Yichen (Henry) Liu

Hi and welcome to my personal website!

I’m a Ph.D. student at Maizie Zhou’s lab, Vanderbilt University. My primary research interest is in computational genomics, with a focus on structural variation, long-read sequencing, and pangenome-based genome inference, with the broader goal of building more accurate and scalable models of human genetic variation. My research has examined the strengths and limitations of alignment- and assembly-based structural variant calling and translated these insights into new computational methods, including VolcanoSV for diploid SV discovery, DipSVFilter for sequence-consistency-based filtering, and panMARS for population-scale pangenome inference. I also explore alternative sequencing strategies for improving variant detection.

Education

University of Electronic Science and Technology of China

2016-2020 Sichuan, China

Vanderbilt University

Feb 2021 - Now TN, USA

Research Experience

Awards

First/Co-first Authored Publications

† These authors contributed equally.

  1. Luo, C. †, Liu, Y. H. †, Liu, H., Zhang, Z., Zhang, L., Peters, B. A., & Zhou, X. M. (2026). A little longer, a lot better: Simulation-guided exploration of extended-length single-end barcoded reads for structural variant detection. Bioinformatics Advances, 6(1), vbag267.
  2. Luo, C. †, Liu, Y. H. †, & Zhou, X. M. (2024). VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing. Nature Communications, 15(1), 6956. (Featured in Vanderbilt News)
  3. Liu, Y. H. †, Luo, C. †, Golding, S. G., Ioffe, J. B., & Zhou, X. M. (2024). Tradeoffs in alignment and assembly-based methods for structural variant detection with long-read sequencing data. Nature Communications, 15(1), 2447. (VUSE Best Paper Award)
  4. Liu, Y. H., Zhu, J., Constantinidis, C., & Zhou, X. (2021). Emergence of prefrontal neuron maturation properties by training recurrent neural networks in cognitive tasks. iScience, 24(10), 103178.
  5. Liu, Y. H., Grubbs, G. L., Zhang, L., Fang, X., Dill, D. L., Sidow, A., & Zhou, X. (2021). Aquila_stLFR: diploid genome assembly based structural variant calling package for stLFR linked-reads. Bioinformatics Advances, 1(1), vbab007

Co-authored Publications

  1. Luo, C., Zhou, Z. J., Liu, Y. H., & Zhou, X. M. (2025). FocalSV enables target region–based structural variant assembly and refinement using single-molecule long-read sequencing data. Genome Research, 35(10), 2252-2272.
  2. Hu, Y., Li, Y., Xie, M., Rao, M., Liu, Y. H., & Zhou, X. M. (2024). MaskGraphene: Advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning. Genome Biology 26(1), 380.
  3. Xie, Y., Liu, Y. H., Constantinidis, C., Zhou, X., Rezayat, E., Clark, K., … & Noudoost, B. The cognitive neuroscience of visual working memory-volume II.
  4. Xie, Y., Liu, Y. H., Constantinidis, C., & Zhou, X. (2022). Neural mechanisms of working memory accuracy revealed by recurrent neural networks. Frontiers in Systems Neuroscience, 16, 760864.
  5. Luo, C., Datar, P. A., Liu, Y. H., & Zhou, X. (2022, December). Haplotype-phasing of long-read HiFi data to enhance structural variant detection through a Skip-Gram model. IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2326-2333).

Preprints/Under Review

  1. Zhu, Y., Hu, Y., Xie, M. B., Qin, H., Szul, Z. J., Young, D. M., Yuan, W., Wang, Q., Liu, Y. H., Shen, W., Meltzer, S., & Zhou, X. M. (2026). Benchmarking cell type annotation in spatial transcriptomics: Resolving cellular hierarchies, biological fidelity, and dynamic cell states. Nature Biomedical Engineering (Under Revision)
  2. Yuan, W., Luo, C., Hu, Y., Zhang, L., Wen, Z., Liu, Y. H., … & Zhou, X. M. (2024). CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data. Science Advances (Under Review)

In Preparation

  1. Liu, Y. H., Wang, H., Lu, Z., & Zhou, X. M. panMARS: Pangenome-based variant inference with copy-number-informed k-mer evidence
  2. Liu, Y. H., & Khurana, R. DipSVFilter: Improving structural variant filtering through k-mer and diploid sequence consistency.
  3. Liu, Y. H., Zhang, X., Wang, Y., & Zhou, X. M. Sequence Ambiguity Defines the Performance Gap Between Short- and Long-Read in SNP and INDEL Calling
  4. Xie, M. B., Zhu, Y., Hu, Y., Kim, M., Liu, Y. H., Li, B., Rafat, M., Meltzer, S., Hwang, T. H., Cang, Z., Zhao, Y., & Zhou, X. M. ComFlow: An optimal transport framework for deciphering intra- and intercellular signaling landscapes in two- and three-dimensional tissue systems.
  5. Hu, Y., Wen, Z., Liu, Y. H., Yuan, W., Qin, H., Lin, Z., Yang, H., Klindt, D., Carja, O., & Zhou, X. M. An interpretable framework for decoding transcriptomic foundation models.
  6. Lei, Y., Luo, C., Yuan, W., Bhattarai, P., Liu, Y. H., Mallory, X., & Zhou, X. M. SPARCAL enables accurate detection of spatially resolved germline and somatic SNVs from 10x Visium spatial transcriptomics data.

Posters and Presentations

  1. Liu, Y. H., & Khurana, R. (2026). DipSVFilter: Improving structural variant filtering through diploid sequence consistency. Intelligent Systems for Molecular Biology (ISMB).

Professional Service