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bacteria:t3e:software
Table of Contents
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Software, Databases and Websites
| Name | Purpose | URL | Reference |
|---|---|---|---|
| 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.mpl.ird.fr/cgi-bin/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| T3SPs | T3E prediction | cic.scu.edu.cn/bioinformatics/T3SPs.zip (outdated) | Yang et al., 2013 |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| T3E prediction | biocomputer.bio.cuhk.edu.hk/softwares/T3_MM (R package), biocomputer.bio.cuhk.edu.hk/T3DB/T3_MM.php (outdated) | Wang et al., 2013 |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| T3DB | T3E database | biocomputer.bio.cuhk.edu.hk/T3DB/ (outdated) | Wang et al., 2012 |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/ | 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 |
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: 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
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
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
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.1601292814.txt.gz · Last modified: 2020/09/28 13:33 by rkoebnik
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