前些天我在生信技能树重新唤起来了大家的记忆：都不知道自己还有一本生物信息学的书 ，因为是中文的接地气的教程合辑，所以受大家喜爱，我已经在出版社的鞭策下努力让他早日现世，这样大家以后带师弟师妹的时候也方便推荐教材。

这里还是推荐另外一个类似的教程，跟我们的目录都差不多，挺好的，可以互相对应着看，查漏补缺。他们的网页里面不仅仅是这本书，还有很多一直在更新的博客教程，类似于我们生信技能树公众号一样：

https://gencore.bio.nyu.edu/bioinformatics/ 感兴趣的朋友甚至可以收藏起来，避免下次找不到这么好的资源了。

This e-book contains resources for mastering NGS analysis. It has been generated by the Bioinformatics team at NYU Center For Genomics and Systems Biology in New York and Abu Dhabi.

TOPICS

• Next-Generation Sequencing Analysis Resources

• Introduction to Linux

• Linux Exercise

• Nano Tutorials

• Pre-Requisites

• NGS Sequencing Technology and File Formats

• How Sequencing Works

• FastA Format

• FastQ Format

• Quality Scores

• SAM/BAM/CRAM Format

• BED Format

• VCF Format

• GFF3 Format

• Alignment

• Trimming with Trimmomatic

• Visualization

• Variant Calling

• Pre-Processing

• Variant Discovery

• RNA-seq Analysis

• Aligning RNA-seq data

• Introduction to R

• DESeq

• DESeq 2

• Gene Set Enrichment Analysis with ClusterProfiler

• Over-Representation Analysis with ClusterProfiler

• Salmon & kallisto: Rapid Transcript Quantification for RNA-Seq Data

• Instructions to install R Modules on Dalma

• HPC

• Resources for editing files on the HPC

• Atom

• SSH Mounts

• Neovim

• SLURM

• Modules

• Gencore Infrastructure

• Gencore Variant Detection Example

• Software

• HPCRunner

• BioX Workflow

• ChipSeq analysis

• CHiP-seq considerations

• Prerequisites, data summary and availability

• Deeptools2 bamCoverage

• Deeptools2 computeMatrix and plotHeatmap using BioSAILs

• Exercise part4 – Alternative approach in R to plot and visualize the data

• De novo genome assembly

• Pre-processing and QC

• Exercise in de novo assembly

• Individual Commands

• Single cell RNA sequencing

• Prerequisites

• Seurat part 1 – Loading the data

• Seurat part 2 – Cell QC

• Seurat part 3 – Data normalization and PCA

• Seurat part 4 – Cell clustering

• Loading your own data in Seurat & Reanalyze a different dataset

• Metagenomics

• Quality Control

• Shotgun Metagenomics

• Taxonomic Classification

• Functional Analysis

• Deep Learning using Keras