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Molecular Diagnosis and Diversity for Regulated Xanthomonas

Bacterial virulence factors

Plant resistance genes

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This DokuWiki is based upon work from COST Action CA16107 EuroXanth, supported by COST (European Cooperation in Science and Technology)

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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 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 Wagner et al., 2022
DeepT3 2.0 T3E prediction Jing et al., 2021
DeepT3_4 T3E prediction Yu et al., 2021
T3SEpp T3E prediction Hui et al., 2020
ACNNT3 T3E prediction Source code available at: Li et al., 2020a
EP3 T3E prediction Li et al., 2020b
PrediTALE TAL effector target prediction Erkes et al., 2019
Phylogenetic profiling T3E prediction Zalguizuri et al., 2019
WEDeepT3 T3E prediction Fu & Yang, 2019
DeepT3 T3E prediction Xue et al., 2019
Bastion3 T3E prediction Wang et al., 2019
Machine-learning algorithm T3E prediction Teper et al., 2016
AnnoTALE Annotation and analysis of TAL effector genes Grau et al., 2016
GenSET T3E prediction Hobbs et al., 2016
pEffect T3E prediction Goldberg et al., 2016
QueTAL Suite for the functional and phylogenetic comparison of TAL effectors Pérez-Quintero et al., 2015
HMM-LDA T3E prediction Yang & Qi, 2014
Talvez TAL effector target prediction Pérez-Quintero et al., 2013
TALgetter TAL effector target prediction Grau et al., 2013
T3SPs T3E prediction (outdated) Yang et al., 2013
cSIEVE T3E prediction Hovis et al., 2013
T3_MM T3E prediction (R package), (outdated) Wang et al., 2013
BEAN T3E prediction Dong et al., 2013; Dong et al., 2015
RalstoT3Edb T3E prediction & database Peeters et al., 2013; Sabbagh et al., 2019
TALE-NT TAL effector target prediction Doyle et al., 2012
T3DB T3E database (outdated) Wang et al., 2012
EffectPred T3E prediction Source code available at: (outdated) Sato et al., 2011
BPBAac T3E prediction (outdated) Wang et al., 2011
HMM (EPIYA motif) T3E prediction Xu et al., 2010
T3SEdb T3E prediction & database (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 (outdated) Löwer & Schneider, 2009
EffectiveT3 T3E prediction Arnold et al., 2009
SIEVE T3E prediction (outdated) Samudrala et al., 2009; McDermott et al., 2011


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: 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: 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

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, 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: 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

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: 10.3389/fpls.2013.00333

bacteria/t3e/software.txt · Last modified: 2022/10/27 15:26 by rkoebnik