Exon First Nucleotide Mutations in Splicing: Evaluation of In Silico Prediction Tools

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Publikace nespadá pod Fakultu sportovních studií, ale pod Středoevropský technologický institut. Oficiální stránka publikace je na webu muni.cz.
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GRODECKÁ Lucie LOCKEROVÁ Pavla RAVČUKOVÁ Barbora BURATTI Emanuele BARALLE Francisco E. DUŠEK Ladislav FREIBERGER Tomáš

Rok publikování 2014
Druh Článek v odborném periodiku
Časopis / Zdroj PLoS One
Fakulta / Pracoviště MU

Středoevropský technologický institut

Citace
www http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0089570
Doi http://dx.doi.org/10.1371/journal.pone.0089570
Obor Neurologie, neurochirurgie, neurovědy
Klíčová slova POLYPYRIMIDINE TRACT RECOGNITION; HUMAN-DISEASE GENES; CONFORMATIONAL SELECTION; COMPUTATIONAL TOOLS; SEQUENCE MOTIFS; FACTOR U2AF(35); MSH2 MISSENSE; SITE; ENHANCERS; U2AF(65)
Přiložené soubory
Popis Mutations in the first nucleotide of exons (E+1) mostly affect pre-mRNA splicing when found in AG-dependent 39 splice sites, whereas AG-independent splice sites are more resistant. The AG-dependency, however, may be difficult to assess just from primary sequence data as it depends on the quality of the polypyrimidine tract. For this reason, in silico prediction tools are commonly used to score 39 splice sites. In this study, we have assessed the ability of sequence features and in silico prediction tools to discriminate between the splicing-affecting and non-affecting E+1 variants. For this purpose, we newly tested 16 substitutions in vitro and derived other variants from literature. Surprisingly, we found that in the presence of the substituting nucleotide, the quality of the polypyrimidine tract alone was not conclusive about its splicing fate. Rather, it was the identity of the substituting nucleotide that markedly influenced it. Among the computational tools tested, the best performance was achieved using the Maximum Entropy Model and Position-Specific Scoring Matrix. As a result of this study, we have now established preliminary discriminative cut-off values showing sensitivity up to 95% and specificity up to 90%. This is expected to improve our ability to detect splicing-affecting variants in a clinical genetic setting.
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