Yang Li (Ph.D.)

Yang Li (Ph.D.)

he/his

Assistant Professor | Expert in Gene Regulatory Networks | Algorithm Design in Omics Data

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

A genome-wide association study prioritizes VRN1-2 as a candidate gene associated with plant height in soybean

Published in Theoretical and Applied Genetics, 2025

Plant height influences soybean yield and stress tolerance, yet its molecular basis remains poorly understood. A genome-wide association study using 6.7 million variants in a global soybean panel identified three QTLs, including one co-localizing with the known gene Dt1 and two novel loci. Within these regions, nine candidate genes were proposed, with VRN1-2 prioritized based on expression patterns and genetic variation. A non-synonymous variant in VRN1-2 was significantly associated with height, and the short-height allele shows signs of domestication selection. These findings reveal novel regulators of plant height and provide targets for molecular breeding.

Recommended citation: Le Wang, Hong Xue, Zhenbin Hu, Yang Li, Tuya Siqin, Hengyou Zhang. (2025). "A genome-wide association study prioritizes VRN1-2 as a candidate gene associated with plant height in soybean." Theoretical and Applied Genetics. 137(84).
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Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Published in Briefings in Bioinformatics, 2024

STREAM advances enhancer-driven gene regulatory network (eGRN) inference by integrating Steiner forest modeling, hybrid biclustering, and submodular optimization to decipher transcription factor (TF)-enhancer-gene relationships from joint single-cell transcriptome and chromatin accessibility data. It outperforms existing methods in TF recovery, enhancer linkage prediction, and gene relation discovery. Applied to Alzheimer’s disease and diffuse small lymphocytic lymphoma datasets, STREAM identifies pseudotime-associated regulatory dynamics and reveals key TF-enhancer-gene interactions underlying tumor cell mechanisms, including critical TF cooperation patterns. This approach enables precise reconstruction of gene regulatory programs in complex biological systems.

Recommended citation: Yang Li, Anjun Ma, Yizhong Wang, Qi Guo, Cankun Wang, Hongjun Fu, Bingqiang Liu, Qin Ma. (2024). "Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data." Briefings in Bioinformatics. 25(5).
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Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph

Published in Frontiers in Genetics, 2024

We propose MPCHG, a novel method for predicting DNA motif pairs using a composite heterogeneous graph model. Unlike existing approaches that rely on single-motif recognition and suffer from local optima, MPCHG identifies motif pairs on paired sequences with significantly improved accuracy. The predicted motifs show increased DNase accessibility and functional consistency, and their associated transcription factor pairs are significantly enriched with known interactions. These findings highlight MPCHG’s potential for uncovering DNA motif cooperativity and advancing our understanding of gene regulation mediated by long-range chromatin interactions.

Recommended citation: Qiuqin Wu, Yang Li, Qi Wang, Xiaoyu Zhao, Duanchen Sun, Bingqiang Liu. (2024). "Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph." Frontiers in Genetics. 15.
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A weighted two-stage sequence alignment framework to identify DNA motifs from ChIP-exo data

Published in Patterns, 2024

TESA is a specialized ChIP-exo motif discovery framework that integrates sequencing coverage with a positional weighting scheme, a graph model, binomial statistics, and a novel “bookend” model to improve motif site prediction. Benchmarking on prokaryotic and eukaryotic datasets demonstrates its superior accuracy and robustness, representing a significant advancement in genomic analysis.

Recommended citation: Yang Li*, Yizhong Wang*, Cankun Wang, Anjun Ma, Qin Ma, Bingqiang Liu. (2024). "A weighted two-stage sequence alignment framework to identify DNA motifs from ChIP-exo data." Patterns. 5(3).
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Human iPSC-based model of COPD to investigate disease mechanisms predict SARS-COV-2 outcome and test preventive immunotherapy

Published in Stem Cells, 2024

Chronic inflammation and impaired repair contribute to COPD, but the lack of accurate ex vivo models limits insight into epithelial-mesenchymal interactions. Using an iPSC-derived alveolosphere co-cultured with fibroblasts, we applied transcriptomic and proteomic analyses to explore COPD pathology and SARS-CoV-2 infection. Single-cell profiling revealed aberrant inflammation, mitochondrial dysfunction, and cell death, mirroring severe alveolar damage in COPD patients with COVID-19. The model also enabled testing of ACE2-neutralizing antibodies, confirming their efficacy in preventing infection. This 3D system offers a powerful platform for dissecting COPD-specific mechanisms and evaluating potential therapeutics.

Recommended citation: Rania Dagher, Aigul Moldobaeva, Elise Gubbins, Sydney Clark, Mia Madel Alfajaro, Craig B Wilen, Finn Hawkins, Xiaotao Qu, Chia Chien Chiang, Yang Li, Lori Clarke, Yasuhiro Ikeda, Charles Brown, Roland Kolbeck, Qin Ma, Mauricio Rojas, Jonathan L Koff, Mahboobe Ghaedi. (2024). "Human iPSC-based model of COPD to investigate disease mechanisms predict SARS-COV-2 outcome and test preventive immunotherapy." Stem Cells. 42(3).
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CEMIG: Prediction of cis-regulatory motif using de Bruijn graph from ATAC-seq

Published in Briefings in Bioinformatics, 2024

CEMIG is a motif discovery algorithm that leverages de Bruijn and Hamming distance graphs to predict transcription factor-binding motifs from ATAC-seq data. Unlike existing methods focused on TF footprints, CEMIG emphasizes motif identification and mapping. Evaluated on 129 ATAC-seq datasets, it outperforms three established tools across four metrics and accurately detects both cell-type-specific and conserved motifs in GM12878 and K562 cell lines. Functional genomic analyses confirm its biological relevance. Developed in C++ for cross-platform use, CEMIG offers a robust, efficient solution for regulatory motif discovery and is freely available at https://github.com/OSU-BMBL/CEMIG.

Recommended citation: Yizhong Wang*, Yang Li*, Cankun Wang, Chan-Wang Jerry Lio, Qin Ma, Bingqiang Li. (2024). "CEMIG: Prediction of cis-regulatory motif using de Bruijn graph from ATAC-seq." Briefings in Bioinformatics. 25(1).
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CD200R signaling contributes to unfavorable tumor microenvironment through regulating production of chemokines by tumor-associated myeloid cells

Published in iScience, 2023

CD200, an immune checkpoint overexpressed in many solid tumors, suppresses anti-tumor immunity. In CD200R-deficient mice, CD200⁺ tumors were more effectively rejected, accompanied by increased infiltration of CD4⁺/CD8⁺ T cells, NK cells, and eosinophils, and reduced neutrophils. Antibody depletion confirmed the essential role of immune effectors. Mechanistically, loss of CD200R altered chemokine expression in tumor-associated myeloid cells, notably increasing CCL24 and eosinophil recruitment. These findings reveal that CD200R signaling shapes an immunosuppressive tumor microenvironment by promoting neutrophil infiltration and excluding effectors, highlighting CD200-CD200R as a promising target for solid tumor immunotherapy.

Recommended citation: Cho-Hao Lin*, Fatemeh Talebian*, Yang Li*, Jianmin Zhu, Jin-Qing Liu, Bolin Zhao, Sujit Basu, Xueliang Pan, Xi Chen, Pearlly Yan, William E. Carson, Gang Xin, Haitao Wen, Ruoning Wang, Zihai Li, Qin Ma, Xue-Feng Bai. (2023). "CD200R signaling contributes to unfavorable tumor microenvironment through regulating production of chemokines by tumor-associated myeloid cells." iScience. 26(6).
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Single-cell biological network inference using a heterogeneous graph transformer

Published in Nature Communications, 2023

Single-cell multi-omics (scMulti-omics) enables simultaneous profiling of multiple molecular layers, revealing cellular heterogeneity and complex mechanisms. We present DeepMAPS, a robust tool for inferring biological networks from scMulti-omics data using a heterogeneous graph model and multi-head graph transformer to capture local and global gene–cell relationships. DeepMAPS outperforms existing methods in cell clustering and network construction, accurately deriving cell-type-specific networks in diverse datasets, including lung tumor CITE-seq and lymphoma scRNA-seq/scATAC-seq data. A user-friendly web server with interactive visualizations enhances accessibility and reproducibility, making DeepMAPS a powerful platform for integrative single-cell analysis.

Recommended citation: Anjun Ma, Xiaoying Wang, Jingxian Li, Cankun Wang, Tong Xiao, Yuntao Liu, Hao Cheng, Juexin Wang, Yang Li, Yuzhou Chang, Jinpu Li, Duolin Wang, Yuexu Jiang, Li Su, Gang Xin, Shaopeng Gu, Zihai Li, Bingqiang Liu, Dong Xu, Qin Ma. (2023). "Single-cell biological network inference using a heterogeneous graph transformer." Nature Communications. 14(1).
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Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer’s disease

Published in Acta Neuropathologica Communications, 2022

The human middle temporal gyrus (MTG) is highly susceptible in early Alzheimer’s disease (AD), yet its molecular vulnerability remains unclear. Using 10× Visium spatial transcriptomics, we profiled AD and control MTG, identifying layer-specific marker genes and differentially expressed genes. Cell-type deconvolution and gene co-expression analyses revealed altered intercellular communication in AD, particularly among microglia, oligodendrocytes, astrocytes, and neurons. Eight gene modules were detected, with four showing significant co-expression changes in AD. Single-molecule FISH validated the cell-type-specific expression of novel and known AD-related genes, providing insights into the spatial and cellular mechanisms underlying MTG vulnerability in early AD.

Recommended citation: Shuo Chen, Yuzhou Chang, Liangping Li, Diana Acosta, Yang Li, Qi Guo, Cankun Wang, Emir Turkes, Cody Morrison, Dominic Julian, Mark E. Hester, Douglas W. Scharre, Chintda Santiskulvong, Sarah XueYing Song, Jasmine T. Plummer, Geidy E. Serrano, Thomas G. Beach, Karen E. Duff, Qin Ma, Hongjun Fu. (2022). "Spatially resolved transcriptomics reveals genes associated with the vulnerability of middle temporal gyrus in Alzheimer’s disease." Acta Neuropathologica Communications. 10(188).
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scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data

Published in Bioinformatics, 2022

Gene expression imputation is vital for single-cell RNA-seq analysis. scGNN 2.0 improves upon its predecessor with a simplified closed-loop architecture, significantly boosting speed and performance. Across eight datasets, it enhances cell clustering accuracy by 85% (adjusted Rand index) and reduces imputation error by 68% (Median L1 Error). Built-in visualizations facilitate interpretation of imputation, clustering, and cell–cell interactions. Additionally, expanded input/output support enhances flexibility, making scGNN 2.0 a powerful and efficient tool for single-cell data analysis.

Recommended citation: Haocheng Gu, Hao Cheng, Anjun Ma, Yang Li, Juexin Wang, Dong Xu, Qin Ma. (2022). "scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data." Bioinformatics. 38(23).
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Microglia coordinate cellular interactions during spinal cord repair in mice

Published in Nature Communications, 2022

Traumatic spinal cord injury (SCI) induces a neuroinflammatory response involving microglia and monocyte-derived macrophages (MDMs), which are hard to distinguish in vivo. By pharmacologically depleting microglia and applying histopathology, tract tracing, and bulk and single-cell RNA sequencing, this study uncovers microglia-specific roles in SCI. Microglia are essential for coordinating CNS glial and immune responses, limiting tissue damage, and promoting recovery. Their absence worsens outcomes, while restoring specific microglia-mediated signaling pathways improves repair. Bioinformatic analysis further suggests that optimal recovery may rely on key ligand–receptor interactions among microglia, astrocytes, and MDMs.

Recommended citation: Faith H Brennan, Yang Li, Cankun Wang, Anjun Ma, Qi Guo, Yi Li, Nicole Pukos, Warren A Campbell, Kristina G Witcher, Zhen Guan, Kristina A Kigerl, Jodie CE Hall, Jonathan P Godbout, Andy J Fischer, Dana M McTigue, Zhigang He, Qin Ma, Phillip G Popovich. (2022). "Microglia coordinate cellular interactions during spinal cord repair in mice." Nature Communications. 13(1).
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Towards cell-type-specific gene regulation in heterogeneous cancer cells

Published in Cancer Research, 2020

CeRIS is a web server designed to identify cell-type-specific alternative regulons (ARs)—groups of chromatin-accessible genes co-regulated by the same transcription regulator (TR)—based on single-cell RNA-Seq data. Unlike conventional regulons, ARs reflect regulatory heterogeneity across cell types, a key factor in cancer evolution and drug response. CeRIS integrates data preprocessing, gene signal modeling, cell clustering, biclustering, motif analysis, and TR mapping to predict ARs. Benchmarking on 30+ datasets shows CeRIS outperforms existing tools in identifying cell-type-specific regulons. Applications to multiple myeloma and human lung data demonstrate its power in discovering oncogenic regulators and mapping heterogeneous gene regulatory landscapes.

Recommended citation: Qin Ma, Anjun Ma, Cankun Wang, Yang Li, Chi Zhang. (2020). "Towards cell-type-specific gene regulation in heterogeneous cancer cells." Cancer Research. 80(16_Supplement).
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