Zhen Wei PhD

Assistant Professor

Zhen Wei is a bioinformatician who joined the faculty at the Department of Biological Sciences at Xi’an Jiaotong-Liverpool University in 2020. His current research focuses on improving the metrics of predictive models in RNA genomics. He is also interested in the batch effect normalization techniques in NGS data, including their applications in various novel functional genomic technologies.


  • PhD in Bioinformatics, University of Liverpool - 2020


  • Assistant Professor in Bioinformatics, XJTLU, 2020 to present


  • Feature engineering in the prediction of genomic markers: contributing to the search for and building of genomic features that will facilitate the machine learning prediction of the genomic molecular markers such as DNA, RNA, and protein modifications distributed non-randomly across the genome coordinates.
  • Software development in R/Bioconductor for the genomic metric extraction: building software packages in R which can facilitate the automatic comprehension of genomic features that are highly interpretable and predictive for the generic genomic data analysis. 

  • Functional genomics: inferring interactive relationships between genes, proteins, and molecular modifications from HTP assays with Bayesian network structural learning.
  • Technical artifact correction in high throughput sequencing: understanding technical biases in NGS assays using a data generating model; creating a computational pipeline that could detect and correct technical artefacts in various NGS datasets.
  • Investigating the effects of technical biases on biological conclusions: exploring improvement of biological conclusions following the correction of major technical artefacts in published NGS studies.


  • Xichen Zhao, Yuxin Zhang, Daiyun Hang, Jia Meng, and Zhen Wei*. "Detecting RNA modification using direct RNA sequencing: a systematic review." Computational and Structural Biotechnology Journal, 2022
  • Bowen Song#, Xuan Wang#, Zhanmin Liang#, Jiongming Ma#, Daiyun Huang, Yue Wang, João Pedro de Magalhães, and Zhen Wei* RMDisease V2. 0: an updated database of genetic variants that affect RNA modifications with disease and trait implications. Nucleic Acids Research, 2022
  • Lian Liu, Bowen Song, Kunqi Chen, Yuxin Zhang, João Pedro De Magalhães, Daniel J. Rigden, Xiujuan Lei, and Zhen Wei*. WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction. Methods, 2022
  • Jiongming Ma#, Bowen Song#, Zhen Wei*, Daiyun Huang, Yuxin Zhang, Jionglong Su, Joao Pedro de Magalhaes, Daniel J. Rigden, Jia Meng, and Kunqi Chen*. m5C-Atlas: A comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome. Nucleic acids research, 2022
  • Bowen Song, Kunqi Chen, Yujiao Tang, Jialin Ma, Jia Meng, Zhen Wei*. PSI-MOUSE: Predicting Mouse Pseudouridine Sites From Sequence and Genome-Derived Features. Evolutionary Bioinformatics, 2020
  • Bowen Song#, Yujiao Tang#, Kunqi Chen*, Zhen Wei, Rong Rong, Zhiliang Lu, Jionglong Su, João Pedro de Magalhães, Daniel J. Rigden5 and Jia Meng. m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human. Bioinformatics, 2020
  • Hao Xue#, Zhen Wei*, Kunqi Chen, Yujiao Tang, Xiangyu Wu, Jionglong Su and Jia Meng. Prediction of RNA methylation status from gene expression data using classification and regression methods. Evolutionary Bioinformatics, 2020
  • Lian Liu#, Xiu-Juan Lei*, Jia Meng, Zhen Wei*. WITMSG: Large-scale prediction of human intronic m6A RNA methylation sites from sequence and genomic features. Current Genomics, 2020
  • Lian Liu#, Xiu-Juan Lei*, Zeng-Qiang Fang, Yu-Jiao Tang, Jia Meng, Zhen Wei*. LITHOPHONE: improving lncRNA methylation site prediction using an ensemble predictor. Frontiers in genetics, 2020
  • Kun-Qi Chen.#, Zhen Wei#, Qing Zhang#, Xiang-Yu Wu#, Rong Rong, Zhi-Liang Lu, Jiong-Long Su, Joao Pedro De Magalhães, Daniel J. Rigden and Jia Meng. WHISTLE: a high-accuracy map of the human N6- methyladenosine (m6A) epitranscriptome predicted using a machine learning approach. Nucleic Acids Research, 2019
  • Zhen Wei #, Subbarayalu Panneerdoss#, Santosh Timilsina#, Jing-Ying Zhu#, Tabrez A. Mohammad, Zhi-Liang Lu, Joao Pedro De Magalhães, Yi-Dong Chen, Rong Rong and Yu-Fei Huang. Topological characterization of human and mouse m5C Epitranscriptome revealed by bisulfite sequencing. International journal of genomics, 2018
  • Xiao-Dong Cui#, Zhen Wei#, Lin Zhang, Hui Liu, Lei Sun, Shao-Wu Zhang, Yu-Fei Huang and Jia Meng*. “Guitar: An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features.” BioMed Research International, 2016
  • Hui Liu#, Huai-Zhi Wang#, Zhen Wei#, Song-Yao Zhang, Gang Hua, Shao-Wu Zhang, Lin Zhang, Shou-Jiang Gao, Jia Meng*, Xing Chen* and Yufei Huang*. Met-Db V2.0: Elucidating Context-Specific Functions of N6- Methyl-Adenosine Methyltranscriptome, Nucleic Acids Research, 2017
Zhen Wei