EuroXanth DokuWiki

User Tools

Site Tools

Trace:
bacteria:t3e:software

Differences

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

Link to this comparison view

Both sides previous revisionPrevious revision
Next revision		Previous revision
bacteria:t3e:software [2023/07/17 11:00] – [References] rkoebnikbacteria:t3e:software [2025/06/05 11:22] (current) – [References] rkoebnik
Line 1: Line 1:
 ====== Software, Databases and Websites ====== ====== 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 [[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.+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/index.php/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 |
 +|Effectidor II |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2025  |
 +|PLM-T3SE |T3E prediction |  |Gao //et al.//, 2025  |
 |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.//, 2022a  |
 |Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022b  | |Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022b  |
Line 40: Line 42:
 |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 |[[http://www.sysbep.org/sieve/|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 =====
Line 55: Line 57:
  
 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]] 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]]
 +
 +Gao M, Song C, Liu T (2025). PLM-T3SE: Accurate prediction of type III secretion effectors using protein language model embeddings. J. Cell. Biochem. 126: e30642. DOI: [[https://doi.org/10.1002/jcb.30642|10.1002/jcb.30642]]
  
 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]]
Line 75: Line 79:
  
 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]]
Line 99: Line 105:
  
 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 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 N, Baumer E, Lyubman I, Shimony Y, Bracha N, Martins L, Potnis N, Chang JH, Teper D, Koebnik R, Pupko T (2025). Effectidor II: A pan-genomic AI-based algorithm for the prediction of type III secretion system effectors. Bioinformatics 41: btaf272. DOI: [[https://doi.org/10.1093/bioinformatics/btaf272|10.1093/bioinformatics/btaf272]]
  
 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]]
bacteria/t3e/software.1689588013.txt.gz · Last modified: 2023/07/17 11:00 by rkoebnik

Page Tools

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Share Alike 4.0 International
CC Attribution-Share Alike 4.0 International Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki