Hello Bioinformatics lovers,

I told a conference organizer that I do not consider myself an expert in AI, and she was surprised.

AI is a buzzword word and it is spreading like wildfire in our lives (I can not live without ChatGPT)

But bioinformatics existed before AI is cool.

Most of my daily work is:

1. formulating the biological question. (This is the hardest)
2. finding the right data to answer it.
3. if no such data is available, work with the wet lab team to generate them
4. after getting the data, do a thorough quality control
5. clean the messy data and metadata (the data that describes the data)
6. do extensive Exploratory data analysis (EDA) by plotting and spotting errors and weirdness in the data
7. simple statistical analysis, correlation calculation, and visualization with these six basic plot types. https://www.youtube.com/watch?v=0Bn0OtzJFt4
8. depending on the tasks, we may use regression, random forest, or XGboost (conventional machine learning techniques, and I do not consider them to be AI).
9. In fact, a solid foundation in statistics and linear algebra (most of our data are just rectangular matrices!) will already take you a long way.
10. conclude and make a deck to communicate the findings to the wet lab team. (help make decisions)
11. new questions arise. repeat and rinse the same process.

It is Okay not to be an “expert” in AI. I talk about what I know.

Bioinformatics != AI

When someone asked me how to learn bioinformatics:

my answer is still: learn the basics and then apply them to a real project.

I am putting the videos for reproducing a genomics paper in this chatomics playlist .

Make sure you subscribe to it and I will make the rest of the videos in the coming days.

That being said, I am actively learning deep learning, read my blog post here https://divingintogeneticsandgenomics.com/post/how-i-am-learning-deep-learning/

Sharing is caring, please send this newsletter to someone you think may benefit from it.

My other posts from the past week

1. ​People ask how can I have so many things to write for bioinformatics. The Secret? 👇 ​
2. ​AWK is a powerhouse for genomics data manipulation. Here’s a quick guide with practical examples ​
3. ​Scary Excel Stories in Genomics​
4. ​Filling Missing Gene Names (NAs) in Genomics Data with {tidyr}​
5. ​How to Reorder Rows in R Using a Custom Order​
6. ​Bash Quirks Every Bioinformatician Should Know​
7. ​How to Quickly Inspect Dataframe Headers in UNIX​
8. ​Two Types of Bioinformaticians—Which One Are You? ​
9. ​The Devil in Genomic Coordinates: 0-Based vs. 1-Based Systems​

PS:

If you want to learn Bioinformatics, there are other ways that I can help:

1. My free YouTube Chatomics channel, make sure you subscribe to it.
2. I have many resources collected on my github here.
3. I have been writing blog posts for over 10 years https://divingintogeneticsandgenomics.com/​.

Stay awesome!