Differences

This shows you the differences between two versions of the page.

--- bacteria:t3e:software [2020/09/29 10:51]
rkoebnik
+++ — (current)
@@ Line 1: / Line 1: @@
-====== 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 |
-|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.mpl.ird.fr/cgi-bin/talvez/talvez.cgi|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 |[[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/ |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: [[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]]
-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]]
-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]]
-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]]
-\\

EuroXanth DokuWiki

User Tools

Site Tools

Differences

Page Tools