-molecule long-read transcriptome
Tilgner, H., Grubert, F., Sharon, D., & Snyder, M. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400447111 · Cited by 266
Impaired Epidermal to Dendritic T Cell Signaling Slows Wound Repair in Aged Skin
Keyes, B. E., Liu, S., Asare, A., Naik, S., Levorse, J., Polak, L., Lu, C. P., Nikolova, M., Pasolli, H. A., & Fuchs, E. (2016) · Cell · doi:10.1016/j.cell.2016.10.052 · Cited by 266
Injury-induced ctgfa directs glial bridging and spinal cord regeneration in zebrafish
Mokalled, M. H., Patra, C., Dickson, A. L., Endo, T., Stainier, D. Y. R., & Poss, K. D. (2016) · Science · doi:10.1126/science.aaf2679 · Cited by 280
Genomic diversity and evolution of the head crest in the rock pigeon
Shapiro, M. D., Kronenberg, Z., Li, C., Domyan, E. T., Pan, H., Campbell, M., Tan, H., Huff, C. D., Hu, H., Vickrey, A. I., et al. (2013) · Science · doi:10.1126/science.1230422 · Cited by 265
Age-Triggered and Dark-Induced Leaf Senescence Require the bHLH Transcription Factors PIF3, 4, and 5
Song, Y., Yang, C., Gao, S., Zhang, W., Li, L., & Kuai, B. (2014) · Molecular Plant · doi:10.1093/mp/ssu109 · Cited by 265
LaCroix, R. A., Sandberg, T. E., O'Brien, E. J., Utrilla, J., Ebrahim, A., Guzmán, G. I., Szubin, R., Palsson, B. Ø., & Feist, A. M. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.02246-14 · Cited by 254
The gene cortex controls mimicry and crypsis in butterflies and moths
Nadeau, N. J., Pardo‐Díaz, C., Whibley, A. C., Supple, M. A., Saenko, S. V., Wallbank, R. W. R., Wu, G. C., Maroja, L. S., Ferguson, L., Hanly, J. J., et al. (2016) · Nature · doi:10.1038/nature17961 · Cited by 254
Zhou, T., Tan, L., Cederquist, G. Y., Fan, Y., Hartley, B. J., Mukherjee, S., Tomishima, M., Brennand, K. J., Zhang, Q., Schwartz, R. E., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.06.017 · Cited by 263
Dissecting childhood asthma with nasal transcriptomics distinguishes subphenotypes of disease
Poole, A. W., Urbanek, C., Eng, C. S., Schageman, J. J., Jacobson, S. R., O’Connor, B. P., Galanter, J., Gignoux, C. R., Roth, L. A., Kumar, R., et al. (2014) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2013.11.025 · Cited by 259
Cold-Inducible RNA-Binding Protein Modulates Circadian Gene Expression Posttranscriptionally
Morf, J., Rey, G., Schneider, K., Stratmann, M., Fujita, J., Naef, F., & Schibler, U. (2012) · Science · doi:10.1126/science.1217726 · Cited by 265
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Aldiri, I., Xu, B., Wang, L., Chen, X. '., Hiler, D. J., Griffiths, L. M., Valentine, M. B., Shirinifard, A., Thiagarajan, S., Sablauer, A., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.022 · Cited by 252
Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization
Roberts, B., Haupt, A., Tucker, A., Grancharova, T., Arakaki, J., Fuqua, M. A., Nelson, A., Hookway, C., Ludmann, S. A., Mueller, I. A., et al. (2017) · Molecular Biology of the Cell · doi:10.1091/mbc.e17-03-0209 · Cited by 262
Sundell, D., Street, N. R., Kumar, M., Mellerowicz, E. J., Kücükoglu, M., Johnsson, C., Kumar, V., Mannapperuma, C., Delhomme, N., Nilsson, O. R., et al. (2017) · The Plant Cell · doi:10.1105/tpc.17.00153 · Cited by 265
Epigenetic (re)programming of caste-specific behavior in the ant Camponotus floridanus
Simola, D. F., Graham, R. J., Brady, C. M., Enzmann, B. L., Desplan, C., Ray, A., Zwiebel, L. J., Bonasio, R., Reinberg, D., Liebig, J., et al. (2016) · Science · doi:10.1126/science.aac6633 · Cited by 242
Nerves Regulate Cardiomyocyte Proliferation and Heart Regeneration
Mahmoud, A. I., O'Meara, C. C., Gemberling, M. P., Zhao, L., Bryant, D. M., Zheng, R., Gannon, J., Cai, L., Choi, W.-Y., Egnaczyk, G. F., et al. (2015) · Developmental Cell · doi:10.1016/j.devcel.2015.06.017 · Cited by 273
Ecker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 258
Lu, S., Zhang, Y., Zhu, K., Yang, W., Ye, J., Chai, L., Xu, Q., & Deng, X. (2018) · PLANT PHYSIOLOGY · doi:10.1104/pp.17.01830 · Cited by 261
Webster, M. W., Chen, Y.-H., Stowell, J. A. W., Alhusaini, N., Sweet, T. J., Graveley, B. R., Coller, J., & Passmore, L. A. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.05.033 · Cited by 258
Extracellular Matrix Remodeling Regulates Glucose Metabolism through TXNIP Destabilization
Sullivan, W. J., Mullen, P. J., Schmid, E. W., Flores, A., Momcilovic, M., Sharpley, M. S., Jelínek, D., Whiteley, A. E., Maxwell, M. B., Wilde, B. R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.017 · Cited by 258
Zhan, S., Fang, G., Cai, M., Kou, Z., Xu, J. Q., Cao, Y., Bai, L., Zhang, Y., Jiang, Y., Luo, X., et al. (2019) · Cell Research · doi:10.1038/s41422-019-0252-6 · Cited by 265
Finding the active genes in deep RNA-seq gene expression studies
Hart, T., Komori, H. K., LaMere, S. A., Podshivalova, K., & Salomon, D. R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-778 · Cited by 256
Sweet, M., Cocciolo, A., Slavov, D. B., Jones, K. L., Sweet, J., Graw, S. L., Reece, T. B., Ambardekar, A. V., Bristow, M. R., Mestroni, L., et al. (2018) · BMC Genomics · doi:10.1186/s12864-018-5213-9 · Cited by 260
Ferroptotic cell death triggered by conjugated linolenic acids is mediated by ACSL1
Beatty, A., Singh, T., Tyurina, Y. Y., Tyurin, V. A., Samovich, S., Nicolas, E., Maslar, K., Zhou, Y., Cai, K. Q., Tan, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22471-y · Cited by 268
Transketolase counteracts oxidative stress to drive cancer development
Xu, I. M.-J., Lai, R. K.-H., Lin, S.-H., Tse, A. P.-W., Chiu, D. K.-C., Koh, H.-Y., Law, C.-T., Wong, C.-M., Cai, Z., Wong, C. C.-L., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508779113 · Cited by 251
Mazor, T., Pankov, A., Johnson, B., Hong, C., Hamilton, E. G., Bell, R. J. A., Smirnov, I. V., Reis, G. F., Phillips, J. J., Barnes, M. J., et al. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.07.012 · Cited by 270
Metadata
- Authors
- Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L.
- Affiliations
- University of Maryland, College Park (US)
- California Institute of Technology (US)
- University of California, Berkeley (US)
- Corresponding author
- Pachter, Lior
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2010-05
- Volume
- 28
- Issue
- 5
- Pages
- 511-515
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3146043
- OpenAlex
- W2141458291
- Cited by
- 15,556
- References
- 31
- Citations by year
- 2026: 449 · 2025: 708 · 2024: 865 · 2023: 1081 · 2022: 1349 · 2021: 1458 · 2020: 1444 · 2019: 1349 · 2018: 1319 · 2017: 1225 · 2016: 1051 · 2015: 936 · 2014: 889 · 2013: 684 · 2012: 447
- FWCI
- 185.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Human Genome Research Institute (U54HG004576, R01HG006102)
- National Institute of General Medical Sciences (R01GM083873)
- U.S. National Library of Medicine (R01LM006845)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- transcript assembly, transcript quantification, Cufflinks, unannotated transcripts, isoform switching, alternative splicing, alternative transcription, cell differentiation, myogenesis, genome annotation
- MeSH terms
- Algorithms, Animals, Cell Differentiation, Cell Line, RNA, Messenger, Software, Proto-Oncogene Proteins c-myc, Genome, Sequence Analysis, RNA, Protein Isoforms, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Mice
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