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bacteria:t3e:software [2022/07/25 11:53] rkoebnik [Software, Databases and Websites] |
bacteria:t3e:software [2022/10/27 15:26] (current) rkoebnik |
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^Name ^Purpose ^URL ^Reference | | ^Name ^Purpose ^URL ^Reference | | ||
|Effectidor |T3E prediction |[[https:// | |Effectidor |T3E prediction |[[https:// | ||
- | |T3SEpp |T3E prediction |www.szu-bioinf.org/ | + | |DeepT3 2.0 |T3E prediction |[[http:// |
+ | |DeepT3_4 |T3E prediction |[[https:// | ||
+ | |T3SEpp |T3E prediction | ||
|ACNNT3 |T3E prediction |Source code available at: [[https:// | |ACNNT3 |T3E prediction |Source code available at: [[https:// | ||
- | |EP3 |T3E prediction |lab.malab.cn/ | + | |EP3 |T3E prediction |[[http://lab.malab.cn/ |
- | |PrediTALE |TAL effector target prediction |galaxy.informatik.uni-halle.de |Erkes //et al.//, 2019 | | + | |PrediTALE |TAL effector target prediction |[[http://galaxy.informatik.uni-halle.de|galaxy.informatik.uni-halle.de]] |
- | |Phylogenetic profiling |T3E prediction |www.iib.unsam.edu.ar/ | + | |Phylogenetic profiling |T3E prediction |
|WEDeepT3 |T3E prediction |[[https:// | |WEDeepT3 |T3E prediction |[[https:// | ||
|DeepT3 |T3E prediction |[[https:// | |DeepT3 |T3E prediction |[[https:// | ||
Line 17: | Line 19: | ||
|GenSET |T3E prediction | |Hobbs //et al.//, 2016 | | |GenSET |T3E prediction | |Hobbs //et al.//, 2016 | | ||
|pEffect |T3E prediction |[[https:// | |pEffect |T3E prediction |[[https:// | ||
- | |QueTAL |Suite for the functional and phylogenetic comparison of TAL effectors |bioinfo-web.mpl.ird.fr/ | + | |QueTAL |Suite for the functional and phylogenetic comparison of TAL effectors |
|HMM-LDA |T3E prediction | |Yang & Qi, 2014 | | |HMM-LDA |T3E prediction | |Yang & Qi, 2014 | | ||
- | |Talvez |TAL effector target prediction |bioinfo.mpl.ird.fr/ | + | |Talvez |TAL effector target prediction |[[http://bioinfo-web.mpl.ird.fr/ |
|TALgetter |TAL effector target prediction |[[http:// | |TALgetter |TAL effector target prediction |[[http:// | ||
|T3SPs |T3E prediction |cic.scu.edu.cn/ | |T3SPs |T3E prediction |cic.scu.edu.cn/ | ||
|cSIEVE |T3E prediction | |Hovis //et al.//, 2013 | | |cSIEVE |T3E prediction | |Hovis //et al.//, 2013 | | ||
|T3_MM |T3E prediction |biocomputer.bio.cuhk.edu.hk/ | |T3_MM |T3E prediction |biocomputer.bio.cuhk.edu.hk/ | ||
- | |BEAN |T3E prediction |systbio.cau.edu.cn/ | + | |BEAN |T3E prediction |[[http://systbio.cau.edu.cn/ |
- | |RalstoT3Edb |T3E prediction & database |iant.toulouse.inra.fr/ | + | |RalstoT3Edb |T3E prediction & database |
|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/ | ||
Line 31: | Line 33: | ||
|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 | | ||
- | |T3SEdb |T3E prediction & database |effectors.bic.nus.edu.sg/ | + | |T3SEdb |T3E prediction & database |effectors.bic.nus.edu.sg/ |
|Classifier |T3E prediction |Discriminant functions available upon request |Kampenusa & Zikmanis, 2010 | | |Classifier |T3E prediction |Discriminant functions available upon request |Kampenusa & Zikmanis, 2010 | | ||
|Classifier |T3E prediction |Method and data available upon request |Yang //et al.//, 2010 | | |Classifier |T3E prediction |Method and data available upon request |Yang //et al.//, 2010 | | ||
|modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/ | |modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/ | ||
- | |EffectiveT3 |T3E prediction |www.effectors.org |Arnold //et al.//, 2009 | | + | |EffectiveT3 |T3E prediction |[[http://www.effectors.org|www.effectors.org]] |
|SIEVE |T3E prediction |www.sysbep.org/ | |SIEVE |T3E prediction |www.sysbep.org/ | ||
- | |||
===== References ===== | ===== References ===== | ||
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Hui X, Chen Z, Lin M, Zhang J, Hu Y, Zeng Y, Cheng X, Ou-Yang L, Sun MA, White AP, Wang Y (2020). T3SEpp: an integrated prediction pipeline for bacterial type III secreted effectors. mSystems 5: e00288-20. DOI: [[https:// | Hui X, Chen Z, Lin M, Zhang J, Hu Y, Zeng Y, Cheng X, Ou-Yang L, Sun MA, White AP, Wang Y (2020). T3SEpp: an integrated prediction pipeline for bacterial type III secreted effectors. mSystems 5: e00288-20. DOI: [[https:// | ||
+ | |||
+ | Jing R, Wen T, Liao C, Xue L, Liu F, Yu L, Luo J (2021). DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning framework. NAR Genom. Bioinform. 3: lqab086. DOI[[https:// | ||
Kampenusa I, Zikmanis P (2010). Distinguishable codon usage and amino acid composition patterns among substrates of leaderless secretory pathways from proteobacteria. Appl. Microbiol. Biotechnol. 86: 285-293. DOI: [[https:// | Kampenusa I, Zikmanis P (2010). Distinguishable codon usage and amino acid composition patterns among substrates of leaderless secretory pathways from proteobacteria. Appl. Microbiol. Biotechnol. 86: 285-293. 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, in press. DOI: [[https:// | + | 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 |
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:// | ||
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Yang Y, Zhao J, Morgan RL, Ma W, Jiang T (2010). Computational prediction of type III secreted proteins from gram-negative bacteria. BMC Bioinformatics 11: S47. DOI: [[https:// | Yang Y, Zhao J, Morgan RL, Ma W, Jiang T (2010). Computational prediction of type III secreted proteins from gram-negative bacteria. BMC Bioinformatics 11: S47. DOI: [[https:// | ||
+ | |||
+ | Yu L, Liu F, Li Y, Luo J, Jing R (2021). DeepT3_4: a hybrid deep neural network model for the distinction between bacterial type III and IV secreted effectors. Front. Microbiol. 12: 605782. DOI: [[https:// | ||
Zalguizuri A, Caetano-Anollés G, Lepek VC (2019). Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems. Brief. Bioinform. 20: 1395-1402. DOI: [[https:// | Zalguizuri A, Caetano-Anollés G, Lepek VC (2019). Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems. Brief. Bioinform. 20: 1395-1402. DOI: [[https:// |