The aim of Cui Lab is developing methods of bioinformatics and systems biology to investigate life sciences or provide helps for biologists and medical scientists in their studies. Currently, we focus on developing bioinformatic methods of noncoding RNAs and network pharmacology for complex diseases such as cardiovascular diseases.

Currently we mainly focus on:

Qinghua Cui
Qinghua Cui(崔庆华)
Professor, PI
Department of Biomedical Informatics
Peking University Health Science Center

38 Xueyuan Rd, Beijing, China, 100191
Email: cuiqinghua@hsc.pku.edu.cn
Tel: 8610-82801585
Fax: 8610-82801001

Lab Members:

Zhou Huang PhD student(2018-present)
Yan Huang visiting Master student(2017-present)
Yiran Zhou PhD student(2017-present)
Yuanxu Gao PhD student(2017-present)
Junpei Wang PhD student(2016-present)
Weili Yang PhD student(2016-present)
Chunmei Cui Master student (2016-present)
Sisi Guo joint PhD student (2015-present)
Xiaowen Feng PhD student (2015-present)
Kaiwen Jia PhD student (2015-present)
Jiangcheng Shi Master student (2015-present)
Pan Zeng PhD student(2014-present)

Alumni:

Chengxiang Qiu Master student(2009-2012), now a bioiformatician in University of Pennsylvania
Dong Wang Master student(2010-2013), now an investment manager in Harvest Fund
Geng Chen Master student(2011-2014), now a reseracher at Fujian Hospital for Infection disease
Jian Tu Master student(2012-2015), now a founder CEO of BYSX Co.Ltd
Jianwei Li Visiting scholar from Hebei University of Technology (2013-2014)
Chuanbo Huang Visiting scholar from HuaQiao University (2014-2015)
Changyu Tao joint PhD student (2015), now a PhD supervised by Prof. Ence Yang
Junpei Wang Master student (2013-2016), now a PhD student
Junyi Wang visiting student, now a PhD student at School of Enineering, Peking University
Zhenzhen Chen PhD student (2014-2017), now a Co-PI in the National Key Lab of Cardiovascular Disease, Fuwai Hospital
Yuchen Wang PhD student (2013-2017), now an assistant professor in the Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College (Beijing, China).
Wei Ma PhD student (2013-2017), now an assistant professor in the PLA NAVY GENERAL HOSPITAL
Yuan Zhou Post-Doctor(2015-2017), now a tenure-track PI in the Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University

  • Cepred
  • Resource type: Web query-driven and downloadable software.
    Description: Cepred is a tool to predict whether a human intronic miRNA is high co-expressed with its host gene or not based on data of genome coordinate, which can be further used to identify the potential functions, biological processes, phenotypes and diseases associated with this miRNA (Wang et al., PLoS one 2009).

  • TAM
  • Resource type: Web query-driven software.
    Description: TAM is a web-accessible program that is in the first version. For a given list of miRNAs, for example the deregulated miRNAs from microarray or deep sequencing experiments, TAM is able to identify enriched miRNA sets and explore novel miRNAs related to the input, and therefore is able to predict novel miRNAs for given function and diseases(Lu et al., BMC Bioinformatics 2010).

  • MISIM
  • Resource type: Web query-driven software.
    Description: MISIM is a tool for the miRNA functional similarity measuring and network construction. Given a miRNA list, MISIM measures the pairwise functional similarity of the given miRNAs. MISIM is also able to generate a miRNA functional network according to the calculated MISIM similarity coefficients for given miRNAs. (Wang et al., Bioinformatics 2010)

  • Drawing of Chromosomes
  • Resource type: Downloadable R source code.
    Description: This R script is able to draw figures of chromosomes with highlighted genes in various colors. (Wang et al., PLoS ONE 2010)

  • Identification of network components
  • Resource type: Downloadable Java source code.
    Description: The java source code is used to identify network components for a given list of network nodes. It is able to identify not only the network components for the whole network but also can identify network components for a subset of network nodes. (Cui Q. PLoS ONE 2010)

  • miREnvironment
  • Resource type: Web query-driven database and software.
    Description: miREnvironment integrated the experimentally supported interactions of miRNA and environmental factors including drugs. Phenotypes are also curated. Currently, two tools that can predict the therapy outcome of cancer treatment and predict environmental factor-disease relationships are also integrated. (Yang et al., Bioinformatics 2011; Qiu et al., Scientific Reports 2012).

  • miRUPnet
  • Resource type: Web query-driven software.
    Description: miRUPnet is a tool to predict the function of a miRNA based on functional enrichment analysis of its upstream interacting context. (Qiu et al., Molecular BioSystems 2012).

  • miR2Gene
  • Resource type: Web query-driven software.
    Description: miR2Gene is a tool to predict the function of protein-coding gene by miRNA set enrichment analysis of miRNAs that regulate the gene. (Qiu et al., BMC Systems Biology 2011).

  • LncRNADisease
  • Resource type: Web query-driven database and software.
    Description: LncRNADisease is a database that integrated experimentally supported long noncoding RNA (lncRNA) and disease associations, and lncRNA interactions. LncRNADisease is also a software platform that integrated tools for prediction novel relationship between lncRNAs and diseases.

  • LncTar
  • Resource type: Standalone software and Web query-driven software.
    Description: LncTar is an efficient tool for predicting the RNA targets of long noncoding RNAs (lncRNAs). LncTar can run under Windows and Linux. LncTar shows the following advantages: 1) it has no limits to RNA size. As we know, it can process the largest lncRNAs and mRNAs in current human genome; 2) it runs fast. LncTar can predict one RNA-RNA interaction ~10 seconds on average on a PC; 3) it presents a quantitative standard to automatically evaluate whether two RNAs can interact with each other; 4) it shows a high accuracy. LncTar achieved an accuracy of 80% to the predictions of current experimentally supported mRNA targets of lncRNAs (Li et al., Brief Bioinform 2014).

  • LncDisease
  • Resource type: Standalone software.
    Description: LncDisease is a novel computational method and tool to predict the associations between lncRNAs and diseases (Wang et al., NAR 2016).

  • SRAMP
  • Resource type: Web query-driven software.
    Description: SRAMP would serve as a useful tool to predict m6A modification sites on the RNA sequences (Zhou et al., NAR 2016).

  • MicroPattern
  • Resource type: Web query-driven software.
    Description: MicroPattern is a tool used to mine regular rules and patterns behind a list of microbes. MicroPattern can also be used to calculate the similarity between the given disease-associated microbe list and the collected micro-disease association dataset.

  • HMDD
  • Resource type: Web query-driven database.
    Description: Human MicroRNA Disease Database (HMDD) is a database that contains the experimentally supported miRNA-disease association data, which are manually curated from publications. The dysfunction evidence or miRNAs and literature PubMed ID are also given. HMDD was released on December 2007 (Lu et al., PLoS one 2008).

  • TransmiR
  • Resource type: Web query-driven database.
    Description: TransmiR integrated the experimentally supported transcription factor and miRNA regulatory relations (Wang et al., NAR 2010).

  • miREnvironment
  • Resource type: Web query-driven database and software.
    Description: miREnvironment integrated the experimentally supported interactions of miRNA and environmental factors including drugs. Phenotypes are also curated. Currently, two tools that can predict the therapy outcome of cancer treatment and predict environmental factor-disease relationships are also integrated (Yang et al., Bioinformatics 2011).

  • LncRNADisease
  • Resource type: Web query-driven database and software.
    Description: LncRNADisease is a database that integrated experimentally supported long noncoding RNA (lncRNA) and disease associations, and lncRNA interactions. LncRNADisease is also a software platform that integrated tools for prediction novel relationship between lncRNAs and diseases (Chen et al., NAR 2013).

  • HMDAD
  • Resource type: Web query-driven database.
    Description: The Human Microbe-Disease Association Database (HMDAD) is a resource which collected and curated the human microbe-disease association data from microbiota studies (Ma et al., Brief Bioinform 2016).

  • TF-miRNA regulatory network
  • Resource type: Downloadable data in txt format
    Description: No elements are isolated but interact with each other to form complex networks. For gene regulation, there exist several regulatory relationships, they are transcription factors (TFs)-genes, TF-miRNAs, and miRNA-genes. This network integrated currently available and experimentally supported gene regulatory relations and represents the first TF-miRNA-target network, which is a potentially valuable platform in biological and medical sciences (Qiu et al., BMC Systems Biology 2010).

  • Human microRNA oncogenes and tumor suppressors
  • Resource type: Downloadable data
    Description: We curated human microRNA oncogenes and tumor suppressors, and their annotation data (Wang et al., PLoS ONE 2010).

  • microRNA profiling in various cells
  • Resource type: Downloadable data
    Description: We generated miRNA expression profiles in a number of cells (Li et al., Biomed Res International 2013).

  • 长期招收博士后,要求如下:
  • 一、职位要求

      

    1、研究背景:生物信息学

      

    2、发表过SCI科研论文、具有良好的英文文献阅读和写作能力;

      

    3、熟悉编程、熟悉人工智能;

      

    4、具备良好的学术道德、沟通能力和团队精神,具有强烈进取心,工作刻苦努力。

      

    二、待遇

      

    按照北京大学医学部博士后相关待遇执行。博士后实行年薪制,博雅博士后年薪24万,其他全职博士后年薪不低于18万,优秀博士后年薪会提高两万元,其它待遇按北京大学博士后的规章制度执行。鼓励并支持以项目负责人身份申请国家级基金。

      

    三、招聘程序

      

    1、应聘者请提供详细个人简介(包括个人基本信息、教育科研经历、科研成果等)、近五年发表的代表性论文全文;

    2、本实验室初审;

      

    3、学院组织面试;

      

    4、报北京大学医学部及相关部门审批。

      

    四、联系方式

      

    接收简历邮箱:cuiqinghua@bjmu.edu.cn,邮件主题请注明“博士后应聘”