Marie Curie Fellow, EMBO Non-Stipendiary Fellow, European Bioinformatics Institute (EMBL-EBI)
Biography
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PhD in Computational Biology, 2019
Monash University
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MSc in Computer Science, 2015
Peking University
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B.Eng in Software Engineering, 2010
Tongji University
Publications
A noncanonical chaperone interacts with drug efflux pumps during their assembly into bacterial outer membranes.
Stubenrauch CJ, Bamert RS, Wang J, Lithgow T.
PLoS Biol, 2022
doi:10.1371/journal.pbio.3001523.
PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria.
Dai W, Li J, Li Q, Cai J, Su J, Stubenrauch C, Wang J.
Nucleic Acids Res, 2022
doi:10.1093/nar/gkab814.
Component Parts of Bacteriophage Virions Accurately Defined by a Machine-Learning Approach Built on Evolutionary Features.
Thung TY, White ME, Dai W, Wilksch JJ, Bamert RS, Rocker A, Stubenrauch CJ, Williams D, Huang C, Schittelhelm R, Barr JJ, Jameson E, McGowan S, Zhang Y, Wang J, Dunstan RA, Lithgow T.
mSystems, 2021
doi:10.1128/msystems.00242-21.
AcrHub: an integrative hub for investigating, predicting and mapping anti-CRISPR proteins.
Wang J, Dai W, Li J, Li Q, Xie R, Zhang Y, Stubenrauch C, Lithgow T.
Nucleic Acids Res, 2021
doi:10.1093/nar/gkaa951.
BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria.
Wang J, Li J, Hou Y, Dai W, Xie R, Marquez-Lago TT, Leier A, Zhou T, Torres V, Hay I, Stubenrauch C, Zhang Y, Song J, Lithgow T.
Nucleic Acids Res, 2021
doi:10.1093/nar/gkaa899.
AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses.
Huang L, Yang B, Yi H, Asif A, Wang J, Lithgow T, Zhang H, Minhas FUAA, Yin Y.
Nucleic Acids Res, 2021
doi:10.1093/nar/gkaa857.
DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.
Xie R, Li J, Wang J, Dai W, Leier A, Marquez-Lago TT, Akutsu T, Lithgow T, Song J, Zhang Y.
Brief Bioinform, 2021
doi:10.1093/bib/bbaa125.
The architecture and stabilisation of flagellotropic tailed bacteriophages.
Hardy JM, Dunstan RA, Grinter R, Belousoff MJ, Wang J, Pickard D, Venugopal H, Dougan G, Lithgow T, Coulibaly F.
Nat Commun, 2020
doi:10.1038/s41467-020-17505-w.
Mapping bacterial effector arsenals: in vivo and in silico approaches to defining the protein features dictating effector secretion by bacteria.
Lee YW, Wang J, Newton HJ, Lithgow T.
Curr Opin Microbiol, 2020
doi:10.1016/j.mib.2020.04.002.
PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins.
Wang J, Dai W, Li J, Xie R, Dunstan RA, Stubenrauch C, Zhang Y, Lithgow T.
Nucleic Acids Res, 2020
doi:10.1093/nar/gkaa432.
PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.
Zhang Y, Yu S, Xie R, Li J, Leier A, Marquez-Lago TT, Akutsu T, Smith AI, Ge Z, Wang J, Lithgow T, Song J.
Bioinformatics, 2020
doi:10.1093/bioinformatics/btz629.
An Outbreak of Carbapenem-Resistant and Hypervirulent Klebsiella pneumoniae in an Intensive Care Unit of a Major Teaching Hospital in Wenzhou, China.
Zhao Y, Zhang X, Torres VVL, Liu H, Rocker A, Zhang Y, Wang J, Chen L, Bi W, Lin J, Strugnell RA, Zhang S, Lithgow T, Zhou T, Cao J.
Front Public Health, 2019
doi:10.3389/fpubh.2019.00229.
Bastion3: a two-layer ensemble predictor of type III secreted effectors.
Wang J, Li J, Yang B, Xie R, Marquez-Lago TT, Leier A, Hayashida M, Akutsu T, Zhang Y, Chou KC, Selkrig J, Zhou T, Song J, Lithgow T.
Bioinformatics, 2019
doi:10.1093/bioinformatics/bty914.
Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.
Zhang Y, Xie R, Wang J, Leier A, Marquez-Lago TT, Akutsu T, Webb GI, Chou KC, Song J.
Brief Bioinform, 2019
doi:10.1093/bib/bby079.
Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches.
Wang J, Yang B, An Y, Marquez-Lago T, Leier A, Wilksch J, Hong Q, Zhang Y, Hayashida M, Akutsu T, Webb GI, Strugnell RA, Song J, Lithgow T.
Brief Bioinform, 2019
doi:10.1093/bib/bbx164.
FusC, a member of the M16 protease family acquired by bacteria for iron piracy against plants.
Grinter R, Hay ID, Song J, Wang J, Teng D, Dhanesakaran V, Wilksch JJ, Davies MR, Littler D, Beckham SA, Henderson IR, Strugnell RA, Dougan G, Lithgow T.
PLoS Biol, 2018
doi:10.1371/journal.pbio.2006026.
Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.
Wang J, Yang B, Leier A, Marquez-Lago TT, Hayashida M, Rocker A, Zhang Y, Akutsu T, Chou KC, Strugnell RA, Song J, Lithgow T.
Bioinformatics, 2018
doi:10.1093/bioinformatics/bty155.
Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.
An Y, Wang J, Li C, Leier A, Marquez-Lago T, Wilksch J, Zhang Y, Webb GI, Song J, Lithgow T.
Brief Bioinform, 2018
doi:10.1093/bib/bbw100.
POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.
Wang J, Yang B, Revote J, Leier A, Marquez-Lago TT, Webb G, Song J, Chou KC, Lithgow T.
Bioinformatics, 2017
doi:10.1093/bioinformatics/btx302.
PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection.
Song J, Wang H, Wang J, Leier A, Marquez-Lago T, Yang B, Zhang Z, Akutsu T, Webb GI, Daly RJ.
Sci Rep, 2017
doi:10.1038/s41598-017-07199-4.
SecretEPDB: a comprehensive web-based resource for secreted effector proteins of the bacterial types III, IV and VI secretion systems.
An Y, Wang J, Li C, Revote J, Zhang Y, Naderer T, Hayashida M, Akutsu T, Webb GI, Lithgow T, Song J.
Sci Rep, 2017
doi:10.1038/srep41031.
Teaching and Supervisions
2024/2025:
- Working on HPC clusters - Trainer
2023/2024:
- Analysis of single cell RNA-seq data - Trainer
- Introduction to UNIX and bash - Trainer
- High Performance Computing: An Introduction - Trainer
