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Hangzhou Repugene Technology Co., Ltd.  is a high-tech advanced biomedical research and development enterprise with the team of Dr. Haigui.

core team

Dr. Li Nan, Director of Bioinformatics and Big Data

Release time:
2019/01/19 17:18
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Doctor of Bioinformatics and Computational Biology, Chinese Academy of Sciences
Postdoctoral fellow at the University of California, San Diego
With many years of overseas R&D experience, he has published many important academic papers in Nature.
Expertise: High-throughput sequence analysis, bioinformatics process design and multiple machine learning methods
He graduated from the School of Life Sciences of Peking University with a Ph.D. in Bioinformatics and Computational Biology from the Chinese Academy of Sciences and a postdoctoral research at the University of California, San Diego.
With many years of experience in bioinformatics and computational biology, the research results are published in internationally renowned journals such as Nature, Nature Communication, Bioinformatics and Molecular & Cellular Proteomics. The total number of citations published has exceeded 450 (Google Scholar). His research interests focus on high-throughput sequence analysis, bioinformatics process design and a variety of machine learning methods, as well as high-performance computing platforms for applications in bioinformatics and computational biology.
The main research experiences include:
Participated in the NIH Roadmap Epigenomics data analysis work and supported the main conclusions in the article;
Participate in the Chroma-Seq data analysis software ChromaSig design and its migration to the high-performance computing platform, reducing the running time to 1/3 of the original version, and implementing parallel computing on the high-performance computing platform;
Designed and implemented a high-performance computing platform for bioinformatics, successfully implementing a high-performance computing platform under high data conditions (0.4-0.5PB);
Participate in and realize the application of a variety of machine learning methods in protein docking and protein-specific recognition. Apply SVM and LASSO Regression methods to solve the problem of protein binding. It has been recognized by the academic community. Related articles have been cited in many reviews. .
Successfully built the development of PB-scale high-performance computing platform and related management sequencing.
The published academic articles mainly include:
1 Nan Li, Richard I. Ainsworth, Meixin Wu, Bo Ding, Wei Wang. MIEC-SVM: Automated Pipeline for Protein Peptide/ligand Interaction Prediction. Bioinformatics, 2015, DOI:10.1093 /bioinformatics/btv666
2 Kai Zhang, Nan Li, Richard I. Ainsworth & Wei Wang. Systematic identification of protein combinations mediating chromatin looping. Nature Communications 7, Article number: 12249 (2016)doi:10.1038/ncomms12249
3 Roadmap Epigenomics Consortium, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015, 518, 317-330. (Contributor to pipeline design)
4 Nan Li#, Richard SL Stein#, Wei He#, Elizabeth Komives, Wei Wang. Identification of Methyllysine Peptides Binding to Chromobox Protein Homolog 6 Chromodomain in the Human Proteome. Molecular & Cellular Proteomics, 2013, 12, O112.025015. # -equal contribution.