• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.167-172
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• 2005

This paper introduces a method which improves the performance of the identification of splice sites in the genomic DNA sequence of eukaryotes. This method combines a low order Markov model in series with a neural network for the predictions of splice sites. The lower order Markov model incorporates the biological knowledge surrounding the splice sites as probabilistic parameters. The Neural network takes the Markov encoded parameters as the inputs and produces the prediction. Two types of neural networks are used for the comparison. This method reduces the computational complexity and shows encouraging accuracy in the predictions of splice sites when applied to several standard splice site dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권11호
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• pp.2784-2799
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• 2012

Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, "unmeaningful" DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research.

• 한국자원식물학회지
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• 제23권6호
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• pp.526-534
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• 2010

Genomes of clusters of related eukaryotes are now being sequenced at an increasing rate. In this paper, we developed an accurate, low-cost method for annotation of gene prediction and exon-intron structure. The gene prediction was adapted for delta 1-pyrroline-5-carboxylate-synthetase (p5cs) gene from China wild-type of the halophytic Leymus chinensis (Trin.), naturally adapted to highly-alkali soils. Due to complex adaptive mechanisms in halophytes, more attentions are being paid on the regulatory elements of stress adaptation in halophytes. P5CS encodes delta 1-pyrroline-5-carboxylate-synthetase, a key regulatory enzyme involved in the biosynthesis of proline, that has direct correlation with proline accumulation in vivo and positive relationship with stress tolerance. Using analysis of reverse transcription-polymerase chain reaction (RT-PCR) and PCR, and direct sequencing, 1076 base pairs (bp) of cDNA in length and 2396 bp of genomic DNA in length were obtained from direct sequencing results. Through gene prediction and exon-intron structure verification, the full-length of cDNA sequence was divided into eight parts, with seven parts of intron insertion. The average lengths of determinated coding regions and non-coding regions were 154.17 bp and 188.57 bp, respectively. Nearly all splice sites displayed GT as the donor sites at the 5' end of intron region, and 71.43% displayed AG as the acceptor sites at the 3' end of intron region. We conclude that this method is a cost-effective way for obtaining an experimentally verified genome annotation.