Analysis and design of RNA sequencing experiments for identifying isoform regulation
Open accessAbstract
Through alternative splicing, most human genes express multiple isoforms that often differ in function. To infer isoform regulation from high-throughput sequencing of cDNA fragments (RNA-seq), we developed the mixture-of-isoforms (MISO) model, a statistical model that estimates expression of alternatively spliced exons and isoforms and assesses confidence in these estimates. Incorporation of mRNA fragment length distribution in paired-end RNA-seq greatly improved estimation of alternative-splicing levels. MISO also detects differentially regulated exons or isoforms. Application of MISO implicated the RNA splicing factor hnRNP H1 in the regulation of alternative cleavage and polyadenylation, a role that was supported by UV cross-linking-immunoprecipitation sequencing (CLIP-seq) analysis in human cells. Our results provide a probabilistic framework for RNA-seq analysis, give functional insights into pre-mRNA processing and yield guidelines for the optimal design of RNA-seq experiments for studies of gene and isoform expression.
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Paired-end reads are preferable to single-end reads for de novo transcript discovery or isoform expression analysis.
“Furthermore, sequencing can involve single-end (SE) or paired-end (PE) reads, although the latter is preferable for de novo transcript discovery or isoform expression analysis [4, 5].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [4]
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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 · see citing works
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558 · see citing works
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181 · see citing works
Self-citationPan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951 · see citing works
The M2 splice isoform of pyruvate kinase is important for cancer metabolism and tumour growth
Christofk, H. R., Vander Heiden, M. G., Harris, M. H., Ramanathan, A., Gerszten, R. E., Wei, R., Fleming, M. D., Schreiber, S. L., & Cantley, L. C. (2008) · Nature · doi:10.1038/nature06734 · Cited by 2,820 · see citing works
Fast and SNP-tolerant detection of complex variants and splicing in short reads
Wu, T. D., & Nacu, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq057 · Cited by 1,894 · see citing works
Mayr, C., & Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.06.016 · Cited by 1,613 · see citing works
Sandberg, R., Neilson, J. R., Sarma, A., Sharp, P. A., & Burge, C. B. (2008) · Science · doi:10.1126/science.1155390 · Cited by 1,349 · see citing works
Self-citationUnderstanding alternative splicing: towards a cellular code
Matlin, A. J., Clark, F., & Smith, C. W. J. (2005) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1645 · Cited by 1,299 · see citing works
Guttman, M., Garber, M., Levin, J. Z., Donaghey, J., Robinson, J., Adiconis, X., Fan, L., Koziol, M. J., Gnirke, A., Nusbaum, C., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt.1633 · Cited by 1,253 · see citing works
Monte Carlo Estimation of Bayesian Credible and HPD Intervals
Chen, M.-H., & Shao, Q.-M. (1999) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1999.10474802 · Cited by 1,251 · see citing works
RNA-Seq gene expression estimation with read mapping uncertainty
Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A., & Dewey, C. N. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp692 · Cited by 1,198 · see citing works
Cooper, T. A., Wan, L., & Dreyfuss, G. (2009) · Cell · doi:10.1016/j.cell.2009.02.011 · Cited by 1,160 · see citing works
Statistical inferences for isoform expression in RNA-Seq
Jiang, H., & Wong, W. H. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp113 · Cited by 453 · see citing works
Molecular mechanisms of eukaryotic pre-mRNA 3′ end processing regulation
Millevoi, S., & Vagner, S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp1176 · Cited by 393 · see citing works
Logistic-Normal Distributions: Some Properties and Uses
Aitchison, J., & Shen, S. (1980) · Biometrika · doi:10.2307/2335470 · Cited by 382 · see citing works
Cancer-associated regulation of alternative splicing
Venables, J. P., Klinck, R., Koh, C., Gervais-Bird, J., Bramard, A., Inkel, L., Durand, M., Couture, S., Froehlich, U., Lapointe, E., et al. (2009) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1608 · Cited by 340 · see citing works
Detection of splice junctions from paired-end RNA-seq data by SpliceMap
Au, K. F., Jiang, H., Lin, L., Xing, Y., & Wong, W. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq211 · Cited by 328 · see citing works
Alternative expression analysis by RNA sequencing
Griffith, M., Griffith, O. L., Mwenifumbo, J. C., Goya, R., Morrissy, A. S., Morin, R. D., Corbett, R., Tang, M., Hou, Y. C., Pugh, T. J., et al. (2010) · Nature Methods · doi:10.1038/nmeth.1503 · Cited by 306 · see citing works
Annotating genomes with massive-scale RNA sequencing
Denœud, F., Aury, J., Silva, C. D., Noël, B., Rogier, O., Delledonne, M., Morgante, M., Valle, G., Wincker, P., Scarpelli, C., et al. (2008) · Genome biology · doi:10.1186/gb-2008-9-12-r175 · Cited by 250 · see citing works
Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing
Yassour, M., Kaplan, T., Fraser, H. B., Levin, J. Z., Pfiffner, J., Adiconis, X., Schroth, G. P., Luo, S. J., Khrebtukova, I., Gnirke, A., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812841106 · Cited by 213 · see citing works
Modeling non-uniformity in short-read rates in RNA-Seq data
Li, J., Jiang, H., & Wong, W. H. (2010) · Genome Biology · doi:10.1186/gb-2010-11-5-r50 · Cited by 193 · see citing works
Identification of Alternative Splicing Markers for Breast Cancer
Venables, J. P., Klinck, R., Bramard, A., Inkel, L., Dufresne‐Martin, G., Koh, C., Gervais-Bird, J., Lapointe, E., Froehlich, U., Durand, M., et al. (2008) · Cancer Research · doi:10.1158/0008-5472.can-08-1769 · Cited by 185 · see citing works
Analysis of the Human Neurexin Genes: Alternative Splicing and the Generation of Protein Diversity
Rowen, L., Young, J. M., Birditt, B., Kaur, A., Madan, A., Philipps, D. L., Qin, S., Minx, P. J., Wilson, R. K., Hood, L., et al. (2002) · Genomics · doi:10.1006/geno.2002.6734 · Cited by 184 · see citing works
PolyA_DB: a database for mammalian mRNA polyadenylation
Zhang, H. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki055 · Cited by 132 · see citing works
Splice site strength–dependent activity and genetic buffering by poly-G runs
Xiao, X. G., Wang, Z., Jang, M., Nutiu, R., Wang, E. T., & Burge, C. B. (2009) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1661 · Cited by 116 · see citing works
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 · see citing works
The RNA-binding Protein CUGBP1 Regulates Stability of Tumor Necrosis Factor mRNA in Muscle Cells
Zhang, L., Lee, J. E., Wilusz, J., et al. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m802803200 · Cited by 90 · see citing works
Honoré, B., Baandrup, U. T., & Vorum, H. (2003) · Experimental Cell Research · doi:10.1016/j.yexcr.2003.11.011 · Cited by 83 · see citing works
Getting Started in Probabilistic Graphical Models
Airoldi, E. M. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030252 · Cited by 71 · see citing works
Self-citationA physical and functional link between splicing factors promotes pre-mRNA 3′ end processing
Millevoi, S., Decorsière, A., Loulergue, C., Iacovoni, J. S., Bernat-Fabre, S., Antoniou, M. N., & Vagner, S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp470 · Cited by 67 · see citing works
The hnRNPs F and H2 bind to similar sequences to influence gene expression
Alkan, S., Martincic, K., & Milcarek, C. (2005) · Biochemical Journal · doi:10.1042/bj20050538 · Cited by 64 · see citing works
A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq
Wang, L., Xi, Y., Yu, J. M., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0008529 · Cited by 40 · see citing works
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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179 · see citing works
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Shen, S., Park, J. W., Lu, Z.-X., Lin, L., Henry, M. D., Wu, Y. N., Zhou, Q., & Xing, Y. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419161111 · Cited by 2,713 · see citing works
FMRP Stalls Ribosomal Translocation on mRNAs Linked to Synaptic Function and Autism
Darnell, J. C., Van Driesche, S. J., Zhang, C., Hung, K. Y. S., Mele, A., Fraser, C. E., Stone, E. F., Chen, C., Fak, J. J., Chi, S. W., et al. (2011) · Cell · doi:10.1016/j.cell.2011.06.013 · Cited by 2,072 · see citing works
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · see citing works
Detecting differential usage of exons from RNA-seq data
Anders, S., Reyes, A., & Huber, W. (2012) · Genome Research · doi:10.1101/gr.133744.111 · Cited by 1,545 · see citing works
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
A large-scale binding and functional map of human RNA-binding proteins
Van Nostrand, E. L., Freese, P., Pratt, G. A., Wang, X., Wei, X., Xiao, R., Blue, S. M., Chen, J.-Y., Cody, N. A. L., Dominguez, D., et al. (2020) · Nature · doi:10.1038/s41586-020-2077-3 · Cited by 1,317 · see citing works
Self-citationGene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Computational methods for transcriptome annotation and quantification using RNA-seq
Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011) · Nature Methods · doi:10.1038/nmeth.1613 · Cited by 1,042 · see citing works
Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues
Merkin, J., Russell, C., Chen, P., & Burge, C. B. (2012) · Science · doi:10.1126/science.1228186 · Cited by 972 · see citing works
Self-citationCTCF-promoted RNA polymerase II pausing links DNA methylation to splicing
Shukla, S., Kavak, E., Gregory, M., Imashimizu, M., Shutinoski, B., Kashlev, M., Oberdoerffer, P., Sandberg, R., & Oberdoerffer, S. (2011) · Nature · doi:10.1038/nature10442 · Cited by 973 · see citing works
Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924 · see citing works
Annotation-free quantification of RNA splicing using LeafCutter
Li, Y. I., Knowles, D. A., Humphrey, J., Barbeira, A. N., Dickinson, S. P., Im, H. K., & Pritchard, J. K. (2018) · Nature Genetics · doi:10.1038/s41588-017-0004-9 · Cited by 765 · see citing works
Assessment of transcript reconstruction methods for RNA-seq
The RGASP Consortium, Steijger, T., Abril, J. F., Engström, P. G., Kokocinski, F., Hubbard, T. J., Guigó, R., Harrow, J., & Bertone, P. (2013) · Nature Methods · doi:10.1038/nmeth.2714 · Cited by 699 · see citing works
Single-cell RNA counting at allele and isoform resolution using Smart-seq3
Hagemann-Jensen, M., Ziegenhain, C., Chen, P., Ramsköld, D., Hendriks, G.-J., Larsson, A. J. M., Faridani, O. R., & Sandberg, R. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0497-0 · Cited by 700 · see citing works
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Di Giammartino, D. C., Nishida, K., & Manley, J. L. (2011) · Molecular Cell · doi:10.1016/j.molcel.2011.08.017 · Cited by 737 · see citing works
RNA G-quadruplexes cause eIF4A-dependent oncogene translation in cancer
Wolfe, A. L., Singh, K., Zhong, Y., Drewe, P., Rajasekhar, V. K., Sanghvi, V. R., Mavrakis, K. J., Jiang, M., Roderick, J. E., Van der Meulen, J., et al. (2014) · Nature · doi:10.1038/nature13485 · Cited by 635 · see citing works
Systematic analysis of telomere length and somatic alterations in 31 cancer types
Barthel, F. P., Wei, W., Tang, M., Martinez-Ledesma, E., Hu, X., Amin, S. B., Akdemir, K. C., Seth, S., Song, X., Wang, Q., et al. (2017) · Nature Genetics · doi:10.1038/ng.3781 · Cited by 614 · see citing works
A new view of transcriptome complexity and regulation through the lens of local splicing variations
Vaquero-Garcia, J., Barrera, A., Gazzara, M. R., González-Vallinas, J., Lahens, N. F., Hogenesch, J. B., Lynch, K. W., & Barash, Y. (2016) · eLife · doi:10.7554/elife.11752 · Cited by 488 · see citing works
Mammalian NET-Seq Reveals Genome-wide Nascent Transcription Coupled to RNA Processing
Nojima, T., Gomes, T., Grosso, A. R. F., Kimura, H., Dye, M. J., Dhir, S., Carmo-Fonseca, M., & Proudfoot, N. J. (2015) · Cell · doi:10.1016/j.cell.2015.03.027 · Cited by 562 · see citing works
Landscape and variation of RNA secondary structure across the human transcriptome
Wan, Y., Qu, K., Zhang, Q. C., Flynn, R. A., Manor, O., Ouyang, Z., Zhang, J., Spitale, R. C., Snyder, M. P., Segal, E., et al. (2014) · Nature · doi:10.1038/nature12946 · Cited by 555 · see citing works
Transcriptome-wide Regulation of Pre-mRNA Splicing and mRNA Localization by Muscleblind Proteins
Wang, E. T., Cody, N. A. L., Jog, S., Biancolella, M., Wang, T. T., Treacy, D. J., Luo, S., Schroth, G. P., Housman, D. E., Reddy, S., et al. (2012) · Cell · doi:10.1016/j.cell.2012.06.041 · Cited by 490 · see citing works
Self-citationRecurrent AAV2-related insertional mutagenesis in human hepatocellular carcinomas
Nault, J.-C., Datta, S., Imbeaud, S., Franconi, A., Mallet, M., Couchy, G., Letouzé, E., Pilati, C., Verret, B., Blanc, J.-F., et al. (2015) · Nature Genetics · doi:10.1038/ng.3389 · Cited by 489 · see citing works
Huelga, S. C., Vu, A. Q., Arnold, J. D., Liang, T. Y., Liu, P. P., Yan, B. Y., Donohue, J. P., Shiue, L., Hoon, S., Brenner, S., et al. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.02.001 · Cited by 470 · see citing works
A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages
Yu, Y., Fuscoe, J. C., Zhao, C., Guo, C., Jia, M., Qing, T., Bannon, D. I., Lancashire, L., Bao, W., Du, T., et al. (2014) · Nature Communications · doi:10.1038/ncomms4230 · Cited by 395 · see citing works
Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis
Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., et al. (2019) · Science · doi:10.1126/science.aav9776 · Cited by 429 · see citing works
Dynamic integration of splicing within gene regulatory pathways
Braunschweig, U., Gueroussov, S., Plocik, A. M., Graveley, B. R., & Blencowe, B. J. (2013) · Cell · doi:10.1016/j.cell.2013.02.034 · Cited by 406 · see citing works
CDK12 regulates DNA repair genes by suppressing intronic polyadenylation
Dubbury, S. J., Boutz, P. L., & Sharp, P. A. (2018) · Nature · doi:10.1038/s41586-018-0758-y · Cited by 318 · see citing works
Gaidatzis, D., Burger, L., Florescu, M., & Stadler, M. B. (2015) · Nature Biotechnology · doi:10.1038/nbt.3269 · Cited by 313 · see citing works
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 · see citing works
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 · see citing works
Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia
Prudencio, M., Humphrey, J., Pickles, S., Brown, A.-L., Hill, S. E., Kachergus, J. M., Shi, J., Heckman, M. G., Spiegel, M. R., Cook, C., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139741 · Cited by 231 · see citing works
Germline Chd8 haploinsufficiency alters brain development in mouse
Gompers, A. L., Su-Feher, L., Ellegood, J., Copping, N. A., Riyadh, M. A., Stradleigh, T. W., Pride, M. C., Schaffler, M. D., Wade, A. A., Catta-Preta, R., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4592 · Cited by 266 · see citing works
Li, S., Tighe, S. W., Nicolet, C. M., Grove, D., Levy, S., Farmerie, W., Viale, A., Wright, C., Schweitzer, P. A., Gao, Y., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2972 · Cited by 240 · see citing works
Song, Y., Botvinnik, O. B., Lovci, M. T., Kakaradov, B., Liu, P., Xu, J. L., & Yeo, G. W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.06.003 · Cited by 204 · see citing works
High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies
Rabani, M., Raychowdhury, R., Jovanovic, M., Rooney, M., Stumpo, D. J., Pauli, A., Hacohen, N., Schier, A. F., Blackshear, P. J., Friedman, N., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.015 · Cited by 226 · see citing works
Chronic cocaine-regulated epigenomic changes in mouse nucleus accumbens
Feng, J., Wilkinson, M., Liu, X., Purushothaman, I., Ferguson, D., Vialou, V., Maze, I., Shao, N., Kennedy, P., Koo, J., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-4-r65 · Cited by 198 · see citing works
Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology · doi:10.1038/nbt.3682 · Cited by 186 · see citing works
Huang, K.-L., Jee, D., Stein, C. B., Elrod, N. D., Henriques, T., Mascibroda, L. G., Baillat, D., Russell, W. K., Adelman, K., & Wagner, E. J. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.08.016 · Cited by 184 · see citing works
CDK13 cooperates with CDK12 to control global RNA polymerase II processivity
Fan, Z., Devlin, J. R., Hogg, S. J., Doyle, M. A., Harrison, P. F., Todorovski, I., Cluse, L. A., Knight, D. A., Sandow, J. J., Gregory, G., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz5041 · Cited by 164 · see citing works
Uddin, M., Tammimies, K., Pellecchia, G., Alipanahi, B., Hu, P., Wang, Z., Pinto, D., Lau, L., Nalpathamkalam, T., Marshall, C. R., et al. (2014) · Nature Genetics · doi:10.1038/ng.2980 · Cited by 163 · see citing works
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Gregersen, L. H., Mitter, R., Ugalde, A. P., Nojima, T., Proudfoot, N. J., Agami, R., Stewart, A., & Svejstrup, J. Q. (2019) · Cell · doi:10.1016/j.cell.2019.04.038 · Cited by 140 · see citing works
Anvar, S. Y., Allard, G., Tseng, E., Sheynkman, G. M., de Klerk, E., Vermaat, M., Yin, R. H., Johansson, H. E., Ariyurek, Y., den Dunnen, J. T., et al. (2018) · Genome Biology · doi:10.1186/s13059-018-1418-0 · Cited by 118 · see citing works
Discovery and mass spectrometric analysis of novel splice-junction peptides using RNA-Seq
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Zatreanu, D., Han, Z., Mitter, R., Tumini, E., Williams, H., Gregersen, L., Dirac-Svejstrup, A. B., Roma, S., Stewart, A., Aguilera, A., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.07.037 · Cited by 124 · see citing works
LEDGF/p75 interacts with mRNA splicing factors and targets HIV-1 integration to highly spliced genes
Singh, P. K., Plumb, M. R., Ferris, A. L., Iben, J. R., Wu, X., Fadel, H. J., Luke, B. T., Esnault, C., Poeschla, E. M., Hughes, S. H., et al. (2015) · Genes & Development · doi:10.1101/gad.267609.115 · Cited by 109 · see citing works
RNA-binding proteins in neurodegeneration: Seq and you shall receive
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Maze, I., Shen, L., Zhang, B., Garcia, B. A., Shao, N., Mitchell, A., Sun, H., Akbarian, S., Allis, C. D., & Nestler, E. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3816 · Cited by 100 · see citing works
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Hitzemann, R., Bottomly, D., Darakjian, P., Walter, N., Iancu, O., Searles, R., Wilmot, B., & McWeeney, S. (2013) · Genes, Brain and Behavior · doi:10.1111/gbb.12007 · Cited by 88 · see citing works
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Liu, Y., Ferguson, J. F., Xue, C., Silverman, I. M., Gregory, B., Reilly, M. P., & Li, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066883 · Cited by 74 · see citing works
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Kissopoulou, A., Jonasson, J., Lindahl, T. L., & Osman, A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081809 · Cited by 69 · see citing works
Highly accurate isoform identification for the human transcriptome
Sommer, M. J., Cha, S., Varabyou, A., Rincon, N., Park, S., Minkin, I., Pertea, M., Steinegger, M., & Salzberg, S. L. (2022) · doi:10.1101/2022.06.08.495354 · Cited by 40 · see citing works
Xu, T., Verhagen, M., Joosten, R., Sun, W., Sacchetti, A., Munoz Sagredo, L., Orian-Rousseau, V., & Fodde, R. (2022) · eLife · doi:10.7554/elife.82006 · Cited by 25 · see citing works
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Hitzemann, R., Darakjian, P., Walter, N., Dan Iancu, O., Searles, R., & McWeeney, S. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00001-1 · Cited by 20 · see citing works
- Authors
- Katz, Y., Wang, E. T., Airoldi, E. M., & Burge, C. B.
- Affiliations
- Harvard–MIT Division of Health Sciences and Technology (US)
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Airoldi, Edoardo M
- Burge, Christopher B
- Venue
- Nature Methods
- Published
- 2010-11-07
- Volume
- 7
- Issue
- 12
- Pages
- 1009-1015
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC3037023
- OpenAlex
- W1575879600
- Cited by
- 1,353
- References
- 36
- Citations by year
- 2026: 26 · 2025: 52 · 2024: 68 · 2023: 69 · 2022: 71 · 2021: 127 · 2020: 113 · 2019: 104 · 2018: 128 · 2017: 102 · 2016: 123 · 2015: 119 · 2014: 112 · 2013: 69 · 2012: 51
- FWCI
- 18.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
- PMC
- Funders
- National Science Foundation
- Colorado State University
- National Institutes of Health
- National Human Genome Research Institute (RC2HG005624, R01HG002439)
- National Institute of General Medical Sciences (R01GM096193, R01GM085319)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- 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
- Keywords
- alternative splicing, isoform regulation, paired-end sequencing, pre-mRNA processing, probabilistic-statistical model
- MeSH terms
- Base Sequence, DNA-Binding Proteins, Exons, Humans, Introns, RNA, RNA, Messenger, Alternative Splicing, Sequence Analysis, RNA, Protein Isoforms, Reverse Transcriptase Polymerase Chain Reaction, Heterogeneous-Nuclear Ribonucleoproteins, NFATC Transcription Factors
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