 So you chose to use feature counts to count the number of reads per gene. So we will launch feature counts on the map BAM. So we first need to put the alignment files, so the BAM files, and they are tied into a collection. So you click on collection and then you automatically have the BAM files which are selected. Specify the strand information and we know now that it's unstranded. For the gene annotation file, it's in the history, so we just select a GFF-GTF in your history. And here we select the Drosophila GTF. The feature that we will use in the GTF is the exam, so we will count only reads that fall into the exam. It's not the one that fall into entrance, and we will use a gene ID as a gene identifier. The output format is indeed the output that is compatible with DSEC2, which is the tool we are going to use. And we want to create a gene length file. This is for downstream analysis. Does the input have read pairs? Yes. And we would like to count them as a single fragment. This is what is done also by star. And we don't check the pair then distance. This is one of the power meter that you could specify to have a more customized analysis. And we will filter for a minimum quality of them. I think that's all I just checked. So we specified, yes, the minimum quality of them. So we can run it. So it generates three outputs. One is the length of each gene. One is the summaries, some statistics, and then it's the real file that we will use downstream. It's the counts. And similarly to what we have done with the star counts, we can aggregate all the summaries using multi-QC. So we just select multi-QC. The tool that we use is feature counts. And the output is feature counts is in a collection. So we click collection. And then it's the summary. And we run the tool. So this tool is slightly slow. So I post the video and I come back when it's done. Feature count has finished. It was much faster than what I had in mind. And so we can check the output. So the feature length, if I click, I generated one per dataset. Which should be identical. I click on the I. And what I will see is that it's a table. It should be displayed with two columns. One is the G90. And the other one is the length. So I come back to the history. And the other one was the summary, which we will aggregate into the multi-QC. And here we have the counts. And if I check it, the preview is a bit slow today. We can see that we have on the first column, the G90s, on the second column, the number of counts. And we need to keep in mind that we have a header, which would be G90 and then the name of the sample. Great. So now multi-QC has finished also. We can check the webpage clicking on the I. And we can see that the number of reads that have been assigned is about 60 patterns. And the reads that were not assigned are mainly because they are multi-mapping. So we have 24% of reads that are multi-mapping. So now we will be able to join the people that chose the start tutorial.