This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
bacteria:t3e:software [2020/09/28 12:29] – old revision restored (2020/09/28 13:18) rkoebnik | bacteria:t3e:software [2023/09/11 17:32] (current) – [References] rkoebnik | ||
---|---|---|---|
Line 1: | Line 1: | ||
====== Software, Databases and Websites ====== | ====== Software, Databases and Websites ====== | ||
- | ^ Name ^ Purpose | + | Based on discusisons during the International Type III Secretion Meeting in Tübingen (Germany) in April 2016, a unified nomenclature for injectisome-type [[https:// |
- | | T3SEpp | + | |
- | | ACNNT3 | + | ^Name ^Purpose ^URL ^Reference | |
- | | EP3 | + | |Effectidor |T3E prediction |[[https:// |
- | | PrediTALE | + | |Effectidor |T3E prediction |[[https:// |
- | | Phylogenetic profiling | + | |DeepT3 2.0 |T3E prediction |[[http:// |
- | | WEDeepT3 | + | |DeepT3_4 |T3E prediction |[[https:// |
- | | DeepT3 | + | |T3SEpp |T3E prediction |[[http:// |
- | | Bastion3 | + | |ACNNT3 |T3E prediction |Source code available at: [[https:// |
- | | Machine-learning algorithm | + | |EP3 |T3E prediction |[[http:// |
- | | AnnoTALE | + | |PrediTALE |TAL effector target prediction |[[http:// |
- | | GenSET | + | |Phylogenetic profiling |T3E prediction |[[http:// |
- | | pEffect | + | |WEDeepT3 |T3E prediction |[[https:// |
- | | QueTAL | + | |DeepT3 |T3E prediction |[[https:// |
- | | HMM-LDA | + | |Bastion3 |T3E prediction |[[http:// |
- | | Talvez | + | |Machine-learning algorithm |T3E prediction | |Teper //et al.//, 2016 | |
- | | TALgetter | + | |AnnoTALE |Annotation and analysis of TAL effector genes |[[http:// |
- | | T3SPs | + | |GenSET |T3E prediction | |Hobbs //et al.//, 2016 | |
- | | cSIEVE | + | |pEffect |T3E prediction |[[https:// |
- | | T3_MM | + | |QueTAL |Suite for the functional and phylogenetic comparison of TAL effectors |[[http:// |
- | | BEAN | T3E prediction | + | |HMM-LDA |T3E prediction | |Yang & Qi, 2014 | |
- | | RalstoT3Edb | + | |Talvez |TAL effector target prediction |[[http:// |
- | | TALE-NT | + | |TALgetter |TAL effector target prediction |[[http://galaxy.informatik.uni-halle.de/ |
- | | T3DB | T3E database | + | |T3SPs |T3E prediction |cic.scu.edu.cn/ |
- | | EffectPred | + | |cSIEVE |T3E prediction | |Hovis //et al.//, 2013 | |
- | | BPBAac | + | |T3_MM |T3E prediction |biocomputer.bio.cuhk.edu.hk/ |
- | | HMM (EPIYA motif) | + | |BEAN |T3E prediction |[[http://systbio.cau.edu.cn/ |
- | | T3SEdb | + | |RalstoT3Edb |T3E prediction & database |[[http://iant.toulouse.inra.fr/ |
- | | Classifier | + | |TALE-NT |TAL effector target prediction |[[https:// |
- | | Classifier | + | |T3DB |T3E database |biocomputer.bio.cuhk.edu.hk/ |
- | | modlab | + | |EffectPred |T3E prediction |Source code available at: [[http://www.p.chiba-u.ac.jp/ |
- | | EffectiveT3 | + | |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/ |
- | | SIEVE | + | |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://www.effectors.org|www.effectors.org]] | ||
+ | |SIEVE |T3E prediction |[[http://www.sysbep.org/ | ||
+ | |// | ||
===== References ===== | ===== References ===== | ||
Line 60: | Line 67: | ||
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:// | ||
Line 66: | Line 75: | ||
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:// | ||
Line 86: | Line 97: | ||
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, 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:// | ||
+ | |||
+ | 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:// | ||
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:// | ||
Line 104: | Line 121: | ||
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:// | ||
Line 111: | Line 130: | ||
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:// | ||
- | \\ | ||