IGV Single-cell Hands-on Roscoff 2022

Sophie Lemoine and Morgane Thomas-Chollier

Introduction

Goal

The aim is to :

View the scRNA-seq signal in its genomic context
Better understand the 3’ biais in 10X Genomics datasets

Connect to the server and set up your environment

Connect to JupytherHub

Sign in to Jupyterhub and open a Terminal
select in the reservation field form_2022_32
In the launcher, click on “Terminal” in “Other” section. You should be in your home directory by default. Check it:
```
pwd
```

2 - Set up your working environment

Go to your project directory
```
cd /shared/projects/<your_project>
```
Create a directory that will contain all results of the upcoming analyses.
```
mkdir ebaii22_igv
```
Go to the newly created directory
```
cd ebaii22_igv
```
Copy the directory containing data

cp -r /shared/projects/form_2022_32/SingleCellRNASeq/Visualization .

Your directory structure should be like this

/shared/projects/<your_project>/ebaii22_igv
│
└───Visualization

You can check your directory structure:

 tree

You should see a BAM file (= mapped reads), a BAI file (for technical reasons this file must be present for IGV) and a BED file (contains a summary of the BAM with alignment position. This file is not mandatory, but it can be useful as less heavy than BAM)

 └── Visualization
    ├── pbmc1k_rdx.bam
    ├── pbmc1k_rdx.bam.bai
    └── pbmc1k_rdx.bed

Visualizing the signal in a genome browser

1 - Download the following files from the server onto your computer (laptop)

In JupytherHub, use the menu on the left to navigate to your project folder.
Once you get to the Visualization folder, right-click on the following files and choose Download to save them on your laptop

You should have these 3 files on your laptop :

pbmc1k_rdx.bam
pbmc1k_rdx.bam.bai
pbmc1k_rdx.bed

2 - Viewing the raw alignment data in IGV

Open IGV on your computer
Keep the default genome (GRCh38/hg38)
Load BAM file :
- File / Load from File…
- Select the BAM file pbmc1k_rdx.bam
Load the BED file pbmc1k_rdx.bed

(the BAI file does not need to be loaded)

Browse around in the genome. Do you see peaks? Where are located the peaks, in reference to the annotated genes ?