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Application of motif-based tools on evolutionary analysis of multipartite single-stranded DNA viruses.


ABSTRACT: Multipartite viruses contain more than one distinctive genome component, and the origin of multipartite viruses has been suggested to evolve from a non-segmented wild-type virus. To explore whether recombination also plays a role in the evolution of the genomes of multipartite viruses, we developed a systematic approach that employs motif-finding tools to detect conserved motifs from divergent genomic regions and applies statistical approaches to select high-confidence motifs. The information that this approach provides helps us understand the evolution of viruses. In this study, we compared our motif-based strategy with current alignment-based recombination-detecting methods and applied our methods to the analysis of multipartite single-stranded plant DNA viruses, including bipartite begomoviruses, Banana bunchy top virus (BBTV) (consisting of 6 genome components) and Faba bean necrotic yellows virus (FBNYV) (consisting of 8 genome components). Our analysis revealed that recombination occurred between genome components in some begomoviruses, BBTV and FBNYV. Our data also show that several unusual recombination events have contributed to the evolution of BBTV genome components. We believe that similar approaches can be applied to resolve the evolutionary history of other viruses.

SUBMITTER: Wang HI 

PROVIDER: S-EPMC3735576 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Application of motif-based tools on evolutionary analysis of multipartite single-stranded DNA viruses.

Wang Hsiang-Iu HI   Chang Chih-Hung CH   Lin Po-Heng PH   Fu Hui-Chuan HC   Tang Chuanyi C   Yeh Hsin-Hung HH  

PloS one 20130806 8


Multipartite viruses contain more than one distinctive genome component, and the origin of multipartite viruses has been suggested to evolve from a non-segmented wild-type virus. To explore whether recombination also plays a role in the evolution of the genomes of multipartite viruses, we developed a systematic approach that employs motif-finding tools to detect conserved motifs from divergent genomic regions and applies statistical approaches to select high-confidence motifs. The information th  ...[more]

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