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--- bacteria:t3e:software [2022/10/27 14:26] – rkoebnik
+++ bacteria:t3e:software [2025/06/05 11:22] (current) – [References] rkoebnik
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 ====== 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 |
-|Effectidor |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2022  |
+|Effectidor II |T3E prediction |[[https://effectidor.tau.ac.il/|https://effectidor.tau.ac.il]] |Wagner //et al.//, 2025  |
-|DeepT3 2.0 |T3E prediction |[[http://advintbioinforlab.com/deept3/]] |Jing //et al.//, 2021  |
+|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.//, 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  |
@@ Line 30: / Line 33: @@
 |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  |
+|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  |
 |HMM (EPIYA motif) |T3E prediction |  |Xu //et al.//, 2010  |
@@ Line 38: / Line 41: @@
 |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  |
-|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 =====
@@ Line 53: / 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]]
+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]]
@@ Line 73: / 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]]
+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]]
@@ Line 94: / Line 102: @@
 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 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]]

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