The 4th Big Data Forum for Life and Health Sciences (October 13-16, 2019)

Biological research has entered the era of big data, including a wide variety of omics data and covering a broad range of health data. Such big data is generated at ever-growing rates and distributed throughout the world with heterogeneous standards and diverse limited access capabilities. However, the promise to translate these big data into big knowledge can be realized only if they are publicly shared. Thus, providing open access to omics & health big data is essential for expedited translation of big data into big knowledge and is becoming increasingly vital in advancing scientific research and promoting human healthcare and precise medical treatment.

Open Biodiversity & Health Big Data



It is our great pleasure to announce that the 2019 Big Data Forum for Life and Health Sciences will be held in October 13-16, 2019. A few renowned biomedical data scientists have agreed to give speeches. Likely, you are also cordially invited to share your work and participate in this excited event.

Looking forward to seeing you in Beijing, China! We will be working hard to ensure your stay not only a fruitful one, but also an enjoyable one!

Organizing Committee

  • Zhang Zhang (Chair, BIG, CAS)
  • Yiming Bao (BIG, CAS)
  • Wenming Zhao (BIG, CAS)
  • Jingfa Xiao (BIG, CAS)
  • Songnian Hu (BIG, CAS)
  • Jun Yu (BIG, CAS)
  • Jingchu Luo (Peking University)

Previous Conferences

Invited Speakers

Peer Bork

Structural and Computational Biology Unit

Janusz M. Bujnicki

Professor of Biological Sciences
Laboratory of Bioinformatics and Protein Engineering
International Institute of Molecular and Cell Biology in Warsaw

Chao Chen

School of Life Sciences
Central South University

Weihua Chen

College of Life Science & Technology
Huazhong University of Science and Technology

Xiangjun Du

School of Public Health (Shenzhen)
Sun Yat-sen University

Lizhi Gao

Kunming Institute of Botany
Chinese Academy of Sciences

Dianjing Guo

Associate Professor
School of Life Sciences
The Chinese University of Hong Kong, China

Heui-Soo Kim

College of Natural Sciences
Pusan National University

Chwan-Chuen King

Epidemiology and Preventive Medicine
Taiwan University

Sofia Kossida

University of Montpellier
IMGT®, the international ImMunoGeneTics information system®

Shelan Liu

Zhejiang Provincial Center for Disease Control and Prevention

Yuwen Liu

Agricultural Genomics Institute
Chinese Academy of Agricultural Sciences

Jian Lu

Center for Bioinformatics
School of Life Sciences, Peking University

Lijia Ma

School of Life Sciences
Westlake University

Jiao Li

Institute of Medical Information and Library
Chinese Academy of Medical Sciences

Rasmus Nielsen

Department of Statistics
University of California, Berkeley
United States

Jeffrey Townsend

Center for Biomedical Data Science
Yale University

Ana Tereza Ribeiro de Vasconcelos

Head of the Bioinformatics Laboratory
National Laboratory of Scientific Computation
Bionformatics Laboratory

Chen Wu

Cancer Hospital
Chinese Academy of Medical Sciences

Wen Wang

Research Center For Ecology and Environmental Science
Northwestern Polytechnical University

Xingming Zhao

Institute of Science and Technology for Brain-Inspired
Fudan University

Zhaolei Zhang

Donnelly Centre for Cellular and Biomolecular Research
University of Toronto

Agenda (tentative)

The conference features selected talks (15mins) and lightning talks (5mins). Please submit your abstract for consideration of oral presentation, particularly for junior researchers/postdocs/graduate students.
Online registration and abstract submission:

October 13 Sunday: Registration
October 14 Monday: Talks
09:00 - 10:20 Session 1, chaired by Zhang Zhang, BIG, CAS
09:00 - 09:25 Welcome and Opening Remarks
Zhang Zhang, On Behalf of the Organizing Committee
Introduction of the Alliance of International Science Organizations
Prof. Likun Ai, Assistant Executive Director of ANSO Secretariat
09:25 - 10:05 Microbiome analysis of the human gut and beyond
Peer Bork, EMBL, Germany

The human microbiome, that is the totality of microbes inhabiting us, is spread over various body sites, the largest and probably medically most relevant one is the human gut. The gut microbiome is a complex system, which is still little understood and it is not even clear how the healthy state(s) should be defined. Yet, an increasing number of diseases is being associated with dysbiosis of the microbiome, often implied by metagenome-wide association studies (MWAS). MWAS do not reveal causalities, but disease associations and thus can still be a starting point for diagnostics, as I will illustrate using colorectal cancer (Wirbel et al., Nature Med., 2019). MWAS results are also often indirect or confounded (Schmidt et al, Cell 2018), the latter we could demonstrate for type 2 diabetes, where the intake of the drug metformin rather than the disease itself leads to the association with the gut microbiome (Forslund et al., Nature 2015). In an in vitro screen of 1200 marketed drugs against each of 40 human gut bacteria we could show that a quarter of all non-antibiotic drugs directly inhibit at least one gut microbial strain (Maier et al., Nature, 2018), a taxonomic resolution level that is becoming also achievable in MWAS (Schloissnig et al. 2013). Using single nucleotide variants (SNVs) we are able, for example, to trace donor and recipient SNVs after faecal microbiota transplantation, a microbial therapy (Li et al., Science 2016), or to trace oral-gut transmission (Schmidt et al, elife 2019). As our microbes are coming from the environment and our planet is one big ecosystem, it is crucial to study biodiversity and interactions of microbes at planetary scale. The feasibility of such a global approach is illustrated by (i) the TARA oceans project, surveying the microbial diversity of this vast ecosystem by studying plankton in all major ocean regions (Bork et al., Science 2015 and refs therein) and (ii) topsoil microbiomics (Bahram et al., Nature, 2018), revealing a global war between fungi and bacteria as well as regional antibiotic resistance gene reservoirs.

10.05 - 10:35 Single Cell Functional Genomics and Endometrium Cell Atlas
Lijia Ma, Westlake University, China

The widely applied single cell genomic technology accelerates the researches on cell heterogeneity at the levels of DNA, gene expression profiles and chromatin topology by labeling individual cells with cellular barcode. We applied this technology in analyzing the endometrium tissue from Recurrent Implantation Failure (RIF) patients and healthy controls and characterized the distinct cell compositions between them. We demonstrated that the diversity of stromal cell of RIF patients is significantly lower than healthy control, which is consistent with their differences in endometrial lining thickness. We also detected a gene expression pattern that matches mid-to-late luteal phase in the RIF patients but not healthy control, which indicates an advanced or accelerated menstrual cycle in these patients. This study provides an insight of applying the single cell technology to decipher the underlying cell composition during endometrium cycle, and directly connected the lower cellular diversity and advanced gene expression profiles to clinic symptom.

10:35 - 10:55 Group Photo and Tea & Coffee Break
10:55 - 12:25 Session 2, chaired by Lijia Ma, Westlake University, China
10:55 - 11:25 Database Resources of the National Genomics Data Center
Yiming Bao, BIG, CAS
11:25 - 11:55 Epigenetic feature in gastric cancer
Dianjing Guo, The Chinese University of Hong Kong
11:55 - 12:25 Mining novel biomarkers from gut metagenomics data for patient stratification
Weihua Chen, Huazhong University of Science and Technology, China
12:25 - 14:00 Lunch and BIG tour
14:00 - 15:40 Session 3, chaired by Yiming Bao, BIG, CAS
14:00 - 14:40 IMGT®, the international ImMunoGeneTics information system®: 30 years of immunoinformatics, present endeavors and perspectives
Sofia Kossida, University of Montpellier, France

IMGT®, the international ImMunoGeneTics information system®,is the global reference in immunogenetics and immunoinformatics, created in 1989 by Marie-Paule Lefranc (Université de Montpellier and CNRS). IMGT® is a high -quality integrated knowledge resource specialized in the immunoglobulins (IG) or antibodies , T cell receptors (TR), major histocompatibility (MH) of human and other vertebrate species , and in the immunoglobulin superfamily (IgSF), MH superfamily (MhSF) and related proteins of the immune system (RPI) of vertebrates and invertebrates. IMGT® provides a common access to sequence, genome and structure Immunogenetics data, based on the concepts of IMGT-ONTOLOGY and on the IMGT Scientific chart rules. IMGT® works in close collaboration with EBI (Europe) , DDBJ (Japan) and NCBI (USA). IMGT® consists of sequence databases, genome database, structure database, and monoclonal antibodies database, Web resources and interactive tools.

14:40 - 15:10 Decoding brain with multi-dimensional data: An introduction of the Zhangjiang International Brainbank
Xingming Zhao, Fudan University, China
15:10 - 15:40 Multi-omic regulation of human brain and related mental disorders
Chao Chen, Central South University

A large number of genetic signals associated with psychiatric diseases have been discovered through genome-wide association studies, but most of the variants have small effect sizes and unclear biological functions. Integration of multi-omics data and network analysis is necessary to identify their roles. In my talk, I will present what we have learnt from nearly 2000 postmortem brains. Through regulation of gene expression network, we highlighted isoform-level dysregulation as a critical mechanism linking genetic risk factors with psychiatric disease pathophysiology, and discovered transcription factor POU3F2 and non-coding RNA DGCR5 are involved in the pathogenesis of schizophrenia. We also found that DNA methylation influences gene expression to systematically affect different phenotypes of psychiatric diseases. Our work demonstrated the feasibility of a multi-omics network strategy to study psychiatric diseases.

15:40 - 16:00 Tea & Coffee Break
16:00 - 17:55 Session 4, chaired by Wenming Zhao, BIG, CAS
16:00 - 16:40 Large-scale ruminant genome data provides insighs into their evolution and distinct traits
Wen Wang, Northwestern Polytechnical University, China
16:40 - 16:55 Co-expressed gene-set enrichment analysis for drug repositioning with examples of psoriasis and periodontal diseases
Zhilong Jia, Chinese PLA General Hospital
16:55 - 17:25 Data-Driven Medical Studies: Efforts from National Scientific Data Center for Population and Health
Jiao Li, Institute of Medical Information and Library, Chinese Academy of Medical Sciences

Medical research paradigm is changing, from symptom-based medicine to evidence-based medicine to precision medicine. Nowadays, at the very beginning of a medical study, the principle investigator needs to make detailed data management plan (DMP), indicating the scientific data storage, data license, data sharing, data preservation and etc. In this talk, I will introduce the efforts made by the National Scientific Data Center for Population and Health on scientific data collection, data curation, data sharing, and data management tools development. Furthermore, I’d like to discuss the technical and management challenges in data-driven medical studies.

17:25 - 17:55 Less is more: Cancer cells evolve to use amino acids more economically
Jian Lu, Peking University, China

Rapidly proliferating cancer cells have much higher demand for proteinogenic amino acids than normal cells. The use of amino acids in human proteomes is largely affected by their bioavailability, which is constrained by the biosynthetic energy cost in the living organisms. Conceptually distinct from gene-based analyses, we introduce the energy cost per amino acid (ECPA) to quantitatively characterize the use of 20 amino acids during protein synthesis in human cells. By analyzing gene expression data from The Cancer Genome Atlas, we find that cancer cells evolve to utilize amino acids more economically by optimizing gene expression profiles. We further validate this pattern in an experimental evolution of xenograft tumors. ECPA not only shows robust prognostic power across many cancer types, but also improves the prediction of tumor response to checkpoint inhibitor immunotherapy. Our ECPA analysis reveals a common principle during cancer evolution.

18:30 - 20:30 Welcome Dinner
October 15 Tuesday: Talks
13:00 - 14:50 Session 5, chaired by Jingfa Xiao, BIG, CAS
13:00 - 13:40 Application of next generation sequencing in leukemia – from bench to bedside
Zhaolei Zhang, University of Toronto, Canada

I will discuss our efforts in applying high-throughput DNA and RNA sequencing in the diagnostics and longitudinal monitoring of leukemias patients. We show that targeted sequencing is effective in ascertaining the mutation load after chemotherapy or allogeneic hematopoietic transplant, which in turn can serve as a good prognostic indicator. I will also discuss how such longitudinal mutation data can be used to trace the clonal evolution in leukemia patients. I will further discuss how patient’s electronic health data can be used in predicting the risk of developing leukemia.

13:40 - 13:55 Genomic Variant Information and Knowledge Databases
Shuhui Song, BIG, CAS
13:55 - 14:35 Genomic Impact of Transposable Elements and Its Biological Function
Heui-Soo Kim, Pusan National University, Korea

Transposable elements can influence gene transcript ion and biological function through various mechanisms. Long terminal repeats (LTRs) of endogenous retroviruses (ERVs) have been shown to influence the expression of neighboring genes. Solitary LTRs contain various transcriptional regulatory elements including promoters, enhancers, and polyadenylation signals. They provide miRNAs during species radiation. Hypomethylation of the LTR element allows the neighboring functional gene to have tissue specific expression. Accumulated changes of the LTR elements in gene regulation are likely to be functional factors for the process of diversification, speciation and evolution consequences. A small minority of such sequences has acquired a role in regulating gene expression, and some of these may be related to differences between individuals, and to expression of disease. They seemed to be a source of alternative splicing, structural change of genomes, chromosome evolution, and could be related to genetic variation and epigenetic regulation linked to various diseases in various species.

14:35 - 15:10 Tea & Coffee Break
15:10 - 17:00 Session 6, chaired by Lina Ma, BIG, CAS
15:10 - 15:50 Structural bioinformatics of RNA molecules: integration of data from various sources at different stages of computational 3D structure modeling
Janusz Bujnicki, International Institute of Molecular and Cell Biology in Warsaw, Poland

Ribonucleic acid (RNA) molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Understanding how these molecules interact to carry out their biological roles requires detailed knowledge of RNA 3D structure and dynamics as well as thermodynamics, which strongly governs the folding of RNA and RNA-RNA interactions as well as a host of other interactions within the cellular environment. Experimental determination of these properties is difficult, and various computational methods have been developed to model the folding of RNA 3D structures and their interactions with other molecules. However, computational methods have limitations, especially when the biological effects demand computation of the dynamics beyond a few hundred nanoseconds. For the researcher confronted with such challenges, a more amenable approach is to resort to coarse-grained modeling to reduce the number of data points and computational demand to a more tractable size, while sacrificing as little critical information as possible.

I will review strategies for computational modeling of RNA structure and dynamics, and for the structure determination of RNA interactions and complexes with other molecules (ions, small molecules, and proteins). I will show applications of SimRNA, a method developed in my laboratory, which uses a coarse-grained representation, relies on the Monte Carlo method for sampling the conformational space, and employs a statistical potential to approximate the energy and identify conformations that correspond to biologically relevant structures. I will also discuss the use of computational approaches for RNA structure prediction that can utilize data from experimental analyses.

15:50 - 16:20 Integrated Epidemiologic Information System for Dengue and Influenza - From Surveillance to Epidemiological Investigation and Public Health Policies
Chwan-Chuen King, Taiwan University, China

Large-scale outbreaks of dengue and human influenza have frequently occurred in Taiwan during summer and winter seasons, respectively. Facing challenges of fast increasing numbers of cases and deaths, an integrated epidemiological information system which provides timely data for decision-makers is necessary. Since March 2004 [i.e. after the outbreak of severe acute respiratory syndrome (SARS) in 2003], we established a hospital emergency department-based syndromic surveillance system (ED-SSS) that involves 11 syndrome groups capable of automatic transmission of daily patient data directly from hospitals with emergency care throughout Taiwan to the Centers for Disease Control in Taiwan (Taiwan-CDC). In addition, school-based infectious disease syndromic surveillance system (SID-SSS) was developed from Taipei City government in response to the 2009 influenza A (H1N1) pandemic. Teachers and nurses from preschools to universities in all 12 districts of Taipei City are required to report cases of symptomatic children or sick leaves on a daily bases through the SID-SSS. The SID-SSS has extended to New Taipei City and other parts in Taiwan. In other words, both ED-SSS and SID-SSS can provide daily epidemi-ological information when outbreaks of emerging infectious diseases caused by novel agents [e.g. enterovirus 71, 2009 swine influenza virus (pdmH1N1/09)] occur.

In 2015, the most severe and largest epidemic of dengue occurred in southern Taiwan. A risk factor-based integrated dengue information system (RF-IDIS) which involve important risk factors (e.g. mosquito indices, meteorological factors, land use, high risk locations etc.) to assist outbreak investigation and risk management. This RF-IDIS was firstly established in Pingtung in 2016 and subsequently extended to other cities/counties in Taiwan. Source reduction of Aedes mosquitoes can start early from entomological surveillance data even before the detection of human cases.

In the future, integrated influenza surveillance systems need to have human vaccine history, viral sequences of the virus derived from different host species, and ecology.

16:20 - 16:50 Big Data Integration Based on Mechanistic Model
Xiangjun Du, School of Public Health (Shenzhen), Sun Yat-sen University, China

Take transmission dynamics of seasonal influenza virus as an example, I will show you the idea of using theoretical modelling to address critical questions in life and health sciences by integrating big data from multiple sources. I will show you the successes of long-term incidence forecasting of seasonal influenza virus recent years for the United States, and the challenges due to the complexity of pathogen evolution. Data integration based on mechanistic model is a way forward for study the complex diseases in life and health sciences.

16:50 - 17:10 Tea & Coffee Break
17:10 - 18:35 Session 7, chaired by Xiangjun Du, School of Public Health (Shenzhen), Sun Yat-sen University
17:10 - 17:50 Multi-omics analysis of esophageal squamous cell carcinoma reveals alcohol drinking-related mutation signature and genomic alterations mediating interactions in tumor ecosystem
Chen Wu, Cancer Hospital Chinese Academy of Medical Sciences
17:50 - 18:05 Computational analysis and visualization tools for 3D genomic study
Juntao Gao, Tsinghua University
18:05 - 18:35 Inheritance and reprogramming of epigenomic landscape
Jiang Liu, Beijing Institute of Genomics, Chinese Academy of Sciences
October 16 Wednesday: Talks
09:00 - 10:10 Session 8, chaired by Zhang Zhang, BIG, CAS
09:00 - 09:40 Mutation, selection, and the somatic evolution of cancer
Jeffrey Townsend, Yale University, United States
09:40 - 10:10 Developing Experimental and Computational Methods to Study the Genetic Basis of Complex Diseases and Traits in the Big Data Era
Yuwen Liu, Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences

With the accumulation of genotype and phenotype data over an increasing big number of individuals, genome-wide association studies have identified many genetic variants associated with complex diseases and traits. However, we lack high-throughput experimental tools to understand the biological functions of these variants. We also lack computational methods to integrate multi-omics data, or differenttypes of “big biology data”, to explore the genetic basis of complex diseases and traits. In my talk, I will present two experimental methods and one computational method that we have built/extended in the past in the field of human genetics. I will also talk a little bit about my future work focusing on utilizing big data in animal genetics to accelerate livestock genetic improvement.

10:10 - 10:20 CGVD: A genomic variation database for Chinese populations
Jingyao Zeng, BIG, CAS
10:20 - 10:40 Tea & Coffee Break
10:40 - 12:20 Session 9, chaired by Rujiao Li, BIG, CAS
10:40 - 11:20 From virus to human genome through bioinformatics
Ana Tereza Ribeiro de Vasconcelos, National Laboratory of Scientific Computation, Brazil

Bringing together the expertise of bioinformaticians and geneticists is crucial, since very specific and fundamental computational approaches are required for virus, microorganism and human genome research, particularly in an era of big data. Improve existing analytical tools, computational resources, data sharing approaches, new diagnostic tools, and bioinformatic training are crucial. In this talk I will present results in collaboration with several research groups in Brazil related to the Zika virus, neglected and genetic diseases and resistance to antibiotics.

11:20 - 11:50 Effects of long-term exposure to air pollution on the resurgence of scarlet fever in China: a 15-year surveillance study
Shelan Liu, Zhejiang Provincial Center for Disease Control and Prevention, China

Background: Some researches have projected a relationship between scarlet fever and meteorology and air pollution in a specific regions or cities, but the resurge mechanism of scarlet fever after 2011 in China has not interpreted in view of nationwide.
Objectives: Aimed to investigate the association between the resurgence of scarlet fever and long-term exposure to air pollutants and weather conditions in China. Methods: Data on scarlet fever were from the National Notifiable Disease Reporting System. Air pollutants were from the National Environmental Protection Department, and weather conditions were from the National Meteorological Information Center. A lag non-linear model (DLNM) was used to estimate the excess risk of scarlet fever associated with air pollutants and weather conditions.
Results: 655,039 scarlet fever cases were reported during 2004-2018. It started to surge in 2011 (4.7638 per 100 000), and peaked in 2018 (5.6736 per 100 000). The average incidence in 2011-18 was twice that in 2004-10 (rate ratio=2.302; 95% CI: 2.289-2.314; p<0.001]. There was a low-moderate correlation between scarlet fever and monthly NO2 concentration (r=0.21), sunlight (r=0.28), and wind speed (r=0.24), but the others were weak [PM10 (r=0.13), ozone (r=0.11), and PM2.5 (r=0.05)]. By contrast, it was inversely correlated with monthly relative humidity (RH, r=-0.37), precipitation (r=-0.24) and mean temperature (r=-0.2). A one-unit increment of NO₂ concentration was associated with scarlet fever increased (RR=1.5, 95% CI: 1.35-1.66). The RR was recorded at lag 0 at 42 μg/m3 NO2 concentration (RR=1.15). NO2 pooled estimates varied substantially across China (RR=1.02~4.16), but were higher in northern parts.
Conclusions: Long-term exposure to ambient NO₂ was associated with scarlet fever resurgence, triggered by low RH, temperature, precipitation, and high wind speed and sunshine. Further interventions to reduce NO2 emissions might suppress the resurgence of scarlet fever.

11:50 - 12:20 The tea tree genome provides insights into tea-processing suitability, tea flavor and independent evolution of caffeine biosynthesis.
Lizhi Gao, Kunming Institute of Botany, Chinese Academy of Sciences
12:20 - 13:30 Lunch
13:30 - 15:15 Special Session: The CAS Youth Innovation Promotion Association
chaired by Shuhui Song and Lili Hao, BIG, CAS
13:30 - 14:15 3 Selected talks, 15min/talk:
  • Transcriptional Heterogeneity of Mouse Megakaryocytes at Single-Cell Resolution
    Shu Sun, BIG, CAS
  • adATAC: adaptive and accurate nucleosome positioning with ATAC-Seq data
    Bingxiang Xu, BIG, CAS
  • m6A promotes R-loop formation to facilitate transcription termination
    Qianlan Liu, BIG, CAS
14:15 - 15:15 12 Lightning talks, 5min/talk:
  • An intelligent recommendation algorithm for genotype imputation with convolutional neural networks
    Shuo Shi, BIG, CAS
  • New genes generated and amplified by transposons in animals
    Shengjun Tan, Institute of Zoology, CAS
  • Genome-wide Methylome Data Uncovered the Dynamic Role of DNA Methylation for Plant Stress Regulation
    Fiaz Ahmad, University of Karachi, Pakistan
  • eRNA interact with target genes via basepairing In Alu Elements
    Bai Xue, BIG, CAS
  • An expanded landscape of human long noncoding RNA
    Shuai Jiang, Peking University, China
  • Dynamic methylome of internal mRNA N7-methylguanosine and its regulatory role in translation
    Lionel MALBEC, BIG, CAS
  • RNA 5-methylcytosine facilitates the maternal-to-zygotic transition by preventing maternal mRNA decay
    Wenlan Yang, BIG, CAS
  • Role of RNA N6-methyladenosine methylation in regulating brain development in mouse
    Xufei Teng, BIG, CAS
  • Transcriptionally active B2 potentially contributes to chromatin dynamic change during Mouse Neural Development
    Xiao Zhang, BIG, CAS
  • Comparison and analysis of lncRNA-mediated ceRNA regulation in different molecular subtypes of glioblastoma
    Qianpeng Li, BIG, CAS
  • Molecular characterization reveals the diagnostic, prognostic and predictive significance of PRKCG in glioma
    Lin Liu, BIG, CAS
  • NucMap: a database of genome-wide nucleosome positioning map across species
    Jinyue Wang, BIG, CAS
15:15 - 15:30 Tea & Coffee Break
15:30 - 16:50 Session 10, chaired by Weiwei Zhai, Institute of Zoology, CAS
15:30 - 15:45 Cross-sectional whole-genome sequencing and epidemiological study of multidrug-resistant Mycobacterium tuberculosis in China
Cuidan Li, BIG, CAS
15:45 - 16:00 Genome Warehouse: A Public Repository Housing Genome-scale Data
Meili Chen, BIG, CAS
16:00 - 16:40 Human adaptation to local environments
Rasmus Nielsen, UC Berkeley, United States
16:40 - 16:50 Closing Remarks
Zhang Zhang, BIG, CAS