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 development programming and somatic cell reprogramming:
A method based on energy indicators to quantitatively describe the cell states of early embryonic development and somatic cell reprogramming in mammals in the Waddington landscape was proposed (Research, 2023, national invention patent ZL 202211397106.0). The multi-omics data resource library and cloud platform EmAtlas for spatiotemporal regulation of early mammalian development and the multi-species embryonic development temporal activation database EmExplorer were built (Open Biol. 2019; Software 2019SR0463259), and the artificial intelligence prediction platform EmPredictor for cell fate transition was established (Bioinformatics, 2021; Brief Bioinform. 2021; Software 2019SR0463259; Science and Technology Daily reported). The molecular barriers of embryonic genome activation (ZGA) in the comprehensive somatic cell reprogramming process were screened (J Cell Mol Med, 2022; Mol Ther Nucleic Acids. 2020), epigenetic regulatory barriers such as R-loop and histone modification coordinated regulation (BBA-Gene Regulatory Mechanisms. 2022a,b; Theriogenology, 2020; Int J Mol Sci. 2021,2022), completed the theoretical biological description of the competitive and coordinated targeted regulation of cell reprogramming by pioneer transcription factors (Comput Struct Biotechnol J, 2019; Brief Bioinform. 2021), improved the species-specific embryonic genome activation comparative transcriptome method (Mol Ther Nucleic Acids. 2020; ESI 1% highly cited paper), and analyzed the sequence basis of the function of the key demethylation proteins TET, ALKBH, and KDM families based on sequence conservation and selective evolutionary pressure (Cell Mol Life Sci. 2021; Brief Bioinform. 2019, 2021; ESI 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
Yanan Zhao, Chunshen Lon, Wenjing Shang, Zhihao Si, Zhigang Liu*, Zhenxing Feng*, Yongchun Zuo*. A composite scaling network of EfficientNet for improving spatial domain identification performance.Communications Biology.2024 7(1):1567(2023 IF:5.2).
Yan Hong, Hanshuang Li, Chunshen Long, Pengfei Liang, Jian Zhou, Yongchun Zuo*.An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.Fundamental Research. 2024 4(4):770-776 (2023 IF:6.2).
Hanshuang Li, Chunshen Long, Yan Hong, Liaofu Luo, Yongchun Zuo*. Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator,Research, 2023, 6: 0118 (2021 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 (2021 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 (2021 IF: 19.160).