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Liu Lab

The research laboratory led by Yunlong Liu, PhD, develops innovative computational and high-throughput genomics approaches in studying gene regulation, with the focus on splicing regulation.

The Liu lab is located in the Center for Computational Biology. Specific research areas include developing computational algorithms and high-throughput genomics experimental approaches in studying functions of genetic variants in complex diseases, designing methodologies on next generation sequencing and single-cell analytics, and understanding regulatory mechanisms and translational impact of alternative splicing in a variety of disease systems, including cancer, diabetes, neurological diseases, and addiction.

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Active Research

The Liu Laboratory (Laboratory for Computational Genomics) uses systems biology approaches to understand regulatory mechanisms of gene expression, including transcriptional regulation, post-transcriptional regulation, and epigenetic regulation. This area involves several interdisciplinary components, including functional genomics, genetics, computational and statistical modeling, computer science/engineering, and data management.
  • Regulation of mRNA Splicing by Intronic Genetic Variants

    PI: Yunlong Liu, PhD

    Agency: NIH-NCI

    Period: 4/1/2016 – 3/31/2020

    The goal of this project is to design and implement a set of bioinformatics algorithms and high-throughput experimental assays for prioritizing function al intronic variants that contribute to phenotypic differences by affecting pre0mRNA splicing.

  • Computational Tools for Top Down Mass Spectrometry Based Proteoform Identification and Proteogenomics

    PI: Yunlong Liu, PhD

    Agency: NIH-NIGMS

    Period: 4/1/2016 – 3/31/2020

    This project addresses the proteoform identification problem by top-down mass spectrometry and by top-down mass spectrometry-based proteogenomics.

Recent Publications

  • 2019
    Lin H, Hargreaves KA, Li R, Reiter JL, Mort M, Cooper DN, Zhou Y, Eadon MT, Dolan ME, Ipe J, Skaar T, Liu Y. RegSNPs-intron: a computational framework for prioritizing intronic single nucleotide variants in Human Genetic Disease, bioRxiv 515171(2019); doi:

    Rao X, Thapa KS, Chen AB, Lin H, Gao H, Reiter JL, Hargreaves KA, Ipe J, Lai D, Xuei X, Gu H, Kapoor M, Farris SP, Tischfield J, Foroud T, Goate AM, Skaar TC, Mayfield RD, Edenberg HJ, Liu Y. Allele-Specific Expression and High-Throughput Reporter Assay Reveal Functional Variants in Human Brains with Alcohol Use Disorders, bioRxiv 514992 (2019); doi:

  • 2017
    Li M, Feng W, Zhang X, Yang Y, Wang K, Mort M, Cooper DN, Wang Y, Zhou Y, Liu Y. ExonImpact: Prioritizing Pathogenic Alternative Splicing Events. Hum Mutat. 2017. doi: 10.1002/humu.23111. [Epub ahead of print] PubMed PMID: 27604408.

    Zhang X, Li M, Lin H, Rao X, Feng W, Yang Y, Mort M, Cooper DN, Wang Y, Wang Y, Wells C, Zhou Y,  Liu Y. RegSNPs-splicing: A tool for prioritizing synonymous single nucleotide substitution, Human Genetics, 2017

    Hao Y, Xue X, Li L, Nakshatri H, Edenberg HJ, and Liu Y. RareVar: A Framework for Detecting Low Frequency Single Nucleotide Variants, Journal of Computational Biology, 2017.

  • 2014
    Zhang X, Lin H, Zhao H, Hao Y, Mort M, Cooper DN, Zhou Y, Liu Y. Impact of human pathogenic micro-insertions and micro-deletions on post-transcriptional regulation. Hum Mol Genet. 2014 Jun 1;23(11):3024-34. doi: 10.1093/hmg/ddu019. Epub 2014 Jan 16. PubMed PMID: 24436305; PubMed Central PMCID: PMC4014196.

Research Team

196-Liu, Yunlong

Yunlong Liu, PhD, MS

Director, Center for Computational Biology & Bioinformatics

Read Bio

Additional Research Team Members

Other research team members in the Liu Lab include Jill Reiter, PhD (Visiting Assistant Research Professor), Wenting Wu, PhD (Assistant Research Professor), Hongyu Gao, PhD (Assistant Scientist), Guanglong Jiang, PhD (Bioinformatics Analyst), Rudong Li, PhD (Post-doctoral Fellow), Andy B. Chen, PhD (Post-doctoral Fellow), Muyi Liu, PhD (Post-doctoral Fellow), Xiaona Chu (Research Analyst/Technician), Kerry Sanders (Research Analyst/Technician), Edward R. Simpson, Steven Chen, Duojiao Chen, and Chuanpeng Dong.