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-====== 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://en.wikipedia.org/wiki/Type_three_secretion_system|type III secretion sytems]] was proposed in 2020 (Wagner & Diepold, 2020). This nomenclature was also advertised in the corresponding [[https://t3sswiki.science/w/index.php?title=Nomenclature_of_Type_III_Secretion_Systems|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/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022  |
-|DeepT3_4 |T3E prediction |[[https://github.com/jingry/autoBioSeqpy/tree/2.0/examples/T3T4|github.com/jingry/autoBioSeqpy/tree/2.0/examples/T3T4]] |Yu //et al.//, 2021  |
-|T3SEpp |T3E prediction |[[http://www.szu-bioinf.org/T3SEpp/|www.szu-bioinf.org/T3SEpp]] |Hui //et al.//, 2020  |
-|ACNNT3 |T3E prediction |Source code available at: [[https://github.com/Lijiesky/ACNNT3|https://github.com/Lijiesky/ACNNT3]] |Li //et al.//, 2020a  |
-|EP3 |T3E prediction |[[http://lab.malab.cn/~lijing/EP3.html|lab.malab.cn/~lijing/EP3.html]] |Li //et al.//, 2020b  |
-|PrediTALE |TAL effector target prediction |[[http://galaxy.informatik.uni-halle.de|galaxy.informatik.uni-halle.de]] |Erkes //et al.//, 2019  |
-|Phylogenetic profiling |T3E prediction |[[http://www.iib.unsam.edu.ar/orgsissec/|www.iib.unsam.edu.ar/orgsissec/]] |Zalguizuri //et al.//, 2019  |
-|WEDeepT3 |T3E prediction |[[https://bcmi.sjtu.edu.cn/~yangyang/WEDeepT3.html|bcmi.sjtu.edu.cn/~yangyang/WEDeepT3.html]] |Fu & Yang, 2019 |
-|DeepT3 |T3E prediction |[[https://github.com/lje00006/DeepT3|github.com/lje00006/DeepT3]] |Xue //et al.//, 2019  |
-|Bastion3 |T3E prediction |[[http://bastion3.erc.monash.edu/|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 |[[http://www.jstacs.de/index.php/AnnoTALE|www.jstacs.de/index.php/AnnoTALE]] |Grau //et al.//, 2016  |
-|GenSET |T3E prediction |  |Hobbs //et al.//, 2016  |
-|pEffect |T3E prediction |[[https://services.bromberglab.org/peffect/|services.bromberglab.org/peffect]] |Goldberg //et al.//, 2016  |
-|QueTAL |Suite for the functional and phylogenetic comparison of TAL effectors |[[http://bioinfo-web.mpl.ird.fr/cgi-bin2/quetal/quetal.cgi|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 |[[http://bioinfo-web.mpl.ird.fr/cgi-bin2/talvez/talvez.cgi|bioinfo-web.mpl.ird.fr/cgi-bin2/talvez/talvez.cgi]] |Pérez-Quintero //et al.//, 2013  |
-|TALgetter |TAL effector target prediction |[[http://galaxy.informatik.uni-halle.de/|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 |[[http://systbio.cau.edu.cn/bean/|systbio.cau.edu.cn/bean/]] |Dong //et al.//, 2013; Dong //et al.//, 2015  |
-|RalstoT3Edb |T3E prediction & database |[[http://iant.toulouse.inra.fr/T3E|iant.toulouse.inra.fr/T3E]] |Peeters //et al.//, 2013; Sabbagh //et al.//, 2019  |
-|TALE-NT |TAL effector target prediction |[[https://boglab.plp.iastate.edu|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 |[[http://www.effectors.org|www.effectors.org]] |Arnold //et al.//, 2009  |
-|SIEVE |T3E prediction |www.sysbep.org/sieve/ **(outdated)**   |Samudrala //et al.//, 2009; McDermott //et al.//, 2011  |
-===== References =====
-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://doi.org/10.1371/journal.ppat.1000376|10.1371/journal.ppat.1000376]]
-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://doi.org/10.1093/database/bav064|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: [[https://doi.org/10.1371/journal.pone.0056632|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: [[https://doi.org/10.1093/nar/gks608|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: [[https://doi.org/10.1371/journal.pcbi.1007206|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: [[https://doi.org/10.1007/s40484-019-0184-7|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: [[https://doi.org/10.1038/srep34516|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: [[https://doi.org/10.1038/srep21077|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: [[https://doi.org/10.1371/journal.pcbi.1002962|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: [[https://doi.org/10.1186/s12864-016-3363-1|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: [[https://doi.org/10.1111/2049-632X.12070|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: [[https://doi.org/10.1128/mSystems|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[[https://doi.org/10.1093/nargab/lqab086|: 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: [[https://doi.org/10.1007/s00253-009-2423-8|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: [[https://doi.org/10.1155/2020/3974598|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: [[https://doi.org/10.1093/bib/bbaa008|10.1093/bib/bbaa008]]
-Löwer M, Schneider G (2009). Prediction of type III secretion signals in genomes of gram-negative bacteria. PLoS One 4: e5917. DOI: [[https://doi.org/10.1371/journal.pone.0005917|10.1371/journal.pone.0005917]]. Erratum in: PLoS One (2009); 4. DOI: [[https://doi.org/10.1371/annotation/78c8fc32-b1e2-4c87-9c92-d318af980b9b|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: [[https://doi.org/10.1128/IAI.00537-10|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: [[https://doi.org/10.1186/1471-2164-14-859|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: [[https://doi.org/10.3389/fpls.2015.00545|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: [[https://doi.org/10.1371/journal.pone.0068464|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: [[https://doi.org/10.7717/peerj.7346|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: [[https://doi.org/10.1371/journal.ppat.1000375|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: [[https://doi.org/10.1186/1471-2105-12-442|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: [[https://doi.org/10.1186/1471-2105-11-S7-S4|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: [[https://doi.org/10.1111/mpp.12288|10.1111/mpp.12288]]
-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://doi.org/10.1093/bioinformatics/btac087|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: [[https://doi.org/10.1007/82_2020_210|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: [[https://doi.org/10.1186/1471-2105-13-66|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: [[https://doi.org/10.1093/bioinformatics/bty914|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: [[https://doi.org/10.1371/journal.pone.0058173|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: [[https://doi.org/10.1093/bioinformatics/btr021|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: [[https://doi.org/10.1186/1471-2164-11-S3-S1|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: [[https://doi.org/10.1093/bioinformatics/bty931|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: [[https://doi.org/10.1371/journal.pone.0084439|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: [[https://doi.org/10.1504/ijdmb.2014.064894|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: [[https://doi.org/10.1186/1471-2105-11-S1-S47|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: [[https://doi.org/10.3389/fmicb.2021.605782|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: [[https://doi.org/10.1093/bib/bby009|10.1093/bib/bby009]]
-===== 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://doi.org/10.3389/fpls.2013.00333|10.3389/fpls.2013.00333]]

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