Postdoctoral recruitment inspiration for the research group of researcher Zou Zhengting of the Institute of Zoology, Chinese Academy of Sciences

The Computational Molecular Evolution Research Group of the Institute of Zoology, Chinese Academy of Sciences, takes computational biology as the main means, and explores the molecular evolution, developmental evolution and other evolutionary biology problems of protein sequences based on comparative genomics, mathematical modeling and simulation, statistical inference, deep learning and other methods. For details, please refer to: http://sourcedb.ioz.cas.cn/zw/zjrc/202009/t20200929_5709540.html

At present, it is planned to recruit 1-2 postdoctoral fellows to engage in evolutionary biology research based on deep learning and molecular sequence and omics data analysis, and the co-supervisor is Researcher Zou Zhengting.

Application requirements

Ability and technology to conduct computational evolutionary biology research, discover and solve specific scientific research problems, deep learning or single-cell sequencing data analysis capabilities are preferred;

Have a sense of responsibility for research topics, take the initiative to think and solve problems, be able to carry out scientific research work independently, be physically and mentally healthy, have good communication skills and teamwork skills;

Be able to read English literature in the field in depth, and have good English writing and oral communication skills;

Have or are about to obtain a Doctorate degree in biology or computational sciences.

Job Responsibilities

Under the guidance of the research team leader, independently undertake scientific research tasks and carry out scientific research;

Assist the research team leader in laboratory management and postgraduate training;

Assist the research group leader or independently apply for research topics.

Salary

Enjoy the treatment given by the Institute of Zoology in accordance with the relevant regulations, as well as the postdoctoral and special research assistant treatment of the Chinese Academy of Sciences, and provide competitive salaries and benefits in Beijing, and the treatment of excellent candidates is excellent.

The research group encourages applications for relevant talent programs and external funding, provides good research conditions and opportunities for international cooperation and exchange, and supports researchers in the next stage of career development.

Application materials

Resume (including a brief introduction to research work);

Letters of recommendation from at least two referees, name, unit and contact information of the referrer;

Other documents that can prove an individual's ability.

Co-mentor profile

Zou Zhengting, male, Ph.D., doctoral supervisor, leader of the Computational Molecular Evolution Research Group at the Institute of Zoology, Chinese Academy of Sciences.

2009 – 2013 B.S., Biological Sciences, Peking University

2013 – 2017 Ph.D., Bioinformatics, University of Michigan

2017 – 2020 Postdoctoral Fellow, Department of Ecology and Evolution, University of Michigan

2020.9 Research Team Leader, Institute of Zoology, Chinese Academy of Sciences

Research Interests: Computational Biology, Molecular Evolution and Phylogenetics, Evolutionary Developmental Biology, Deep Learning Applications

Introduction to the direction of the study group

Biological evolution is the most fundamental formation law and principle of living systems. Therefore, the goal of evolutionary biology research is to use qualitative or quantitative theories to explain life phenomena and data at various scales such as molecules, cells, morphologies, populations and species, based on evolutionary principles, and to discover more evolutionary laws. Based on comparative analysis, mathematical modeling and simulation, deep learning and other methods, this research group explores the core and frontier issues related to biological evolution from the level of molecular sequences and other aspects.

The evolution of molecular sequences

As a mathematical model of the evolutionary processes of DNA and protein sequences, molecular evolution models are one of the core tools for bioinformatics analysis.

In recent years, with the accumulation and analysis of a large number of omics data, we have gained more understanding of the patterns and laws of molecular sequence evolution. For example, differences in the rate of evolution between different species and between different genomic sequence loci (heterogeneity), and different loci of molecular sequences do not evolve independently, but interact (superior effect epitasis) and so on.

Based on the available multi-species sequence data, the research team hopes to explore the biological mechanisms and influencing factors of these sequence evolution patterns, and how to combine these factors for molecular evolution and phylogenetic analysis, and to more carefully and accurately characterize and model the patterns and laws of sequence evolution, adaptive evolution and other phenomena.

Evolutionary analysis applications of deep learning

Deep learning is a rapidly developing computational method in recent years, capable of pattern extraction and prediction based on a large number of complex and diverse data. In view of the complex influencing factors and high heterogeneity of biological data, the research team hopes to use deep learning methods to try to identify and predict patterns such as phylogenetics and classification of molecular sequences and other data.

Developmental evolutionary patterns and mechanisms

Differentiation of cell types determines the different physiological functions of multicellular organisms and is the basis for biodiversity and adaptive evolution. In recent years, single-cell sequencing techniques have produced a large amount of developmental data for different species. We hope to compare and analyze omics data from the perspective of molecular evolution and phylogeny, combine information such as gene regulatory networks, and explore the evolutionary laws of developmental processes at biological scales above the sequence.

In addition, the research group maintains a wide range of interests in topics such as adaptive evolution, convergent evolution, comparative genomics, phenotypic evolution, and phylogenetic trees.

Representative treatises

#共同第一作者, *Corresponding Author

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14. Si S, Xu X*, Zhuang Y, Gao X, Zhang H, Zou Z*, and Luo SJ*. The genetics and evolution of eye color in domestic pigeons (Columba livia). PLOS Genet., 2021, 17:e1009770. doi: 10.1371/journal.pgen.1009770

13. Zou Z, and Zhang J. Are nonsynonymous transversions generally more deleterious than nonsynonymous transitions? Mol. Biol. Evol., 2021, 38:181-191. doi: 10.1093/molbev/msaa200

12. Lyons DM#, Zou Z#, Xu H, Zhang J. Idiosyncratic epistasis creates universals in mutational effects and evolutionary trajectories. Nat. Ecol. Evol., 2020, 4:1685-1693. doi: 10.1038/s41559-020-01286-y

11. Zou Z, and Zhang J. The nature and phylogenomic impact of sequence convergence. Phylogenetics in the Genomic Era (C. Scornavacca, et al., eds). No commercial publisher | Authors open access book, pp.4.6:1-4.6:17, 2020. ffhal-02536347

10. Zou Z#, Zhang H#, Guan Y, Zhang J. Deep residual neural networks resolve quartet molecular phylogenies. Mol. Biol. Evol., 2020, 37:1495-1507. doi: 10.1093/molbev/msz307

9. Zou Z, and Zhang J. Amino acid exchangeabilities vary across the tree of life. Sci. Adv., 2019, 5: eaax3124. doi: 10.1126/sciadv.aax3124

8. Ding X, Zou Z, Brooks III CL. 2019. Deciphering protein evolution and fitness landscapes with latent space models. Nat. Commun., 10: 5644. doi: 10.1038/s41467-019-13633-0

7. Zou Z, and Zhang J. Gene tree discordance does not explain away the temporal decline of convergence in mammalian protein sequence evolution. Mol. Biol. Evol., 2017, 34: 1682-1688. doi: 10.1093/molbev/msx109

6. Zou Z, and Zhang J. Morphological and molecular convergences in mammalian phylogenetics. Nat. Commun., 2016, 7: 12758. doi: 10.1038/ncomms12758

5. Oetjens MT, Shen F, Emery SB, Zou Z and Kidd JM. 2016. Y-Chromosome structural diversity in the bonobo and chimpanzee lineages. Genome Biol. Evol., 8: 2231-2240. doi: 10.1093/gbe/evw150

4. Zou Z, and Zhang J. Are convergent and parallel amino acid substitutions in protein evolution more prevalent than neutral expectations? Mol. Biol. Evol., 2015, 32: 2085-2096. doi: 10.1093/molbev/msv091

3. Zou Z, and Zhang J. No genome-wide convergence for echolocation. Mol. Biol. Evol., 2015, 32: 1237-1241. doi: 10.1093/molbev/msv014

2. Zou Z-T, Uphyrkina O, Fomenko P, Luo S-J. The development and application of a multiplex short tandem repeat (STR) system for identifying subspecies, individuals and sex in tigers. Integr. Zool., 2015, 10: 376-388. doi: 10.1111/1749-4877.12136

1. Xu X, Dong G-X, Hu X-S, Miao L, Zhang X-L, Zhang D-L, Yang H-D, Zhang T-Y, Zou Z-T, Zhang T-T, Zhuang Y, Bhak J, Cho YS, Dai W-T, Jiang T-J, Xie C, Li R-Q, Luo S-J. 2013. The genetic basis of white tigers. Curr. Biol., 23: 1031-1035. doi: 10.1016/j.cub.2013.04.054