Classification of genomic components and prediction of genes of Begomovirus based on subsequence natural vector and support vector machine

Abstract

Background Begomoviruses are widely distributed and causing devastating diseases in many crops. According to the number of genomic components, a begomovirus is known as either monopartite or bipartite begomovirus. Both the monopartite and bipartite begomoviruses have the DNA-A component which encodes all essential proteins for virus functions, while the bipartite begomoviruses still contain the DNA-B component. The satellite molecules, known as betasatellites, alphasatellites or deltasatellites, sometimes exist in the begomoviruses. So, the genomic components of begomoviruses are complex and varied. Different genomic components have different gene structures and functions. Classifying the components of begomoviruses is important for studying the virus origin and pathogenic mechanism. Methods We propose a model combining Subsequence Natural Vector (SNV) method with Support Vector Machine (SVM) algorithm, to classify the genomic components of begomoviruses and predict the genes of begomoviruses. First, the genome sequence is represented as a vector numerically by the SNV method. Then SVM is applied on the datasets to build the classification model. At last, recursive feature elimination (RFE) is used to select essential features of the subsequence natural vectors based on the importance of features. Results In the investigation, DNA-A, DNA-B, and different satellite DNAs are selected to build the model. To evaluate our model, the homology-based method BLAST and two machine learning algorithms Random Forest and Naive Bayes method are used to compare with our model. According to the results, our classification model can classify DNA-A, DNA-B, and different satellites with high accuracy. Especially, we can distinguish whether a DNA-A component is from a monopartite or a bipartite begomovirus. Then, based on the results of classification, we can also predict the genes of different genomic components. According to the selected features, we find that the content of four nucleotides in the second and tenth segments (approximately 150-350 bp and 1,450–1,650 bp) are the most different between DNA-A components of monopartite and bipartite begomoviruses, which may be related to the pre-coat protein (AV2) and the transcriptional activator protein (AC2) genes. Our results advance the understanding of the unique structures of the genomic components of begomoviruses.

Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging Images in Analysis of Therapeutic Effects on Cerebral Ischemic Stroke

“…Taken together, it suggested that the MCRSVMC algorithm was highly efficient and stable. e activated brain areas in the healthy control group during the verb association task mainly included auxiliary motor area, right Broca area, right anterior cingulate gyrus, and left fusiform gyrus, indicating that the verb association task mainly activated the frontal parietal cortex, and the bilateral occipital area was activated by a small area [21][22][23]. e Scientific Programming activated brain areas for picture naming mainly included the right visual cortex, left fusiform gyrus, and left Broca, indicating that the picture naming mainly activated bilateral occipital lobes in the visual cortex, which was consistent with the results of Tan et al [24].…”

An efficient numerical representation of genome sequence: natural vector with covariance component

“…The biological similarity between two sequences can be measured using the Euclidean distance of their corresponding 18-dimensional natural vectors, which is commonly used in our previous studies ( Deng et al, 2011 ; Zhao, Tian & Yau, 2018 ; Zhao et al, 2019 ; Sun et al, 2021 ; He et al, 2020 ; Pei et al, 2020 ).…”

“…This natural vector consists of the count, average position and central moment of each nucleotide. It has been successfully applied to many research fields, especially the clustering ( Zhao, Tian & Yau, 2018 ; Zhao et al, 2019 ; Sun et al, 2021 ) and classification ( He et al, 2020 ; Pei et al, 2020 ) of biological sequences. The 12-dimensional natural vector only considers the respective distribution of single nucleotide but ignores the relationship between the two nucleotides.…”