RESUMEN
Computational approaches have been developed to prioritize candidate genes in disease gene identification. They are based on different pieces of evidences associating each gene with the given disease. In this study, 648 genes underlying genodermatoses have been compared to 1808 genes involved in other genetic diseases using a bioinformatic approach. These genes were studied at the structural, evolutionary and functional levels. Results show that genes underlying genodermatoses present longer CDS and have more exons. Significant differences were observed in nucleotide motif and amino-acid compositions. Evolutionary conservation analysis revealed that genodermatoses genes have less paralogs, more orthologs in Mouse and Dog and are less conserved. Functional analysis revealed that genodermatosis genes seem to be involved in immune system and skin layers. The Bayesian network model returned a rate of good classification of around 80%. This computational approach could help investigators working in the field of dermatology by prioritizing positional candidate genes for mutation screening.
Asunto(s)
Enfermedades Cutáneas Genéticas/genética , Animales , Teorema de Bayes , Bovinos , Perros , Evolución Molecular , Genoma Humano , Genómica , Humanos , Ratones , Motivos de Nucleótidos , Estructura Secundaria de Proteína , Proteínas/genética , RatasRESUMEN
Intronless genes (IGs) fraction varies between 2.7 and 97.7% in eukaryotic genomes. Although many databases on exons and introns exist, there was no curated database for such genes which allowed their study in a concerted manner. Such a database would be useful to identify the functional features and the distribution of these genes across the genome. Here, a new database of IGs in eukaryotes based on GenBank data was described. This database, called IGD (Intronless Gene Database), is a collection of gene sequences that were annotated and curated. The current version of IGD contains 687 human intronless genes with their protein and CDS sequences. Some features of the entries are given in this paper. Data was extracted from GenBank release 183 using a Perl script. Data extraction was followed by a manual curation step. Intronless genes were then analyzed based on their RefSeq annotation and Gene Ontology functional class. IGD represents a useful resource for retrieval and in silico study of intronless genes. IGD is available at http://www.bioinfo-cbs.org/igd with comprehensive help and FAQ pages that illustrate the main uses of this resource.