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bacteria:t3e:software [2020/09/28 13:32] rkoebnik [Table] |
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- | ====== Software, Databases and Websites ====== | ||
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- | ^ 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 | ||
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- | ===== References ===== | ||
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- | Arnold R, Brandmaier S, Kleine F, Tischler P, Heinz E, Behrens S, Niinikoski A, Mewes HW, Horn M, Rattei T (2009). Sequence-based prediction of type III secreted proteins. PLoS Pathog. 5: e1000376. DOI: [[https:// | ||
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- | Dong X, Lu X, Zhang Z (2015). BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors. Database (Oxford) 2015: bav064. DOI: [[https:// | ||
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- | Dong X, Zhang YJ, Zhang Z. Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes. PLoS One. 2013; | ||
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- | Doyle EL, Booher NJ, Standage DS, Voytas DF, Brendel VP, Vandyk JK, Bogdanove AJ (2012). TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: tools for TAL effector design and target prediction. Nucleic Acids Res. 40: W117-W122. DOI: [[https:// | ||
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- | Erkes A, Mücke S, Reschke M, Boch J, Grau J (2019). PrediTALE: A novel model learned from quantitative data allows for new perspectives on TALE targeting. PLoS Comput. Biol. 15: e1007206. DOI: [[https:// | ||
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- | Fu X, Yang Y (2019). WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning. Quant. Biol. 7: 293-301. DOI: [[https:// | ||
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- | Goldberg T, Rost B, Bromberg Y (2016). Computational prediction shines light on type III secretion origins. Sci. Rep. 6: 34516. DOI: [[https:// | ||
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- | Grau J, Reschke M, Erkes A, Streubel J, Morgan RD, Wilson GG, Koebnik R, Boch J (2016). AnnoTALE: bioinformatics tools for identification, | ||
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- | Grau J, Wolf A, Reschke M, Bonas U, Posch S, Boch J (2013). Computational predictions provide insights into the biology of TAL effector target sites. PLoS Comput. Biol. 9: e1002962. DOI: [[https:// | ||
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- | Hobbs CK, Porter VL, Stow ML, Siame BA, Tsang HH, Leung KY (2016). Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes. BMC Genomics 17: 1048. DOI: [[https:// | ||
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- | Hovis KM, Mojica S, McDermott JE, Pedersen L, Simhi C, Rank RG, Myers GS, Ravel J, Hsia RC, Bavoil PM @013). Genus-optimized strategy for the identification of chlamydial type III secretion substrates. Pathog. Dis. 69: 213-222. DOI: [[https:// | ||
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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:// | ||
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- | 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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- | Li J, Li Z, Luo J, Yao Y (2020a). ACNNT3: Attention-CNN framework for prediction of sequence-based bacterial type III secreted effectors. Comput. Math. Methods Med. 2020: 3974598. DOI: [[https:// | ||
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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:// | ||
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- | 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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- | McDermott JE, Corrigan A, Peterson E, Oehmen C, Niemann G, Cambronne ED, Sharp D, Adkins JN, Samudrala R, Heffron F (2011). Computational prediction of type III and IV secreted effectors in gram-negative bacteria. Infect. Immun. 79: 23-32. DOI: [[https:// | ||
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- | Peeters N, Carrère S, Anisimova M, Plener L, Cazalé AC, Genin S (2013). Repertoire, unified nomenclature and evolution of the Type III effector gene set in the //Ralstonia solanacearum// | ||
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- | Pérez-Quintero AL, Lamy L, Gordon JL, Escalon A, Cunnac S, Szurek B, Gagnevin L (2015). QueTAL: a suite of tools to classify and compare TAL effectors functionally and phylogenetically. Front. Plant Sci. 6: 545. DOI: [[https:// | ||
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- | Pérez-Quintero AL, Rodriguez-R LM, Dereeper A, López C, Koebnik R, Szurek B, Cunnac S (2013). An improved method for TAL effectors DNA-binding sites prediction reveals functional convergence in TAL repertoires of // | ||
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- | Sabbagh CRR, Carrere S, Lonjon F, Vailleau F, Macho AP, Genin S, Peeters N (2019). Pangenomic type III effector database of the plant pathogenic // | ||
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- | Samudrala R, Heffron F, McDermott JE (2009). Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems. PLoS Pathog. 5: e1000375. DOI: [[https:// | ||
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- | Sato Y, Takaya A, Yamamoto T (2011). Meta-analytic approach to the accurate prediction of secreted virulence effectors in gram-negative bacteria. BMC Bioinformatics 12: 442. DOI: [[https:// | ||
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- | Tay DM, Govindarajan KR, Khan AM, Ong TY, Samad HM, Soh WW, Tong M, Zhang F, Tan TW (2010). T3SEdb: data warehousing of virulence effectors secreted by the bacterial Type III Secretion System. BMC Bioinformatics 11: S4. DOI: [[https:// | ||
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- | Teper D, Burstein D, Salomon D, Gershovitz M, Pupko T, Sessa G (2016). Identification of novel // | ||
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- | 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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- | Wang J, Li J, Yang B, Xie R, Marquez-Lago TT, Leier A, Hayashida M, Akutsu T, Zhang Y, Chou KC, Selkrig J, Zhou T, Song J, Lithgow T (2019). Bastion3: a two-layer ensemble predictor of type III secreted effectors. Bioinformatics 35: 2017-2028. DOI: [[https:// | ||
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- | Wang Y, Sun M, Bao H, White AP (2013). T3_MM: a Markov model effectively classifies bacterial type III secretion signals. PLoS One 8: e58173. DOI: [[https:// | ||
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- | Wang Y, Zhang Q, Sun MA, Guo D (2011). High-accuracy prediction of bacterial type III secreted effectors based on position-specific amino acid composition profiles. Bioinformatics 27: 777-784. DOI: [[https:// | ||
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- | Xu S, Zhang C, Miao Y, Gao J, Xu D (2010). Effector prediction in host-pathogen interaction based on a Markov model of a ubiquitous EPIYA motif. BMC Genomics 11: S1. DOI: [[https:// | ||
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- | Xue L, Tang B, Chen W, Luo J (2019). DeepT3: deep convolutional neural networks accurately identify Gram-negative bacterial type III secreted effectors using the N-terminal sequence. Bioinformatics 35: 2051-2057. DOI: [[https:// | ||
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- | Yang X, Guo Y, Luo J, Pu X, Li M (2013). Effective identification of Gram-negative bacterial type III secreted effectors using position-specific residue conservation profiles. PLoS One 8: e84439. DOI: [[https:// | ||
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- | Yang Y, Qi S (2014). A new feature selection method for computational prediction of type III secreted effectors. Int. J. Data Min. Bioinform. 10: 440-454. 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:// | ||
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- | 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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- | ===== Further Reading ===== | ||
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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:// | ||
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