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bacteria:t3e:software [2020/09/28 10:55] – rkoebnikbacteria:t3e:software [2023/09/11 17:32] (current) – [References] rkoebnik
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 ====== Software, Databases and Websites ====== ====== Software, Databases and Websites ======
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 +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 | ^Name ^Purpose ^URL ^Reference |
-|T3SEpp |T3E prediction |[[http://www.szu-bioinf.org/T3SEpp|www.szu-bioinf.org/T3SEpp]] |Hui //et al.//, 2020  |+|Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022a  | 
 +|Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022b  | 
 +|DeepT3 2.0 |T3E prediction |[[http://advintbioinforlab.com/deept3/|http://advintbioinforlab.com/deept3/]] |Jing //et al.//, 2021  | 
 +|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  | |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  | |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  | |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  | |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  | |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  | |Bastion3 |T3E prediction |[[http://bastion3.erc.monash.edu/|bastion3.erc.monash.edu]] |Wang //et al.//, 2019  |
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 |GenSET |T3E prediction |  |Hobbs //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  | |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|http://bioinfo-web.mpl.ird.fr/cgi-bin2/quetal/quetal.cgi]] |Pérez-Quintero //et al.//, 2015  |+|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 | |HMM-LDA |T3E prediction |  |Yang & Qi, 2014 |
-|Talvez |TAL effector target prediction |[[http://bioinfo.mpl.ird.fr/cgi-bin/talvez/talvez.cgi|http://bioinfo.mpl.ird.fr/cgi-bin/talvez/talvez.cgi]] |Pérez-Quintero //et al.//, 2013  | +|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  |+|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  | |T3SPs |T3E prediction |cic.scu.edu.cn/bioinformatics/T3SPs.zip **(outdated)**   |Yang //et al.//, 2013  |
 |cSIEVE |T3E prediction |  |Hovis //et al.//, 2013  | |cSIEVE |T3E prediction |  |Hovis //et al.//, 2013  |
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 |TALE-NT |TAL effector target prediction |[[https://boglab.plp.iastate.edu|boglab.plp.iastate.edu]] |Doyle //et al.//, 2012  | |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  | |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  |+|EffectPred |T3E prediction |Source code available at: [[http://www.p.chiba-u.ac.jp/lab/bisei/software/index.html|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  | |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/softwares/BPBAac/ **(outdated)**   |Wang //et al.//, 2011  |
 |HMM (EPIYA motif) |T3E prediction |  |Xu //et al.//, 2010  | |HMM (EPIYA motif) |T3E prediction |  |Xu //et al.//, 2010  |
-|T3SEdb |T3E prediction & database |[[http://effectors.bic.nus.edu.sg/T3SEdb/|effectors.bic.nus.edu.sg/T3SEdb/]] |Tay //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 |Discriminant functions available upon request |Kampenusa & Zikmanis, 2010 |
 |Classifier |T3E prediction |Method and data available upon request |Yang //et al.//, 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 | |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  | |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  |+|SIEVE |T3E prediction |[[http://www.sysbep.org/sieve/|www.sysbep.org/sieve/]] **(outdated)**   |Samudrala //et al.//, 2009; McDermott //et al.//, 2011  | 
 +|//Pseudomonas//–Plant Interaction website  |T3E database |[[http://www.pseudomonas-syringae.org|www.pseudomonas-syringae.org]] |Lindeberg //et al.//, 2005  |
  
 ===== References ===== ===== References =====
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 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]] 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]] 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]]
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 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]] 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]] 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]]
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 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]] 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]]
 +
 +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: [[https://doi.org/10.1094/MPMI-18-0275|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: [[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]] 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]]
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 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]] 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, 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: [[https://doi.org/10.3389/fpls.2022.1024405|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: [[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 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]]
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 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]] 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]] 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]]
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 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]] 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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bacteria/t3e/software.1601286900.txt.gz · Last modified: 2023/01/09 10:20 (external edit)

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