Bioinformatics Tutorial

《生物信息学实践教程》

Teaching Philosophy

🎦 Study and Practice | 格物致知知行合一

"Tell me and I forget. Teach me and I remember. Involve me and I learn." - Benjamin Franklin

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

Courses

本书主要用于清华大学本科生课《生物信息学》和博士生课《生物信息学实践》

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：

google
wikipedia
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>>

Courses required before this tutorial

基本生物课程：如《遗传学》和/或《分子生物学》
基本统计课程：如《概率论》和/或《生物统计》
基本数学课程：如《微积分》和《线性代数》
基本计算机课程：如《Linux》和《C或Python语言》

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

Contact Us

Lu Lab 鲁志实验室
School of Life Sciences, Tsinghua University, Beijing, China
e-mail: lulab1 AT tsinghua.edu.cn
Homepage: www.ncRNAlab.org ( lulab.life.tsinghua.edu.cn )
Software: software.ncRNAlab.org
Courses: courses.ncRNAlab.org
Books: book.ncRNAlab.org ( bioinfo.gitbook.io )
Docs: docs.ncRNAlab.org ( lulab.gitbook.io | lulab.github.io )

Copyright

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

2016-2024年于清华园

Last updated 2 months ago

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：

google
wikipedia
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>>

Section

Major Authors