Appendix I. Keep Learning

在生物信息学的学习和应用中，最重要的、最有用的基本工具和技能，过去一直是，我相信将来的很长一段时间也会是：

1. google

2. wikipedia

3. 论坛（知乎，Seqanswers，Biostars, etc)

⭐: 必读 ✨: 推荐

PDFs for Text-books and Education Papers

1) Recommended Books

(1) 参考书 - 综合

选择性阅读的案头书

• ✨ 《生物信息学》 101 教材

• 《生物信息学》 樊龙江 主编

• 《生物信息学》 李霞，雷健波，李亦学 等 编

(2). 参考书 - 工具书

按需阅读和练习

Better to learn and practice 3 basic techniques (完成任何一个要求即可：1. 1000行以上的程序； 2. 认可证书，例如在线课程的正式）

1. R (or MATLAB)

2. Python (or Perl)

3. Linux (Editor (e.g. VIM) and Shell Script (e.g. bash))

1. ⭐ Quick R (online) OR 《R语言实战》 (《R in action》)
2. ⭐ 《笨办法学 Python》（《Learn Python The Hard Way》）OR 《Beginning Perl for Bioinformatics》

3. ⭐ 《鸟哥的Linux私房菜-基础学习篇》 (推荐章节)

Linux 推荐章节：

• 第5章: 5.3.1 man page; 第6章: 6.1用户与用户组; 6.2 LINUX文件权限概念; 6.3 LINUX目录配置

• 第7章: 7.1目录与路径; 7.2文件与目录管理; 7.3文件内容查阅; 7.5命令与文件的查询; 7.6权限与命令间的关系; 第8章: 8.2文件系统的简单操作

• 第9章: 9.1压缩文件的用途与技术; 9.2 Linux系统常见的压缩命令; 9.3打包命令：tar

• 第10章 vim程序编辑器

• 第11章 认识与学习bash; 第12章 正则表达式与文件格式化处理；第13章 学习shell script

• 第25章 LINUX备份策略: 25.2.2完整备份的差异备份; 25.3鸟哥的备份策略; 25.4灾难恢复的考虑; 25.5重点回顾

Linux 重点学习:

1. Editor (e.g. VIM)

2. Shell Script (e.g. bash)

(3) 参考书 - 统计类

• 《Principles of Biostatistics》 by Marcello Pagano, Kimberlee Gauvreau

• Statistics for biologist by Nature

2) Recommended on-line Courses

• 生物信息导论和方法 (北大 @MOOC)
• Bioinformatics Specialization (UC SanDiego @coursera)
• Genomics of Human Diseases (DragonStar Course @github)

3) Recommended Tips

• ✨One Tip Per Day (e.g. How to tell which library type to use )

4)✨ [Education Papers] Computational Biology Primers

This is a list of explanatory papers that have appeared as primer in the Computational Biology section of the journal Nature Biotechnology, in reverse chronological order. (Last addition November 2013 / checked March 2016).

— Nature Biotechnology

(1) Basics

The anatomy of successful computational biology software

(Stephen Altschul, Barry Demchak, Richard Durbin, Robert Gentleman, Martin Krzywinski, Heng Li, Anton Nekrutenko, James Robinson, Wayne Rasband, James Taylor & Cole Trapnell)

October 2013, Vol 31, No 10; pp 894 - 897

doi: 10.1038/nbt.2721 (google)

Understanding genome browsing

(Melissa S Cline & W James Kent)

February 2009, Vol 27, No 2; pp 153 - 155

doi: 10.1038/nbt0209-153 (google)

(2) Basic Statistics

How does multiple testing correction work?

(William S Noble)

December 2009, Vol 27, No 12 ; pp 1135 - 1137

doi: 10.1038/nbt1209-1135 (google)

What is Bayesian statistics?

(Sean R Eddy)

September 2004, Volume 22, No 9; pp 1177 - 1178

doi: 10.1038/nbt0904-1177 (google)

(3) Basic Algorithms

How to map billions of short reads onto genomes

(Cole Trapnell & Steven L Salzberg)

May 2009, Vol 27, No 5; pp 455 - 457

doi: 10.1038/nbt0509-455 (google)

Where did the BLOSUM62 alignment score matrix come from?

(Sean R Eddy)

August 2004, Volume 22, No 8; pp 1035 - 1036

doi: 10.1038/nbt0804-1035 (google)

What is dynamic programming?

(Sean R Eddy)

July 2004, Volume 22, No 7; pp 909 - 910

doi: 10.1038/nbt0704-909 (google)

How do RNA folding algorithms work?

(Sean R Eddy)

November 2004, Volume 22, No 11; pp 1457 - 1458

doi: 10.1038/nbt1104-1457 (google)

(4) Machine Learning

What is a hidden Markov model?

(Sean R Eddy)

October 2004, Volume 22, No 10; pp 1315 - 1316

doi: 10.1038/nbt1004-1315 (google)

What is the expectation maximization algorithm?

(Chuong B Do & Serafim Batzoglou)

August 2008, Volume 26 No 8; pp 897 - 899

doi: 10.1038/nbt1406 (google)

What are decision trees?

(Carl Kingsford & Steven L Salzberg)

September 2008, Volume 26, No 9; pp 1011 - 1013

doi: 10.1038/nbt0908-1011 (google)

What is a support vector machine?

(William S Noble)

December 2006, Volume 24, No 12; pp 1565 - 1567

doi: 10.1038/nbt1206-1565 (google)

Inference in Bayesian networks

(Chris J Needham, James R Bradford, Andrew J Bulpitt & David R Westhead)

January 2006, Volume 24, No 1; pp 51 - 53

doi: 10.1038/nbt0106-51 (google)

What are artificial neural networks?

(Anders Krogh)

February 2008, Volume 26, No 2; pp 195 - 197

doi: 10.1038/nbt1386 (google)

How does gene expression clustering work?

(Patrik D'haeseleer)

December 2005, Volume 23, No 12; pp 1499 - 1501

doi: 10.1038/nbt1205-1499 (google)

What is principal component analysis?

(Markus Ringnér)

March 2008, Volume 26, No 3; pp 303 - 304

doi: 10.1038/nbt0308-303 (google)

(5) Others

What are DNA sequence motifs?

(Patrik D'haeseleer)

April 2006, Volume 24, No 4; pp 423 - 425

doi: 10.1038/nbt0406-423 (google)

How does DNA sequence motif discovery work?

(Patrik D'haeseleer)

August 2006, Volume 24, No 8; pp 959 - 961

doi: 10.1038/nbt0806-959 (google)

How to apply de Bruijn graphs to genome assembly

(Phillip E C Compeau, Pavel A Pevzner & Glenn Tesler)

November 2011, Vol 29, No 11; pp 987 - 991

doi: 10.1038/nbt.2023 (google)

How does eukaryotic gene prediction work?

(Michael R Brent)

August 2007, Volume 25, No 8; pp 883 - 885

doi: 10.1038/nbt0807-883 (google)

Analyzing 'omics data using hierarchical models

(Hongkai Ji & X Shirley Liu)

April 2010, Vol 28, No 4; pp 337 - 340

doi: 10.1038/nbt.1619 (google)

What is flux balance analysis?

(Jeffrey D Orth, Ines Thiele & Bernhard Ø Palsson)

March 2010, Vol 28, No 3; pp 245 - 248

doi: 10.1038/nbt.1614 (google)

How to visually interpret biological data using networks

(Daniele Merico, David Gfeller & Gary D Bader)

October 2009, Vol 27 No 10 ; pp 921 - 924

doi: 10.1038/nbt.1567 (google)

SNP imputation in association studies

(Eran Halperin & Dietrich A Stephan)

April 2009, Vol 27, No 4; pp 349 - 351

doi: 10.1038/nbt0409-349 (google)

Maximizing power in association studies

(Eran Halperin & Dietrich A Stephan)

March 2009, Vol 27, No 3; pp 255 - 256

doi: 10.1038/nbt0309-255 (google)

How do shotgun proteomics algorithms identify proteins?

(Edward M Marcotte)

July 2007, Volume 25, No 7; pp 755 - 757

doi: 10.1038/nbt0707-755 (google)

5) ✨[Education Papers] Getting Started in Something

Several Captions have been used to indicate educationally relevant papers in Plos CompBio. Here we have collected some other papers. — PloS Computational Biology

Getting Started in Computational Immunology.

(Kleinstein SH )

PLoS Comput Biol (2008) 4(8): e1000128;

doi: 10.1371/journal.pcbi.1000128 (google)

(1) Basics

Getting Started in Gene Orthology and Functional Analysis

(Fang G, Bhardwaj N, Robilotto R, Gerstein MB)

PLoS Comput Biol (2010) 6(3): e1000703;

doi: 10.1371/journal.pcbi.1000703 (google)

Getting Started in Biological Pathway Construction and Analysis.

(Viswanathan GA, Seto J, Patil S, Nudelman G, Sealfon SC )

PLoS Comput Biol (2008) 4(2): e16;

doi: 10.1371/journal.pcbi.0040016 (google)

Getting Started in Structural Phylogenomics

(Sjölander K )

PLoS Comput Biol (2010) 6(1): e1000621 ;

doi: 10.1371/journal.pcbi.1000621 (google)

(2) Advanced

Getting Started in Text Mining

(Cohen KB, Hunter L)

PLoS Comput Biol (2008) 4(1): e20;

doi: 10.1371/journal.pcbi.0040020 (google)

Getting Started in Text Mining: Part Two.

(Rzhetsky A, Seringhaus M, Gerstein MB)

PLoS Comput Biol (2009) 5(7): e1000411. ;

doi: 10.1371/journal.pcbi.1000411 (google)

Getting Started in Probabilistic Graphical Models.

(Airoldi EM )

PLoS Comput Biol (2007) 3(12): e252. ;

doi: 10.1371/journal.pcbi.0030252 (google)

(3) MS and Array

Getting Started in Computational Mass Spectrometry-Based Proteomics.

(Vitek O)

PLoS Comput Biol (2009) 5(5): e1000366. ;

doi: 10.1371/journal.pcbi.1000366 (google)

Getting Started in Gene Expression Microarray Analysis

(Slonim DK, Yanai I)

PLoS Comput Biol (2009) 5(10): e1000543;

doi: 10.1371/journal.pcbi.1000543 (google)

Getting Started in Tiling Microarray Analysis

(Liu XS)

PLoS Comput Biol (2007) 3(10): e183;

doi: 10.1371/journal.pcbi.0030183 (google)

6) Advanced for AI

⭐: 必读 ✨: 推荐

(1) Recommended Books

• ⭐ 《Bioinformatics Data Skills》 by Vince Buffalo
• ✨ 《Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids》 (English | 中文) by Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
• ✨ 《机器学习》 -- 周志华

(2) Recommended On-line Courses

• ⭐ StatQuest Video List (StatQuest: Machine Learning @Youtube)
• ✨ Machine Learning by Andrew Ng 吴恩达 (CS229): @coursera
• ✨ 李沐: Practical Machine Learning (李沐: 实用机器学习-斯坦福2021秋@Bilibili)
• ✨ 李沐-动手学深度学习 PyTorch版@Bilibili
• 李沐AI论文解读@Bilibili

(4) Recommended Educational Papers

• Statistics for biologist by Nature

(5) More Books

edited based on Xiaofan Liu's list

1. 数学基础 (建议根据自己的基础进行复习)

   1. 《高等数学》

   2. 《线性代数》

   3. 《数理统计与概率论》

2. 入门书籍 (其中1、2可选一本精读，数学基础好的推荐选2)

   1. 《机器学习》，周志华著 (★★★推荐)

   2. 《统计学习方法》，李航著 (★★★推荐)

   3. 《多元统计分析》，何晓群著

3. Python编程书籍

   1. 《Python机器学习基础教程》，[德]安德里亚斯·穆勒（Andreas C.Müller，[美]莎拉·吉多（Sarah Guido）著，张亮（hysic）译 (★★★推荐)

   2. 《python高性能编程》，Micha，Gorelick，戈雷利克，Ian，Ozsvald ...著

4. 深度学习类书籍 (希望加强对模型数学原理的理解，并且进一步学习深度学习的同学可选读)

   1. 《深度学习[deep learning]》，[美] Ian，Goodfellow，[加] Yoshua，Bengio，[加] Aaron ... 著(★★★推荐)

   2. 《模式识别与机器学习(Pattern Recognition and Machine Learning)》,Christopher M. Bishop著

   3. 《机器学习：从概率的视角分析(The Machine Learning: A Probabilistic Perspective)》，Kevin P. Murphy著

   注：PRML和MLAPP两本书难度较大

5. 深度学习编程与实践书籍 (工具类书籍，不是必读)

   1. 《Keras深度学习实战》，[意大利]安东尼奥·古利

   2. 《深度学习入门之PyTorch》，廖星宇著

   3. 《深度学习框架PyTorch快速开发与实战》，邢梦来，王硕，孙洋洋著

   4. 《TensorFlow实战》，黄文坚，唐源著

(6) More Online Resources

edited based on Xiaofan Liu's list

1. 机器学习入门课程

   1. 浙江大学公开课：概率论与数理统计 (根据自己基础选择复习)
   2. Machine Learning by Andrew Ng 吴恩达 (CS229): @coursera (★★★推荐)
2. 深度学习课程

   1. Deep Learning by Andrew Ng 吴恩达 (CS230): @coursera | @bilibili (★★★推荐)
   2. Keras快速搭建神经网络 (★★★推荐)
   3. 李宏毅深度学习2017
   4. 不用博士学位玩转Tensorflow深度学习