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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 has both computational and experimental components, 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. Group members have contributed to many areas of bioinformatics including:

  • Identification of transcription factors and their co-regulatory factor motifs
  • De novo motif discovery
  • Identification of enriched genomic loci in ChIP-seq and mRNA-seq data
  • Accurate anchoring alignment of divergent sequences
  • Sample classification/prediction and feature selection
  • Complete deconvolution of bulk gene expression data

Three years ago, Li established a wet lab so that computational discoveries through the group’s datamining efforts could be further pursued biologically. Currently, Li dedicates half of his resources on “wet” lab research.

Li’s team includes Staff Scientist Yuanyuan Li, Ph.D., Research Fellow Kai Kang (former), Ph.D., in methods development, and two wet lab Postdoctoral Fellows - Payel Sil, Ph.D. and Wenling Li (former), Ph.D. The fellows in the wet lab work closely with Dr. Xiaoling Li’s group in the NIEHS Signal Transduction Laboratory. Two new fellows will be joining the lab in 2021.

At present, Li’s group focuses on the following areas: 1) methods and applications for classification and regression (dry lab) and 2) investigation of the role of a gene in the interplay between autoimmunity and tumorigenesis (wet lab).

The "Dry" Lab

The dry lab focuses on methods and applications for mining large-scale genomic data. The methods we developed or implemented are broadly applicable to data of environmental relevance for hypothesis generation and biomarker identification. Although the lab routinely encompasses a variety of computational/statistical approaches, the lab specializes in two complimentary approaches—the genetic algorithm/k-nearest neighbor (GA/KNN) method that we developed years ago and classification and regression tree (CART)-based methods that we have implemented. We continue to improve and refine these tools to adapt to the changes of scientific questions and nature of data. The lab uses both methods for lab-initiated projects and data generated by their collaborators.

The "Wet" Lab

The wet lab is studying one gene and its role in cancer progression using both in vitro cell lines and in vivo mouse models. The candidate gene was identified by the group’s bioinformatics effort. Two years into the project, the lab is poised to have its first wet-lab manuscript for publication. The lab hopes to be the first to demonstrate that deficiency in the candidate gene plays an important role in tumorigenesis. Stay tuned…

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.
  • 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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