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 Code
    • 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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On this page
  • 1) Table of Contents
  • 2) Recommended Tools for RNA-seq
  • 3) Pipelines for RNA-seq
  • 4) Teaching Videos
  • 5) References

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  1. Part III. NGS DATA ANALYSES

2.RNA-seq

Previous1.2 bedtools and samtoolsNext2.1.Expression Matrix

Last updated 2 years ago

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  • 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 (请参考一节)

  • Spliced Mapping/Alignment (请参考一节)

  • Differential analysis:

    • : 利用负二项分布广义线性模型进行差异分析

    • : 和DESeq2类似,利用负二项分布广义线性模型进行差异分析。

    • : 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

5) References

( - Genome Biology 2021)

see Videos in the

-- Genome Biology 2016

- Genome Biology 2021

2.1 Expression Matrix
2.2 Differential Expression with Cufflinks
2.2 Differential Expression with DEseq2 and edgeR
2.4 Alternative Splicing
mapping
fastqc
cutadapt
trim_galore
fastp
fastx_toolkit
mapping
hisat2
STAR
DESeq2
edgeR
limma
RNA-seq analysis pipeline
Best practices on the differential expression analysis of multi-species RNA-seq
lncRNA analysis pipeline
Files needed
A survey of best practices for RNA-seq data analysis
Best practices on the differential expression analysis of multi-species RNA-seq