Deciphering the splicing code

By Yoseph Barash, John A. Calarco, Weijun Gao, Qun Pan, Xinchen Wang, O. Shai, Benjamin J. Blencowe, Brendan J. Frey. 2010

884 citations recorded by the source corpus.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Integrated genome and transcriptome sequencing identifies a noncoding mutation in the genome replication factor DONSON as the cause of microcephaly-micromelia syndrome (2017, background): “quence (Barash et al. 2010; Xiong et al. 2015), much of the genomic code mediating splicing remains unknown (Lewandowska 2013; Guigo and Valcarcel 2015; Lee and Rio 2015).”
Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs (2012, background): “primary transcript sequence (Wang et al. 2004; Barash et al. 2010), it is currently close to impossible to predict from the analysis of mammalian primary RNA sequence alone neither the entire exon– intron structure of transcripts nor their tissue specific expression”
Alternative pre-mRNA splicing regulation in cancer: pathways and programs unhinged. (2010, background): “One clue to a potential function of E9 inclusion comes from a recent study that identified a class of AS event in which increased inclusion in differentiated cells of a PTC-inducing exon acts as a means of posttranscriptional gene control, resulting in NMD-mediated down-regulation of the gene produc”
Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution. (2019, background): “Analysis of these cis-elements by machine learning approaches can infer context-dependent and combinatorial mechanisms to derive “splicing codes” that predict splice site selection in genomic sequence, and in some cases also the regulation of alternative splicing across cell types and tissues (Baeza”
The human splicing code reveals new insights into the genetic determinants of disease (2014, methodology): “Unlike existing methods (3, 11, 12), our computational model was derived using human data, incorporates over 300 new sequence features, and outputs realvalued absoluteC values for individual tissues, rather than categorical C values for tissue differences (13).”