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bacteria:t3e:software [2022/10/27 14:26] – rkoebnik | bacteria:t3e:software [2023/09/11 17:32] (current) – [References] rkoebnik | ||
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^Name ^Purpose ^URL ^Reference | | ^Name ^Purpose ^URL ^Reference | | ||
- | |Effectidor |T3E prediction |[[https:// | + | |Effectidor |T3E prediction |[[https:// |
- | |DeepT3 2.0 |T3E prediction |[[http:// | + | |Effectidor |T3E prediction |[[https:// |
+ | |DeepT3 2.0 |T3E prediction |[[http:// | ||
|DeepT3_4 |T3E prediction |[[https:// | |DeepT3_4 |T3E prediction |[[https:// | ||
|T3SEpp |T3E prediction |[[http:// | |T3SEpp |T3E prediction |[[http:// | ||
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|TALE-NT |TAL effector target prediction |[[https:// | |TALE-NT |TAL effector target prediction |[[https:// | ||
|T3DB |T3E database |biocomputer.bio.cuhk.edu.hk/ | |T3DB |T3E database |biocomputer.bio.cuhk.edu.hk/ | ||
- | |EffectPred |T3E prediction |Source code available at: www.p.chiba-u.ac.jp/ | + | |EffectPred |T3E prediction |Source code available at: [[http:// |
|BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/ | |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/ | ||
|HMM (EPIYA motif) |T3E prediction | |Xu //et al.//, 2010 | | |HMM (EPIYA motif) |T3E prediction | |Xu //et al.//, 2010 | | ||
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|modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/ | |modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/ | ||
|EffectiveT3 |T3E prediction |[[http:// | |EffectiveT3 |T3E prediction |[[http:// | ||
- | |SIEVE |T3E prediction |www.sysbep.org/ | + | |SIEVE |T3E prediction |[[http://www.sysbep.org/ |
+ | |// | ||
===== References ===== | ===== References ===== | ||
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Li J, Wei L, Guo F, Zou Q (2020b). EP3: an ensemble predictor that accurately identifies type III secreted effectors. Brief. Bioinform., in press (bbaa008). DOI: [[https:// | Li J, Wei L, Guo F, Zou Q (2020b). EP3: an ensemble predictor that accurately identifies type III secreted effectors. Brief. Bioinform., in press (bbaa008). DOI: [[https:// | ||
+ | |||
+ | Lindeberg M, Stavrinides J, Chang JH, Alfano JR, Collmer A, Dangl JL, Greenberg JT, Mansfield JW, Guttman DS (2005). Proposed guidelines for a unified nomenclature and phylogenetic analysis of type III Hop effector proteins in the plant pathogen // | ||
Löwer M, Schneider G (2009). Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4: e5917. DOI: [[https:// | Löwer M, Schneider G (2009). Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4: e5917. DOI: [[https:// | ||
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Teper D, Burstein D, Salomon D, Gershovitz M, Pupko T, Sessa G (2016). Identification of novel // | Teper D, Burstein D, Salomon D, Gershovitz M, Pupko T, Sessa G (2016). Identification of novel // | ||
- | Wagner N, Avram O, Gold-Binshtok D, Zerah B, Teper D, Pupko T (2022). Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors. Bioinformatics 38: 2341-2343. DOI: [[https:// | + | Wagner N, Alburquerque M, Ecker N, Dotan E, Zerah B, Pena MM, Potnis N, Pupko T (2022a). Natural language processing approach to model the secretion signal of type III effectors. Front. Plant Sci. 13: 1024405. DOI: [[https:// |
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+ | Wagner N, Avram O, Gold-Binshtok D, Zerah B, Teper D, Pupko T (2022b). Effectidor: an automated machine-learning based web server for the prediction of type-III secretion system effectors. Bioinformatics 38: 2341-2343. DOI: [[https:// | ||
Wagner S, Diepold A (2020). A unified nomenclature for injectisome-type type III secretion systems. Curr. Top. Microbiol. Immunol. 427: 1-10. doi: [[https:// | Wagner S, Diepold A (2020). A unified nomenclature for injectisome-type type III secretion systems. Curr. Top. Microbiol. Immunol. 427: 1-10. doi: [[https:// |