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bacteria:t3e:software [2022/07/25 12:02]
rkoebnik [Software, Databases and Websites]
bacteria:t3e:software [2022/10/27 15:26] (current)
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.//, 2022  |
 +|DeepT3 2.0 |T3E prediction |[[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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 |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/softwares/BPBAac/ **(outdated)**   |Wang //et al.//, 2011  | |BPBAac |T3E prediction |biocomputer.bio.cuhk.edu.hk/softwares/BPBAac/ **(outdated)**   |Wang //et al.//, 2011  |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 |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|Tay //et al.//, 2010  |+|T3SEdb |T3E prediction & database |effectors.bic.nus.edu.sg/T3SEdb/ **(outdated)**   |Tay //et al.//, 2010  |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 |modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/T3SS_prediction/T3SS_prediction.html **(outdated)**   |Löwer & Schneider, 2009 | |modlab |T3E prediction |gecco.org.chemie.uni-frankfurt.de/T3SS_prediction/T3SS_prediction.html **(outdated)**   |Löwer &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 |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 |www.sysbep.org/sieve/ **(outdated)**   |Samudrala //et al.//, 2009; McDermott //et al.//, 2011  |
- 
  
 ===== 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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 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.1658743329.txt.gz · Last modified: 2022/07/25 12:02 by rkoebnik