IBW2017·哈尔滨 | 参会准备 | 预习报告人的研究兴趣
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IBW2017·哈尔滨，生信新手要去参会吗？能听懂多少？新手，要做好预习，把收获最大化，并为下次做poster、做报告做准备。
看前人分享的经验：
参加学术会议，绝不仅仅是听听报告而已——论年轻人参会三部曲 ——陆朋玮
参加学术会议不带脑子？看看这个 ——刘哲雨，郝晓鑫
三个要点
会前了解报告人和报告题目；见本文后半部分小丫整理的报告题目和报告人研究兴趣；
会上听思路和八卦，记笔记，交朋友；
会后整理、总结。本次报告有小记者总结参会体验，稍后在本站发布，敬请关注。IBW2017·哈尔滨后天开幕 | 小记者招募 | 与生信大咖近距离接触
本次会议首页给出了大会报告人实验室主页链接，点击进入，查看研究兴趣，浏览一下发表的论文Publication，研究哪个生物学问题，开发了哪些算法/工具/数据库。对应报告题目，做到心中有数。
会议结束后，这些报告人就不只是名字，他们会在你心中形成丰富的人的形象，以后读到他们的paper，就像见到了老朋友。
小丫把大会报告人报告题目和实验室主页上的研究兴趣集中到这里，按报告顺序排列，以便速查。
Yi Xing，Illuminating dark matters of the human transcriptome
The long-term goal of our research is to elucidate how genomic and environmental variation of RNA regulatory networks impacts phenotypic traits and diseases. We are a hybrid computational and experimental lab. We combine genomic, bioinformatic, molecular, and network approaches to study mRNA processing and post-transcriptional gene regulation in mammalian cells. We develop novel computational and statistical methods for analysis of massive genome and transcriptome data. We integrate computational studies with high-throughput and experimental research to systematically investigate the variation and dynamics of RNA regulatory networks between species, within human populations, and in response to developmental and disease signals.
韩敬东，Integrative data analysis for development and aging
Our research is focused on three areas: 1) Integration of genomic and functional genomics data to formulate biological hypotheses for human disease-related processes. 2) Understanding complex human diseases through structure, dynamics and function of disease-related networks. 3) Genetic robustness or fail-safe mechanisms engineered to genetic networks. Using data-mining, statistics approaches and network theories, we first try to generate biological hypotheses and computational models, and then use molecular biology, cell biology and systems biology approaches to validate and refine models and hypotheses.
Ying Xu，Cell cycle in cancer cells
Our group's research and development center around the following areas:
Cancer Computational and Systems Biology: We are interested in developing integrated computational and omic techniques for (a) identification of biomarkers for a number of human cancers, detetable through analyses of serum/urine samples, and (b) understanding the relationships between molecular signatures and cancer formation & development. Our work involves microarray gene expression data generation and analyses, comparative genome analyses and analyses of other experimental data.
Study of Microbial Genome Structure and Application to Pathway & Network Inference: We are interested in understanding both the micro- and macro-structures of microbial genomes through computational studies and experimental validation, and in understanding why microbial genomes are organized the way they are. We are also interested applying the knowledge and information gained through such studies to prediction of pathways and networks in microbes.
王亚东，Challenges of analyzing big biological data
主要研究生物信息学、人工智能、知识工程、机器学习等。具体包括：基因组数据分析算法、数据可视化、知识挖掘与整合、网络分析、生物医药领域搜索等。
赵方庆，Ciruclar RNAs: composition, expression and evolution
1. Genetic variation and Precision medicine
2. Bioinformatics in Circular RNAs
3. Metagenomics and Human health
Ting Wang，The evolution of human epigenomes
Our research is to understand the evolution and adaption of human regulatory networks, with a focus on the impact of these processes on human health and disease. In particular, we investigate the evolutionary model of mobile elements (or transposable elements) and their roles in basic biology and cancer, including their genetic and epigenetic regulation.
We use integrative and systems methods. We develop statistical and computational algorithms to explore the human genome, to integrate cross-species comparative and high-throughput genomics data. We test our hypothesis and validate our predictions in the wet lab.
Our interests span areas of genomics, epigenomics, evolution, computation, systems biology and many more. We also have a general interest in large data integration and visualization, including developing genome and genomics browsers, and developing tools for analyzing high-throughput genomics data, including next-gen sequencing data.
彭绍亮，天河超级计算机上的生物医药大数据和机器人医生
在国防科技大学从事高性能计算、生物医药大数据、金融大数据、移动计算等研究工作，并担任高性能计算生命科学方向负责人，华大基因研究院 “特聘教授”。主持国家自科基金项目3项(重点1项)和国家发改委项目等，一作出版专著和论文50余篇（Nature Communication, ACM/ IEEE Transactions,《中国科学》等），参与天河系列超级计算机应用软件研发工作，获军队科技进步一等奖1项。CCF高性能计算和传感器网络专委会委员、CCF高级会员、YOCSEF长沙2015-2016副主席、ACM/IEEE会员。
高歌，Accurately annotate genetic variants: what we learned from 1.1 PB omics data
课题组以生物信息学分析技术、方法与平台开发为基础，通过综合运用大数据与统计学习(statistical learning)等计算方法，整合高通量遗传学与功能基因组学数据，探索新表达调控因子的功能与演化及其对生物体新性状和新功能的贡献。目前课题组主要研究方向包括1) 非编码RNA对干细胞命运决定过程的调控、与2) 基因组中适应性基因获得/丢失对调控网络演化的影响。
Wei Wang，Genomics and epigenomics
We are interested in understanding the regulatory mechanisms underlying cell fate decision. Especially, we take a multi-scale approach that integrates computational and experimental investigation of epigenetic regulation in cell fate decision from molecular level to genomic level then to systems level. We aim to build computational and theoretical models to uncover fundamental principles that govern cell fate decision in development and cellular reprogramming and design strategies to intelligently manipulate cell state.
Our research is highly interdisciplinary. The methods we use range from molecular modeling of protein structures, to bioinformatics analysis of epigenomic data generated by sequencing technology, to statistical learning of genetic network, to biophysical modeling of epigenetic landscape. The theoretical work is tightly coupled with experimental investigation that exploits molecular biology, biochemistry, cell biology and genomic techniques.
杨力，Genome-wide characterization and evolutionary analysis of backspliced circRNAs
We are carrying out a series of work to focus on decoding the regulatory network of poly(A)- lncRNAs across species. First, we further improve computational algorithms for poly(A)- lncRNA identification and their function predication in different species. Second, with the application of high-throughput poly(A)- RNA sequencing and aforementioned algorithms, we are in the progress of systematically profiling various of poly(A)- lncRNAs from different species and cell lines. In addition, we plan to knock down/knock out some key enzymes that are known to involve in lncRNA biogenesis to identify more poly(A)- lncRNA across species. Moreover, we will predict and validate possible conserved/consensus motifs in poly(A)- lncRNAs across species to decipher their importance for lncRNA biogenesis during evolution. Furthermore, we plan to identify any possible protein, RNA and DNA partners that bind to specific poly(A)- lncRNAs by performing state-of-the-art binding assays, such as CHART (Capture Hybridization Analysis of RNA Targets) and ChIRP (Chromatin Isolation by RNA Purification). Finally, we have obtain human embryonic stem cells with specific poly(A)- lncRNA repression and are ready to fully address their potential functions by checking the genome-wide gene expression changes.
Jian Ma，New Algorithms to Decode Genome Organization
Our long-term goal is to develop novel computational methods to address a fundamental challenge in biology and biomedicine, i.e., how the changes in genome sequence give rise to the differences in phenotypes (at both cellular and organism levels). Such insights will shed new light on disease mechanisms. We develop algorithms to explore the human genome to identify different types of genomic changes and study their impact on genome function, chromatin and nuclear genome organization, and gene regulation. We develop systems biology approaches to identifying key genetic variants in cancer development and progression.
徐书华，Identifying and prioritizing adaptive variants from whole-genome sequences of Tibetan highlanders
群体基因组学是通过运用计算生物学的方法，从群体层面上理解基因组的进化动力学机制，从而为基因医学提供进化史的理论基础。目前，我们着眼于分析遗传结构、推测遗传历史、检测自然选择，以及在群体中刻画与复杂疾病相关的基因图谱，识别出不同表达水平的基因。此外，最新的研究热点还在于新近混合人群的等位基因互作机制，以及基因与基因之间的互相作用。
研究方向
人类基因组的遗传结构
人群混合及环境适应性
数据库构建及软件开发
Yunlong Liu，Roles of alternative splicing in complex disease
The Liu Laboratory (Laboratory for Computational Genomics) uses systems biology approaches to understand regulatory mechanisms of gene expression, including transcriptional regulation, post-transcriptional regulation, and epigenetic regulation. This area involves several interdisciplinary components, including functional genomics, genetics, computational and statistical modeling, computer science/engineering, and data management.
Wei Li，3'UTR Shortening Represses Tumor Suppressors in trans by Disrupting ceRNA Crosstalk
Our lab is focused on the design and application of bioinformatics algorithms to elucidate global epigenetic mechanisms and transcription dynamics in normal development and diseases such as cancer.
We have developed a number of widely used algorithms to analyze next generation sequencing data from ChIP-seq (MACS, MACE), DNA methylation Bisulfite-seq (BSMAP/RRBSMAP, BSeQC, MOABS), nucleosome positioning MNase-seq (DANPOS), and RNA-seq (CPAT, RSeQC, DaPars). These algorithms have 47 32385 47 15288 0 0 2879 0 0:00:11 0:00:05 0:00:06 2984 been broadly adopted by thousands of academic users. For example, the MACS algorithm has gathered >3000 citations since 2008.
In collaboration with experimental biologists, we used these algorithms to gain novel biological insights from various biological processes and disease models.
李亦学，Genomic Analysis Reveals Hypoxia Adaptation in the Tibetan Mastiff by Introgression of the Grey Wolf from the Tibetan Plateau
在代谢组学的理论计算研究中提出了基于涨落谱分析的生化代谢网络重构与动态行为模拟的新方法，成功地应用于心肌缺血以及C3植物光合作用等生化代谢通路的研究。
在蛋白质组生物信息学研究上取得重要成果。提出了基于泊松距离研究蛋白质翻译后修饰的功能重要性的进化分析方法，阐明蛋白质修饰的发生并不保证其有确定的生物学功能，建立了筛选功能性修饰位点的方法。
在转录组和基因调控网络研究上取得重要成果。发展了人类基因组的基因可变剪切模式的统计识别方法，研究肿瘤的发生和发展与特异性的基因可变剪切模式之间的关系；建立了双向启动子统计识别和表观遗传级联调控网络分析方法。
在疾病基因组学研究方面取得重要成果。绘制了骆驼基因组草图，发现了与糖尿病发生发展相关的快速进化的基因簇和可能的抗代谢综合症的基因模块。成果发表在Nature系列期刊上。
此外他通过建立统计数学模型精确计算并发现了病毒株之间的DNA序列变异与疾病爆发和流行之间的关系，为我国首篇Science关于SARS研究论文发表提供了生物信息学的关键支撑。
Yun Li，Hugin: Hi-C Unifying Genomic Interrogator
The focus of my research is on the development of statistical methods and their application to the genetic dissection of complex diseases and traits. In particular, I have developed genotype imputation methods (implemented in software MaCH and MaCH-Admix) that have become standard in the analysis of genome-wide association scans. I have developed methods for meta-analysis, imputation, local ancestry inference, and region-based association analysis of rare variants in both genetically homogeneous populations and in admixed populations, and assessed different approaches to handle imputation uncertainty in subsequent association analysis. I have worked on genomewide scans for genetic variants underlying several metabolic, auto-immune and cardiovascular diseases and related quantitative traits. In addition, I have developed methods to accommodate low-coverage sequencing data for genotype calling and for association testing (implemented in software thunder [component of GotCloud], BETASEQ, UNCcombo) and have been actively involved in a number of next-generation sequencing (NGS) based studies including the 1000 Genomes Project (Project Leader on calling SNP genotypes from low-coverage pilot), identification of RNA-DNA differences (RDDs), targeted sequencing of selected exons in >14,000 individuals, the WHI whole exome sequencing project (WHISP), and whole genome sequencing based studies for type 2 diabetes, for cannabis and stimulant dependence, and for blood lipid levels, Exome Sequencing Project (ESP), and TOPMed project. More recently, I have worked on method development for Hi-C data, particularly to aid in the annotation of GWAS associated regulatory variants in terms of their target gene(s) and potential causal mechanism. I have also developed methods for DNA methylation data and actively participated in multiple epigenomewide association studies.
叶凯，Systematic discovery of complex insertions and deletions in human cancers
叶凯教授长期从事生物信息学和基因组学领域研究工作，是首批接触第二代高通量测序技术和数据的研究人员，以主要成员的身份参与了千人基因组计划、美国肿瘤基因组路线图计划等国际重大科学工程。
Sheng Zhong，Systematic mapping of RNA-chromatin interactions in vivo
We study gene regulation and cellular behavior by developing statistical and experimental methods. Our primary goal is to develop new technologies to map molecular networks, including RNA-RNA interactome [Nat Comm, 2016], RNA-chromatin interactome [Curr Biol, 2017], and protein-protein interactome. Our secondary quest is to model the variations of these networks in three axes, namely developmental time, personal difference, and evolutionary change. Our major tools include epigenomic and single-cell assays, single-molecule imaging, statistical modeling, and large scale computation.
钱文峰，Genetic interaction network as a major determinant of gene order in the genome
The long term goal of our lab is to understand basic rules in gene expression regulation and biological adaptation. There are four major ongoing research topics in our group.
(1) Regulatory mechanisms of transcription and translation, aiming to understand the function of each nucleotide;
(2) Single-cell DNA methylome and transcriptome, aiming to understand how robust transcriptome is maintained among isogenic cells that are in the same environment;
(3) The rates of point mutation and homologous recombination, and their impacts on the rate of adaptation;
(4) Ploidy and aneuploidy variation, and their impacts on the growth rate of cells (including tumor cells).
张治华，Delta: An integrative analysis and visualization platform for 3D genome
ZHANG’s lab combines the methods of computational systems biology with the cutting-edge experimental technologies to study the structure and function of 3D genome (Fig 1). There are four major research interests in our group.
1. 3D Genome. Gene transcription in eukaryotes is tightly regulated by a series of regulatory elements, e.g promoters, enhancers, inhibitors. Many of these regulatory elements located far away from their targeting genes. To determine the interactions between the distal regulatory elements and genes is critical in understanding gene regulatory network (GRN). We are interested in the questions like: a) how to predict target genes of a given distal regulatory element in this complex interaction network; b) how to predict the spatiotemporal expressive pattern by the feature of GRN; c) what is the spatial structure of chromatin fibers in nuclear and its relationship with the GRN functions; d) what is the connection between the 3D genome and variations detected in GWAS studies.
2. Long noncoding RNAs. In the formation of 3D genome structure in nuclear, and the regulatory activity of distal elements, noncoding RNA plays an important role. We are interested in the questions like: a) how lncRNAs interact with chromatin associated proteins in the context of chromatin-chromatin interactions. b) How to predict the targeting genes of a long noncoding RNA ; c) the genesis, process and regulation of enhancer associated RNAs and circularRNAs.
3. The micro-evolution of the gene regulatory network (GRN). We are focusing on how the topology and network dynamics change by the mutation in the process of tumorgenesis, the formation and development of tumor heterogeneity and tumor metastasis and how the changes affect the evolution of tumor in vivo. We anticipate developing a new computational method to identify the genetic or epigenetic variations which play important roles in this process from the point of view of the dynamic evolution of the GRN.
4. Artificial intelligence and biomedical big data. We are focusing on using artificial intelligence to deep mining biomedical big data to understand the relationship between genetic or epigenetic variations and complex diseases, such as cancer and type 2 diabetes.
Han Liang，The Pan-Cancer Analysis using TCGA and ICGC data
The fundamental question driving our research paradigm is how to take full advantage of cancer genomic data to elucidate the molecular basis of human cancer and develop effective prognostic and therapeutic strategies, thereby contributing to the true promise of personalized or precision cancer therapy. Combining both computational and experimental approaches, my group research focuses on the following areas.
Develop cutting-edge computational algorithms and bioinformatic tools for better analyzing cancer genomic data
Pan-cancer analyses using The Cancer Genome Atlas (TCGA) data
Investigate the functional role of RNA editing in cancer
Develop systems-biology approaches for inferring driver molecular events from next-generation sequencing data
汪小我，Regulation by competing: a hidden layer of gene regulatory networks
主要从事生物信息学与系统生物学方面的研究，包括：
微小RNA（microRNA）的识别、进化模式分析及转录调控研究。
基因表达调控网络分析
表观遗传学研究
高通量测序数据的分析与算法开发
合成生命系统的设计方法
Xiaole Liu，Widespread B cell clonal expansions and related mechanisms of immune evasions in human cancers
Our research focuses on algorithm development and integrative mining from high throughput data to understand gene regulation in cancer biology. We have developed a number of widely used algorithms for transcription factor motif finding, ChIP-chip / ChIP-seq / DNase-seq / CRISPR screen data analysis. Through integrating genome-wide transcription factor binding, chromatin dynamics, gene expression profiles, and chemical and functional screens, we try to model the specificity and function of transcription factors, chromatin regulators, RNA binding proteins, kinases, and lncRNAs in tumor development, progression, drug response and resistance.
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