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bacteria:t3e:software [2022/07/25 11:03] – [Software, Databases and Websites] rkoebnikbacteria:t3e:software [2023/09/11 17:32] (current) – [References] rkoebnik
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 ^Name ^Purpose ^URL ^Reference | ^Name ^Purpose ^URL ^Reference |
-|Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022  |+|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  | |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  |
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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 |T3DB |T3E database |biocomputer.bio.cuhk.edu.hk/T3DB/ **(outdated)**   |Wang //et al.//, 2012  | |T3DB |T3E database |biocomputer.bio.cuhk.edu.hk/T3DB/ **(outdated)**   |Wang //et al.//, 2012  |
-|EffectPred |T3E prediction |Source code available at: www.p.chiba-u.ac.jp/lab/bisei/software/index.html **(outdated)**   |Sato //et al.//, 2011  |+|EffectPred |T3E prediction |Source code available at: [[http://www.p.chiba-u.ac.jp/lab/bisei/software/index.html|www.p.chiba-u.ac.jp/lab/bisei/software/index.html]] **(outdated)**   |Sato //et al.//, 2011  |
 |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/softwares/BPBAac/ **(outdated)**   |Wang //et al.//, 2011  | |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/softwares/BPBAac/ **(outdated)**   |Wang //et al.//, 2011  |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  |
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 |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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 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, 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 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]] 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]]
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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]]
bacteria/t3e/software.1658743422.txt.gz · Last modified: 2023/01/09 10:20 (external edit)

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