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.
Speaker in The 12th International Biocuration Conference, 2019 Cambridge, UK.
Speaker in the 10th Fudan PhD student academic forum, 2018 Shanghai, China. (second prize)
Poster: 3rd International Symposium of Epigenetic Mechanism and Human Health, 2018 Shenzhen, China, EWAS Atlas: a curated knowledgebase of epigenome-wide association studies (best poster award)
Speaker in the 3rd Big Data Forum for Life and Health Sciences, 2018, Beijing, China, EWAS Atlas: a curated knowledgebase of epigenome-wide association studies
Volunteer in the Workshop on DNA Methylation and Precision Medicine, 2017, Beijing, China
Volunteer in the 2nd Big Forum for Life and Health Sciences, 2017, Beijing, China
Volunteer in the 8th International Biocuration Conference, 2015, Beijing, China
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 studies. Nucleic Acids Res 2019, in press. [PMID=30364969]
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 plants. Nucleic Acids Res 2019, in press. [PMID=30364952]
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 human. Nucleic Acids Res 2019, in press. [PMID=30357418]
Yin H, Li M#, Xia L, He C, Zhang Z: Computational determination of gene age and characterization of evolutionary dynamics in human. Briefings in Bioinformatics 2018, doi: 10.1093/bib/bby074. [PMID=30184145]
Li M as co-first author in BIG Data Center Members: Database Resources of the BIG Data Center in 2019. Nucleic Acids Res 2019, in press. [PMID=30365034]
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 species. Nucleic Acids Res 2018, 46(D1):D288-D295. [PMID=29161430]
Li M as co-first author in BIG Data Center Members: Database Resources of the BIG Data Center in 2018. Nucleic Acids Res 2018, 46(D1):D14-D20. [PMID=29036542]
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 rice. Journal of Genetics and Genomics, 2017, 44(5):235-241. [PMID=28529082]
BIG Data Center Members: The BIG Data Center: from deposition to integration to translation. Nucleic Acids Res 2017, 45(D1):D18-D24. [PMID=27899658]
Yin HY, Ma LN, Wang GY, Li M, Zhang Z: Old genes experience stronger translational selection than young genes. Gene 2016, 590(1):29-34. [PMID=27259662]
李萌伟, 李芳菲, 樊志宏,等. 紫丁香蘑ISSR-PCR反应体系的正交优化与建立[J]. 生物技术, 2013(6):55-59. [CNKI]