1. Bacterial Genome Assembly | Victor Jongeneel Radhika S. Khetani
RNA-Seq Lab
Radhika S. Khetani
Powerpoint by Casey Hanson
RNA-Seq Lab v5 | Radhika S. Khetani

2. Bacterial Genome Assembly | Victor Jongeneel
Exercise
Use the Tuxedo Suite to:
Align RNA-Seq reads using TopHat(splice-aware aligner).
Perform reference-based transcriptome assembly with CuffLinks.
Obtain a new transcriptome using CuffLinks & CuffMerge.
Use CuffDiff to obtain a list of differentially expressed genes.
Report a list of significantly expressed genes.
Use a genome browser and visualization tool to observe the aligned data and the new transcriptome.
RNA-Seq Lab v5 | Radhika S. Khetani

3. Bacterial Genome Assembly | Victor Jongeneel
Premise
Question: Is their a difference in our results if the Tuxedo Suit is run 2 different ways?
1. Procedure:
Run 1A: Allow TopHat to select splice junctions de novo and proceed through the steps without giving the software known genes/gene models.
Run 1B: Force TopHat to use only known splice junctions (i.e. known genes/gene models) and proceed through the steps making sure we are doing our analysis in the context of these gene models.
2. Evaluation:
a metrics: # of mapped reads and # of significantly different identified genes
b. Compare new transcriptome to known genes.
RNA-Seq Lab v5 | Radhika S. Khetani

4. Data Sources sample replicate # fastq name # reads control Replicate 1
RNA-Seq: 100 bp, single end data
sample
replicate #
fastq name
# reads
control
Replicate 1
thrombin_control.txt
10,953
experimental
thrombin_expt.txt
12,027
Genome & gene information
name
description
chr22.fa
Fasta file with the sequence of chromosome 22 from the human genome (hg19 – UCSC)
genes-chr22.gtf
GTF file with gene annotation, known genes (hg19 – UCSC)
RNA-Seq Lab v5 | Radhika S. Khetani

5. Step 0A: Accessing the IGB Biocluster
Open Putty.exe
In the hostname textbox type:
biocluster.igb.Illinois.edu
Click Open
If popup appears, Click Yes
Enter login credentials assigned to you; example, user class45.
Now you are all set!
RNA-Seq Lab v5 | Radhika S. Khetani

6. RNA-Seq Lab v5 | Radhika S. Khetani
Step 0B: Lab Setup
The lab is located in the following directory: ~/mayo/khetani This directory contains the finished version of the lab (i.e. the version of the lab after the tutorial). Consult it if you unsure about your runs. You don’t have write permissions to the lab directory. Create a working directory of this lab in your home directory for your output to be stored. Note ~ is a symbol in unix paths referring to your home directory. Copy the files Make sure you login to a machine on the cluster using the qsub command. The exact syntax for this command is given below. This particular command will login you into a reserved computer (denoted by classroom) with 4 cpus with an interactive session. You only need to do this once.
$ mkdir ~/khetani # Make working directory in your home directory,
$ cp ~/mayo/khetani/data/* ~/khetani # Copy data to your working directory.
$ qsub –I –l ncpus=4 # Login to a computer on cluster.
RNA-Seq Lab v5 | Radhika S. Khetani

7. Step 0C: Shared Desktop Directory
For viewing and manipulating files on the classroom computers, we provide a shared directory in the following folder on the desktop: classes/mayo In today’s lab, we will be using the following folder in the shared directory: classes/mayo/khetani
RNA-Seq Lab v5 | Radhika S. Khetani

8. Step 1: Access the Biocluster
We will login to the biocluster and examine various files.
$ qsub –I –l ncpus=4 # Open session on node with 4 cpus SKIP IF DONE
$ cd ~/khetani/ # Change to the directory of today’s lab session.
$ head chr22.fa # Examine first 10 lines of chr22 sequence file.
$ head –n 12 thrombin_control.txt
# Examine first 12 lines (first 3 reads) from control.
$ tail –n 12 thrombin_expt.txt
# Examine last 12 lines (last 3 reads) from experimental sample.
$ head genes-chr22.gtf # Examine first 10 lines of chr22 genes file.
RNA-Seq Lab v5 | Radhika S. Khetani

9. Step 2: Create an Index using Bowtie
Assume the data is of good quality and quality trimming has taken place.
We will now use Bowtie in the Tuxedo Suite to build a chromosome index of chr22.
$ module load tophat2/2.0.8 # Load Tophat2 and dependencies. $ bowtie2-build chr22.fa chr22 # Make Bowtie index for chr22. # This index is essential for the rest of the exercise. # Make sure you use the correct bowtie version (type bowtie-build).
RNA-Seq Lab v5 | Radhika S. Khetani

10. Run 1A: de novo Alignment.
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.
RNA-Seq Lab v5 | Radhika S. Khetani

11. Step 3A: Align Reads Using TopHat
We will now align RNA-Seq reads to chr22 using mostly default parameters.
Note: We are not providing gene information. TopHat will find splice junctions de novo.
$ tophat –p 4 –o ctrl chr22 thrombin_control.txt # Run TopHAT using chr22 as reference and sequences in the control. # -p indicates the number of cpus (4). # -o indicates the output directory (ctrl). $ tophat –p 4 –o expt chr22 thrombin_expt.txt # Run TopHAT using chr22 as reference and sequences in the experimental sample. # -o indicates the output directory (expt).
RNA-Seq Lab v5 | Radhika S. Khetani

12. Step 4A: Evaluate de novo Alignment
We will now evaluate our alignment by observing how many reads DID NOT align to the reference genome chr22.
$ samtools view –c ctrl/unmapped.bam # -c instructs the view tool to count the unmapped reads. # The result should be 101 unmapped reads. $ samtools view –c expt/unmapped.bam # The result should be 163 unmapped reads.
RNA-Seq Lab v5 | Radhika S. Khetani

13. Step 5A: Create Index for Mapped Reads
For the purpose of visualization, we will create an index for the mapped reads.
$ samtools index ctrl/accepted_hits.bam # Create index of mapped reads in control. $ samtools index expt/accepted_hits.bam # Create index of mapped reads in experimental sample.
RNA-Seq Lab v5 | Radhika S. Khetani

14. Run 1B: Informed Alignment.
In this exercise, we will be aligning RNA-Seq reads to a reference genome in the presence of gene information. This obviates the need for TopHat to find splice junctions de novo.
RNA-Seq Lab v5 | Radhika S. Khetani

15. Step 3B: Align Reads Using Gene Info
We will now align RNA-Seq reads to chr22 using mostly default parameters for TopHat and information on the location of genes on chr22.
$ tophat –p 4 –G genes-chr22.gtf –o ctrl-genes chr22 thrombin_control.txt # Run TopHAT using chr22 as reference and sequences in the control. # -p indicates the number of cpus (4). # -o indicates the output directory (ctrl-genes). # -G indicates the gene file to use to aid in alignment. $ tophat –p 4 –G genes-chr22.gtf –o expt-genes chr22 thrombin_expt.txt # Run TopHAT using chr22 as reference and sequences in the experimental sample. # -o indicates the output directory (expt-genes).
RNA-Seq Lab v5 | Radhika S. Khetani

16. Step 4B: Evaluate Informed Alignment
We will now evaluate our informed alignment by observing how many reads DID NOT align to the reference genome chr22.
$ samtools view –c ctrl-genes/unmapped.bam # -c instructs the view tool to count the unmapped reads. # The result should be 27 unmapped reads. $ samtools view –c expt-genes/unmapped.bam # The result should be 39 unmapped reads.
RNA-Seq Lab v5 | Radhika S. Khetani

17. Step 5B: Create Index for Mapped Reads
For the purpose of visualization, we will create an index for the mapped reads in the informed alignment.
$ samtools index ctrl-genes/accepted_hits.bam # Create index of mapped reads in control. $ samtools index expt-genes/accepted_hits.bam # Create index of mapped reads in experimental sample.
RNA-Seq Lab v5 | Radhika S. Khetani

18. Checkpoint 1: Comparison of Alignments
sample #
fastq name
# reads
Unmapped Reads
de novo (Run 1A)
Informed (Run 1B)
control
thrombin_control.txt
10,953
101 27
experimental
thrombin_expt.txt
12,027 63 39
Conclusions
There are fewer unmapped reads with the informed alignment, or Run 1B (i.e. when we use the known genes, and known splice sites)!
TopHat’s prediction of splice junctions de novo is not working very well for this dataset. (This is likely due to the low number of reads in our dataset.)
RNA-Seq Lab v5 | Radhika S. Khetani

19. Finding Differentially Expressed Genes.
Next, we will utilize our RNA-Seq alignments to assembly gene transcripts, thereby permitting us to get relative gene abundances between the two samples (control and experimental).
RNA-Seq Lab v5 | Radhika S. Khetani

20. Step 6: Assemble Transcripts using Cufflinks
For the de-novo alignment (Run 1A) , we will run the program CuffLinks in order to obtain gene transcripts from our aligned RNA-Seq reads .
There is no need to conduct this step for the informed alignment (Run 1B) because we have the locations of known genes already.
$ module load cufflinks/2.1.1 # Load cufflinks v2 and dependencies $ cufflinks –p 4 –o cuff-ctrl ctrl/accepted_hits.bam # -p indicates the number of processors to use (4) # -o indicates the output directory (cuff-ctrl) $ cufflinks -p 4 –o cuff-expt expt/accepted_hits.bam # -o indicates the output directory (cuff-expt)
RNA-Seq Lab v5 | Radhika S. Khetani

21. Step 7: Merge Transcripts Using CuffMerge
For the de-novo alignment (Run 1A) , we will run the program CuffMerge in order to merge our assembled transcripts.
There is no need to conduct this step for Run 1B.
$ echo -e "cuff-ctrl/transcripts.gtf\ncuff-expt/transcripts.gtf" > gtf.list.txt # Create a text file named gtf.list.txt with the following contents: cuff-ctrl/transcripts.gtf cuff-expt/transcripts.gtf $ cuffmerge –o cuffmerge gtf.list.txt # -o indicates the output directory (cuffmerge)
RNA-Seq Lab v5 | Radhika S. Khetani

22. Step 8A: Gene Expression Using CuffDiff
For both alignments, de-novo (Run 1A) and informed (Run 1B), we aim to collected the abundances of the expressed genes. To do this, we will utilize the CuffDiff program.
We need only a gene (.gtf) file and alignment (.bam) files to calculate differentially expressed genes between the different sample groups (control and experimental).
$ cuffdiff –p 4 –o cuffdiff cuffmerge/merged.gtf expt/accepted_hits.bam ctrl/accepted_hits.bam # -p indicates the number of processors to use (4) # -o indicates the output directory(cuffdiff) $ cuffdiff –p 4 –o cuffdiff-genes genes-chr22.gtf expt/accepted_hits.bam ctrl/accepted_hits.bam # -o indicates the output directory (cuffdiff-genes)
RNA-Seq Lab v5 | Radhika S. Khetani

23. Step 8B: Gene Expression Using CuffDiff
Bacterial Genome Assembly | Victor Jongeneel
Step 8B: Gene Expression Using CuffDiff
For both alignments, de-novo (Run 1A) and informed (Run 1B), we aim to collected the abundances of the expressed genes. To do this, we will utilize the CuffDiff program.
$ head cuffdiff-genes/gene_exp.diff # Examine first 10 lines of file. # We want all rows where the 14th column is yes. The awk command is convenient for file parsing. We’ll see a Galaxy interface for awk in a later lab. $ awk ‘{if ($14==“yes”) print $0}’ cuffdiff/gene_exp.diff > cuffdiff/gene_exp.SIG.diff $ awk ‘{if ($14==“yes”) print $0}’ cuffdiff-genes/gene_exp.diff > cuffdiff- genes/gene_exp.SIG.diff
RNA-Seq Lab v5 | Radhika S. Khetani

24. Step 8C: Gene Expression Using CuffDiff
For both alignments, de-novo (Run 1A) and informed (Run 1B), we aim to collected the abundances of the expressed genes. To do this, we will utilize the CuffDiff program.
We will count the lines in the .SIG.DIFF files to see how many genes are differentially expressed in each of the two alignments.
$ wc –l cuffdiff/gene_exp.SIG.diff cuffdiff-genes/gene_exp.SIG.DIFF # Count the number of lines in each .SIG.DIFF files # Output 3 cuffdiff/gene_exp.SIG.diff 0 cuffdiff-genes/gene_exp.SIG.diff
Only the de-novo alignment (Run 1A) reports any differentially expressed genes between experiment and control !
RNA-Seq Lab v5 | Radhika S. Khetani

25. 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 de-novo alignment (Run 1A) for the two samples (control and experimental), the new transcriptome generated by CuffMerge, and the differentially expressed genes.
RNA-Seq Lab v5 | Radhika S. Khetani

26. RNA-Seq Lab v5 | Radhika S. Khetani
Step 9: Start IGV
In this step, we will start IGV and load the chr22.fa file, the known genes file
(genes-chr22.gtf), the hits for both sample groups, and the merged transcriptome. These files are located in classes/mayo/khetani on the desktop.
Graphical Instruction: Load Genome
1. Within IGV, click the FILE tab on the menu bar.
2. Click the the ‘Load Genome from File’ option.
3. In the browser window, select chr22.fa (genome).
Graphical Instruction: Load Other Files
Within IGV, click the FILE tab on the menu bar.
Click the ‘Load from File’ option.
Select the genes-chr22.gtf file (known genes file).
Perform Steps 1-3 for the files to the right.
Files to Load
genes-chr22.gtf
ctrl_accepted_hits.bam
expt_accepted_hits.bam
merged.gtf
RNA-Seq Lab v5 | Radhika S. Khetani

27. Step 10A: Visualization With IGV
Your browser window should look similar to the picture below:
RNA-Seq Lab v5 | Radhika S. Khetani

28. Step 10B: Visualization With IGV
Click here and type the following location of a differentially expressed gene:
chr22:
Move to the left and right of the gene. What do you see?
RNA-Seq Lab v5 | Radhika S. Khetani

29. Step 10C: Visualization with IGV
Looks like the new transcriptome (merged.gtf) compares poorly to the known gene models. This is very likely due to the very low number of reads in our dataset. We can see that there are many more reads for one dataset compared to the other. Hence, it makes sense that the gene was called as being differentially expressed. Note the intron spanning reads.
RNA-Seq Lab v5 | Radhika S. Khetani

30. Bacterial Genome Assembly | Victor Jongeneel
Conclusion
Today we did the following:
Used the Tuxedo Suite to:
Aligned RNA-Seq reads using TopHat(splice-aware aligner).
Performed reference-based transcriptome assembly with CuffLinks.
Obtained a new transcriptome using CuffLinks & CuffMerge.
Used CuffDiff to obtain a list of differentially expressed genes.
Reported a list of significantly expressed genes.
Used a genome browser and visualization tool to observe the aligned data and the new transcriptome.
RNA-Seq Lab v5 | Radhika S. Khetani