Joining a course

the general principle in taking online course - pick your favorite cherry

The Griffith's lab

https://github.com/griffithlab/rnaseq_tutorial https://rnabio.org/ (These two links are essentially the same in terms of content)

This is a course that has been held multiple times for years conducted by Washington University. I cannot comment too much because this course uses AWS which requires credit card information if you are not enrolled to their course. The documents and everything in their repo, nevertheless, are very useful and I would recommend anyone to actually attend the course.

Coursera - Command Line Tools for Genomic Data Science

https://www.coursera.org/learn/genomic-tools

I started with this one because this course uses virtual machine for the Linux setup so you do not need to worry about your computer and focus on getting the hang of things. You will be working on the RNA-seq analysis R package such as Cufflinks, generating .SAM, .BED, so essentially a side by side tutorial of the analysis. But let everyone, including myself, be reminded that this course was made somewhere around 2015 and essentially some of the stuffs such as tophat and cuff-packages are falling out of favor. The concept is similar though so I consider this to be useful still.

This course also bothers to hold your hands through basic command line. To me command line is not too overwhelming at the age of 35 because I started Linux-ing in my teens, though I did not stick with it, but I do remember what is cd, mv, rm, etc. So I couldn't really make a good assessment here but the course conductor bothers to tell you cd means change directory and ls means list and so on, that one, no offense intended, is quite unique among command line people.

But when it comes to manipulating text files which virtually could only be done in the Terminal, I would recommend you to just get a general ideas from this course and pave your own way in (Goolging!). Terminal command is an interactive programming experience in my wordings. That means there are more than one way to solve the same problem.

Once you are comfortable with terminal and Linux environment, you would be more equipped to embrace R.

Galaxy

https://training.galaxyproject.org/ https://usegalaxy.org/ (The above is the tutorial of using Galaxy and the below is the actual web platform to utilize Galaxy)

The training material for Galaxy is not totally designed to teach oneself homebrew RNA-Seq cause it is a web platform with graphic user interface to work with, from file fetching to data visualization. But it does provide a comprehensive exercise for Galaxy application and that could give you an idea of how a pipeline works and all. It also gives you the example of solving some common research problems in the computational way. For instance it spells out how to understand the fastQC report here which you can just easily skip the Galaxy related content to stay relevant.

Only one caveat - it is not straight forward to go to and fro between Linux and Galaxy in one single workflow because Galaxy will (literally) insert something to the file after running each package. It doesn't restrict you to analyse the same data on both though.

Datacamp

It is an interactive and partially free place to get to know basic R (and python) syntax, among others. This is potentially powerful when one needs to deviate from standard procedure to manipulate their own data.