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dc.contributor.authorSperschneider, J.
dc.contributor.authorWilliams, A.
dc.contributor.authorHane, James
dc.contributor.authorSingh, K.
dc.contributor.authorTaylor, J.
dc.date.accessioned2017-01-30T12:11:06Z
dc.date.available2017-01-30T12:11:06Z
dc.date.created2016-02-03T19:30:16Z
dc.date.issued2015
dc.identifier.citationSperschneider, J. and Williams, A. and Hane, J. and Singh, K. and Taylor, J. 2015. Evaluation of secretion prediction highlights differing approaches needed for oomycete and fungal effectors. Frontiers in Plant Science. 6 (Article 1168): pp. 1-14.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/18969
dc.identifier.doi10.3389/fpls.2015.01168
dc.description.abstract

© 2015 Sperschneider, Williams, Hane, Singh and Taylor. The steadily increasing number of sequenced fungal and oomycete genomes has enabled detailed studies of how these eukaryotic microbes infect plants and cause devastating losses in food crops. During infection, fungal and oomycete pathogens secrete effector molecules which manipulate host plant cell processes to the pathogen's advantage. Proteinaceous effectors are synthesized intracellularly and must be externalized to interact with host cells. Computational prediction of secreted proteins from genomic sequences is an important technique to narrow down the candidate effector repertoire for subsequent experimental validation. In this study, we benchmark secretion prediction tools on experimentally validated fungal and oomycete effectors. We observe that for a set of fungal SwissProt protein sequences, SignalP 4 and the neural network predictors of SignalP 3 (D-score) and SignalP 2 perform best. For effector prediction in particular, the use of a sensitive method can be desirable to obtain the most complete candidate effector set. We show that the neural network predictors of SignalP 2 and 3, as well as TargetP were the most sensitive tools for fungal effector secretion prediction, whereas the hidden Markov model predictors of SignalP 2 and 3 were the most sensitive tools for oomycete effectors. Thus, previous versions of SignalP retain value for oomycete effector prediction, as the current version, SignalP 4, was unable to reliably predict the signal peptide of the oomycete Crinkler effectors in the test set. Our assessment of subcellular localization predictors shows that cytoplasmic effectors are often predicted as not extracellular. This limits the reliability of secretion predictions that depend on these tools. We present our assessment with a view to informing future pathogenomics studies and suggest revised pipelines for secretion prediction to obtain optimal effector predictions in fungi and oomycetes.

dc.publisherFRONTIERS MEDIA SA
dc.titleEvaluation of secretion prediction highlights differing approaches needed for oomycete and fungal effectors
dc.typeJournal Article
dcterms.source.volume6
dcterms.source.numberDEC
dcterms.source.issn1664-462X
dcterms.source.titleFrontiers in Plant Science
curtin.note

This open access article is distributed under the Creative Commons license http://creativecommons.org/licenses/by/4.0/

curtin.departmentCentre for Crop Disease Management
curtin.accessStatusOpen access


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