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RNA-Seq Lab
Radhika S. Khetani
Powerpoint by Casey Hanson
RNA-Seq Lab v5 | Radhika S. Khetani
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Exercise
1. Use the Tuxedo Suite to:
a. Align RNA-Seq reads using TopHat(splice-aware aligner).
b. Perform reference-based transcriptome assembly with CuffLinks.
c.
Obtain a new transcriptome using CuffLinks & CuffMerge.
d. Use CuffDiff to obtain a list of differentially expressed genes.
e. Report a list of significantly expressed genes.
2. Use a genome browser and visualization tool to observe the aligned
data and the new transcriptome.
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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.
2 metrics: # of mapped reads and # of significantly different identified gene s
b.
Compare new transcriptome to known genes.
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Data Sources
RNA-Seq: 100 bp, single end data
sample
replicate #
fastq name
# reads
control
Replicate 1
thrombin_control.txt
10,953
experimental
Replicate 1
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)
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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!
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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
$ cp
# Make working directory in your home directory,
~/mayo/khetani/data/*
$ qsub –I –l ncpus=4
~/khetani
# Copy data to your working directory.
# Login to a computer on cluster.
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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
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Step 1: Access the Biocluster
We will login to the biocluster and examine various files.
$ qsub –I –l ncpus=4
$ cd ~/khetani/
# Open session on node with 4 cpus.
SKIP IF DONE
# 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.
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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).
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.
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.
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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).
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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.
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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.
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.
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.
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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).
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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.
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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.
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Checkpoint 1: Comparison of Alignments
sample #
fastq name
# reads
control
thrombin_control.txt
experimental
thrombin_expt.txt
Unmapped Reads
de novo (Run 1A)
Informed (Run 1B)
10,953
101
27
12,027
63
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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.)
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.
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).
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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)
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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)
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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)
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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 > cuffdiffgenes/gene_exp.SIG.diff
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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 !
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.
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.
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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
1.
Within IGV, click the FILE tab on the menu bar.
2.
Click the ‘Load from File’ option.
3.
Select the genes-chr22.gtf file (known genes file).
4.
Perform Steps 1-3 for the files to the right.
RNA-Seq Lab v5 | Radhika S. Khetani
Files to Load
genes-chr22.gtf
ctrl_accepted_hits.bam
expt_accepted_hits.bam
merged.gtf
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Step 10A: Visualization With IGV
Your browser window should look similar to the picture below:
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Step 10B: Visualization With IGV
Click here and type the following location of a differentially expressed gene:
chr22:19960675-19963235
Move to the left and right of the gene. What do you see?
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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.
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Conclusion
Today we did the following:
1. Used the Tuxedo Suite to:
a. Aligned RNA-Seq reads using TopHat(splice-aware aligner).
b. Performed reference-based transcriptome assembly with CuffLinks.
c.
Obtained a new transcriptome using CuffLinks & CuffMerge.
d. Used CuffDiff to obtain a list of differentially expressed genes.
e. Reported a list of significantly expressed genes.
2. Used a genome browser and visualization tool to observe the aligned
data and the new transcriptome.
RNA-Seq Lab v5 | Radhika S. Khetani
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