Xun Chen
Principal Investigator
Shanghai, China
Summary
👤
Senior🎓
Top SchoolXun Chen is a Principal Investigator and genomics-focused computational biologist with nine years of post-PhD experience developing multi-omics sequencing technologies, machine learning models, and bioinformatics algorithms for infection-, immunity- and cancer-related research. Trained in plant genetics and genomics with a minor in computer science, he blends deep wet-lab insight with strong HPC, R and Perl skills to tackle human genomics and epigenetics problems. His work spans evolutionary genomics, transposable elements and viral etiology, and has guided vaccine and precision-medicine efforts across labs in the US, Japan and China. Notably, he transitioned from postdoctoral research at the University of Vermont to independent group leadership at Shanghai Institute of Immunity and Infection, building methods that emphasize individual variability in disease response.
9 years of coding experience
4 years of employment as a software developer
Minor degree, Computer Science, Minor degree, Computer Science at Huazhong University of Science and TechnologyDoctor of Philosophy (Ph.D.), Plant genetics and genomics, Doctor of Philosophy (Ph.D.), Plant genetics and genomics at Huazhong Agricultural University
Chinese, English
Github Skills (21)
next-generation-sequencing10
sequencing10
htslib9
database8
command-line8
structural-variation7
genomes6
computation5
genome5
bioinformatics4
reporter4
genomics4
rna-seq4
interaction4
xmake2
Programming languages (3)
Github contributions (5)
xunchen85/RFAPtools
Jun 2017 - Mar 2019
Contributions:22 commits, 20 pushes, 1 branch in 1 year 8 months
xunchen85/ERVcaller
May 2018 - Feb 2022
ERVcaller is a tool designed to accurately detect and genotype non-reference unfixed endogenous retroviruses (ERVs) and other transposable elements (TEs) in the human genome using next-generation sequencing (NGS) data. We evaluated the tools using both simulated and real benchmark whole-genome sequencing (WGS) datasets. ERVcaller is capable to accurately detect various TE insertions of any lengths, particularly ERVs. It allows for the use of a TE reference library regardless of sequence complexity, such as the entire RepBase database. It is easy to install and use with command lines.
Contributions:4 releases, 142 commits, 3 PRs in 3 years 9 months
command-linedatabasenext-generation-sequencingsequencing
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