Biostatistics & Computational Biology Branch

Leping Li, Ph.D.
Leping Li, Ph.D.
Principal Investigator
Tel 984-287-3836
Fax 919-541-4311
[email protected]
Biographical Sketch (199KB)
NIH Intramural ProfileLeping Li, Ph.D.
P.O. Box 12233
Mail Drop A3-03
Durham, NC 27709

Research Summary

Leping Li, Ph.D., is a senior investigator in the Biostatistics and Computational Biology Branch. His research program focuses on computational biology, and his staff is a multidisciplinary team. The early focus of the group was the development and implementation of computational/statistical methods to mine high-dimensional genomic data.

A few years ago, the team has started a new initiative – mining a large collection of sleep electroencephalogram (EEG) data for disease classification and biomedical discovery for translational research. The team has obtained ‘in-laboratory’ EEG data for more than 5,000 patients from the University of North Carolina at Chapel Hill. Additional EEG data continue to be collected.

The team discovered that patients with diabetes had significantly reduced sleep spindle wave density than controls; the size of that difference was correlated with markers of disease severity (complications and glycemic control). To follow up this finding, the team is designing prospective human studies and using animal models to try to understand the bidirectional relationship between brain and diabetes.

Ongoing projects in the lab include:

Bidirectional Relationship Between Brain and Diabetes – The team is collaborating with Dr. Janice Hwang, M.D., M.H.S., Chief of the Division of Endocrinology and Metabolism at UNC-Chapel Hill and Zheng (Jane) Fan, M.D., Director of Sleep Lab at UNC-Chapel Hill to design a prospective study to try to understand how brain affects diabetes and diabetes affects the brain’s ability to sense and regulation peripheral glucose level.

Deep Learning (DL) on EEG Data – This is one of our major focuses as part of our ongoing project on disease classification. We are developing and implementing DL algorithms, especially, convolutional deep neural networks, long short-term memory (LSTM), and variational autoencoder (VAE) for both the time-domain and frequency-domain EEG data.

Disease Classification Using EEG Power Data – The team has nominated a set of novel EEG features for disease classification based on the power in various frequency bands of the EEG data. Particularly, the group is interested in identifying EEG features that may be indicative of neurological diseases such as major depression, post-traumatic stress disorders (PTSD), autism, and Alzheimer’s disease.

Sleep Cycle and Spectrogram Analysis and Visualization – The team had developed an easy-to-use tool for sleep cycle identification and visualization (SSAVE). SSAVE is the first open-source software that takes sleep-stage annotations and EEG signals as input, identifies and characterizes the sleep cycles, and produces a hypnogram and its time-matched EEG spectrogram. SSAVE fills an important gap for the rapidly growing field of sleep medicine by providing an. SSAVE can be used as a Python package, as a desktop standalone tool or through a web portal.

Li’s team includes Staff Scientist Yuanyuan Li, Ph.D., Computer Scientists Amlan Talukder, Ph.D. (Deep Learning) and Nishanth Anandanadarajah, Ph.D. (Deep Learning), Electrical Engineer Deryck Yeung, Ph.D. (Special Volunteer from Trinity University), Statistician David Umbach, Ph.D., and student Ethan Xu (Special Volunteer).

Software

  • ART
    Set of Simulation Tools
  • coMotif
    A three-component mixture framework to model the joint distribution of two motifs as well as the situation where some sequences contain only one or none of the motifs.
  • GADEM
    An unbiased de novo motif discovery tool implementing an expectation-maximization (EM) algorithm.
  • GA/KNN
    Selects the most discriminative variables for sample classification and may be used for analysis of microarray gene expression data, proteomic data or other high-dimensional data.
  • SSAVE 
    SSAVE: Sleep Cycle and Spectrogram Analysis and Visualization for Electroencephalography Data
  • T-KDE
    T-KDE will identify the locations of constitutive binding sites. T-KDE, which combines a binary range tree with a kernel density estimator, is applied to ChIP-seq data from multiple cell lines.

Selected Publications

  1. Talukder A, Yeung D, Li Y, Anandanadarajah N, Umbach DM, Fan Z, Li L. Comparison of power spectra from overnight electroencephalography between patients with Down syndrome and matched control subjects. J Sleep Res. 2024 Feb 27:e14187. doi: 10.1111/jsr.14187. [Abstract]
  2. Yeung D, Talukder A, Shi M, Umbach DM, Li Y, Motsinger-Reif A, Fan Z, Li L. Differences in sleep spindle wave density between patients with diabetes mellitus and matched controls: implications for sensing and regulation of peripheral blood glucose. medRxiv [Preprint]. 2024 Apr 12:2024.04.11.24305676. doi: 10.1101/2024.04.11.24305676. [Abstract]
  3. Talukder A, Li Y, Yeung D, Shi M, Umbach, DM, Fan, Z, Li L. OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics. Computers in Biology and Medicine, to appear.
  4. Li L, Talukder A, Yeung D, Li Y, Umbach, DM, Gilmore, J, Fan, Z. Comparison of overnight trends in relative power for specific frequency bands, sleep stages, and brain regions between patients with depressive disorder and matched control subjects. In review.
  5. Li L, Perera L, Varghese SA, Shiloh-Malawsky Y, Hunter SE, Sneddon TP, Powell CM, Matera AG, Fan Z. 2023. A homozygous missense variant in the YG box domain in an individual with severe spinal muscular atrophy: a case report and variant characterization. Front Cell Neurosci. 17:1259380. doi: 10.3389/fncel.2023.1259380. [Abstract]
  6. Li W, Nakano H, Fan W, Li Y, Sil P, Nakano K, Zhao F, Karmaus PW, Grimm SA, Shi M, Xu X, Mizuta R, Kitamura D, Wan Y, Fessler MB, Cook DN, Shats I, Li X, Li L. 2023. DNASE1L3 enhances antitumor immunity and suppresses tumor progression in colon cancer. JCI Insight. 8(17):e168161. doi: 10.1172/jci.insight.168161. [Abstract]
  7. Talukder A, Li Y, Yeung D, Umbach DM, Fan Z, Li L. SSAVE: A tool for analysis and visualization of sleep periods using electroencephalography data. 2023. Front Sleep. 2:1102391. doi: 10.3389/frsle.2023.1102391. [Abstract]
  8. Li L, Umbach DM, Li Y, Halani P, Shi M, Ahn M, Yeung DSC, Vaughn B, Fan ZJ. Sleep apnoea and hypoventilation in patients with five major types of muscular dystrophy. 2023. BMJ Open Respir Res. 10(1):e001506. doi: 10.1136/bmjresp-2022-001506. [Abstract]
  9. Nodzenski M, Shi M, Krahn JM, Wise AS, Li Y, Li L, Umbach DM, Weinberg CR. GADGETS: a genetic algorithm for detecting epistasis using nuclear families. 2022. Bioinformatics. 38(4):1052-1058. doi: 10.1093/bioinformatics/btab766. [Abstract]
  10. Tang S, Zhang Z, Oakley RH, Li W, He W, Xu X, Ji M, Xu Q, Chen L, Wellman AS, Li Q, Li L, Li JL, Li X, Cidlowski JA, Li X. 2021. Intestinal epithelial glucocorticoid receptor promotes chronic inflammation-associated colorectal cancer. JCI Insight. 6(24):e151815. doi: 10.1172/jci.insight.151815. PMID: 34784298. [Abstract]
  11. Li Y, Umbach DM, Krahn JM, Shats I, Li X, Li L. 2021. Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines. BMC Genomics. 22(1):272. [Abstract Li Y, Umbach DM, Krahn JM, Shats I, Li X, Li L. 2021. Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines. BMC Genomics. 22(1):272.]
  12. Gagliano T, Shah K, Gargani S, Lao L, Alsaleem M, Chen J, Ntafis V, Huang P, Ditsiou A, Vella V, Yadav K, Bienkowska K, Bresciani G, Kang K, Li L, Carter P, Benstead-Hume G, O'Hanlon T, Dean M, Pearl FM, Lee SC, Rakha EA, Green AR, Kontoyiannis DL, Song E, Stebbing J, Giamas G. 2020. PIK3Cδ expression by fibroblasts promotes triple-negative breast cancer progression. J Clin Invest; doi: 10.1172/JCI128313 [Online 3 March 2020]. [Abstract Gagliano T, Shah K, Gargani S, Lao L, Alsaleem M, Chen J, Ntafis V, Huang P, Ditsiou A, Vella V, Yadav K, Bienkowska K, Bresciani G, Kang K, Li L, Carter P, Benstead-Hume G, O'Hanlon T, Dean M, Pearl FM, Lee SC, Rakha EA, Green AR, Kontoyiannis DL, Song E, Stebbing J, Giamas G. 2020. PIK3Cδ expression by fibroblasts promotes triple-negative breast cancer progression. J Clin Invest; doi: 10.1172/JCI128313 [Online 3 March 2020].]
  13. Yuanyuan Li, David M. Umbach, Adrienna Bingham, Qi-Jing Li, Yuan Zhuang and Leping Li. Putative Biomarkers for Predicting Tumor Sample Purity Based on Gene Expression Data. BMC Genomics Volume 20, Article number: 1021 (2019). [Abstract Yuanyuan Li, David M. Umbach, Adrienna Bingham, Qi-Jing Li, Yuan Zhuang and Leping Li. Putative Biomarkers for Predicting Tumor Sample Purity Based on Gene Expression Data. BMC Genomics Volume 20, Article number: 1021 (2019).]
  14. Kang K, Meng Q, Shats I, Umbach DM, Li M, Li Y, Li X, Li L. CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. PLoS Comput Biol., 2019,15(12):e1007510. [Abstract Kang K, Meng Q, Shats I, Umbach DM, Li M, Li Y, Li X, Li L. CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data. PLoS Comput Biol., 2019,15(12):e1007510.]
  15. Igor Shats, Jason G. Williams, Juan Liu, Leesa J. Deterding, Chaemin Lim, Xiaojiang Xu, Thomas A. Randall, Ethan Lee, Wenling Li, Wei Fan, Jian-Liang Li, Marina Sokolsky, Alexander V. Kabanov, Leping Li, Jason W. Locasale and Xiaoling Li. Bacteria Boost Mammalian Host NAD Metabolism by Engaging the Deamidated Biosynthesis Pathway. Cell Metabolism, accepted. [Abstract Igor Shats, Jason G. Williams, Juan Liu, Leesa J. Deterding, Chaemin Lim, Xiaojiang Xu, Thomas A. Randall, Ethan Lee, Wenling Li, Wei Fan, Jian-Liang Li, Marina Sokolsky, Alexander V. Kabanov, Leping Li, Jason W. Locasale and Xiaoling Li. Bacteria Boost Mammalian Host NAD Metabolism by Engaging the Deamidated Biosynthesis Pathway. Cell Metabolism, accepted.]
  16. Nguyen TA, Grimm SA, Bushel PR, Li J, Li Y, Bennett BD, Lavender CA, Ward JM, Fargo DC, Anderson CW, Li L, Resnick MA, Menendez D. Revealing a human p53 universe. Nucleic Acids Res, 2018, 46(16):8153-8167. [Abstract Nguyen TA, Grimm SA, Bushel PR, Li J, Li Y, Bennett BD, Lavender CA, Ward JM, Fargo DC, Anderson CW, Li L, Resnick MA, Menendez D. Revealing a human p53 universe. Nucleic Acids Res, 2018, 46(16):8153-8167.]
  17. Ungewitter EK, Rotgers E, Kang HS, Lichti-Kaiser K, Li L, Grimm SA, Jetten AM, Yao HH. Loss of Glis3 causes dysregulation of retrotransposon silencing and germ cell demise in fetal mouse testis. Sci Rep. 2018, 8(1):9662. [Abstract Ungewitter EK, Rotgers E, Kang HS, Lichti-Kaiser K, Li L, Grimm SA, Jetten AM, Yao HH. Loss of Glis3 causes dysregulation of retrotransposon silencing and germ cell demise in fetal mouse testis. Sci Rep. 2018, 8(1):9662.]
  18. Miao YL, Gambini A, Zhang Y, Jefferson WN, Padilla-Banks E, Bernhardt ML, Huang W, Li L, Williams CJ. Mediator complex component MED13 regulates the mouse oocyte-to-embryo transition and is required for postimplantation development. Biol Reprod. 2018, 98(4):449-464. [Abstract Miao YL, Gambini A, Zhang Y, Jefferson WN, Padilla-Banks E, Bernhardt ML, Huang W, Li L, Williams CJ. Mediator complex component MED13 regulates the mouse oocyte-to-embryo transition and is required for postimplantation development. Biol Reprod. 2018, 98(4):449-464.]
  19. Roy S, Moore AJ, Love C, Reddy A, Rajagopalan D, Dave S, Li L, Murre C, Zhuang Y. Id proteins suppress E2A-driven innate-like T cell development prior to TCR selection. Front Immunol. 2018, 9:42. [Abstract Roy S, Moore AJ, Love C, Reddy A, Rajagopalan D, Dave S, Li L, Murre C, Zhuang Y. Id proteins suppress E2A-driven innate-like T cell development prior to TCR selection. Front Immunol. 2018, 9:42.]
  20. Li Y, Krahn JM, Flake GP, Umbach DM, Li L. Toward predicting metastatic progression of melanoma based on gene expression data. Pigment cell & melanoma research 2015 28(4):453-463. [Abstract Li Y, Krahn JM, Flake GP, Umbach DM, Li L. Toward predicting metastatic progression of melanoma based on gene expression data. Pigment cell & melanoma research 2015 28(4):453-463.]
  21. Wells, M.L., Washington, O.L., Hicks, S.N., Nobile, C.J., Hartooni, N., Wilson, G.M., Zucconi, B.E., Huang, W., Li, L., Fargo, D.C., Blackshear, P.J. Post-transcriptional regulation of transcript abundance by a conserved member of the tristetraprolin family in Candida albicans. Mol. Microbiol., 2015, 95(6):1036-1053.   [Abstract Wells, M.L., Washington, O.L., Hicks, S.N., Nobile, C.J., Hartooni, N., Wilson, G.M., Zucconi, B.E., Huang, W., Li, L., Fargo, D.C., Blackshear, P.J. Post-transcriptional regulation of transcript abundance by a conserved member of the tristetraprolin family in Candida albicans. Mol. Microbiol., 2015, 95(6):1036-1053.  ]
  22. Choi, Y.-J., Lai, W.S., Fedic, R., Stumpo, D.J, Huang, W., Li, L., Perera, L., Brewer, B.Y., Brewer, B.Y., Wilson, G.M., Mason, J.M., Blackshear, P.J. The Drosophila Tis11 protein and its effects on mRNA expression in flies. J. Biol. Chem., 2014, 289(51):35042-60. [Abstract Choi, Y.-J., Lai, W.S., Fedic, R., Stumpo, D.J, Huang, W., Li, L., Perera, L., Brewer, B.Y., Brewer, B.Y., Wilson, G.M., Mason, J.M., Blackshear, P.J. The Drosophila Tis11 protein and its effects on mRNA expression in flies. J. Biol. Chem., 2014, 289(51):35042-60.]
  23. Niu L, Huang W, Umbach DM, Li L. IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data. BMC genomics, 2014, 15:862. [Abstract Niu L, Huang W, Umbach DM, Li L. IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data. BMC genomics, 2014, 15:862.]
  24. Zhang, X., Li, B., Ma, L., Li, L., Zheng, D., Li W., Chu, M., Mailman, R.B., Archer, T.K., Wang, Y. Transcriptional repression by specific SWI/SNF components affects pluripotency of human embryonic stem cells. Stem Cell Report, 2014, 3(3):460-474. [Abstract Zhang, X., Li, B., Ma, L., Li, L., Zheng, D., Li W., Chu, M., Mailman, R.B., Archer, T.K., Wang, Y. Transcriptional repression by specific SWI/SNF components affects pluripotency of human embryonic stem cells. Stem Cell Report, 2014, 3(3):460-474.]
  25. Hewitt, S.C., Li, L., Grimm, S.A., Winuthayanon, W., Hamilton, K.J., Pockette, B., Rubel, CA., Pedersen, L.C., Fargo, D., Lanz, R.B., DeMayo, F.J., Schutz, G., Korach, K.S. Novel DNA motif binding activity observed in vivo with an estrogen receptor alpha mutant mouse. Mol. Endocrinol. 2014, 28(6):899-911. [Abstract Hewitt, S.C., Li, L., Grimm, S.A., Winuthayanon, W., Hamilton, K.J., Pockette, B., Rubel, CA., Pedersen, L.C., Fargo, D., Lanz, R.B., DeMayo, F.J., Schutz, G., Korach, K.S. Novel DNA motif binding activity observed in vivo with an estrogen receptor alpha mutant mouse. Mol. Endocrinol. 2014, 28(6):899-911.]
  26. Li, Y., Umbach, D.M., Li, L. T-KDE: A method for analyzing genome-wide protein binding pat-terns from ChIP-seq data. BMC Genomics, 2014, 15:27. [Abstract Li, Y., Umbach, D.M., Li, L. T-KDE: A method for analyzing genome-wide protein binding pat-terns from ChIP-seq data. BMC Genomics, 2014, 15:27.]
  27. Li, Y., Hamilton, K.J., Lai, A.Y., Burns, K.A., Li, L., Wade, P.A., Korach, K.S. Diethylstilbestrol (DES)-stimulated hormonal toxicity is mediated by ERalpha alteration of target gene methylation patterns and epigenetic modifiers (DNMT3A, MBD2, and HDAC2) in the mouse seminal vesicle. Environ. Health Perspect., 2014, 122(3):262-8. [Abstract Li, Y., Hamilton, K.J., Lai, A.Y., Burns, K.A., Li, L., Wade, P.A., Korach, K.S. Diethylstilbestrol (DES)-stimulated hormonal toxicity is mediated by ERalpha alteration of target gene methylation patterns and epigenetic modifiers (DNMT3A, MBD2, and HDAC2) in the mouse seminal vesicle. Environ. Health Perspect., 2014, 122(3):262-8.]
  28. Madenspacher, J., Azzam, K., Gowdy, K., Malcolm, K., Nick, J., Aloor, D. J., Draper, D., Guardiola, J., Shatz, M., Menendez, D., Lowe, J., Lu, J., Bushel, P., Li, Leping, Merrick, A., Resnick, M.A. and Fessler, M. p53 Integrates host defense and cell fate during bacterial pneumonia. J. Experimental Medicine:  891-904, 2013.   [Abstract Madenspacher, J., Azzam, K., Gowdy, K., Malcolm, K., Nick, J., Aloor, D. J., Draper, D., Guardiola, J., Shatz, M., Menendez, D., Lowe, J., Lu, J., Bushel, P., Li, Leping, Merrick, A., Resnick, M.A. and Fessler, M. p53 Integrates host defense and cell fate during bacterial pneumonia. J. Experimental Medicine:  891-904, 2013.  ]
  29. Tennant, B., Robertson, A.G., Kramer, M., Li, L., Zhang, X., Beach, M., Thiessen, N., Chiu, R., Mungall, K., Whiting, C., Sabatini, P., Kim, A., Gottardo, R., Marra, M., Lynn, F., Jones, S.J.M., Hoodless, P.A., Hoffman, B.G. Identification and analysis of pancreatic islet enhancers. Diabetologia, 2013, 56(3):542-552. [Abstract Tennant, B., Robertson, A.G., Kramer, M., Li, L., Zhang, X., Beach, M., Thiessen, N., Chiu, R., Mungall, K., Whiting, C., Sabatini, P., Kim, A., Gottardo, R., Marra, M., Lynn, F., Jones, S.J.M., Hoodless, P.A., Hoffman, B.G. Identification and analysis of pancreatic islet enhancers. Diabetologia, 2013, 56(3):542-552.]
  30. Li Y, Huang W, Niu L, Umbach DM, Covo S, Li L. Characterization of constitutive CTCF/cohesin loci: a possible role in establishing topological domains in mammalian genomes, BMC Genomics, 2013, 14:553. [Abstract Li Y, Huang W, Niu L, Umbach DM, Covo S, Li L. Characterization of constitutive CTCF/cohesin loci: a possible role in establishing topological domains in mammalian genomes, BMC Genomics, 2013, 14:553.]
  31. Huang W, Loganantharaj R, Schroeder B, Fargo D, Li L. PAVIS: a tool for Peak Annotation and Visualization, Bioinformatics, 2013, 29(23):3097-9. [Abstract Huang W, Loganantharaj R, Schroeder B, Fargo D, Li L. PAVIS: a tool for Peak Annotation and Visualization, Bioinformatics, 2013, 29(23):3097-9.]