作者
Jana Sperschneider, Donald M Gardiner, Peter N Dodds, Francesco Tini, Lorenzo Covarelli, Karam B Singh, John M Manners, Jennifer M Taylor
发表日期
2016/4
期刊
New Phytologist
卷号
210
期号
2
页码范围
743-761
简介
- Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant–pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N‐terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine‐rich, which suffers from poor accuracy.
- We present EffectorP which pioneers the application of machine learning to fungal effector prediction.
- EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence‐derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are …
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J Sperschneider, DM Gardiner, PN Dodds, F Tini… - New Phytologist, 2016