Based on discusisons during the International Type III Secretion Meeting in Tübingen (Germany) in April 2016, a unified nomenclature for injectisome-type type III secretion sytems was proposed in 2020 (Wagner & Diepold, 2020). This nomenclature was also advertised in the corresponding Wiki entry. At the same time, it was suggested to continue using the original name for T3SS chaperones and effectors. Algorithms to predict bacterial type III effectors are listed below.
| Name | Purpose | URL | Reference |
|---|---|---|---|
| Effectidor | T3E prediction | https://effectidor.tau.ac.il | Wagner et al., 2022a |
| Effectidor | T3E prediction | https://effectidor.tau.ac.il | Wagner et al., 2022b |
| DeepT3 2.0 | T3E prediction | http://advintbioinforlab.com/deept3/ | Jing et al., 2021 |
| DeepT3_4 | T3E prediction | github.com/jingry/autoBioSeqpy/tree/2.0/examples/T3T4 | Yu et al., 2021 |
| T3SEpp | T3E prediction | www.szu-bioinf.org/T3SEpp | Hui et al., 2020 |
| ACNNT3 | T3E prediction | Source code available at: https://github.com/Lijiesky/ACNNT3 | Li et al., 2020a |
| EP3 | T3E prediction | lab.malab.cn/~lijing/EP3.html | Li et al., 2020b |
| PrediTALE | TAL effector target prediction | galaxy.informatik.uni-halle.de | Erkes et al., 2019 |
| Phylogenetic profiling | T3E prediction | www.iib.unsam.edu.ar/orgsissec/ | Zalguizuri et al., 2019 |
| WEDeepT3 | T3E prediction | bcmi.sjtu.edu.cn/~yangyang/WEDeepT3.html | Fu & Yang, 2019 |
| DeepT3 | T3E prediction | github.com/lje00006/DeepT3 | Xue et al., 2019 |
| Bastion3 | T3E prediction | bastion3.erc.monash.edu | Wang et al., 2019 |
| Machine-learning algorithm | T3E prediction | Teper et al., 2016 | |
| AnnoTALE | Annotation and analysis of TAL effector genes | www.jstacs.de/index.php/AnnoTALE | Grau et al., 2016 |
| GenSET | T3E prediction | Hobbs et al., 2016 | |
| pEffect | T3E prediction | services.bromberglab.org/peffect | Goldberg et al., 2016 |
| QueTAL | Suite for the functional and phylogenetic comparison of TAL effectors | bioinfo-web.mpl.ird.fr/cgi-bin2/quetal/quetal.cgi | Pérez-Quintero et al., 2015 |
| HMM-LDA | T3E prediction | Yang & Qi, 2014 | |
| Talvez | TAL effector target prediction | bioinfo-web.mpl.ird.fr/cgi-bin2/talvez/talvez.cgi | Pérez-Quintero et al., 2013 |
| TALgetter | TAL effector target prediction | galaxy.informatik.uni-halle.de | Grau et al., 2013 |
| T3SPs | T3E prediction | cic.scu.edu.cn/bioinformatics/T3SPs.zip (outdated) | Yang et al., 2013 |
| cSIEVE | T3E prediction | Hovis et al., 2013 | |
| T3_MM | T3E prediction | biocomputer.bio.cuhk.edu.hk/softwares/T3_MM (R package), biocomputer.bio.cuhk.edu.hk/T3DB/T3_MM.php (outdated) | Wang et al., 2013 |
| BEAN | T3E prediction | systbio.cau.edu.cn/bean/ | Dong et al., 2013; Dong et al., 2015 |
| RalstoT3Edb | T3E prediction & database | iant.toulouse.inra.fr/T3E | Peeters et al., 2013; Sabbagh et al., 2019 |
| TALE-NT | TAL effector target prediction | boglab.plp.iastate.edu | Doyle et al., 2012 |
| T3DB | T3E database | biocomputer.bio.cuhk.edu.hk/T3DB/ (outdated) | Wang et al., 2012 |
| EffectPred | T3E prediction | Source code available at: www.p.chiba-u.ac.jp/lab/bisei/software/index.html (outdated) | Sato et al., 2011 |
| BPBAac | T3E prediction | biocomputer.bio.cuhk.edu.hk/softwares/BPBAac/ (outdated) | Wang et al., 2011 |
| HMM (EPIYA motif) | T3E prediction | Xu et al., 2010 | |
| T3SEdb | T3E prediction & database | effectors.bic.nus.edu.sg/T3SEdb/ (outdated) | Tay et al., 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 |
| modlab | T3E prediction | gecco.org.chemie.uni-frankfurt.de/T3SS_prediction/T3SS_prediction.html (outdated) | Löwer & Schneider, 2009 |
| EffectiveT3 | T3E prediction | www.effectors.org | Arnold et al., 2009 |
| SIEVE | T3E prediction | www.sysbep.org/sieve/ (outdated) | Samudrala et al., 2009; McDermott et al., 2011 |
| Pseudomonas–Plant Interaction website | T3E database | www.pseudomonas-syringae.org | Lindeberg et al., 2005 |
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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: 10.1093/database/bav064
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;8(2):e56632. DOI: 10.1371/journal.pone.0056632
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: 10.1093/nar/gks608
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: 10.1371/journal.pcbi.1007206
Fu X, Yang Y (2019). WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning. Quant. Biol. 7: 293-301. DOI: 10.1007/s40484-019-0184-7
Goldberg T, Rost B, Bromberg Y (2016). Computational prediction shines light on type III secretion origins. Sci. Rep. 6: 34516. DOI: 10.1038/srep34516
Grau J, Reschke M, Erkes A, Streubel J, Morgan RD, Wilson GG, Koebnik R, Boch J (2016). AnnoTALE: bioinformatics tools for identification, annotation, and nomenclature of TALEs from Xanthomonas genomic sequences. Sci. Rep. 6: 21077. DOI: 10.1038/srep21077
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: 10.1371/journal.pcbi.1002962
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: 10.1186/s12864-016-3363-1
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: 10.1111/2049-632X.12070
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: 10.1128/mSystems
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: 10.1093/nargab/lqab086
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: 10.1007/s00253-009-2423-8
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: 10.1155/2020/3974598
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: 10.1093/bib/bbaa008
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 Pseudomonas syringae. Mol. Plant Microbe Interact. 18: 275-282. DOI: 10.1094/MPMI-18-0275
Löwer M, Schneider G (2009). Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4: e5917. DOI: 10.1371/journal.pone.0005917. Erratum in: PLoS One (2009); 4. DOI: 10.1371/annotation/78c8fc32-b1e2-4c87-9c92-d318af980b9b
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: 10.1128/IAI.00537-10
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 species complex. BMC Genomics 14: 859. DOI: 10.1186/1471-2164-14-859
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: 10.3389/fpls.2015.00545
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 Xanthomonas oryzae strains. PLoS One 8: e68464. DOI: 10.1371/journal.pone.0068464
Sabbagh CRR, Carrere S, Lonjon F, Vailleau F, Macho AP, Genin S, Peeters N (2019). Pangenomic type III effector database of the plant pathogenic Ralstonia spp. PeerJ 7: e7346. DOI: 10.7717/peerj.7346
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: 10.1371/journal.ppat.1000375
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: 10.1186/1471-2105-12-442
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: 10.1186/1471-2105-11-S7-S4
Teper D, Burstein D, Salomon D, Gershovitz M, Pupko T, Sessa G (2016). Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine-learning approach. Mol. Plant Pathol. 17: 398-411. DOI: 10.1111/mpp.12288
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: 10.3389/fpls.2022.1024405
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: 10.1093/bioinformatics/btac087
Wagner S, Diepold A (2020). A unified nomenclature for injectisome-type type III secretion systems. Curr. Top. Microbiol. Immunol. 427: 1-10. doi: 10.1007/82_2020_210
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: 10.1186/1471-2105-13-66
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: 10.1093/bioinformatics/bty914
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: 10.1371/journal.pone.0058173
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: 10.1093/bioinformatics/btr021
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: 10.1186/1471-2164-11-S3-S1
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: 10.1093/bioinformatics/bty931
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: 10.1371/journal.pone.0084439
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: 10.1504/ijdmb.2014.064894
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: 10.1186/1471-2105-11-S1-S47
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: 10.3389/fmicb.2021.605782
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: 10.1093/bib/bby009
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