Denti, M. A., Viero, G., Provenzani, A., Quattrone, A. & Macchi, P. mRNA fate: life and death of the mRNA in the cytoplasm. RNA Biol. 10, 360–366 (2013).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Nikom, D. & Zheng, S. Alternative splicing in neurodegenerative disease and the promise of RNA therapies. Nat. Rev. Neurosci. 24, 457–473 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Bradley, R. K. & Anczuków, O. RNA splicing dysregulation and the hallmarks of cancer. Nat. Rev. Cancer 23, 135–155 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ule, J. & Blencowe, B. J. Alternative splicing regulatory networks: functions, mechanisms, and evolution. Mol. Cell 76, 329–345 (2019).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Rogalska, M. E., Vivori, C. & Valcárcel, J. Regulation of pre-mRNA splicing: roles in physiology and disease, and therapeutic prospects. Nat. Rev. Genet. 24, 251–269 (2022).ArticleÂ
PubMedÂ
Google ScholarÂ
Wang, Z. & Burge, C. B. Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. RNA 14, 802–813 (2008).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ule, J. et al. An RNA map predicting Nova-dependent splicing regulation. Nature 444, 580–586 (2006).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wang, Z., Xiao, X., Van Nostrand, E. & Burge, C. B. General and specific functions of exonic splicing silencers in splicing control. Mol. Cell 23, 61–70 (2006).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
de Boer, C. G. & Taipale, J. Hold out the genome: a roadmap to solving the cis-regulatory code. Nature 625, 41–50 (2024).ArticleÂ
PubMedÂ
Google ScholarÂ
Scotti, M. M. & Swanson, M. S. RNA mis-splicing in disease. Nat. Rev. Genet. 17, 19–32 (2016).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Rowlands, C. F., Baralle, D. & Ellingford, J. M. Machine learning approaches for the prioritization of genomic variants impacting pre-mRNA splicing. Cells 8, 1513 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Hwang, H., Jeon, H., Yeo, N. & Baek, D. Big data and deep learning for RNA biology. Exp. Mol. Med. 56, 1293–1321 (2024).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shapiro, M. B. & Senapathy, P. RNA splice junctions of different classes of eukaryotes: sequence statistics and functional implications in gene expression. Nucleic Acids Res. 15, 7155–7174 (1987).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Carmel, I., Tal, S., Vig, I. & Ast, G. Comparative analysis detects dependencies among the 5′ splice-site positions. RNA 10, 828–840 (2004).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Berglund, J. A., Abovich, N. & Rosbash, M. A cooperative interaction between U2AF65 and mBBP/SF1 facilitates branchpoint region recognition. Genes Dev. 12, 858–867 (1998).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Paggi, J. M. & Bejerano, G. A sequence-based, deep learning model accurately predicts RNA splicing branchpoints. RNA 24, 1647–1658 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Breathnach, R., Benoist, C., O’Hare, K., Gannon, F. & Chambon, P. Ovalbumin gene: evidence for a leader sequence in mRNA and DNA sequences at the exon-intron boundaries. Proc. Natl Acad. Sci. USA 75, 4853–4857 (1978).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yoshida, H. et al. Elucidation of the aberrant 3′ splice site selection by cancer-associated mutations on the U2AF1. Nat. Commun. 11, 4744 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ast, G. How did alternative splicing evolve? Nat. Rev. Genet. 5, 773–782 (2004).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Parker, M. T. et al. m6A modification of U6 snRNA modulates usage of two major classes of pre-mRNA 5′ splice site. eLife 11, e78808 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shenasa, H. & Bentley, D. L. Pre-mRNA splicing and its cotranscriptional connections. Trends Genet. 39, 672–685 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Izaurralde, E. et al. A nuclear cap binding protein complex involved in pre-mRNA splicing. Cell 78, 657–668 (1994).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Cooke, C., Hans, H. & Alwine, J. C. Utilization of splicing elements and polyadenylation signal elements in the coupling of polyadenylation and last-intron removal. Mol. Cell. Biol. 19, 4971–4979 (1999).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Licatalosi, D. D. et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature 456, 464 (2008).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Rot, G. et al. High-resolution RNA maps suggest common principles of splicing and polyadenylation regulation by TDP-43. Cell Rep. 19, 1056–1067 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Fiszbein, A., Krick, K. S., Begg, B. E. & Burge, C. B. Exon-mediated activation of transcription starts. Cell 179, 1551–1565.e17 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Furger, A., O’Sullivan, J. M., Binnie, A., Lee, B. A. & Proudfoot, N. J. Promoter proximal splice sites enhance transcription. Genes Dev. 16, 2792–2799 (2002).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Reimer, K. A., Mimoso, C. A., Adelman, K. & Neugebauer, K. M. Co-transcriptional splicing regulates 3′ end cleavage during mammalian erythropoiesis. Mol. Cell 81, 998–1012.e7 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tilgner, H. et al. Nucleosome positioning as a determinant of exon recognition. Nat. Struct. Mol. Biol. 16, 996–1001 (2009).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kfir, N. et al. SF3B1 association with chromatin determines splicing outcomes. Cell Rep. 11, 618–629 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Deutsch, M. & Long, M. Intron–exon structures of eukaryotic model organisms. Nucleic Acids Res. 27, 3219–3228 (1999).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Santoni, M. J. et al. Differential exon usage involving an unusual splicing mechanism generates at least eight types of NCAM cDNA in mouse brain. EMBO J. 8, 385–392 (1989).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Piovesan, A. et al. 1.1: a tool to summarize data from NCBI gene datasets and its application to an update of human gene statistics. Database 2016, baw153 (2016).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Amit, M. et al. Differential GC content between exons and introns establishes distinct strategies of splice-site recognition. Cell Rep. 1, 543–556 (2012).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Black, D. L. Mechanisms of alternative pre-messenger RNA splicing. Annu. Rev. Biochem. 72, 291–336 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Witten, J. T. & Ule, J. Understanding splicing regulation through RNA splicing maps. Trends Genet. 27, 89–97 (2011).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Erkelenz, S. et al. Position-dependent splicing activation and repression by SR and hnRNP proteins rely on common mechanisms. RNA 19, 96–102 (2013).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
SliÅ¡ković, I., Eich, H. & Müller-McNicoll, M. Exploring the multifunctionality of SR proteins. Biochem. Soc. Trans. 50, 187–198 (2022).ArticleÂ
PubMedÂ
Google ScholarÂ
Ule, J. et al. CLIP identifies nova-regulated RNA networks in the brain. Science 302, 1212–1215 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Hallegger, M. et al. TDP-43 condensation properties specify its RNA-binding and regulatory repertoire. Cell 184, 4680–4696.e22 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sharma, D. et al. The kinetic landscape of an RNA-binding protein in cells. Nature 591, 152–156 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Keller, E. B. & Noon, W. A. Intron splicing: a conserved internal signal in introns of animal pre-mRNAs. Proc. Natl Acad. Sci. USA 81, 7417–7420 (1984).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Fairbrother, W. G., Yeh, R.-F., Sharp, P. A. & Burge, C. B. Predictive identification of exonic splicing enhancers in human genes. Science 297, 1007–1013 (2002).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Fairbrother, W. G. et al. RESCUE-ESE identifies candidate exonic splicing enhancers in vertebrate exons. Nucleic Acids Res. 32, W187–W190 (2004).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cartegni, L. et al. A web resource to identify exonic splicing enhancers. Nucleic Acids Res. 31, 3568–3571 (2003).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wang, Z. et al. Systematic identification and analysis of exonic splicing silencers. Cell 119, 831–845 (2004).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kupfer, D. M. et al. Introns and splicing elements of five diverse fungi. Eukaryot. Cell 3, 1088–1100 (2004).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Desmet, F.-O. et al. Human splicing finder: an online bioinformatics tool to predict splicing signals. Nucleic Acids Res. 37, e67 (2009).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sonnenburg, S., Schweikert, G., Philips, P., Behr, J. & Rätsch, G. Accurate splice site prediction using support vector machines. BMC Bioinformatics 8, (Suppl. 10), S7 (2007).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zhang, X. H.-F., Leslie, C. S. & Chasin, L. A. Computational searches for splicing signals. Methods 37, 292–305 (2005).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Yeo, G. & Burge, C. B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Salzberg, S. L. A method for identifying splice sites and translational start sites in eukaryotic mRNA. Comput. Appl. Biosci. 13, 365–376 (1997).CASÂ
PubMedÂ
Google ScholarÂ
Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Reese, M. G., Eeckman, F. H., Kulp, D. & Haussler, D. Improved splice site detection in Genie. J. Comput. Biol. 4, 311–323 (1997).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Barash, Y. et al. Deciphering the splicing code. Nature 465, 53–59 (2010). This paper reports the original splicing code model, describing an integrative model containing more than 1,000 input features and taking on a tissue-specific prediction task that is still challenging today.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Xiong, H. Y., Barash, Y. & Frey, B. J. Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context. Bioinformatics 27, 2554–2562 (2011).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Xiong, H. Y. et al. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806 (2015).ArticleÂ
PubMedÂ
Google ScholarÂ
Leung, M. K. K., Xiong, H. Y., Lee, L. J. & Frey, B. J. Deep learning of the tissue-regulated splicing code. Bioinformatics 30, i121–i129 (2014).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zhang, Z. et al. Deep-learning augmented RNA-seq analysis of transcript splicing. Nat. Methods 16, 307–310 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Xu, Y., Wang, Y., Luo, J., Zhao, W. & Zhou, X. Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision. Nucleic Acids Res. 45, 12100–12112 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kim, S., Kim, H., Fong, N., Erickson, B. & Bentley, D. L. Pre-mRNA splicing is a determinant of histone H3K36 methylation. Proc. Natl Acad. Sci. USA 108, 13564–13569 (2011).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Bhattacharya, S. et al. The methyltransferase SETD2 couples transcription and splicing by engaging mRNA processing factors through its SHI domain. Nat. Commun. 12, 1443 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kolasinska-Zwierz, P. et al. Differential chromatin marking of introns and expressed exons by H3K36me3. Nat. Genet. 41, 376–381 (2009).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hu, Q., Greene, C. S. & Heller, E. A. Specific histone modifications associate with alternative exon selection during mammalian development. Nucleic Acids Res. 48, 4709–4724 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548.e24 (2019). This paper describes SpliceAI, which uses dilated convolutional residual neural networks for splice site prediction, enabling efficient training of deeper networks with wider sequence context, improving prediction accuracy.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zeng, T. & Li, Y. I. Predicting RNA splicing from DNA sequence using Pangolin. Genome Biol. 23, 103 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cheng, J. et al. MMSplice: modular modeling improves the predictions of genetic variant effects on splicing. Genome Biol. 20, 48 (2019).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Rentzsch, P., Schubach, M., Shendure, J. & Kircher, M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 13, 31 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Smith, C. & Kitzman, J. O. Benchmarking splice variant prediction algorithms using massively parallel splicing assays. Genome Biol. 24, 294 (2023). This paper shows that independent benchmarking of splicing models using MPRA data provides valuable insights into areas for future model improvement.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cheng, J., Çelik, M. H., Kundaje, A. & Gagneur, J. MTSplice predicts effects of genetic variants on tissue-specific splicing. Genome Biol. 22, 94 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ling, J. P. et al. ASCOT identifies key regulators of neuronal subtype-specific splicing. Nat. Commun. 11, 137 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Xu, C. et al. Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences. Genome Res. 34, 1052–1056 (2024).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Linder, J., Srivastava, D., Yuan, H., Agarwal, V. & Kelley, D. R. Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation. Preprint at bioRxiv https://doi.org/10.1101/2023.08.30.555582 (2023).Celaj, A. et al. An RNA foundation model enables discovery of disease mechanisms and candidate therapeutics. Preprint at bioRxiv https://doi.org/10.1101/2023.09.20.558508 (2023).Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ghanbari, M. & Ohler, U. Deep neural networks for interpreting RNA-binding protein target preferences. Genome Res. 30, 214–226 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, e05005 (2015).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chen, K. et al. Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction. Brief. Bioinform. 25, bbae163 (2024).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Karollus, A. et al. Species-aware DNA language models capture regulatory elements and their evolution. Genome Biol. 25, 83 (2024).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
de Almeida, B. P. et al. SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models. Preprint at bioRxiv https://doi.org/10.1101/2024.03.14.584712 (2024).Dalla-Torre, H. et al. The nucleotide transformer: building and evaluating robust foundation models for human genomics. Preprint at bioRxiv https://doi.org/10.1101/2023.01.11.523679 (2023).Zoonomia Consortium. A comparative genomics multitool for scientific discovery and conservation. Nature 587, 240–245 (2020).ArticleÂ
CASÂ
Google ScholarÂ
Gupta, K. et al. Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing. Genome Biol. 25, 23 (2024). This article describes an interpretable-by-design model, in which prior knowledge of RBP motifs is refined by convolutional neural networks that adjust in vitro-derived motif representations to more accurately represent in vivo binding.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Liao, S. E., Sudarshan, M. & Regev, O. Deciphering RNA splicing logic with interpretable machine learning. Proc. Natl Acad. Sci. USA 120, e2221165120 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
McCue, K. & Burge, C. B. An interpretable model of pre-mRNA splicing for animal and plant genes. Sci. Adv. 10, eadn1547 (2024).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Bretschneider, H., Gandhi, S., Deshwar, A. G., Zuberi, K. & Frey, B. J. COSSMO: predicting competitive alternative splice site selection using deep learning. Bioinformatics 34, i429–i437 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
GTEx Consortium The GTEx consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).ArticleÂ
Google ScholarÂ
Tapial, J. et al. An atlas of alternative splicing profiles and functional associations reveals new regulatory programs and genes that simultaneously express multiple major isoforms. Genome Res. 27, 1759–1768 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sibley, C. R., Blazquez, L. & Ule, J. Lessons from non-canonical splicing. Nat. Rev. Genet. 17, 407–421 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Glinos, D. A. et al. Transcriptome variation in human tissues revealed by long-read sequencing. Nature 608, 353–359 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sharon, D., Tilgner, H., Grubert, F. & Snyder, M. A single-molecule long-read survey of the human transcriptome. Nat. Biotechnol. 31, 1009–1014 (2013).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40, 1780–1793 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lebrigand, K., Magnone, V., Barbry, P. & Waldmann, R. High throughput error corrected Nanopore single cell transcriptome sequencing. Nat. Commun. 11, 4025 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hardwick, S. A. et al. Single-nuclei isoform RNA sequencing unlocks barcoded exon connectivity in frozen brain tissue. Nat. Biotechnol. 40, 1082–1092 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shiau, C.-K. et al. High throughput single cell long-read sequencing analyses of same-cell genotypes and phenotypes in human tumors. Nat. Commun. 14, 4124 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Gilbert, W. V. & Nachtergaele, S. mRNA regulation by RNA modifications. Annu. Rev. Biochem. 92, 175–198 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Novakovsky, G., Dexter, N., Libbrecht, M. W., Wasserman, W. W. & Mostafavi, S. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat. Rev. Genet. 24, 125–137 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Kainth, A. S., Haddad, G. A., Hall, J. M. & Ruthenburg, A. J. Merging short and stranded long reads improves transcript assembly. PLoS Comput. Biol. 19, e1011576 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Joglekar, A. et al. Single-cell long-read mRNA isoform regulation is pervasive across mammalian brain regions, cell types, and development. Preprint at bioRxiv https://doi.org/10.1101/2023.04.02.535281 (2023).Baeza-Centurion, P. et al. Deep indel mutagenesis reveals the regulatory and modulatory architecture of alternative exon splicing. Preprint at bioRxiv https://doi.org/10.1101/2024.04.21.590414 (2024).Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Karollus, A., Mauermeier, T. & Gagneur, J. Current sequence-based models capture gene expression determinants in promoters but mostly ignore distal enhancers. Genome Biol. 24, 56 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ji, Y., Zhou, Z., Liu, H. & Davuluri, R. V. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome. Bioinformatics 37, 2112–2120 (2021). This paper describes the introduction of natural language processing concepts to DNA sequence modelling.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
da Silva, P. T. et al. Nucleotide dependency analysis of DNA language models reveals genomic functional elements. Preprint at bioRxiv https://doi.org/10.1101/2024.07.27.605418 (2024).Jha, A. et al. Enhanced integrated gradients: improving interpretability of deep learning models using splicing codes as a case study. Genome Biol. 21, 149 (2020). This study goes from deep learning to testing biological insight at the bench, a great example of what is possible with crosstalk between explainable AI and experimental biology.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ray, D. et al. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat. Biotechnol. 27, 667–670 (2009).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Sutandy, F. X. R. et al. In vitro iCLIP-based modeling uncovers how the splicing factor U2AF2 relies on regulation by cofactors. Genome Res. 28, 699–713 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Hafner, M. et al. CLIP and complementary methods. Nat. Rev. Methods Prim. 1, 20 (2021).ArticleÂ
CASÂ
Google ScholarÂ
Briese, M. et al. A systems view of spliceosomal assembly and branchpoints with iCLIP. Nat. Struct. Mol. Biol. 26, 930–940 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wolin, E. et al. SPIDR: a highly multiplexed method for mapping RNA-protein interactions uncovers a potential mechanism for selective translational suppression upon cellular stress. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2023.06.05.543769v1 (2023).Lorenz, D. A. et al. Multiplexed transcriptome discovery of RNA-binding protein binding sites by antibody-barcode eCLIP. Nat. Methods 20, 65–69 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
West, C. et al. nf-core/clipseq-a robust Nextflow pipeline for comprehensive CLIP data analysis. Wellcome Open Res. 8, 286 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Katsantoni, M., van Nimwegen, E. & Zavolan, M. Improved analysis of (e)CLIP data with RCRUNCH yields a compendium of RNA-binding protein binding sites and motifs. Genome Biol. 24, 77 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Boyle, E. A. et al. Skipper analysis of eCLIP datasets enables sensitive detection of constrained translation factor binding sites. Cell Genom. 3, 100317 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Capitanchik, C. et al. Flow: a web platform and open database to analyse, store, curate and share bioinformatics data at scale. Preprint at bioRxiv https://doi.org/10.1101/2023.08.22.544179 (2023).Horlacher, M. et al. Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning. Genome Biol. 24, 180 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zhu, H. et al. Dynamic characterization and interpretation for protein–RNA interactions across diverse cellular conditions using HDRNet. Nat. Commun. 14, 6824 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shen, X. & Li, X. Reformer: deep learning model for characterizing protein-RNA interactions from sequence at single-base resolution. Preprint at bioRxiv https://doi.org/10.1101/2024.01.14.575540 (2024).Quinn, T. P., Nguyen, D., Gupta, S. & Venkatesh, S. A neural model of RNA splicing: learning motif distances with self-attention and toeplitz max pooling. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2021.05.24.445518v1 (2021).Welzel, M., Di Liddo, A., Möckel, M. M. & Zarnack, K. FUBP1 is a general splicing factor facilitating 3′ splice site recognition and splicing of long introns. Mol. Cell 83, 2653–2672 (2023).ArticleÂ
PubMedÂ
Google ScholarÂ
Signal, B., Gloss, B. S., Dinger, M. E. & Mercer, T. R. Machine learning annotation of human branchpoints. Bioinformatics 34, 920–927 (2018).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ye, R. et al. Capture RIC-seq reveals positional rules of PTBP1-associated RNA loops in splicing regulation. Mol. Cell 83, 1311–1327.e7 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Liu, N. et al. N6-methyladenosine-dependent RNA structural switches regulate RNA-protein interactions. Nature 518, 560–564 (2015).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Barrass, J. D. et al. Transcriptome-wide RNA processing kinetics revealed using extremely short 4tU labeling. Genome Biol. 16, 282 (2015).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Spitale, R. C. & Incarnato, D. Probing the dynamic RNA structurome and its functions. Nat. Rev. Genet. 24, 178–196 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Rangan, R. et al. RNA structure landscape of S. cerevisiae introns. Preprint at bioRxiv https://doi.org/10.1101/2022.07.22.501175 (2024).Wang, J. et al. RNA structure profiling at single-cell resolution reveals new determinants of cell identity. Nat. Methods 21, 411–422 (2024).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wang, R., Helbig, I., Edmondson, A. C., Lin, L. & Xing, Y. Splicing defects in rare diseases: transcriptomics and machine learning strategies towards genetic diagnosis. Brief. Bioinform. 24, bbad284 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Liu, E. Y. et al. Loss of nuclear TDP-43 is associated with decondensation of LINE retrotransposons. Cell Rep. 27, 1409–1421.e6 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sparber, P. et al. Deciphering the impact of coding and non-coding SCN1A gene variants on RNA splicing. Brain 147, 1278–1293 (2023).ArticleÂ
Google ScholarÂ
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Walker, L. C. et al. Using the ACMG/AMP framework to capture evidence related to predicted and observed impact on splicing: recommendations from the ClinGen SVI Splicing Subgroup. Am. J. Hum. Genet. 110, 1046–1067 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Riepe et al. Benchmarking deep learning splice prediction tools using functional splice assays. Hum. Mutat. 42, 799–810 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wagner, N. et al. Aberrant splicing prediction across human tissues. Nat. Genet. 55, 861–870 (2023). In this study, tissue-specific splice site usage is quantified transcriptome-wide and used to build Absplice, a model that predicts the probability that a given variant causes aberrant splicing in a given tissue.ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Dawes, R., Joshi, H. & Cooper, S. T. Empirical prediction of variant-activated cryptic splice donors using population-based RNA-Seq data. Nat. Commun. 13, 1655 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Dawes, R. et al. SpliceVault predicts the precise nature of variant-associated mis-splicing. Nat. Genet. 55, 324–332 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Havens, M. A. & Hastings, M. L. Splice-switching antisense oligonucleotides as therapeutic drugs. Nucleic Acids Res. 44, 6549–6563 (2016).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Baughn, M. W. et al. Mechanism of STMN2 cryptic splice-polyadenylation and its correction for TDP-43 proteinopathies. Science 379, 1140–1149 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Aslesh, T. & Yokota, T. Restoring SMN expression: an overview of the therapeutic developments for the treatment of spinal muscular atrophy. Cells 11, 417 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Villemaire, J., Dion, I., Elela, S. A. & Chabot, B. Reprogramming alternative pre-messenger RNA splicing through the use of protein-binding antisense oligonucleotides. J. Biol. Chem. 278, 50031–50039 (2003).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Peacey, E., Rodriguez, L., Liu, Y. & Wolfe, M. S. Targeting a pre-mRNA structure with bipartite antisense molecules modulates tau alternative splicing. Nucleic Acids Res. 40, 9836–9849 (2012).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Denichenko, P. et al. Specific inhibition of splicing factor activity by decoy RNA oligonucleotides. Nat. Commun. 10, 1590 (2019).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Sergeeva, O. V., Shcherbinina, E. Y., Shomron, N. & Zatsepin, T. S. Modulation of RNA splicing by oligonucleotides: mechanisms of action and therapeutic implications. Nucleic Acid Ther. 32, 123–138 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Konermann, S. et al. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cell 173, 665–676.e14 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Li, J. D., Taipale, M. & Blencowe, B. J. Efficient, specific, and combinatorial control of endogenous exon splicing with dCasRx-RBM25. Mol. Cell 84, 2573–2589 (2024).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Recinos, Y. et al. CRISPR-dCas13d-based deep screening of proximal and distal splicing-regulatory elements. Nat. Commun. 15, 3839 (2024).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Goyenvalle, A., Babbs, A., van Ommen, G.-J. B., Garcia, L. & Davies, K. E. Enhanced exon-skipping induced by U7 snRNA carrying a splicing silencer sequence: promising tool for DMD therapy. Mol. Ther. 17, 1234–1240 (2009).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ling, J. P., Pletnikova, O., Troncoso, J. C. & Wong, P. C. TDP-43 repression of nonconserved cryptic exons is compromised in ALS-FTD. Science 349, 650–655 (2015). This work revealed that pathological aggregation of a splicing regulator in neurodegenerative disease results in new exons being expressed in mature mRNA, which has led to numerous potential new therapeutic approaches for these diseases.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Taskiran, I. I. et al. Cell-type-directed design of synthetic enhancers. Nature 626, 212–220 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Monteys, A. M. et al. Regulated control of gene therapies by drug-induced splicing. Nature 596, 291–295 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ling, J. P. et al. Cell-specific regulation of gene expression using splicing-dependent frameshifting. Nat. Commun. 13, 5773 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Stanley, R. F. & Abdel-Wahab, O. Dysregulation and therapeutic targeting of RNA splicing in cancer. Nat. Cancer 3, 536–546 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wilkins, O. G. et al. Creation of de novo cryptic splicing for ALS/FTD precision medicine. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2023.11.15.565967 (2023). This paper presents SpliceNouveau, which enables computational design of therapeutic transgenes that are regulated by alternative splicing events; they are expressed only upon disease-activated splicing, thus ensuring that gene therapies are activated only in diseased cells and ensuring that the correct dosage is delivered via autoregulation.Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). SSO Schweiz. Monatsschr. Zahnheilkd. 16, 199–231 (2001).
Google ScholarÂ
Sapoval, N. et al. Current progress and open challenges for applying deep learning across the biosciences. Nat. Commun. 13, 1728 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cheng, J., Çelik, M. H., Nguyen, T. Y. D., Avsec, Ž. & Gagneur, J. CAGI 5 splicing challenge: improved exon skipping and intron retention predictions with MMSplice. Hum. Mutat. 40, 1243–1251 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
He, S. et al. Ribonanza: deep learning of RNA structure through dual crowdsourcing. Preprint at bioRxiv https://doi.org/10.1101/2024.02.24.581671 (2024).Barbosa-Morais, N. L. et al. The evolutionary landscape of alternative splicing in vertebrate species. Science 338, 1587–1593 (2012).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Merkin, J., Russell, C., Chen, P. & Burge, C. B. Evolutionary dynamics of gene and isoform regulation in mammalian tissues. Science 338, 1593–1599 (2012). This paper and Barbosa-Morais et al. (ref. 162) calculate alternative splicing measurements across species, finding that alternative splicing is frequently lineage specific, with conservation dependent partly on the tissue in which the exon is most highly included.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Mazin, P. V., Khaitovich, P., Cardoso-Moreira, M. & Kaessmann, H. Alternative splicing during mammalian organ development. Nat. Genet. 53, 925–934 (2021). This paper finds that alternative splicing events that dynamically change during organ development are substantially more conserved than non-dynamic events.ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Agarwal, V. & Kelley, D. R. The genetic and biochemical determinants of mRNA degradation rates in mammals. Genome Biol. 23, 245 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Karollus, A., Avsec, Ž. & Gagneur, J. Predicting mean ribosome load for 5′UTR of any length using deep learning. PLoS Comput. Biol. 17, e1008982 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Braun, S. et al. Decoding a cancer-relevant splicing decision in the RON proto-oncogene using high-throughput mutagenesis. Nat. Commun. 9, 3315 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Julien, P., Miñana, B., Baeza-Centurion, P., Valcárcel, J. & Lehner, B. The complete local genotype-phenotype landscape for the alternative splicing of a human exon. Nat. Commun. 7, 11558 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Baeza-Centurion, P., Miñana, B., Schmiedel, J. M., Valcárcel, J. & Lehner, B. Combinatorial genetics reveals a scaling law for the effects of mutations on splicing. Cell 176, 549–563.e23 (2019).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ke, S. et al. Saturation mutagenesis reveals manifold determinants of exon definition. Genome Res. 28, 11–24 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Gergics, P. et al. High-throughput splicing assays identify missense and silent splice-disruptive POU1F1 variants underlying pituitary hormone deficiency. Am. J. Hum. Genet. 108, 1526–1539 (2021).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Smith, C. et al. High-throughput splicing assays identify known and novel WT1 exon 9 variants in nephrotic syndrome. Kidney Int. Rep. 8, 2117–2125 (2023).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cortés-López, M., Schulz, L. & Enculescu, M. High-throughput mutagenesis identifies mutations and RNA-binding proteins controlling CD19 splicing and CART-19 therapy resistance. Nat. Commun. 13, 5570 (2022).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Soemedi, R. et al. Pathogenic variants that alter protein code often disrupt splicing. Nat. Genet. 49, 848–855 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chiang, H.-L. et al. Mechanism and modeling of human disease-associated near-exon intronic variants that perturb RNA splicing. Nat. Struct. Mol. Biol. 29, 1043–1055 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Adamson, S. I., Zhan, L. & Graveley, B. R. Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency. Genome Biol. 19, 71 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cheung, R. et al. A multiplexed assay for exon recognition reveals that an unappreciated fraction of rare genetic variants cause large-effect splicing disruptions. Mol. Cell 73, 183–194.e8 (2019).ArticleÂ
CASÂ
Google ScholarÂ
Rosenberg, A. B., Patwardhan, R. P., Shendure, J. & Seelig, G. Learning the sequence determinants of alternative splicing from millions of random sequences. Cell 163, 698–711 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Ke, S. et al. Quantitative evaluation of all hexamers as exonic splicing elements. Genome Res. 21, 1360–1374 (2011).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Mikl, M., Hamburg, A., Pilpel, Y. & Segal, E. Dissecting splicing decisions and cell-to-cell variability with designed sequence libraries. Nat. Commun. 10, 4572 (2019).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Nguyen, E. et al. HyenaDNA: long-range genomic sequence modeling at single nucleotide resolution. Preprint at arXiv https://doi.org/10.48550/arXiv.2306.15794 (2023).Lucks, J. B. et al. Multiplexed RNA structure characterization with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Proc. Natl Acad. Sci. USA 108, 11063–11068 (2011).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lu, Z., Gong, J. & Zhang, Q. C. PARIS: psoralen analysis of RNA interactions and structures with high throughput and resolution. Methods Mol. Biol. 1649, 59–84 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Cai, Z. et al. RIC-seq for global in situ profiling of RNA-RNA spatial interactions. Nature 582, 432–437 (2020).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Turunen, J. J., Niemelä, E. H., Verma, B. & Frilander, M. J. The significant other: splicing by the minor spliceosome. Wiley Interdiscip. Rev. RNA 4, 61–76 (2013).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zarnack, K. et al. Direct competition between hnRNP C and U2AF65 protects the transcriptome from the exonization of Alu elements. Cell 152, 453–466 (2013).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Attig, J. et al. Heteromeric RNP assembly at LINEs controls lineage-specific RNA processing. Cell 174, 1067–1081.e17 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Ilık, Ä°. A. et al. Autonomous transposons tune their sequences to ensure somatic suppression. Nature 626, 1116–1124 (2024).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Attig, J. et al. Splicing repression allows the gradual emergence of new Alu-exons in primate evolution. eLife 5, e19545 (2016).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Darman, R. B. et al. Cancer-associated SF3B1 hotspot mutations induce cryptic 3’ splice site selection through use of a different branch point. Cell Rep. 13, 1033–1045 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kakaradov, B., Xiong, H. Y., Lee, L. J., Jojic, N. & Frey, B. J. Challenges in estimating percent inclusion of alternatively spliced junctions from RNA-seq data. BMC Bioinformatics 13, S11 (2012).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Venables, J. P. et al. Identification of alternative splicing markers for breast cancer. Cancer Res. 68, 9525–9531 (2008).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Pervouchine, D. D., Knowles, D. G. & Guigó, R. Intron-centric estimation of alternative splicing from RNA-seq data. Bioinformatics 29, 273–274 (2013).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Herzel, L. & Neugebauer, K. M. Quantification of co-transcriptional splicing from RNA-Seq data. Methods 85, 36–43 (2015).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Dent, C. I. et al. Quantifying splice-site usage: a simple yet powerful approach to analyze splicing. NAR Genom. Bioinform. 3, lqab041 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Jha, A., Gazzara, M. R. & Barash, Y. Integrative deep models for alternative splicing. Bioinformatics 33, i274–i282 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wachutka, L., Caizzi, L., Gagneur, J. & Cramer, P. Global donor and acceptor splicing site kinetics in human cells. eLife 8, e45056 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wachutka, L. & Gagneur, J. Measures of RNA metabolism rates: toward a definition at the level of single bonds. Transcription 8, 75–80 (2017).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Windhager, L. et al. Ultrashort and progressive 4sU-tagging reveals key characteristics of RNA processing at nucleotide resolution. Genome Res. 22, 2031–2042 (2012).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Schwalb, B. et al. TT-seq maps the human transient transcriptome. Science 352, 1225–1228 (2016).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yuan, J. et al. Genetic modulation of RNA splicing with a CRISPR-guided cytidine deaminase. Mol. Cell 72, 380–394.e7 (2018).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ