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Alternative splicing

About: Alternative splicing is a research topic. Over the lifetime, 20764 publications have been published within this topic receiving 1113366 citations. The topic is also known as: alternative nuclear mRNA splicing, via spliceosome & GO:0000380.


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Journal ArticleDOI
16 Jun 2011-Nature
TL;DR: The results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder.
Abstract: Autism spectrum disorder (ASD) is a common, highly heritable neurodevelopmental condition characterized by marked genetic heterogeneity. Thus, a fundamental question is whether autism represents an aetiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain. Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1 (also known as FOX1), and a module enriched for immune genes and glial markers. Using high-throughput RNA sequencing we demonstrate dysregulated splicing of A2BP1-dependent alternative exons in the ASD brain. Moreover, using a published autism genome-wide association study (GWAS) data set, we show that the neuronal module is enriched for genetically associated variants, providing independent support for the causal involvement of these genes in autism. In contrast, the immune-glial module showed no enrichment for autism GWAS signals, indicating a non-genetic aetiology for this process. Collectively, our results provide strong evidence for convergent molecular abnormalities in ASD, and implicate transcriptional and splicing dysregulation as underlying mechanisms of neuronal dysfunction in this disorder.

1,671 citations

Journal ArticleDOI
Yasushi Okazaki, Masaaki Furuno, Takeya Kasukawa1, Jun Adachi, Hidemasa Bono, S. Kondo, Itoshi Nikaido2, Naoki Osato, Rintaro Saito3, Harukazu Suzuki, Itaru Yamanaka, H. Kiyosawa2, Ken Yagi, Yasuhiro Tomaru4, Yuki Hasegawa2, A. Nogami2, Christian Schönbach, Takashi Gojobori, Richard M. Baldarelli, David P. Hill, Carol J. Bult, David A. Hume5, John Quackenbush6, Lynn M. Schriml7, Alexander Kanapin, Hideo Matsuda8, Serge Batalov9, Kirk W. Beisel10, Judith A. Blake, Dirck W. Bradt, Vladimir Brusic, Cyrus Chothia11, Lori E. Corbani, S. Cousins, Emiliano Dalla, Tommaso A. Dragani, Colin F. Fletcher12, Colin F. Fletcher9, Alistair R. R. Forrest5, K. S. Frazer13, Terry Gaasterland14, Manuela Gariboldi, Carmela Gissi15, Adam Godzik16, Julian Gough11, Sean M. Grimmond5, Stefano Gustincich17, Nobutaka Hirokawa18, Ian J. Jackson19, Erich D. Jarvis20, Akio Kanai3, Hideya Kawaji8, Hideya Kawaji1, Yuka Imamura Kawasawa21, Rafal M. Kedzierski21, Benjamin L. King, Akihiko Konagaya, Igor V. Kurochkin, Yong-Hwan Lee6, Boris Lenhard22, Paul A. Lyons23, Donna Maglott7, Lois J. Maltais, Luigi Marchionni, Louise M. McKenzie, Harukata Miki18, Takeshi Nagashima, Koji Numata3, Toshihisa Okido, William J. Pavan7, Geo Pertea6, Graziano Pesole15, Nikolai Petrovsky24, Ramesh S. Pillai, Joan Pontius7, D. Qi, Sridhar Ramachandran, Timothy Ravasi5, Jonathan C. Reed16, Deborah J Reed, Jeffrey G. Reid, Brian Z. Ring, M. Ringwald, Albin Sandelin22, Claudio Schneider, Colin A. Semple19, Mitsutoshi Setou18, K. Shimada25, Razvan Sultana6, Yoichi Takenaka8, Martin S. Taylor19, Rohan D. Teasdale5, Masaru Tomita3, Roberto Verardo, Lukas Wagner7, Claes Wahlestedt22, Y. Wang6, Yoshiki Watanabe25, Christine A. Wells5, Laurens G. Wilming26, Anthony Wynshaw-Boris27, Masashi Yanagisawa21, Ivana V. Yang6, L. Yang, Zheng Yuan5, Mihaela Zavolan14, Yunhui Zhu, Anne M. Zimmer28, Piero Carninci, N. Hayatsu, Tomoko Hirozane-Kishikawa, Hideaki Konno, M. Nakamura, Naoko Sakazume, K. Sato4, Toshiyuki Shiraki, Kazunori Waki, Jun Kawai, Katsunori Aizawa, Takahiro Arakawa, S. Fukuda, A. Hara, W. Hashizume, K. Imotani, Y. Ishii, Masayoshi Itoh, Ikuko Kagawa, A. Miyazaki, K. Sakai, D. Sasaki, K. Shibata, Akira Shinagawa, Ayako Yasunishi, Masayasu Yoshino, Robert H. Waterston29, Eric S. Lander30, Jane Rogers26, Ewan Birney, Yoshihide Hayashizaki 
05 Dec 2002-Nature
TL;DR: The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.
Abstract: Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences These are clustered into 33,409 'transcriptional units', contributing 901% of a newly established mouse transcriptome database Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome 41% of all transcriptional units showed evidence of alternative splicing In protein-coding transcripts, 79% of splice variations altered the protein product Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics

1,663 citations

Posted ContentDOI
09 Aug 2019-bioRxiv
TL;DR: It is shown that U1 AMO also modulates cancer cells’ phenotype, dose-dependently increasing migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect.
Abstract: Stimulated cells and cancer cells have widespread shortening of mRNA 3’-utranslated regions (3’UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in the last exon and in introns. U1 snRNA (U1), vertebrates’ most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread mRNA shortening. Here we show that U1 AMO also modulates cancer cells’ phenotype, dose-dependently increasing migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3’UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected link between U1 regulation and oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.

1,660 citations

Journal ArticleDOI
TL;DR: ESEfinder (http://exon.cshl.edu/ESE/) is a web-based resource that facilitates rapid analysis of exon sequences to identify putative ESEs responsive to the human SR proteins SF2/ASF, SC35, SRp40 and SRp55, and to predict whether exonic mutations disrupt such elements.
Abstract: Point mutations frequently cause genetic diseases by disrupting the correct pattern of pre-mRNA splicing. The effect of a point mutation within a coding sequence is traditionally attributed to the deduced change in the corresponding amino acid. However, some point mutations can have much more severe effects on the structure of the encoded protein, for example when they inactivate an exonic splicing enhancer (ESE), thereby resulting in exon skipping. ESEs also appear to be especially important in exons that normally undergo alternative splicing. Different classes of ESE consensus motifs have been described, but they are not always easily identified. ESEfinder (http://exon.cshl.edu/ESE/) is a web-based resource that facilitates rapid analysis of exon sequences to identify putative ESEs responsive to the human SR proteins SF2/ASF, SC35, SRp40 and SRp55, and to predict whether exonic mutations disrupt such elements.

1,546 citations

Journal ArticleDOI
TL;DR: A new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data, which uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates.
Abstract: Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.

1,546 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023983
20221,178
2021873
2020866
2019823
2018769