Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses

Rapid reconstruction of porcine reproductive and respiratory syndrome virus using synthetic DNA fragments

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most challenging infectious disease of pig populations causing devastating economic loss to swine industry. Reverse genetics allow to engineer modified viruses such attenuated strains for vaccine development. Some reverse genetic systems were described for PRRSVs but, due to genome complexity of PRRSVs, construction and modification of such systems remain laborious and time-consuming. In this study, we described a reverse genetics approach based on the "Infectious-Subgenomic Amplicons" (ISA) method to rescue infectious PRRSV particles. Permissive cells were transfected with 4 overlapping synthetic DNA fragments covering the entire genome of PRRSV which allowed the rapid reconstruction of the complete virus genome and the subsequent generation of infectious wild-type particles within days. The ISA method represent a rapid alternative of conventional reverse genetic systems. This method will help to generate genetically modified and attenuated strains for the development of sanitary countermeasures in the future.

A novel method for splice sites prediction using sequence component and hidden Markov model

With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.M1 classifier. Our proposed DNA encoding method is based on multi-scale component (MSC) and first order Markov model (MM1). It has been applied to the HS3D dataset with repeated 10 fold cross validation. The experimental results indicate that the new method has increased the classification accuracy and outperformed some current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MS C+Pos(+APR)-SVM.

Classification of splice-junction sequences via weighted position specific scoring approach

The prediction of the complete structure of genes is one of the very important tasks of bioinformatics, especially in eukaryotes. A crucial part in the gene structure prediction is to determine the splice sites in the coding region. Identification of splice sites depends on the precise recognition of the boundaries between exons and introns of a given DNA sequence. This problem can be formulated as a classification of sequence elements into 'exon-intron' (EI), 'intron-exon' (IE) or 'None' (N) boundary classes. In this study we propose a new Weighted Position Specific Scoring Method (WPSSM) to recognize splice sites which uses a position-specific scoring matrix constructed by nucleotide base frequencies. A genetic algorithm is used in order to tune the weight and threshold parameters of the positions on. This method consists of two phases: learning phase and identification phase. The proposed WPSS method poses efficient results compared with the performance of many methods proposed in the literature. Computational experiments are performed on the DNA sequence datasets from 'UCI Repository of machine learning databases'.