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bacteria:t3e:software [2020/08/17 12:43] – [Table] rkoebnik | bacteria:t3e:software [2023/09/11 17:32] (current) – [References] rkoebnik | ||
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====== Software, Databases and Websites ====== | ====== Software, Databases and Websites ====== | ||
- | ^ Name ^ Purpose | + | Based on discusisons during the International Type III Secretion Meeting in Tübingen |
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- | | T3SPs | T3E prediction | + | |
- | | cSIEVE | + | |
- | | T3_MM | T3E prediction | + | |
- | | BEAN | T3E prediction | + | |
- | | EffectPred | + | |
- | | BPBAac | + | |
- | | HMM (EPIYA motif) | + | |
- | | T3SEdb | + | |
- | | Classifier | + | |
- | | Classifier | + | |
- | | modlab | + | |
- | | EffectiveT3 | + | |
- | | SIEVE | T3E prediction | + | |
- | \\ | + | ^Name ^Purpose ^URL ^Reference | |
+ | |Effectidor |T3E prediction |[[https:// | ||
+ | |Effectidor |T3E prediction |[[https:// | ||
+ | |DeepT3 2.0 |T3E prediction |[[http:// | ||
+ | |DeepT3_4 |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: [[http:// | ||
+ | |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 |[[http:// | ||
+ | |// | ||
===== References ===== | ===== References ===== | ||
- | McDermott JE, Corrigan A, Peterson E, Oehmen C, Niemann G, Cambronne ED, Sharp D, Adkins JN, Samudrala R, Heffron F (2011). Computational | + | Arnold R, Brandmaier S, Kleine F, Tischler P, Heinz E, Behrens S, Niinikoski A, Mewes HW, Horn M, Rattei T (2009). Sequence-based |
- | Samudrala R, Heffron F, McDermott JE (2009). Accurate prediction of secreted substrates | + | Dong X, Lu X, Zhang Z (2015). BEAN 2.0: an integrated web resource for the identification |
- | \\ | + | 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; |
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | Goldberg T, Rost B, Bromberg Y (2016). Computational prediction shines light on type III secretion origins. Sci. Rep. 6: 34516. DOI: [[https:// | ||
+ | |||
+ | Grau J, Reschke M, Erkes A, Streubel J, Morgan RD, Wilson GG, Koebnik R, Boch J (2016). AnnoTALE: bioinformatics tools for identification, | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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 // | ||
+ | |||
+ | Sabbagh CRR, Carrere S, Lonjon F, Vailleau F, Macho AP, Genin S, Peeters N (2019). Pangenomic type III effector database of the plant pathogenic // | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | 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:// | ||
+ | |||
+ | ===== Further Reading ===== | ||
+ | |||
+ | 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:// | ||
- | \\ | ||