Selenoproteins are proteins containing an uncommon amino acid selenocysteine 
(Sec). Sec is inserted by a specific translational machinery that recognizes a 
stem-loop structure, the SECIS element, at the 3' UTR of selenoprotein genes and 
recodes a UGA codon within the coding sequence. As UGA is normally a 
translational stop signal, selenoproteins are generally misannotated and 
designated tools have to be developed for this class of proteins. Here, we 
present two new computational methods for selenoprotein identification and 
analysis, which we provide publicly through the web servers at 
http://gladyshevlab.org/SelenoproteinPredictionServer or 
http://seblastian.crg.es. SECISearch3 replaces its predecessor SECISearch as a 
tool for prediction of eukaryotic SECIS elements. Seblastian is a new method for 
selenoprotein gene detection that uses SECISearch3 and then predicts 
selenoprotein sequences encoded upstream of SECIS elements. Seblastian is able 
to both identify known selenoproteins and predict new selenoproteins. By 
applying these tools to diverse eukaryotic genomes, we provide a ranked list of 
newly predicted selenoproteins together with their annotated cysteine-containing 
homologues. An analysis of a representative candidate belonging to the AhpC 
family shows how the use of Sec in this protein evolved in bacterial and 
eukaryotic lineages.