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Deciphering the splicing code. Nature, 465, 53-59

Biomedical Engineering, Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto M5S 3G4, Canada.
Nature (Impact Factor: 42.35). 05/2010; 465(7294):53-9. DOI: 10.1038/nature09000
Source: PubMed

ABSTRACT Alternative splicing has a crucial role in the generation of biological complexity, and its misregulation is often involved in human disease. Here we describe the assembly of a 'splicing code', which uses combinations of hundreds of RNA features to predict tissue-dependent changes in alternative splicing for thousands of exons. The code determines new classes of splicing patterns, identifies distinct regulatory programs in different tissues, and identifies mutation-verified regulatory sequences. Widespread regulatory strategies are revealed, including the use of unexpectedly large combinations of features, the establishment of low exon inclusion levels that are overcome by features in specific tissues, the appearance of features deeper into introns than previously appreciated, and the modulation of splice variant levels by transcript structure characteristics. The code detected a class of exons whose inclusion silences expression in adult tissues by activating nonsense-mediated messenger RNA decay, but whose exclusion promotes expression during embryogenesis. The code facilitates the discovery and detailed characterization of regulated alternative splicing events on a genome-wide scale.

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    • " of discriminatory contributions from binding sites for known splicing regulators was the result of not including sufficient numbers of potential binding site se - quences in our RNA feature compendium , we added addi - tional 5mers , 6mers , and 7mers of known binding sites for splicing regulators as well as codon frequencies ( Yeo et al . 2007 ; Barash et al . 2010 ) to increase the RNA feature compen - dium to 826 features . However , training our data sets with the extended RNA fea - ture compendium did not improve the performance of our splicing code ( Sup - plemental Fig . 2 ) , nor did we observe a change in the identity of the RNA features that displayed the highest information gain ( Supple"
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    • "Although primarily characterized as a repressive splicing regulator, it can also activate some splice sites and this has been related to differential positions of binding relative to regulated exons (Xue et al, 2009; Llorian et al, 2010). Although PTB can act alone as a regulator (Amir-Ahmady et al, 2005), genome-wide analyses suggest that it cooperates with a number of other proteins as a component of 'tissue spicing codes' (Castle et al, 2008; Wang et al, 2008; Barash et al, 2010; Bland et al, 2010; Llorian et al, 2010). Structure-function analysis has indicated that despite their similar RNA-binding preferences , the four RRMs of PTB show functional diversification (Liu et al, 2002; Robinson & Smith, 2006; Mickleburgh et al, 2014). "
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