Splice Site Prediction Using Artificial Neural Networks

Johansen, Öystein; Ryen, Tom; Eftesol, Trygve; Kjosmoen, Thomas; Ruoff, Peter

doi:10.1007/978-3-642-02504-4_9

“…In (Rebello et al, 2011) ANN was applied for predicting genes on a Streptococcus genome, and they were also used on the prediction of splice sites of a gene in (Johansen et al, 2009). Decision trees were performed for finding genes in vertebrate DNA sequences , and a variation (called Randomized oblique decision trees) combined with other learning techniques were applied for gene modeling in (Allen et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

Hidden Markov models for gene sequence classification

Mesa¹,

Basterrech²,

Guerberoff³

et al. 2015

Pattern Anal Applic

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The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.

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“…In biological sequence analysis Artificial Neural Network (ANN) have been also used. In (Rebello et al, 2011) ANN was applied for predicting genes on a Streptococcus genome, and they were also used on the prediction of splice sites of a gene in (Johansen et al, 2009). Decision trees were performed for finding genes in vertebrate DNA sequences , and a variation (called Randomized oblique decision trees) combined with other learning techniques were applied for gene modeling in (Allen et al, 2004).…”

Section: Related Workmentioning

confidence: 99%

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

Mesa,

Basterrech,

Guerberoff

et al. 2015

Preprint

0

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The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.

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“…There are many methods for splice site prediction, such as hidden Markov model [1], combinatorial methods [2], support vector machine [3], genetic algorithm [4], grammar based algorithms [5] artificial neural network [6][7][8][9][10][11] and neural network hybrid methods [12][13][14][15][16][17]. Neural networks have been widely used in splice site detection methods because of their ability to learn and solve many real time problems.…”

Section: Introductionmentioning

confidence: 99%

Splice Site Detection in DNA Sequences using Probabilistic Neural Network

Nassa¹,

Singh²,

Goel³

2013

IJCA

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With numerous of genomes sequenced, gene prediction has become a challenging problem in bioinformatics. Gene prediction helps in identifying physical and mental features of different organisms. A large number of gene prediction tools have been developed in the past two decades. Splice site detection method lies at the heart of ab-initio gene prediction tools and plays an important role in detecting the exon boundaries. In this paper, a method for detecting splice sites by using generalized regression neural network is proposed. The proposed method uses conditional probabilities to preprocess the input which enables it to incorporate the already known sequence features from biological knowledge. The experimental results show that the application of this new architecture to splice site detection has greatly improved the training time and reduces the false positive predictions.

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Splice Site Prediction Using Artificial Neural Networks

Cited by 7 publications

References 3 publications

Hidden Markov models for gene sequence classification

Hidden Markov models for gene sequence classification

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

Splice Site Detection in DNA Sequences using Probabilistic Neural Network

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