References(59)
[1]
A. R. Kornblihtt, I. E. Schor, M. Alló, G. Dujardin, E. Petrillo, and M. J. Muñoz, Alternative splicing: A pivotal step between eukaryotic transcription and translation, Nat. Rev. Mol. Cell Biol., vol. 14, no. 3, pp. 153-165, 2013.
[2]
S. Chaudhary, W. Khokhar, I. Jabre, A. S. N. Reddy, L. J. Byrne, C. M. Wilson, and N. H. Syed, Alternative splicing and protein diversity: Plants versus animals, Front. Plant Sci., vol. 10, p. 708, 2019.
[3]
F. E. Baralle and J. Giudice, Alternative splicing as a regulator of development and tissue identity, Nat. Rev. Mol. Cell Biol., vol. 18, no. 7, pp. 437-451, 2017.
[4]
S. A. Bhuiyan, S. Ly, M. Phan, B. Huntington, E. Hogan, C. C. Liu, J. Liu, and P. Pavlidis, Systematic evaluation of isoform function in literature reports of alternative splicing, BMC Genomics, vol. 19, no. 1, p. 37, 2018.
[5]
G. Biamonti, A. Amato, E. Belloni, A. Di Matteo, L. Infantino, D. Pradella, and C. Ghigna, Alternative splicing in Alzheimer’s disease, Aging Clin. Exp. Res., vol. 33, no. 4, pp. 747-758, 2019.
[6]
E. El Marabti and I. Younis, The cancer spliceome: Reprograming of alternative splicing in cancer, Front. Mol. Biosci., vol. 5, p. 80, 2018.
[7]
A. C. H. Wong, J. E. J. Rasko, and J. J. L. Wong, We skip to work: Alternative splicing in normal and malignant myelopoiesis, Leukemia, vol. 32, no. 5, pp. 1081-1093, 2018.
[8]
A. Paschalis, A. Sharp, J. C. Welti, A. Neeb, G. V. Raj, J. Luo, S. R. Plymate, and J. S. De Bono, Alternative splicing in prostate cancer, Nat. Rev. Clin. Oncol., vol. 15, no. 11, pp. 663-675, 2018.
[9]
E. Fraile-Bethencourt, A. Valenzuela-Palomo, B. Díez-Gómez, E. Goina, A. Acedo, E. Buratti, and E. A. Velasco, Mis-splicing in breast cancer: Identification of pathogenic BRCA2 variants by systematic minigene assays, J. Pathol., vol. 248, no. 4, pp. 409-420, 2019.
[10]
P. A. F. Galante, N. J. Sakabe, N. Kirschbaum-Slager, and S. J. De Souza, Detection and evaluation of intron retention events in the human transcriptome, RNA, vol. 10, no. 5, pp. 757-765, 2004.
[11]
N. J. Sakabe and S. J. De Souza, Sequence features responsible for intron retention in human, BMC Genomics, vol. 8, no. 1, p. 59, 2007.
[12]
R. Louro, A. S. Smirnova, and S. Verjovski-Almeida, Long intronic noncoding RNA transcription: Expression noise or expression choice? Genomics, vol. 93, no. 4, pp. 291-298, 2009.
[13]
C. Cenik, A. Derti, J. C. Mellor, G. F. Berriz, and F. P. Roth, Genome-wide functional analysis of human 5’ untranslated region introns, Genome Biol., vol. 11, no. 3, p. R29, 2010.
[14]
C. I. Castillo-Davis, S. L. Mekhedov, D. L. Hartl, E. V. Koonin, and F. A. Kondrashov, Selection for short introns in highly expressed genes, Nat. Genet., vol. 31, no. 4, pp. 415-418, 2002.
[15]
Q. Zhang, H. Li, H. Jin, H. B. Tan, J. Zhang, and S. T. Sheng, The global landscape of intron retentions in lung adenocarcinoma, BMC Med. Genomics, vol. 7, no. 1, p. 15, 2014.
[16]
D. Wang, J. Zavadil, L. Martin, F. Parisi, E. Friedman, D. Levy, H. Harding, D. Ron, and L. B. Gardner, Inhibition of nonsense-mediated RNA decay by the tumor microenvironment promotes tumorigenesis, Mol. Cell. Biol., vol. 31, no. 17, pp. 3670-3680, 2011.
[17]
C. T. Ong and S. Adusumalli, Increased intron retention is linked to Alzheimer’s disease, Neural Regen. Res., vol. 15, no. 2, pp. 259-260, 2020.
[18]
H. Jung, D. Lee, J. Lee, D. Park, Y. J. Kim, W. Y. Park, D. W. Hong, P. J. Park, and E. Lee, Intron retention is a widespread mechanism of tumor-suppressor inactivation, Nat. Genet., vol. 47, no. 11, pp. 1242-1248, 2015.
[19]
H. Dvinge and R. K. Bradley, Widespread intron retention diversifies most cancer transcriptomes, Genome Med., vol. 7, no. 1, p. 45, 2015.
[20]
S. R. Zhao, Alternative splicing, RNA-Seq and drug discovery, Drug Discov. Today, vol. 24, no. 6, pp. 1258-1267, 2019.
[21]
J. Feng, K. Chen, X. Dong, X. L. Xu, Y. X. Jin, X. Y. Zhang, W. B. Chen, Y. J. Han, L. Shao, Y. Gao, et al., Genome-wide identification of cancer-specific alternative splicing in circRNA, Mol. Cancer, vol. 18, no. 1, p. 35, 2019.
[22]
V. Van Giau, E. Bagyinszky, Y. S. Yang, Y. C. Youn, S. S. A. An, and S. Y. Kim, Genetic analyses of early-onset Alzheimer’s disease using next generation sequencing, Sci. Rep., vol. 9, no. 1, p. 8368, 2019.
[23]
Y. Bai, S. F. Ji, and Y. D. Wang, IRcall and IRclassifier: Two methods for flexible detection of intron retention events from RNA-Seq data, BMC Genomics, vol. 16, no. 2, p. S9, 2015.
[24]
H. Pimentel, J. G. Conboy, and L. Pachter, Keep me around: Intron retention detection and analysis, arXiv preprint arXiv: 1510.00696, 2015.
[25]
A. Roberts and L. Pachter, Streaming fragment assignment for real-time analysis of sequencing experiments, Nat. Methods, vol. 10, no. 1, pp. 71-73, 2013.
[26]
R. Middleton, D. D. Gao, A. Thomas, B. Singh, A. Au, J. J. L. Wong, A. Bomane, B. Cosson, E. Eyras, and J. E. J. Rasko, et al., IRFinder: Assessing the impact of intron retention on mammalian gene expression, Genome Biol., vol. 18, no. 1, p. 51, 2017.
[27]
H. D. Li, C. C. Funk, and N. D. Price, iREAD: A tool for intron retention detection from RNA-Seq data, BMC Genomics, vol. 21, no. 1, p. 128, 2020.
[28]
Y. Katz, E. T. Wang, E. M. Airoldi, and C. B. Burge, Analysis and design of RNA sequencing experiments for identifying isoform regulation, Nat. Methods, vol. 7, no. 12, pp. 1009-1015, 2010.
[29]
S. H. Shen, J. W. Park, J. Huang, K. A. Dittmar, Z. X. Lu, Q. Zhou, R. P. Carstens, and Y. Xing, MATS: A Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data, Nucleic Acids Res., vol. 40, no. 8, p. e61, 2012.
[30]
S. H. Shen, J. W. Park, Z. X. Lu, L. Lin, M. D. Henry, Y. N. Wu, Q. Zhou, and Y. Xing, rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data, Proc. Natl. Acad. Sci. USA, vol. 111, no. 51, pp. E5593-E5601, 2014.
[31]
S. Anders, A. Reyes, and W. Huber, Detecting differential usage of exons from RNA-Seq data, Nat. Prec., .
[32]
Y. F. Li, X. Y. Rao, W. W. Mattox, C. I. Amos, and B. Liu, RNA-Seq analysis of differential splice junction usage and intron retentions by DEXSeq, PLoS One, vol. 10, no. 9, p. e0136653, 2015.
[33]
W. W. Wu, J. Zong, N. Wei, J. Cheng, X. X. Zhou, Y. M. Cheng, D. Chen, Q. H. Guo, B. Zhang, and Y. Feng, CASH: A constructing comprehensive splice site method for detecting alternative splicing events, Brief. Bioinform., vol. 19, no. 5, pp. 905-917, 2018.
[34]
L. Broseus and W. Ritchie, Challenges in detecting and quantifying intron retention from next generation sequencing data, Comput. Struct. Biotechnol. J., vol. 18, pp. 501-508, 2020.
[35]
H. Li, B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Homer, G. Marth, G. Abecasis, R. Durbin, and 1000 Genome Project Data Processing Subgroup, The sequence alignment/map format and SAMtools, Bioinformatics, vol. 25, no. 16, pp. 2078-2079, 2009.
[36]
G. R. Grant, M. H. Farkas, A. D. Pizarro, N. F. Lahens, J. Schug, B. P. Brunk, C. J. Stoeckert, J. B. Hogenesch, and E. A. Pierce, Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM), Bioinformatics, vol. 27, no. 18, pp. 2518-2528, 2011.
[37]
Y. I. Li, D. A. Knowles, J. Humphrey, A. N. Barbeira, S. P. Dickinson, H. K. Im, and J. K. Pritchard, Annotation-free quantification of RNA splicing using LeafCutter, Nat. Genetics, vol. 50, pp. 151-158, 2018.
[38]
H. D. Li, GTFtools: A Python package for analyzing various modes of gene models, bioRxiv, .
[39]
A. R. Quinlan and I. M. Hall, BEDTools: A flexible suite of utilities for comparing genomic features, Bioinformatics, vol. 26, no. 6, pp. 841-842, 2010.
[40]
M. D. Robinson, D. J. McCarthy, and G. K. Smyth, edgeR: A bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, vol. 26, no. 1, pp. 139-140, 2010.
[41]
G. C. Yu, L. G. Wang, Y. Y. Han, and Q. Y. He, clusterProfiler: An R package for comparing biological themes among gene clusters, OMICS: A J. Integr. Biol., vol. 16, no. 5, pp. 284-287, 2012.
[42]
M. R. Duggan, S. Joshi, Y. F. Tan, M. Slifker, E. A. Ross, M. Wimmer, and V. Parikh, Transcriptomic changes in the prefrontal cortex of rats as a function of age and cognitive engagement, Neurobiol. Learn. Mem., vol. 163, p. 107035, 2019.
[43]
A. De Lillo, G. A. Pathak, F. De Angelis, M. Di Girolamo, M. Luigetti, M. Sabatelli, F. Perfetto, S. Frusconi, D. Manfellotto, M. Fuciarelli, et al., Epigenetic profiling of Italian patients identified methylation sites associated with hereditary transthyretin amyloidosis, medRxiv, .
[44]
A. Hamosh, A. F. Scott, J. S. Amberger, C. A. Bocchini, and V. A. McKusick, Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders, Nucleic Acids Res., vol. 33, no. S1, pp. D514-D517, 2005.
[45]
C. H. Wu, R. Apweiler, A. Bairoch, D. A. Natale, W. C. Barker, B. Boeckmann, S. Ferro, E. Gasteiger, H. Z. Huang, R. Lopez, et al., The Universal Protein resource (UniProt): An expanding universe of protein information, Nucleic Acids Res., vol. 34, no. suppl_1, pp. D187-D191, 2006.
[46]
Z. X. Bai, G. C. Han, B. Xie, J. J. Wang, F. H. Song, X. Peng, and H. X. Lei, AlzBase: An integrative database for gene dysregulation in Alzheimer’s disease, Mol. Neurobiol., vol. 53, no. 1, pp. 310-319, 2016.
[47]
J. Piñero, À. Bravo, N. Queralt-Rosinach, A. Gutiérrez-Sacristán, J. Deu-Pons, E. Centeno, J. García-García, F. Sanz, and L. I. Furlong, DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants, Nucleic Acids Res., vol. 45, no. D1, pp. D833-D839, 2017.
[48]
D. P. Vanichkina, U. Schmitz, J. J. L. Wong, and J. E. J. Rasko, Challenges in defining the role of intron retention in normal biology and disease, Semin. Cell Dev. Biol., vol. 75, pp. 40-49, 2018.
[49]
A. C. Smart, C. A. Margolis, H. Pimentel, M. X. He, D. A. Miao, D. Adeegbe, T. Fugmann, K. K. Wong, and E. M. Van Allen, Intron retention as a novel source of cancer neoantigen, bioRxiv, .
[50]
D. X. Zhang, Q. Hu, X. Z. Liu, Y. B. Ji, H. P. Chao, Y. Liu, A. Tracz, J. Kirk, S. Buonamici, and P. Zhu, et al., Intron retention is a hallmark and spliceosome represents a therapeutic vulnerability in aggressive prostate cancer, Nat. Commun., vol. 11, no. 1, p. 2089, 2020.
[51]
D. Kim, M. Shivakumar, S. Han, M. S. Sinclair, Y. J. Lee, Y. L. Zheng, O. I. Olopade, D. Kim, and Y. Lee, Population-dependent intron retention and DNA methylation in breast cancer, Mol. Cancer Res., vol. 16, no. 3, pp. 461-469, 2018.
[52]
H. D. Li, R. Menon, G. S. Omenn, and Y. F. Guan, The emerging era of genomic data integration for analyzing splice isoform function, Trends Genet., vol. 30, no. 8, pp. 340-347, 2014.
[53]
H. D. Li, C. H. Yang, Z. M. Zhang, M. Y. Yang, F. X. Wu, G. S. Omenn, and J. X. Wang, IsoResolve: Predicting splice isoform functions by integrating gene and isoform-level features with domain adaptation, Bioinformatics, vol. 37, no. 4, pp. 522-530, 2021.
[54]
R. Eksi, H. D. Li, R. Menon, Y. C. Wen, G. S. Omenn, M. Kretzler, and Y. F. Guan, Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-Seq data, PLOS Comput. Biol., vol. 9, no. 11, p. e1003314, 2017.
[55]
Z. Y. Fang, C. X. Lin, Y. P. Xu, H. D. Li, and Q. S. Xu, REBET: A method to determine the number of cell clusters based on batch effect removal, Brief. Bioinform., .
[56]
J. T. Zheng, C. X. Lin, Z. Y. Fang, and H. D. Li, Intron retention as a mode for RNA-Seq data analysis, Front. Genet., vol. 11, p. 586, 2020.
[57]
A. Y. Zhang, S. A. Su, A. P. Ng, A. Z. Holik, M. L. Asselin-Labat, M. E. Ritchie, and C. W. Law, A data-driven approach to characterising intron signal in RNA-Seq data, bioRxiv, .
[58]
H. D. Li, C. C. Funk, K. McFarland, E. B. Dammer, M. Allen, M. M. Carrasquillo, Y. Levites, P. Chakrabarty, J. D. Burgess, and X. Wang, et al., Integrative functional genomic analysis of intron retention in human and mouse brain with Alzheimer’s disease, Alzheimer’s Dementia, vol. 17, no. 6, pp. 984-1004, 2021.
[59]
D. An, H. X. Cao, C. S. Li, K. Humbeck, and W. Q. Wang, Isoform sequencing and state-of-art applications for unravelling complexity of plant transcriptomes, Genes, vol. 9, no. 1, p. 43, 2018.