Bioinformatics Tutorial - Basic
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Bioinformatics Tutorial - Basic
Getting Started
Part I. Basic Skills
1.Setup
2.Linux
3.R
4.Python
PART II. BASIC ANALYSES
1.Blast
2.Conservation Analysis
3.Function Analysis
4.Clinical Analyses
Part III. NGS DATA ANALYSES
1.Mapping
2.RNA-seq
2.1.Expression Matrix
2.2.Differential Expression with Cufflinks
2.3.Differential Expression with DEseq2 and edgeR
2.4.Alternative Splicing
3.ChIP-seq
4.Motif
5.Network
6.RNA Regulation Analyses
Part IV. MACHINE LEARNING
1.Machine Learning Basics
2.Machine Learning with R
3.Machine Learning with Python
Part V. Quiz
1.Precision Medicine - exSEEK
2.RNA Regulation - RiboShape
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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2.RNA-seq
  • 常规RNA-seq最直接的作用在于对基因表达进行定量。在本章中我们主要来讨论和基因表达定量相关的RNA-seq数据分析。

1) Table of Contents

2) Recommended Tools for RNA-seq

  • Raw Data QC and preprocessing (请参考mapping一节)
  • Spliced Mapping/Alignment (请参考mapping一节)
  • Differential analysis:
    • ​DESeq2: 利用负二项分布广义线性模型进行差异分析
    • ​edgeR: 和DESeq2类似，利用负二项分布广义线性模型进行差异分析。
    • ​limma: 最早针对microarray数据开发的差异分析工具。开发者后来又进行了多次改进使其也适用于RNA-seq的分析。

3) Pipelines for RNA-seq

4) Teaching Videos (link)

  • PART III: Differential Expression
  • PART III: Alternative Splicing

5) References

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Outline
1) Table of Contents
2) Recommended Tools for RNA-seq
3) Pipelines for RNA-seq
4) Teaching Videos (link)
5) References