National Genomics Data Center

Mengwei Li

PhD Candidate

Email: limengwei (AT)

Tel: +86 177-3021-4280


  • PhD in Bioinformatics, Beijing Institute of Genomics, Chinese Academy of Sciences, China, 2020 (expected)

  • BS in Bioengineering, Shenyang Agricultural University, China, 2014


  • DNA methylation data analysis and integration

  • Tissue-specific DNA methylation

  • Aging


  • EWAS Atlas & EWAS Data Hub

    Epigenome-Wide Association Study (EWAS) has become increasingly significant in identifying the associations between epigenetic variations and different biological traits. In these two study, we integrated DNA methylation array data as well as EWAS associations from public data sources and publications to aid the retrieval and discovery of methylation-based biomarkers for phenotype characterization, clinical treatment and health care. I involved in almost all parts of these two projects, including database design, data process, metadata curation and web development.

  • Gaussian Mixture Quantile Normalization (GMQN)

    To remove the batch effects and other unwanted noise, we developed Gaussian Mixture Quantile Normalization (GMQN), a reference based method that removes unwanted technical variations at signal intensity level. GMQN adjusts batch effects as well as bias associated with type II probe values in 450k and EPIC/850K studies. The principle behind this method is that the signal intensity of each channel displays a Gaussian mixture distribution. The first component is the background signal which has a mean slightly greater than 0. The second component is the signal from probes which have been hybridized to input DNA successfully. Variance of the second component is much larger than the first component because the degrees of hybridization are different among probes. The object of GMQN is to rescale the signal intensity to make the two Gaussian component from different array have the same mean and variance. 

  • Systematic analysis of tissue-specific DNA methylation in 31 normal tissues (ongoing project)

    DNA methylation plays important roles in tissue differentiation. Systematic analysis of tissue-specific DNA methylation (TS DNAm) will promote understanding of tissue-specific differentiation process. The previous studies of TS DNAm are limited in sample size and tissue type. Meanwhile, the comparison between TS DNAm and TS expression is lacked. To address these problems and provide deep insights into TS DNAm, we systematically identify the TS DNAm using 5,211 samples of 31 normal tissues from EWAS Data Hub.   



  • 2019-11 The "2019 National Scholarship for Doctoral Student"

  • 2019-06 The "Triple-A Student" in the University of Chinese Academy of Science


  1. Li M#, Zou D, Li Z, Gao R, Sang J, Zhang Y, Li R, Xia L, Zhang T, Niu G, Bao Y, Zhang Z: EWAS Atlas: a curated knowledgebase of epigenome-wide association studiesNucleic Acids Res 2019, 47(D1):D983-D988[PMID=30364969]

  2. Xiong Z, Li M#, Yang F, Ma Y, Sang J, Li R, Li Z, Zhang Z, Bao Y: EWAS Data Hub: a resource of DNA methylation array data and metadataNucleic Acids Res 2020, in press. [PMID=31584095]

  3. Sang J, Zou D, Wang ZN, Wang F, Zhang YS, Xia L, Li ZH, Ma LN, Li MW, Xu BX, Liu XN, Wu SY, Liu L, Niu GY, Li M, Luo YF, Hu SN, Hao LL, Zhang Z: Rice Genome Reannotation Using Massive RNA-Seq Data in IC4RGenomics Proteomics & Bioinformatics 2019, in press.

  4. Li M, Xia L, Zhang Y, Niu G, Li M, Wang P, Zhang Y, Sang J, Zou D, Hu S, Hao L, Zhang Z: Plant Editosome Database: a curated database of RNA editosome in plantsNucleic Acids Res 2019, 47(D1):D170-D174. [PMID=30364952]

  5. Niu G, Zou D, Li M#, Zhang Y, Sang J, Xia L, Li M, Liu L, Cao J, Zhang Y, Wang P, Hu S, Hao L, Zhang Z: Editome Disease Knowledgebase (EDK): A curated knowledgebase of editome-disease associations in humanNucleic Acids Res 2019, 47(D1):D78-D83. [PMID=30357418]

  6. Yin H, Li M#, Xia L, He C, Zhang Z: Computational determination of gene age and characterization of evolutionary dynamics in humanBriefings in Bioinformatics 2018, doi: 10.1093/bib/bby074. [PMID=30184145]

  7. Li M as co-first author in BIG Data Center Members: Database Resources of the BIG Data Center in 2019Nucleic Acids Res 2019, 47(D1):D8-D14. [PMID=30365034]

  8. Li RJ, Liang F, Li W#, Zou D, Sun SX, Zhao YB, Zhao WM, Bao YM, Xiao JF, Zhang Z: MethBank 3.0: a database of DNA methylomes across a variety of speciesNucleic Acids Res 2018, 46(D1):D288-D295. [PMID=29161430]

  9. Li M as co-first author in BIG Data Center Members: Database Resources of the BIG Data Center in 2018Nucleic Acids Res 2018, 46(D1):D14-D20. [PMID=29036542]

  10. Xia L, Zou D, Sang J, Xu XJ, Yin HY, Li M, Wu SY, Hu SN, Hao LL, Zhang Z: Rice Expression Database (RED): an integrated RNA-Seq-derived gene expression database for riceJournal of Genetics and Genomics, 2017, 44(5):235-241. [PMID=28529082]

  11.  BIG Data Center MembersThe BIG Data Center: from deposition to integration to translationNucleic Acids Res 2017, 45(D1):D18-D24. [PMID=27899658]

  12. Yin HY, Ma LN, Wang GY, Li M, Zhang Z: Old genes experience stronger translational selection than young genesGene 2016, 590(1):29-34. [PMID=27259662]

  13. Li M#, Li F, Fan Z, Yu X, Lv S, Ma D: Optimization and Establishment of Inter-simple Sequence Repeat PCR Reaction System for Lepista nuda. Biotechnology2013(6):55-59. [CNKI]