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bacteria:t3e:software [2020/09/28 13:32] rkoebnik [Table] |
bacteria:t3e:software [2022/10/27 08:18] rkoebnik [References] |
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====== Software, Databases and Websites ====== | ====== Software, Databases and Websites ====== | ||
- | \\ | + | Based on discusisons during the International Type III Secretion Meeting in Tübingen (Germany) in April 2016, a unified nomenclature for injectisome-type [[https:// |
+ | |||
+ | ^Name ^Purpose ^URL ^Reference | | ||
+ | |Effectidor |T3E prediction |[[https:// | ||
+ | |T3SEpp |T3E prediction |[[http:// | ||
+ | |ACNNT3 |T3E prediction |Source code available at: [[https:// | ||
+ | |EP3 |T3E prediction |[[http:// | ||
+ | |PrediTALE |TAL effector target prediction |[[http:// | ||
+ | |Phylogenetic profiling |T3E prediction |[[http:// | ||
+ | |WEDeepT3 |T3E prediction |[[https:// | ||
+ | |DeepT3 |T3E prediction |[[https:// | ||
+ | |Bastion3 |T3E prediction |[[http:// | ||
+ | |Machine-learning algorithm |T3E prediction | |Teper //et al.//, 2016 | | ||
+ | |AnnoTALE |Annotation and analysis of TAL effector genes |[[http:// | ||
+ | |GenSET |T3E prediction | |Hobbs //et al.//, 2016 | | ||
+ | |pEffect |T3E prediction |[[https:// | ||
+ | |QueTAL |Suite for the functional and phylogenetic comparison of TAL effectors |[[http:// | ||
+ | |HMM-LDA |T3E prediction | |Yang & Qi, 2014 | | ||
+ | |Talvez |TAL effector target prediction |[[http:// | ||
+ | |TALgetter |TAL effector target prediction |[[http:// | ||
+ | |T3SPs |T3E prediction |cic.scu.edu.cn/ | ||
+ | |cSIEVE |T3E prediction | |Hovis //et al.//, 2013 | | ||
+ | |T3_MM |T3E prediction |biocomputer.bio.cuhk.edu.hk/ | ||
+ | |BEAN |T3E prediction |[[http:// | ||
+ | |RalstoT3Edb |T3E prediction & database |[[http:// | ||
+ | |TALE-NT |TAL effector target prediction |[[https:// | ||
+ | |T3DB |T3E database |biocomputer.bio.cuhk.edu.hk/ | ||
+ | |EffectPred |T3E prediction |Source code available at: www.p.chiba-u.ac.jp/ | ||
+ | |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/ | ||
+ | |HMM (EPIYA motif) |T3E prediction | |Xu //et al.//, 2010 | | ||
+ | |T3SEdb |T3E prediction & database |effectors.bic.nus.edu.sg/ | ||
+ | |Classifier |T3E prediction |Discriminant functions available upon request |Kampenusa & Zikmanis, 2010 | | ||
+ | |Classifier |T3E prediction |Method and data available upon request |Yang //et al.//, 2010 | | ||
+ | |modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/ | ||
+ | |EffectiveT3 |T3E prediction |[[http:// | ||
+ | |SIEVE |T3E prediction |www.sysbep.org/ | ||
- | \\ | ||
- | ^ Name ^ Purpose | ||
- | | T3SEpp | ||
- | | ACNNT3 | ||
- | | EP3 | T3E prediction | ||
- | | PrediTALE | ||
- | | Phylogenetic profiling | ||
- | | WEDeepT3 | ||
- | | DeepT3 | ||
- | | Bastion3 | ||
- | | Machine-learning algorithm | ||
- | | AnnoTALE | ||
- | | GenSET | ||
- | | pEffect | ||
- | | QueTAL | ||
- | | HMM-LDA | ||
- | | Talvez | ||
- | | TALgetter | ||
- | | T3SPs | T3E prediction | ||
- | | cSIEVE | ||
- | | T3_MM | T3E prediction | ||
- | | BEAN | T3E prediction | ||
- | | RalstoT3Edb | ||
- | | TALE-NT | ||
- | | T3DB | T3E database | ||
- | | EffectPred | ||
- | | BPBAac | ||
- | | HMM (EPIYA motif) | ||
- | | T3SEdb | ||
- | | Classifier | ||
- | | Classifier | ||
- | | modlab | ||
- | | EffectiveT3 | ||
- | | SIEVE | T3E prediction | ||
===== References ===== | ===== References ===== | ||
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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 S, Diepold A (2020). A unified nomenclature for injectisome-type type III secretion systems. Curr. Top. Microbiol. Immunol. 427: 1-10. doi: [[https:// | ||
Wang Y, Huang H, Sun M, Zhang Q, Guo D (2012). T3DB: an integrated database for bacterial type III secretion system. BMC Bioinformatics 13: 66. DOI: [[https:// | Wang Y, Huang H, Sun M, Zhang Q, Guo D (2012). T3DB: an integrated database for bacterial type III secretion system. BMC Bioinformatics 13: 66. 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:// | ||
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Noël LD, Denancé N, Szurek B (2013). Predicting promoters targeted by TAL effectors in plant genomes: from dream to reality. Front. Plant Sci. 4: 333. DOI: [[https:// | Noël LD, Denancé N, Szurek B (2013). Predicting promoters targeted by TAL effectors in plant genomes: from dream to reality. Front. Plant Sci. 4: 333. DOI: [[https:// | ||
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