1. Introduction to RNA-Seq & Transcriptome Analysis
Bacterial Genome Assembly | Victor Jongeneel
Introduction to RNA-Seq & Transcriptome Analysis
Jessica Holmes
PowerPoint by Shounak Bhogale
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

2. Bacterial Genome Assembly | Victor Jongeneel
Exercise
Use the RNA-STAR to align RNA-Seq reads
Use htseqCounts to count the reads.
Use edgeR to find differential expressed genes.
Use IGV for visualization (If time permits).
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

3. RNA-Seq Lab | Shounak Bhogale | 2019v
Pipeline Overview
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

4. Bacterial Genome Assembly | Victor Jongeneel
Input Data
RNA-Seq: 100 bp, single end data
sample
replicate #
fastq name
# reads
TP0
Replicate 1,2
a_0.fastaq, b_0.fastaq
100,000
TP8
a_8.fastaq, b_8.fastaq
Genome & gene information
name
description
mouse_chr12.fna
Fasta file with the sequence of chromosome 12 from the mouse genome
Mouse_chr12.gtf
GTF file with gene annotation, known genes
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

5. Step 1A: Logging into Galaxy
Go to: Click Enter Click Login Input your login credentials. Click Login.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

6. Step 1B: Galaxy Start Screen
The resulting screen should look like the figure below:
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

7. Step 2A: Accessing Input Files
At the top of the page, click Shared Data. Then click Histories.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

8. Step 2B: Accessing Input Files
Bacterial Genome Assembly | Victor Jongeneel
Step 2B: Accessing Input Files
Click sb_transcriptomics You should see this page. Click Import History.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

9. Step 2C: Accessing Input Files
Click Import You should see an imported history like the following.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

10. Step 1: Alignment using RNA-STAR.
In this exercise, we will be aligning RNA-Seq reads to a reference genome in the absence of gene models. Splice junctions will be found de novo.
Remember, we are not going to provide any genic structure information.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

11. RNA-Seq Lab | Shounak Bhogale | 2019
Search RNA STAR in the search box.
Click on RNA STAR under NGS: RNA Analysis
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RNA-Seq Lab | Shounak Bhogale | 2019

12. RNA-Seq Lab | Shounak Bhogale | 2019
You should see this on the screen.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

13. RNA-Seq Lab | Shounak Bhogale | 2019
Set fastaq file as a_0.fastq
Set reference genomes as input.
Select mouse_chr12.fna as
the fasta file.
Set mouse_chr12.gtf as the
input gene model.
Set length as 99.
Click execute after making
these changes.
Keep rest of the parameters as default.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

14. RNA-Seq Lab | Shounak Bhogale | 2019
Once the run is complete three files generated will turn green.
Click on the ’pencil’ symbol next to the dataset 9 to change the name of the dataset.
Change the name in the name databox to a_0.bam and then click save.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

15. RNA-Seq Lab | Shounak Bhogale | 2019
Repeat above steps for remaining three fastaq sets – b_0.fastq, a_8.fastq and
b_8.fastq
Similarly change the names of remaining bam files once they are generated.
Now we are ready for the next step.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

16. Step 2: Read aligned counts
Use htseq-count to generate the aligned counts for each bam files generated in step 1.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

17. RNA-Seq Lab | Shounak Bhogale | 2019
Search htseq-count in the search box.
Click on htseq-count to open the tool.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

18. RNA-Seq Lab | Shounak Bhogale | 2019
Select a_0.bam as
the input.
Change stranded to Reverse.
Keep rest of the parameters
as default and click Execute.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

19. RNA-Seq Lab | Shounak Bhogale | 2019
Change the name of the following datasets.
Here we generated the counts for each gene in the 4 datasets.
Now we are ready for the final step.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

20. Step 3: Finding differentially expressed genes
Now we will use edgeR to analyze the count files generated in step 2 to find differentially expressed genes between two time points.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

21. RNA-Seq Lab | Shounak Bhogale | 2019
Search edgeR in the search box on the
left.
Click on edgeR to open the tool.
This should come up on the screen.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

22. RNA-Seq Lab | Shounak Bhogale | 2019
Select Single Count Matrix
Select all.txt as the input
dataset.
Enter Mouse as factor name.
Enter groups as
TP8,TP8,TP0,TP0
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

23. RNA-Seq Lab | Shounak Bhogale | 2019
Set contrast as TP8-TP0.
Click on Filter low counts.
Set Yes to filter lowly expressed genes.
Set Counts.
Set minimum Counts as 5.
Set Yes for normalized counts table and rscript.
And now Execute!
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

24. RNA-Seq Lab | Shounak Bhogale | 2019
3 files will be generated after the run is complete.
Click on the eye symbol next to the report
file.
Scroll down through the report.
For our dataset there are no DEGs.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

25. Bacterial Genome Assembly | Victor Jongeneel
Conclusion
We did the following today.
Alligned RNA-seq data using RNA-STAR
Used htseq-count to count the aligned reads for each gene.
Used edgeR to find DEGs in the data
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

26. RNA-Seq Lab | Shounak Bhogale | 2019
Useful links
Online resources for RNA-Seq analysis questions –
- Biostar (Bioinformatics explained)
- SEQanswers (the next generation sequencing community)
Most tools have a dedicated lists
Information about the various parts of the Tuxedo suite is available here -
Genome Browsers tutorials –
- IGV tutorials
- UCSC browser tutorials
(openhelix is a great place for tutorials, UIUC has a campus-wide subscription)
Contact us at:
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

27. RNA-Seq Lab | Shounak Bhogale | 2019
Extra Material
IGV
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RNA-Seq Lab | Shounak Bhogale | 2019

28. Visualization Using IGV.
The Integrative Genomics Viewer (IGV) is a tool that supports the visualization of mapped reads to a reference genome, among other functionalities. We will use it to observe where hits were called for the alignment for the two samples (TP0 and TP8), and the differentially(!) expressed genes.
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

29. RNA-Seq Lab | Shounak Bhogale | 2019
Step 9: Start IGV
In this step, we will start IGV and load the select mouse_chr12.fna file, the known genes file
(mouse_chr12.fna), the hits for both sample groups, and the merged transcriptome.
Graphical Instruction: Load Genome
1. Within IGV, click the ‘Genomes’ tab on the menu bar.
2. Click the the ‘Load Genome from File’ option.
3. In the browser window, select mouse_chr12.fna(genome).
Graphical Instruction: Load Other Files
Within IGV, click the FILE tab on the menu bar.
Click the ‘Load from File’ option.
Select the mouse_chr12.gtf file (known genes file).
Perform Steps 1-3 for the files to the right.
Files to Load
mouse_chr12.fna
a_0.bam
b_0.bam
a_8.bam
b_8.bam
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019

30. Step 10: Visualization With IGV
Click here and type the following location of a non-differentially expressed gene:
NC_ :17,788,398-17,793,435
7/5/2019
RNA-Seq Lab | Shounak Bhogale | 2019