五星推荐一个特别详细的 NGS 分析教程网址 bioinform

一份非常好的数据分析学习资料（一切源于我要做 WGBS 分析的 PPT）

链接：https://bioinformatics.ca/workshops/2018-epigenomic-data-analysis/

视频链接：
https://www.youtube.com/channel/UCKbkfKk65PZyRCzUwXOJung/featured

github 链接:
https://github.com/bioinformatics-ca

twitter 主页：
https://twitter.com/bioinfodotca

各种主页:
https://bioinformaticsdotca.github.io/

youtube 链接:
https://www.youtube.com/channel/UCKbkfKk65PZyRCzUwXOJung

最重要：有视频、有实战，并且都是讲的特别详细。

容我打开 2019 资料网站：https://bioinformaticsdotca.github.io/

点进去界面是这样的：

再往下滑动：

好了，我们可以清楚的看到分为几大块。

High-throughput Biology: From Sequence to Networks

这部分主要讲从序列到最终的调控网络，也包括了一些基础的 UNIX/R 的学习。（这部分 PDF 421 页）

准备工作：

1. R Preparation tutorials:
2. UNIX Preparation tutorials:
3. Sequencing Terminology
4. Cytoscape Preparation tutorials: Complete the introductory tutorial to Cytoscape

培训前需要查看的文献

• Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

• Genome structural variation discovery and genotyping

• A survey of sequence alignment algorithms for next-generation sequencing

• Genotype and SNP calling from next-generation sequencing data

• Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud

• Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

• ENCODE RNA-Seq Standards

• Methods to study splicing from high-throughput RNA sequencing data

• A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium

• De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

• Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing

• Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap

• iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

• The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function

• GeneMANIA Prediction Server 2013 Update

• How to visually interpret biological data using networks

• g:Profiler–a web-based toolset for functional profiling of gene lists from large-scale experiments

• g:Profiler–a web server for functional interpretation of gene lists (2011 update)

• Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

• Expression data analysis with reactome

接下来就是一周的课程安排

• Module 1: Introduction to High-throughput Sequencing
• Module 2: Data Visualization
• Module 3: Genome Alignment
• Module 4: Small-Variant Calling and Annotation
• Module 5: Structural Variant Calling
• Module 6: De Novo Assembly
• Module 7: Introduction to RNA Sequencing Analysis
• Module 8: RNA-seq Alignment and Visualization
• Paper: Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing
• Module 9: Expression and Differential Expression
• Module 10: Reference Free Alignment
• Module 11: Isoform Discovery and Alternative Expression
• Module 12: Introduction to Pathway and Network Analysis
• Module 13: Finding Over-Represented Pathways
• Module 14: Network Visualization and Analysis with Cytoscape and Reactome
• Module 15: More Depth on Network and Pathway Analysis and Cytoscape Enrichment map
• Module 16: Gene Function Prediction
• Module 17: Regulatory Network Analysis

Introduction to R

两天

Exploratory Analysis of Biological Data Using R

两天

Bioinformatics for Cancer Genomics

这部分PDF 316 + 49 + 52 页
这部分学癌症相关的应该是大有用处

• Module 1: Introduction to Cancer Genomics
• Module 2: Ethics of Data Usage and Security
• Module 3: Databases and Visualization Tools
• Module 4: Genome Alignment
• Module 5: Genome Assembly-
• Module 6: Copy Number Variants
• Module 7: Somatic Mutations and Annotations
• Module 8: Gene Expression Profiling
• Module 9: Gene Fusion and Rearrangements
• Module 10: Genes to Pathways
• Module 11: Variants to Networks
• Module 12: Integration of Clinical Data

Informatics for RNA-Seq Analysis

这部分就是我们最基础的 RNA-seq 分析所需要做的内容 这部分PDF 131 页

• Module 1: Introduction to Cloud Computing
• Module 2: Introduction to RNA Sequencing Analysis
• Module 3: RNA-seq Alignment and Visualization
• Module 4: Expression and Differential Expression
• Module 5: Reference Free Alignment
• Module 6: Isoform Discovery and Alternative Expression
• Module 7: Genome Guided and Genome-Free Transcriptome Assembly
• Module 8: Functional Annotation and Analysis of Transcripts

Informatics on High-Throughput Sequencing Data

这部分PDF 182 页

• Module 1: Introduction to High-throughput Sequencing
• Module 2: Data Visualization
• Module 3: Genome Alignment
• Module 4: Small-Variant Calling and Annotation
• Module 5: Structural Variant Calling
• Module 6: De Novo Assembly

Pathway and Network Analysis of -omics Data

这部分对于做调控网络的应该是大有帮助 这部分PDF 186 页

• Module 1: Introduction to Pathway and Network Analysis
• Module 2 Finding Over-Represented Pathways
• Module 3: Network Visualization and Analysis with Cytoscape
• Module 4: More Depth on Network and Pathway Analysis
• Module 5: Gene Function Prediction
• Module 6: Regulatory Network Analysis

Using Clouds for Big Cancer Data Analysis

上面就是 2019 年培训资料相关的。

当然这只是一部分，和我开头说的表观方面干系不大啊？然后我就找到了他们表观数据分析的资料存放地方。

Epigenomic Data Analysis：这部分是 2018 年的

https://bioinformatics.ca/workshops/2018-epigenomic-data-analysis/

讲什么呢?

Participants will gain practical experience and skills to be able to:

• Align ChIP-seq and WGBS sequence data to a reference genome (required)
• Identify narrow and broad peaks from ChIP-seq data
• Identify methylated levels from WGBS data
• Visualize and summarize the output of ChIP-Seq and WGBS analyses
• Explore integrative tools for epigenomic data sets

你可以看到这里就是讲 ChIP-seq 和 WGBS 分析，当然肯定也有基础的 UNIX 和 R 。
最最最重要的这部分都是有视频的。

哦，对了，这个国内进不去，自己想想办法吧。这年头干这行没点手段怎么更好的学习。

附上 2019 年的相关资料 PDF ：https://ws28.cn/f/1y1n77osfon （有效期至 2020.03.3 五点）