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

PeerJ

article

Shaojun Pei¹  Rui Dong¹  Yiming Bao²  Rong Lucy He⁴  Stephen S.-T. Yau¹ 

[1] Department of Mathematical Sciences, Tsinghua University;National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation;University of Chinese Academy of Sciences;Department of Biological Sciences, Chicago State University

关键词: Begomovirus;    Classification;    Support vector machine;    Recursive feature elimination;    Subsequence natural vector;   

DOI  :  10.7717/peerj.9625

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundBegomoviruses 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.MethodsWe 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.ResultsIn 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.

【 授权许可】

CC BY