Visiting Scientist — Division of Bioinformatics and Biostatistics (Research-to-Review and Return Branch)

Dan Li, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov  

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About  |  Publications  |  Lab Members

Background

Dr. Dan Li joined the Division of Bioinformatics and Biostatistics at FDA’s National Center for Toxicological Research (NCTR) as a postdoctoral fellow after receiving a Ph.D. in Bioinformatics in 2018, and then became a visiting scientist in 2019. Dr. Li has experience in a broad range of computer science and bioinformatics. He started his research at NCTR by being involved in the Sequencing Quality Control Phase 2 (SEQC2) and Massive Analysis and Quality Control Society (MAQC) projects. Dr. Li was also involved in the design and development of tools and applications to support and enhance FDA’s drug review processes. Dr. Li has received several awards for his work at FDA, including the FDA Scientific Achievement Award (Group) and NCTR Director’s Publication Award for Data.

Research Interests

Dr. Li's primary interests include the development of computational algorithms and tools to analyze various types of data—such as genomic and textual data—to provide evidence for quality control, clinical applications, and regulatory activities. His current research focuses on uncovering signals from advanced genomic data—such as deep targeted sequencing—and providing reproducible, high-quality results for regulatory science and precision medicine. Additionally, Dr. Li is interested in Artificial Intelligence (AI) and Machine Learning (ML) algorithms and applications. He is working on developing predictive AI/ML models using various types of datasets to discover safety and efficacy signals and assist with the pharmacovigilance of FDA-regulated products.
 

Professional Societies/National and International Groups

Arkansas Bioinformatics Consortium
Member
2018 – Present

Massive Analysis and Quality Control Society
Member
2019 – Present

MidSouth Computational Biology and Bioinformatics Society
Member
2015 – Present
 

Selected Publications

Deep Oncopanel Sequencing Reveals within Block Position-Dependent Quality Degradation in FFPE Processed Samples.
Zhang Y., Blomquist T.M., Kusko R., Stetson D., Zhang Z., Yin L., Sebra R., Gong B., Lococo J.S., Mittal V.K., Novoradovskaya N., Yeo J.-Y., Dominiak N., Hipp J., Raymond A., Qiu F., Arib H., Smith M.L., Brock J.E., Farkas D.H., Craig D.J., Crawford E.L., Li D., Morrison T., Tom N., Xiao W., Yang M., Mason C.E., Richmond T.A., Jones W., Johann D.J., Shi L., Tong W., Willey J.C., and Xu J. 
Genome Biology. 2022, 23(1):1-21. doi: 10.1186/s13059-022-02709-8.

FDA-Led Consortium Studies Advance Quality Control of Targeted Next Generation Sequencing Assays for Precision Oncology.
Li D., Kusko R., Ning B., Tong W., Johann D.J., Jr., and Xu J.
Precision Cancer Medicine. 2021, 4. doi: 10.21037/pcm-21-29.

Cross-Oncopanel Study Reveals High Sensitivity and Accuracy with Overall Analytical Performance Depending on Genomic Regions.
SEQC2/MAQC Consortium.
Genome Biology. 2021, 22(1):1-23. doi: 10.1186/s13059-021-02315-0.

Evaluating the Analytical Validity of Circulating Tumor DNA Sequencing Assays for Precision Oncology.
SEQC2/MAQC Consortium.
Nature Biotechnology. 2021, 39(9):1115-1128. doi: 10.1038/s41587-021-00857-z.

Gene Regulation Analysis Reveals Perturbations of Autism Spectrum Disorder during Neural System Development.
Li D., Xu J.,  and Yang M.Q. 
Genes. 2021, 12(12):1901. doi: 10.3390/genes12121901.

Linking Pharmacogenomic Information on Drug Safety and Efficacy with Ethnic Minority Populations.
Li D., Xie A.H., Liu Z., Li D., Ning B., Thakkar S., Tong W., and Xu J.
Pharmaceutics. 2020, 12(11):1021. doi: 10.3390/pharmaceutics12111021.