Shixiang Sun, Ph.D.

Research Fellow.


Email: shixiang.sun at



Education and Training


Postdoctoral   Albert Einstein College of Medicine, USA, 2017 - present


Ph.D.   Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China, 2017


Research interests


Dr. Sun' research in this laboratory is focused on the computational analysis of various types of genome instability from the single cell, whole genome sequence, and transcriptome sequence information in normal tissues of human in relation to aging.


Project 1: Single cell, computational analysis of telomere attrition in aging.

Telomere instability, mainly presented as telomere shortening, has been hypothesized to causally contribute to the functional decline and increased disease risk during aging. Advances of single-cell whole genome sequencing technology and single-cell RNA-seq provides a great opportunity to resolve the landscape of telomere attrition effects in each chromosome end during development and the aging process. In this project, Dr. Sun will analyze the telomere shortening process and test its effects on telomeric and subtelomeric regions of single chromosomes during aging, using existing datasets of single-cell whole-genome sequences from B cells of humans. Then he will test position effects of telomere shortening on subtelomeric gene expression with single-cell RNA-sequencing in human B cells from young and aged subjects.


Project 2: The transcriptomics of immortality in Hydra oligactis

Hydra oligactis is a small freshwater polyp and has been proposed as a model organism for aging research.  This is not only due to its high regenerative capacity and budding capabilities but also a cold-resistant trait, which essentially allows asexual hydra to escape aging phenotypes. However, one sub-strain of hydra is cold-sensitive and undergoes a transition to gametogenesis from asexual budding during temperature drop leading to rapidly age. In this project, Dr. Sun will assemble a de novo Hydra oligactis transcriptome using RNA-seq to understand the molecular mechanism underlying this phenomenon. The differentially expressed genes represent sexual reproduction and aging phenotype will be enriched and annotated for functional analysis in GO terms. Then all expressed hydra genes will be enriched for detecting hallmark gene sets in regulating behavior transition.





  1. S Sun, RR White, KE Fischer, Z Zhang, SN Austad, J Vijg. Inducible aging in Hydra oligactis implicates sexual reproduction, loss of stem cells, and genome maintenance as major pathways. Geroscience 42 (4), 1119-1132, 2020.

  2. K Brazhnik, S Sun, O Alani, M Kinkhabwala, AW Wolkoff, AY Maslov, et al. Single-cell analysis reveals different age-related somatic mutation profiles between stem and differentiated cells in human liver. Science Advances, 6 (5), eaax2659, 2020.

  3. Database Resources of the BIG Data Center in 2018. Nucleic acids research 46 (D1), D14-D20, 2018

  4. R Li, F Liang, M Li, D Zou, S Sun, Y Zhao, W Zhao, Y Bao, J Xiao, Z Zhang. MethBank 3.0: a database of DNA methylomes across a variety of species. Nucleic acids research 46 (D1), D288-D295, 2017

  5. W Su, X Li, M Chen, W Dai, S Sun, S Wang, X Sheng, S Sun, C Gao, et al. Synonymous codon usage analysis of hand, foot and mouth disease viruses: A comparative study on coxsackievirus A6, A10, A16, and enterovirus 71 from 2008 to 2015. Infection, Genetics and Evolution 53, 212-217, 2017

  6. BIG Data Center Members. The BIG Data Center: from deposition to integration to translation. Nucleic acids research 45 (D1), D18-D24, 2016

  7. S Sun, J Xiao, H Zhang, Z Zhang. Pangenome Evidence for Higher Codon Usage Bias and Stronger Translational Selection in Core Genes of Escherichia coli. Frontiers in microbiology 7, 1180, 2016

  8. G Wang, S Sun, Z Zhang. Randomness in Sequence Evolution Increases over Time. PloS one 11 (5), e0155935, 2016

  9. D Zou, S Sun, R Li, J Liu, J Zhang, Z Zhang. MethBank: a database integrating next-generation sequencing single-base-resolution DNA methylation programming data. Nucleic acids research 43 (D1), D54-D58, 2014

  10. W Wang, B Feng, J Xiao, Z Xia, X Zhou, P Li, W Zhang, Y Wang, BL Møller, et al. Cassava genome from a wild ancestor to cultivated varieties. Nature communications 5, ncomms6110, 2014

  11. Y Zhao, J Wu, J Yang, S Sun, J Xiao, J Yu. PGAP: pan-genomes analysis pipeline. Bioinformatics 28 (3), 416-418,  2011