Bioinformatics Tutorial - Basic

生物信息学实践教程 - 基础篇 (2020版)

Teaching Philosophy

🎦 Study and Practice | 格物致知 知行合一

We teach professional skills in bioinformatics. These skills are not just running software. They will give you the freedom of exploring various real data.

Aim

写在前面的话

相对于过去,突然地,我们发现数据不是太少而是太多,信息不是匮乏而是繁杂,新一代人的重要能力是“鉴别”和“挖掘”。

对生物信息学的工作而言,最重要的、最有用的基本工具和技能过去一直是,我相信很长一段时间也会始终是:

  1. google

  2. wikipedia

  3. 知乎

We aim to teach basic data skills that give you freedom.

  • Running bioinformatics software isn’t all that difficult, doesn’t take much skill, and it doesn’t embody any of the significant challenges of bioinformatics.…These data skills give you freedom

  • I believe these two qualities — reproducibility and robustness.

  • So what is a reproducible bioinformatics project? At the very least, it’s sharing your project’s code and data.

  • In wet lab biology, when experiments fail, it can be very apparent, but this is not always true in computing. Electrophoresis gels that look like Rorschach blots rather than tidy bands clearly indicate something went wrong. Unfortunately, without prior expectations, it can be quite difficult to distinguish good results from bad results.

  • The easy way to ensure everything is working properly is to adopt a cautious attitude , and check everything between computational steps.

  • You will almost certainly have to rerun an analysis more than once.

  • Write Code for Humans, Write Data for Computers

  • Use Existing Libraries Whenever Possible

  • Treat Data as Read-Only

  • Document Everything (-- Too geeky?) Just as a well-organized laboratory makes a scientist’s life easier, a well-organized and well-documented project makes a bioinformatician’s life easier.

-- <<Bioinformatics Data Skills>>

Major Authors

Yumin Zhu, Gang Xu, Zhuoer Dong, Yinghui Chen, Meifeng Zhou, Xupeng Chen, Xiaocheng Xi, Xi Hu, Jingyi Cao, Xiaofan Liu, Weihao Zhao, Siqi Wang and Zhi J. Lu

Section

Major Authors

Part I. Basic Skills

1.Setup

Zhi John Lu

1.1 Docker

Gang Xu

1.2 Cluster

Gang Xu

2.Linux

Zhi John Lu

2.1 Basic Command

Xi Hu

2.2 Practice Guide

Xi Hu/Zhuoer Dong

2.3 Linux Bash

Gang Xu

3.R

3.1 R Basics

Zhuoer Dong

3.2 Plot with R

Xiaochen Xi/Zhuoer Dong

4.Python

PART II. BASIC ANALYSES

1.Blast

Gang Xu

2.Conservation Analysis

Xi Hu

3.Function Analysis

3.1 GO

Gang Xu

3.2 KEGG

Gang Xu

3.3 GSEA

Zhuoer Dong

Part III. NGS DATA ANALYSES

1.Mapping

Meifeng Zhou/Yumin Zhu

2.RNA-seq

2.1 Differential Expression

Meifeng Zhou

2.2 Alternative Splicing

Zhuoer Dong

3.ChIP-seq

Jingyi Cao

4.Network

4.1.Co-expression Network

Xiaochen Xi

4.2.miRNA Targets

Yumin Zhu

4.3.RBP-RNA Interactions

Yumin Zhu

5.Motif

5.1.Sequence Motif

Yumin Zhu

5.2.Structure Motif

Yumin Zhu

6.RNA Regulation Analyses

6.1.RNA Editing

Yumin Zhu

6.2.APA (Alternative Polyadenylation)

Yumin Zhu

6.3.Ribo-seq

Yumin Zhu

6.4.Structure-seq

Yumin Zhu

6.5.Chimeric RNA

Yinghui Chen

6.6.SNV Calling

Yinghui Chen

7.Clinical Analyses

7.1.ROC Curve

Weihao Zhao/Yumin Zhu

7.2.PCA/tSNE

Xupeng Chen/Xiaofan Liu

7.3.Survival Analysis

Xiaochen Xi/Yumin Zhu

Part IV. MACHINE LEARNING

1.Machine Learning Basics

Xupeng Chen/Zhi John Lu

2. Machine Learning with R

Xupeng Chen/Xiaofan Liu

3. Machine Learning with Python

Xupeng Chen/Xiaofan Liu

Appendix

Appendix I. Keep Learning

Zhi John Lu

Appendix II. Databases & Servers

Yumin Zhu

Appendix III. How to Backup

Gang Xu/Zhi John Lu

Appendix IV. Teaching Materials

Gang Xu/Zhi John Lu

Contact Us

Copyright

Copyright © 2020 Lu Lab

https://www.apache.org/licenses/LICENSE-2.0

2016-2020年于清华园

本书在清华大学《生物信息学导论》课和《生物信息学实践》课上机指南的基础上编写。