Navegando por Autor "Carvalho, Sávio Gonçalves"

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Genome-wide identification of novel microRNAs and their target genes in the human parasite Schistosoma mansoni.
(2011) Gomes, Matheus de Souza; Muniyappa, Mohan Kumar; Carvalho, Sávio Gonçalves; Cota, Renata Guerra de Sá; Spillane, Charles
Mature microRNAs (miRNAs) are small, non-coding regulatory RNAs which can elicit post-transcriptional repression of mRNA levels of target genes. Here, we report the identification of 67 mature and 42 precursor miRNAs in the Schistosoma mansoni parasite. The evolutionarily conserved S. mansoni miRNAs consisted of 26 precursor miRNAs and 35 mature miRNAs, while we identified 16 precursor miRNAs and 32 mature miRNAs that displayed no conservation. These S. mansoni miRNAs are located on seven autosomal chromosomes and a sex (W) chromosome. miRNA expansion through gene duplication was suggested for at least two miRNA families miR-71 and mir-2. miRNA target finding analysis identified 389 predicted mRNA targets for the identified miRNAs and suggests that the sma-mir-71 may be involved in female sexual maturation. Given the important roles of miRNAs in animals, the identification and characterization of miRNAs in S. mansoni will facilitate novel approaches towards prevention and treatment of Schistosomiasis.
The impact of sequence length and number of sequences on promoter prediction performance.
(2015) Carvalho, Sávio Gonçalves; Cota, Renata Guerra de Sá; Merschmann, Luiz Henrique de Campos
Background: The advent of rapid evolution on sequencing capacity of new genomes has evidenced the need for data analysis automation aiming at speeding up the genomic annotation process and reducing its cost. Given that one important step for functional genomic annotation is the promoter identification, several studies have been taken in order to propose computational approaches to predict promoters. Different classifiers and characteristics of the promoter sequences have been used to deal with this prediction problem. However, several works in literature have addressed the promoter prediction problem using datasets containing sequences of 250 nucleotides or more. As the sequence length defines the amount of dataset attributes, even considering a limited number of properties to characterize the sequences, datasets with a high number of attributes are generated for training classifiers. Once high dimensional datasets can degrade the classifiers predictive performance or even require an infeasible processing time, predicting promoters by training classifiers from datasets with a reduced number of attributes, it is essential to obtain good predictive performance with low computational cost. To the best of our knowledge, there is no work in literature that verified in a systematic way the relation between the sequences length and the predictive performance of classifiers. Thus, in this work, we have evaluated the impact of sequence length variation and training dataset size (number of sequences) on the predictive performance of classifiers. Results: We have built sixteen datasets composed of different sized sequences (ranging in length from 12 to 301 nucleotides) and evaluated them using the SVM, Random Forest and k NN classifiers. The best predictive performances reached by SVM and Random Forest remained relatively stable for datasets composed of sequences varying in length from 301 to 41 nucleotides, while k-NN achieved its best performance for the dataset composed of 101 nucleotides. We have also analyzed, using sequences composed of only 41 nucleotides, the impact of increasing the number of sequences in a dataset on the predictive performance of the same three classifiers. Datasets containing 14,000, 80,000, 100,000 and 120,000 sequences were built and evaluated. All classifiers achieved better predictive performance for datasets containing 80,000 sequences or more. Conclusion: The experimental results show that several datasets composed of shorter sequences achieved better predictive performance when compared with datasets composed of longer sequences, and also consumed a significantly shorter processing time. Furthermore, increasing the number of sequences in a dataset proved to be beneficial to the predictive power of classifiers.