REVEALING RNA EDITING SITES BY USING PROBABILISTIC GRAPHICAL MODELS

MARÍA VIRGINIA SANCHEZ PUERTA; ALEJANDRO EDERA

Buenos Aires

Congreso; XXXV Internacional Meeting of the Willi Hennig Society; 2016

Willi Hennig Society - MACNBR, CONICET

p { margin-bottom: 0.1in; direction: ltr; color: rgb(0, 0, 0); line-height: 120%; }p.western { font-family: "Liberation Serif","Times New Roman",serif; font-size: 12pt; }p.cjk { font-family: "Droid Sans Fallback"; font-size: 12pt; }p.ctl { font-family: "FreeSans"; font-size: 12pt; }a.western:link { }a.ctl:link { }Post-transcriptional eventsexplain the large and complex phenotypic diversity arisen from alimited number of genes. RNA editing is a post-transcriptionalprocess by which transcripts are modified respect to the informationencoded in the DNA. In flowering plants, RNA editing involves the substitution of cytidines touridines at very specific positions of mitochondrial and plastidtranscripts. RNA editing tends to increase amino acid conservationacross species. Comparisons among different plant lineages revealthat the pattern of RNA editing is variable (sometimes highly homoplasious) and may lead tounexpected relationships when editing sites are included inphylogenetic analyses. Computational methods can be used to predictediting sites, but, because amino acid conservation is used as aprior knowledge, they are strongly biased to predict exclusively nonsynonymous editing sites in protein-coding genes. Previous studieshave shown that synonymous editing sites arefrequent and that editing also occurs in non-coding regions. We havedeveloped a method for predicting all type of editing sites based onprobabilistic graphical models. From a dataset exclusively composedof windows of nucleotides surrounding edited and non-edited cytosinesfrom several species, statistical interactions between the targetsite and the other positions are exploited to automatically learn agraphical model. This model is then used to predict both synonymousand nonsynonymous editing sites at any region in a genome. Based onthe predicted RNA editing sites in plant sequences, the evolution ofediting sites can be inferred by standard techniques of ancestralcharacter reconstruction to analyze editing tendencies over time.