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ARS Home » Pacific West Area » Corvallis, Oregon » Horticultural Crops Research » Research » Publications at this Location » Publication #352648

Research Project: Integrated Disease Management of Exotic and Emerging Plant Diseases of Horticultural Crops

Location: Horticultural Crops Research

Title: effectR: An expandable R package to predict candidate effectors

Author
item Tabima, Javier - Oregon State University
item Grunwald, Niklaus - Nik

Submitted to: Molecular Plant-Microbe Interactions
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/19/2019
Publication Date: 3/21/2019
Citation: Tabima, J.F., Grunwald, N.J. 2019. effectR: An expandable R package to predict candidate effectors. Molecular Plant-Microbe Interactions. https://doi.org/10.1101/398404.
DOI: https://doi.org/10.1101/398404

Interpretive Summary: Effectors are by one definition small, secreted proteins that facilitate infection of host plants by all major groups of plant pathogens. Effector protein identification in oomycetes relies on finding specific amino acid motifs in genome data. To date, identification of effectors relies on custom scripts. Here, we developed the R package effectR that provides a convenient tool for rapid prediction of effectors in oomycete genomes, or with custom scripts for any genome, in a reproducible way. The effectR package has been validated with published oomycete genomes. This package provides a convenient tool for reproducible identification of candidate effectors in oomycete genomes.

Technical Abstract: Effectors are by one definition small, secreted proteins that facilitate infection of host plants by all major groups of plant pathogens. Effector protein identification in oomycetes relies on identification of open reading frames with certain amino acid motifs among additional minor criteria. To date, identification of effectors relies on custom scripts to identify motifs in candidate open reading frames. Here, we developed the R package effectR that provides a convenient tool for rapid prediction of effectors in oomycete genomes, or with custom scripts for any genome, in a reproducible way. The effectR package relies on a combination of regular expressions statements and hidden Markov model approaches to predict candidate RxLR and CRN effectors. Other custom motifs for novel effectors can easily be implemented and added to package updates. The effectR package has been validated with published oomycete genomes. This package provides a convenient tool for reproducible identification of candidate effectors in oomycete genomes.