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Your Environment. Your Health.

Leping Li, Ph.D.

Biostatistics & Computational Biology Branch

Leping Li, Ph.D.
Leping Li, Ph.D.
Deputy Chief, Biostatistics & Computational Biology Branch and Principal Investigator
Tel 984-287-3836
Fax 919-541-4311
li3@niehs.nih.gov
P.O. Box 12233
Mail Drop A3-03
Durham, N.C. 27709

Research Summary

Leping Li, Ph.D., is the Deputy Chief and 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 year and a half 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.

Ongoing projects in the lab include:

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. 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.]
  2. 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].]
  3. 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).]
  4. 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.]
  5. 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.]
  6. 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.]
  7. 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.]
  8. 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.]
  9. 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.]
  10. 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.]
  11. 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.  ]
  12. 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.]
  13. 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.]
  14. 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.]
  15. 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.]
  16. 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.]
  17. 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.]
  18. 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.  ]
  19. 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.]
  20. 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.]
  21. 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.]
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