My Resume

Basic Information

I am a Tenure-track Associate Professor and Researcher in Institute for Math & AI, Wuhan University. I obtained Ph.D. (2019) in Computational Mathematics from School of Mathematics and Statistics, Wuhan University, supervised by Prof. Xiufen Zou. During graduate stage, I used to be a visiting scholar in Department of Internal Medicine, University of Iowa (2017-2018) and Department of Biomedical Informatics, The Ohio State University (2018-2019), respectively. After graduation, I started my postdoc at The Ohio State University and University of Michigan, Ann Arbor, under the supervision of Prof. Kin Fai Au, from 2019 to 2024. Prior to joining Wuhan University, I am a Research Fellow in Department of Applied Mathematics at The Hong Kong Polytechnic University. Currently, I serve as a Youth Editorial Board Member for iMeta (2026 IF = 44.4), Genomics, Proteomics and Bioinformatics and GigaScience, and as a reviewer for several international journals, including Nature Biotechnology, Genome Biology, Bioinformatics, Communication Biology, and Artificial Intelligence Review.

I am actively seeking highly motivated Postdoc, PhD and Master students from Mathematic, Statistic, Computer Science, Bioinformatics, or similar fields. Contact me (djwang@whu.edu) if you are interested, please include your CV and brief research statement.

Research Interests

My research lies at the intersection of mathematics, AI, and biomedical sciences, with a focus on bioinformatics, computational systems biology, and complex network analysis. We develop innovative, interpretable algorithms and statistical models to characterize transcriptome complexity and gene regulation using long-read sequencing technologies, particularly Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT).

A central goal of our works is to enable accurate quantification and functional interpretation of gene isoforms and transposable elements (TEs) from bulk, single-cell, and spatial transcriptomic data. By integrating long-read sequencing with multi-omics data and network-based modeling, we aim to build robust computational frameworks that reveal context-specific regulatory mechanisms in complex biological systems.

Our research interests include:

Developing AI-driven and statistical methods for the identification and quantification of gene isoforms and TEs from long-read RNA sequencing data.
Designing reliable and interpretable analytical frameworks for long-read bulk, single-cell, and spatial transcriptomics.
Constructing integrative gene- and isoform-level regulatory networks using diverse genomic and epigenomic data.
Developing network-based approaches to predict and distinguish functional differences among gene isoforms.
Investigating gene isoform diversity and the transcriptional and epigenetic regulation of TEs in early embryogenesis, acute myeloid leukemia, prostate cancer, and endangered species, including the giant panda and red panda, through interdisciplinary collaborations with domain experts.

Through these efforts, we seek to establish scalable bioinformatics platforms that advance a comprehensive understanding of isoform- and TE-mediated regulation in health, disease, development, and biodiversity conservation.

Research Experience

Tenure-track Associate Professor and Researcher

2025.02 - present

Institute for Math & AI, Wuhan University, Wuhan, China

Research Fellow

2024.10 - 2025.01

Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China

Postdoctoral Fellow

2023.02 - 2024.04

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA

Postdoctoral Fellow

2019.06 - 2023.01

Department of Biomedical Informatics, The Ohio State University, USA

Visiting Scholar

2018.10 - 2019.06

Department of Biomedical Informatics, The Ohio State University, USA

Visiting Scholar

2017.11 - 2018.10

Department of Internal Medicine, University of Iowa, USA

Publications

*Co-first author, †Co-corresponding author

Deciphering Transcriptome Complexity via Long-read Sequencing

Chuwen Xu*, Jia Li*, Chenxi Yin*, Keying Li*, ..., Ana Conesa, Yunhao Wang†, Dingjie Wang†

iMeta, Accepted in principle, 2026.

Nanopore Direct RNA Sequencing and the Epitranscriptome: Toward a Complete Map of RNA Regulation

Tianyuan Zhang*, Jia Li*, Chao Tang*, You Wu*, Hao Wu*, Xi-Tong Zhu*, Ziyang Luo*, Hang Qin*, Lishan Ding*, ..., Hu Chen†, Sanqi An†, Xiang Yu†, Lu Chen†, Dingjie Wang†, Shengli Li†, Ling-Ling Chen†, Yanqiang Li†

iMeta, 5, e70136, 2026.

Improving gene isoform quantification with miniQuant

Haoran Li*, Dingjie Wang*, Qi Gao*, Puwen Tan*, Yunhao Wang*, Xiaoyu Cai*, Aifu Li, Yue Zhao, Andrew L. Thurman, Seyed Amir Malekpour, Ying Zhang, Roberta Sala, Andrea Cipriano, Chia-Lin Wei, Vittorio Sebastiano, Chi Song, Nancy Ruonan Zhang, Kin Fai Au†

Nature Biotechnology, 44, 477–489, 2026.

Zygotic activation of transposable elements during zebrafish early embryogenesis

Bo Li*, Ting Li*, Dingjie Wang*, Ying Yang, Puwen Tan, Yunhao Wang, Yungui Yang†, Shunji Jia† and Kin Fai Au†

Nature Communications, 16, Article number: 3692, 2025.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

Francisco Pardo-Palacios*, Dingjie Wang*, Fairlie Reese*, Mark Diekhans*, Sílvia Carbonell-Sala*, Brian Williams*, ..., Christopher Vollmers†, Adam Frankish†, Kin Fai Au†, Gloria M. Sheynkman†, Ali Mortazavi†, Ana Conesa†, Angela N. Brooks†

Nature Methods, 21:1349–1363, 2024.

A network-based computational framework to predict and differentiate functions for gene isoforms using exon-level expression data

Dingjie Wang, Xiufen Zou†, Kin Fai Au†

Methods, 189:54-64, 2021.

Tensor-based mathematical framework and new centralities for temporal multilayer networks

Dingjie Wang, Wei Yu†, Xiufen Zou†

Information Sciences, 512:563-580, 2020.

A new centrality measure of nodes in multilayer networks under the framework of tensor computation

Dingjie Wang, Xiufen Zou

Applied Mathematical Modelling, 54:46-63, 2018.

Trajectory control in nonlinear networked systems and its applications to complex biological systems

Suoqin Jin*, Dingjie Wang*, Xiufen Zou†

SIAM Journal on Applied Mathematics, 78(1):629-649, 2018.

Identifying key nodes in multilayer networks based on tensor decomposition

Dingjie Wang, Haitao Wang, Xiufen Zou

Chaos, 27(6):063108, 2017.

Control energy and controllability of multilayer networks

Dingjie Wang, Xiufen Zou

Advances in Complex Systems, 20(04n05):1750008, 2017.

Crosstalk between pathways enhances the controllability of signalling networks

Dingjie Wang, Suoqin Jin, Xiufen Zou

IET Systems Biology, 10(1):2-9, 2016.

Estimation of control energy and control strategies for complex networks

Dingjie Wang, Suoqin Jin, Fang-xiang Wu, Xiufen Zou

Advances in Complex Systems, 18(07n08):1550018, 2015.

Control of multilayer biological networks and applied to target identification of complex diseases

Wei Zheng, Dingjie Wang, Xiufen Zou

BMC bioinformatics. 20, 271, 2019.

Optimization of controllability and robustness of complex networks by edge directionality

Man Liang, Suoqin Jin, Dingjie Wang, Xiufen Zou

The European Physical Journal B, 89:186, 2016.

Submitted

*Co-first author, †Co-corresponding author

Benchmarking reveals the transcriptional complexity of transposable elements and highlights the quantification advantages of long-read sequencing

Dingjie Wang*†, Shiwen Gao* and Xiufen Zou†

In revision.

Tensor-based multiscale mathematical framework and centrality measures for temporal high-order networks

Dingjie Wang, Yijun Lou and Xiufen Zou

Submitted.

Hypergraph-informed centrality uncovers structural heterogeneity and prioritizes key proteins in tissue-specific higher-order protein interaction networks

Zhentang Xue, Xiufen Zou and Dingjie Wang†

In revision.

Tool Development

LRGASP - The Long-read RNA-seq Genome Annotation Assessment Project (LRGASP) Consortium is organizing a systematic evaluation of different methods for transcript computational identification and quantification using long-read sequencing technologies such as PacBio and Oxford Nanopore.

miniQuant - miniQuant is highly-accurate bioinformatics tool, which ranks genes with quantification errors caused by the ambiguity of read alignments and integrates the complementary strengths of long reads and short reads with optimal combination in a gene- and data-specific manner to achieve more accurate quantification.

miniSim - MiniSim is a realisitic long reads RNA-seq data simulator, which utilizes kernel density estimation (KDE) models trained based on real ONT data to estimate 5' and 3' truncation length.

LongTEQuant - LongTEQuant is a comprehensive tool for identifying and quantifying transposable element (TE)–derived or TE-associated transcripts from long-read RNA-seq data. It integrates transcript abundance estimation with TE-overlap analysis to classify transcripts by their relationship to TEs, including Gene-alone, TE-alone, and TE–Gene chimeric transcripts.

HSCM - HSCM (Higher-order Structural Centrality Methods) are centrality methods based on the higher-order structure of the network, which extract the higher-order structure of network, that is, the situation of nodes participating in the formation of cliques, and then cooperates with other imformation of the original network to weighting the base centrality methods.

IsoNet - IsoNet is a novel tool for inferring co-expression networks based on exon-level expression data.

ENMNFinder - ENMNFinder is an easy and accessible user interface for the calculation and visualization of centralities in multilayer networks.

Grants

Fundamental theories and algorithms of biological big data analysis - National Overseas High-level Talent Program for Youth, Role: PI, 2025-2027.

Multilayer high-order network approaches for gene isoform identification and functional prediction - Project number: 62502351, Natural Science Foundation of China, Role: PI, 2026-2028.

Quantitative and function analysis platform for repetitive genes and gene isoforms in pluripotency regulation and differentiations - Project number: R01HG011469, National Institutes of Health in USA, Role: Key project member, PI: Prof. Kin Fai Au, 2021-2025.

Multilayer network modeling and optimal control of complex diseases - Project number: 61672388, Natural Science Foundation of China, Role: Key project member, PI: Prof. Xiufen Zou, 2017-2020.