Research Direction
Welcome to the Bioinformatics Lab of Inner Mongolia University. Our research interests mainly focus on:
Research on “digital embryos” related to early embryonic developmental programming and somatic cell reprogramming:
Developed 3d-OT, a spatial multi-omics heterogeneous-section alignment algorithm, and reconstructed the 3D spatiotemporal developmental trajectories of mouse embryos (Nature Methods, 2026); proposed a method for quantitatively characterizing cellular energy states during mammalian early embryonic development and somatic cell reprogramming within the Waddington landscape (Research, 2023; Chinese invention patent ZL 202211397106.0); constructed EmAtlas, one of the most comprehensive multi-species, multi-omics resources for mammalian “digital development” (Nucleic Acids Research, 2023), and LivestockDev, a database for livestock reproduction and development (Zoological Research, 2025); established EmPredictor, an artificial intelligence-based platform for predicting cell fate transitions (Bioinformatics, 2021; Briefings in Bioinformatics, 2021); systematically identified molecular barriers to embryonic genome activation (ZGA) during somatic cell reprogramming, together with epigenetic regulatory barriers involving coordinated histone modifications (BBA – Gene Regulatory Mechanisms, 2022a,b; Communications Biology, 2026); provided a theoretical biological framework for the competitive and cooperative targeting of cell reprogramming by pioneer transcription factors (Briefings in Bioinformatics, 2021); and proposed a modular strategy based on the central dogma (DNA–RNA–protein) to elucidate the non-homologous functional divergence of the three demethylase families TET, ALKBH, and KDM, as well as their substrate-adaptation patterns for the specific catalytic modification of DNA, RNA, and proteins (Cellular and Molecular Life Sciences, 2021; Briefings in Bioinformatics, 2019, 2021; ESI Top 1% Highly Cited Paper).
Research on bioinformatics algorithms for multimodal data of single-cell omics and spatiotemporal omics:
A method IDTI (Fundamental Research. 2024) for temporal single-cell trajectory inference based on minimum discrete increments was proposed, and a method EfNST (Communications Biology. 2024) based on the EfficientNet convolutional neural network for spatial domain analysis of spatiotemporal transcriptomes was proposed. The first method and platform for predicting probiotics based on artificial intelligence models was constructed (Brief Bioinform. 2021, national invention patent ZL202211397170.9). Is it possible to use fewer amino acid categories to analyze and design protein sequences, structures, and functions from scratch? We have developed artificial intelligence (AI) research on protein function based on the reduced amino acid alphabet (RAAC) (Peptides. 2009), built the analysis and feature extraction platform RaacBook (Bioinformatics. 2017; ESI 1% highly cited paper; Database, 2019; Software Author 2019SR0467812), and developed RaacLogo (Brief Bioinform. 2021; ESI 1% highly cited paper) and RaacFold (Nucleic Acids Research, 2022; reported by the official website of the People's Government of Inner Mongolia). Early work also includes: developing a method for establishing a promoter machine learning prediction model based on DNA spatial geometric description parameters (Prog Biochem Biophys 2009; Physica A 2010; Genomics 2011), organically combining discrete increments (ID) with the K nearest neighbor algorithm, and successively proposing the K nearest neighbor average discrete increment algorithm K-MID (Amino Acids 2010) and the K nearest neighbor minimum discrete increment algorithm KNN-ID (Amino Acids. 2013; Mol Biosyst. 2015), etc.
New Publications
Bingjie Dai, Litai Yi, Peizhuo Wang, Hanshuang Li, Pengwei Hu, Yancheng Song, Jixiang Xing, Zhenxing Feng, Zhiyuan Yuan*, Yongchun Zuo*. 3d-OT: A Deep Geometry-aware Framework for Heterogeneous Slices Alignment of Spatial Multi-omics, Nature Methods. 2026, 23:760–771.(IF:28.3).
Xinyu Zhao, Jie Wu, Yingxue Che, Chunshen Long, Yongqiang Xing, Hanshuang Li*, Yongchun Zuo*.Machine and Deep Learning Reveal Sequence Determinants Encoding Bivalent Histone Modifications, Communications Biology, 2026, 9(1):491. (IF:5.8).
Hanshuang Li, Chunshen Long, Yan Hong, Liaofu Luo, Yongchun Zuo*. Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator,Research, 2023, 6: 0118 (IF: 11.036).
Lei Zheng, Pengfei Liang, Chunshen Long, Haicheng Li, Hanshuang Li, Yuchao Liang, Xiang He, Qilemuge Xi, Yongqiang Xing*, Yongchun Zuo*. EmAtlas: a comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis.Nucleic Acids Research, 2023, 51(D1), D924-D932 (IF: 19.160).
Lei Zheng, Dongyang Liu, Yuan Alex Li, Siqi Yang, Yuchao Liang, Yongqiang Xing*, Yongchun Zuo*.RaacFold: a webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets.Nucleic Acids Research, 2022, 50(W1), W633-W638 (IF: 19.160).