Bioinformatics Tutorial
Files Needed
  • Getting Started
    • Setup
    • Run jobs in a Docker
    • Run jobs in a cluster [Advanced]
  • Part I. Programming Skills
    • 1.Linux
      • 1.1.Basic Command
      • 1.2.Practice Guide
      • 1.3.Linux Bash
    • 2.R
      • 2.1.R Basics
      • 2.2.Plot with R
    • 3.Python
  • PART II. BASIC ANALYSES
    • 1.Blast
    • 2.Conservation Analysis
    • 3.Function Analysis
      • 3.1.GO
      • 3.2.KEGG
      • 3.3.GSEA
    • 4.Clinical Analyses
      • 4.1.Survival Analysis
  • Part III. NGS DATA ANALYSES
    • 1.Mapping
      • 1.1 Genome Browser
      • 1.2 bedtools and samtools
    • 2.RNA-seq
      • 2.1.Expression Matrix
      • 2.2.Differential Expression with Cufflinks
      • 2.3.Differential Expression with DEseq2 and edgeR
    • 3.ChIP-seq
    • 4.Motif
      • 4.1.Sequence Motif
      • 4.2.Structure Motif
    • 5.RNA Network
      • 5.1.Co-expression Network
      • 5.2.miRNA Targets
      • 5.3. CLIP-seq (RNA-Protein Interaction)
    • 6.RNA Regulation - I
      • 6.1.Alternative Splicing
      • 6.2.APA (Alternative Polyadenylation)
      • 6.3.Chimeric RNA
      • 6.4.RNA Editing
      • 6.5.SNV/INDEL
    • 7.RNA Regulation - II
      • 7.1.Translation: Ribo-seq
      • 7.2.RNA Structure
    • 8.cfDNA
      • 8.1.Basic cfDNA-seq Analyses
  • Part IV. MACHINE LEARNING
    • 1.Machine Learning Basics
      • 1.1 Data Pre-processing
      • 1.2 Data Visualization & Dimension Reduction
      • 1.3 Feature Extraction and Selection
      • 1.4 Machine Learning Classifiers/Models
      • 1.5 Performance Evaluation
    • 2.Machine Learning with R
    • 3.Machine Learning with Python
  • Part V. Assignments
    • 1.Precision Medicine - exSEEK
      • Help
      • Archive: Version 2018
        • 1.1.Data Introduction
        • 1.2.Requirement
        • 1.3.Helps
    • 2.RNA Regulation - RiboShape
      • 2.0.Programming Tools
      • 2.1.RNA-seq Analysis
      • 2.2.Ribo-seq Analysis
      • 2.3.SHAPE Data Analysis
      • 2.4.Integration
    • 3.RNA Regulation - dsRNA
    • 4.Single Cell Data Analysis
      • Help
  • 5.Model Programming
  • Appendix
    • Appendix I. Keep Learning
    • Appendix II. Databases & Servers
    • Appendix III. How to Backup
    • Appendix IV. Teaching Materials
    • Appendix V. Software and Tools
    • Appendix VI. Genome Annotations
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  • 1) Table of Contents
  • 2) Files Needed
  • Method 1: Use docker
  • 5.1 Co-expression Network
  • 5.2 miRNA target prediction
  • 5.3 CLIP-seq data analysis
  • Method 2: Directly Download
  • 3) Teaching Video

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  1. Part III. NGS DATA ANALYSES

5.RNA Network

Previous4.2.Structure MotifNext5.1.Co-expression Network

Last updated 2 years ago

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1) Table of Contents

在一节中,我们介绍了如何通过CHIP-seq数据分析确定特定转录因子和DNA的相互作用关系。本章中我们将介绍更多的推断生物大分子相互关系的生物信息学方法:

  • : 从表达矩阵出发,判断哪些基因有相似的表达模式

  • : 从序列出发,用生物信息学工具预测mirna的靶标

  • : 通过CLIP-seq数据分析确定特定RNA结合蛋白(RBP)和RNA的相互作用关系

2) Files Needed

Method 1: Use docker

5.1 Co-expression Network

docker images的下载链接如所示,下载 bioinfo-coexp.tar.gz 启动新的docker.

# load image tarball
docker load -i ~/Downloads/bioinfo_coexp.tar.gz # 请根据下载文件的实际位置调整输入内容
docker run -dt --name coexpression --restart unless-stopped -v {host.machine.shared.directory}:/data gangxu/coexpression:1.5
docker exec -it coexpression bash
cd /home/bioc

5.2 miRNA target prediction

docker load -i ~/Downloads/bioinfo_mirna_target.tar.gz
docker run -dt --name mirna --restart unless-stopped -v {host.machine.shared.directory}:/data mirna_targets:1.0
docker exec -it mirna bash
cd /home/test/mirna

5.3 CLIP-seq data analysis

Method 2: Directly Download

3) Teaching Video

如果不使用docker,也可以直接从 中的Files/ 路径下的相应文件夹中下载所需要的文件。

see Videos in the

Files needed
Files needed
CHIP-seq
5.1.Co-expression Network
5.2.miRNA Targets
5.3.CLIP-seq (RNA-protein interactions)
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