As with all computational experiments (yes, they are experiments, don't let your pipette-toting friends tell you otherwise), keeping track of what you did is key. In the old days, I kept a written notebook of commands that I ran. Sounds silly, but there were many times that I went back to that notebook to see exactly what the parameters were for a given run using a piece of software.

Today, there are better options. You are using one of the better ones right now. Notebooks, including RMarkdown (mainly for R) and Jupyter (mainly for Python), are a great way to keep track of what you did as well as give justification or explanation for your analyses using plain 'ol English.

Trust me, one day you will be glad you used them. The Methods section of your paper is never fun to write without them.

• The paper describing rMATS
• The documentation for the latest version of rMATS

Last time we talked about using STAR to align RNAseq reads to the transcriptome. We talked about why this was important for alternative splicing analysis given that we need to know exactly where in transcripts these reads are mapping.

Today we will talk about taking the alignments that we produced (OK not those exact alignments, but still) and using them to determine how the inclusion of alternative exons in transcripts varies between conditions.

Today we will continue our focus on the analysis of alternative splicing.

As is the case for many computational biology questions, there are many software choices when it comes to alternative splicing. The one that we will use is called rMATS.

rMATS will take data about where reads align and where alternative exons are to determine how often an exon is included in a sample. It will quantify this using a metric called PSI (Percent Spliced In).

rMATS will then compare PSI values between replicates and across experimental conditions to identify alternative splicing events whoe PSI values are significantly different across conditions.

As you may know, the vast majority of mammalian protein-coding genes contain introns. In fact most contain many introns, meaning they have many exons. Not every one of these exons, or pieces of an exon, has to be included in every transcript.

The inclusion of these exons is often regulated through the binding of RNA binding proteins to specific sequences in the exon and in the flanking introns.

The data we will examine today came from an experiment probing the function of one of these RNA binding proteins, RBFOX2. More on that later.

In order tell which exons were included in a transcript, we need to know more than just which transcript a read came from. We need to know where in the transcript it came from.

The most informative reads with regards to alternative splicing are those that cross a splice junction. Those reads tell us unambiguously that two exons were connected together in the transcript. If we are considering the inclusion of an alternative exon, a junction read could link the alternative exon to one of its neighbors, indicating that the alternative exon was included. Alternatively 😄 (was this funny this time?), a junction read could link the two neighbor exons together, indiciating that the alternative exon was not included.

We can use the relative number of reads that support inclusion or exclusion to learn something about how often an exon was included in a sample.

Consider the plots on the right. These are called sashimi 🍣 plots.

We are quantifying the inclusion of the middle exon. Each row is an RNAseq sample where the height of the color indicates read depth at that location. The arcs that connect exons are junction reads. In the red samples, there are many more reads that support exon inclusion (i.e. go from the middle exon to a flanking exon) than support exclusion (i.e. go from one flanking exon to the other). In the orange samples, this trend is reversed.

We can therefore say that the exon is more often included in the red samples than the orange samples.

A common strategy in the study of RNA processing is to take an RBP you are interested in, perturb it, and ask how the transcriptome looks before and after this perturbation. This is part of what the authors did in today's paper.

Imagine you were studying an RBP that promoted inclusion of an alternative exon by binding the intron downstream of exons that it controls.

Now imagine that you perturbed that RBP, say, by knocking its expression down. What would happen?

Exons that depended on that RBP for efficient inclusion would now be included in transcripts much less frequently. In this example, their PSI values would decrease.

However, other exons don't really care about whether this RBP is around or not. Their PSI values will be generally unaffected.