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Abstract(s)
Íntrons são sequências intervenientes de nucleotídeos que estão presentes no genoma. São caracterizadas por dividir os genes em éxons, que são as regiões codificantes. Os íntrons não são codificados no processo de tradução do DNA, uma vez que são removidos num processo anterior, denominado splicing. Apesar de não serem sequências codificantes, apresentam funções importantes nos organismos e mutações nessas sequências podem gerar danos, como doenças. Nesse sentido, a bioinformática mostra-se como uma ferramenta promissora na caracterização de íntrons: sítios de splicing, junções de éxons, sequências intrônicas em DNA genômico, entre outros. O objetivo deste estudo foi apresentar algumas ferramentas de bioinformática, disponíveis na web, para a caracterização de íntrons. Com recurso à literatura científica, quatro ferramentas foram escolhidas para a discussão: DNA functional site miner, NNSPLICE, GENSCAN e Human Splicing Finder. Essas ferramentas utilizam diferentes algoritmos para a predição sítios de splicing, junções íntrons-éxons, predição múltiplos genes em sequências parciais e completas e também para a predição de mutações em íntrons e variantes de genes. Concluímos que as ferramentas se complementam entre si e que depende da situação em que se deseja utilizar. Além disso, as predições in silico são relevantes e promissoras, mas baseadas em estatísticas, o que não descarta a necessidade de validação in vivo.
Introns are intervening sequences of nucleotides that are present in the genome. They are characterized by dividing genes into exons, which is the coding region. Introns are not encoded in the translation process since they are removed in a previous process, called splicing. Although they are not coding sequences, they have important functions in organisms and mutations in the sequences can generate damages and lead to diseases. Thus, bioinformatics is a promising tool for the characterization of introns, being able to determine splicing sites, junctions of exons, intronic sequences in genomic DNA, among others. This study aims to present some bioinformatics web-based tools available for the characterization of introns. Resorting to the available scientific literature, we choose four bioinformatics tools for discussion: DNA functional site miner, NNSPLICE, GENSCAN, and Human Splicing Finder. These tools are developed from different algorithms for splicing sites prediction, introns-exons junctions, multiple genes prediction in partial or complete DNA sequences and also for prediction of mutation in introns e genes variants. Therefore, we conclude that these bioinformatic tools are complementary and the selection of one is dependent on the desired application. Besides, in silico predictions are relevant and promising, but statistic-based, which does not discard the need for in vivo validation.
Introns are intervening sequences of nucleotides that are present in the genome. They are characterized by dividing genes into exons, which is the coding region. Introns are not encoded in the translation process since they are removed in a previous process, called splicing. Although they are not coding sequences, they have important functions in organisms and mutations in the sequences can generate damages and lead to diseases. Thus, bioinformatics is a promising tool for the characterization of introns, being able to determine splicing sites, junctions of exons, intronic sequences in genomic DNA, among others. This study aims to present some bioinformatics web-based tools available for the characterization of introns. Resorting to the available scientific literature, we choose four bioinformatics tools for discussion: DNA functional site miner, NNSPLICE, GENSCAN, and Human Splicing Finder. These tools are developed from different algorithms for splicing sites prediction, introns-exons junctions, multiple genes prediction in partial or complete DNA sequences and also for prediction of mutation in introns e genes variants. Therefore, we conclude that these bioinformatic tools are complementary and the selection of one is dependent on the desired application. Besides, in silico predictions are relevant and promising, but statistic-based, which does not discard the need for in vivo validation.
Description
Keywords
Biologia molecular Biologia computacional Genômica estrutural
Citation
Lourenço, Darling Andrade; Choupina, Altino (2019). Bioinformática aplicada à caracterização de íntrons. Adolescência: Revista Júnior de Investigação. ISSN 2182 - 6277, p. 62-70