跟着纽约大学NGS中心学生信

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

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