Transcript de novo

Introduction to RNA-Seq & Transcriptome Analysis
Jessica Kirkpatrick
PowerPoint by Casey Hanson
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Exercise
Use the Tuxedo Suite to:
1. Align RNA-Seq reads using TopHat (splice-aware aligner).
2. Perform reference-based transcriptome assembly with CuffLinks.
3. Obtain a new transcriptome using CuffLinks & CuffMerge.
4. Use CuffDiff to obtain a list of differentially expressed genes.
5. Report a list of significantly expressed genes.
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Tuxedo Suite
Bowtie and Bowtie use Burrows-Wheeler indexing for
aligning reads. With bowtie2 there is no upper limit on
the read length
Tophat uses either Bowtie or Bowtie2 to align reads in a
splice-aware manner and aids the discovery of new splice
junctions
The Cufflinks package has 4 components, the 2 major
ones are listed below –
Cufflinks does reference-based transcriptome assembly
Cuffdiff does statistical analysis and identifies
differentially expressed transcripts in a simple pairwise
comparison, and a series of pairwise comparisons in a
time-course experiment
Trapnell et al., Nature Protocols, March 2012
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v
Pipeline Overview
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Premise
Question: Is there a difference in our results if the Tuxedo Suit is run two 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 genes
b. Compare new transcriptome to known genes.
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Input Data
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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Accessing the IGB Biocluster
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Step 1A: Sign into Illinois Galaxy
Open Chrome and go to https://galaxy.illinois.edu/
Click Login and enter your Biocluster username and password.
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Step 1B: How Galaxy works with the Biocluster
Biocluster
Signing up - http://biocluster.igb.illinois.edu/
Usage and cost - http://help.igb.illinois.edu/Biocluster
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Step 1C: Interface
You should see a workspace similar to the one below:
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Step 1B: Changing History Name
Click on Unnamed History in the History Pane on the right side :
Type RNA – Seq workshop and press Enter.
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Step 2A: Accessing Input Files
At the top of the page, click Shared Data.
Then click Publish Histories.
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Step 2B: Accessing Input Files
Click RNA-Seq_Chr_22 Data
You should see this page.
Click Import History.
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Step 2C: Accessing Input Files
Click start_using_this_history
You should see an imported history like the following.
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Step 2D: Accessing Input Files
Click the gear icon at the top of the History pane.
Click Copy Datasets.
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Step 2E: Accessing Input Files
Under Source History, select 1: imported: RNA-Seq history.
Check the files in the image below:
Under Destination History, select 2: RNA – Seq workshop history.
Click Copy History Items.
Click the Copy History Items button.
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Step 2F: Accessing Input Files
You should see the following confirmation at the top of the page:
Click the RNA – Seq workshop link.
The history should look like this :
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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.
Remember, we are not going to provide any genic structure information.
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Step 3A: Align Reads de novo Using TopHat2
At the top right of the page, click the search box :
Type TopHat2
Select TopHat2 under NGS: RNA Analysis
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Step 3B: Align Reads de novo Using TopHat2
You should a page similar to the one below. We will run TopHat2 first
on the thrombin experimental data.
Make sure your inputs match the screenshot below:
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Step 3C: Align Reads de novo Using TopHat2
The rest of the page contains parameters.
We will change the following parameters:
1. Library Type: FR Unstranded
2. Minimum Intron Length: 70
3. Maximum Intron Length: 500000
4. Maximum number of alignment to be allowed: 20
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Step 3C: Align Reads de novo Using TopHat2
The rest of the page contains parameters.
We will change the following parameters:
5. Number of mismatches allowed in each segment alignments for
reads mapped independently : 2
6. Use Own Junctions: No
7. Use Coverage Search: Yes
8. Maximum intron length that may be found during coverage search:
500000
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Step 3E: Align Reads de novo Using TopHat2
The rest of the page contains parameters.
We will change the following parameters:
9.
10.
11.
12.
Use Microexon Search: No
Do Fusion Search: No
Set Bowtie2 settings: No
Specify read group: No
Click Execute when you have set the parameters.
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Step 3F: Align Reads de novo Using TopHat2
You will see confirmation in the
Main Pane denoting which tracks
have been added to run.
You should see the tracks at the top of
the History Pane
A gray track means the job isn't running.
A yellow track means the job is running.
A green track means the job is finished.
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Step 3G: Align Reads de novo Using TopHat2
You will see confirmation in the
Main Pane denoting which tracks
have been added to run.
You should see the tracks at the top of
the History Pane
A gray track means the job isn't running.
A yellow track means the job is running.
A green track means the job is finished.
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Step 3H: Align Reads de novo Using TopHat2
We want to run TopHat2 for the control dataset now.
Navigate to the TopHat2 page again.
This time use 1: thrombin_control.fastq for RNA-Seq FASTQ file.
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Step 3I: Align Reads de novo Using TopHat2
Configure the parameters as before (below) and click execute:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
Library Type: FR Unstranded
Minimum Intron Length: 70
Maximum Intron Length: 500000
Maximum number of alignment to be allowed: 20
Number of mismatches allowed in each segment alignments for
reads mapped independently : 2
Use Own Junctions: No
Use Coverage Search: Yes
Maximum intron length that may be found during coverage search:
500000
Use Microexon Search: No
Do Fusion Search: No
Set Bowtie2 settings: No
Specify read group: No
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Step 4A: Renaming Files
In galaxy, it is important to rename output files to something
meaningful.
For example, to rename 9: Tophat2_on_data2_and data4:accepted_hits
Click the pencil icon
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Step 4B: Renaming Files
On the next page, enter expt_accepted_hits for the Name: field.
Click Save.
Track 9 show have the name change:
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Step 4C: Renaming Files
In this manner, rename the following tracks with the respective names:
5.
6.
7.
8.
expt_align_summary
expt_insertions
expt_deletions
expt_splice_junctions
10.
11.
12.
13.
14.
ctrl_align_summary
ctrl_insertions
ctrl_deletions
ctrl_splice_junctions
ctrl_accepted_hits
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Step 5A: Evaluating de novo Alignment
Click the eye icon 5: expt_align_summary
You should see the results on the screen, like below :
In the experimental group, 147 reads were not aligned.
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Step 5B: Evaluating de novo Alignment
Click the eye icon 10: ctrl_align_summary
You should see the results on the screen, like below :
In the control group, 101 reads were not aligned.
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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 6A: Informed Align Reads Using TopHat2
We want to re-run the analysis for the
experimental group, but using a gene-model
annotation this time.
Instead of repeating the previous steps, we can
save some time by clicking on the update icon on
track 9: expt_accepted_hits.
Click on track 9.
Click the update icon.
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Step 6B: Informed Align Reads Using TopHat2
Keep the same parameters as before, but change the following:
1.
2.
3.
4.
5.
Use Own Junctions: Yes
Use Gene Annotation Model: Yes
Gene Model Annotations: 3: genes-chr22.gtf
Use Raw Junctions: No
Only look for supplied junctions: No
Click Execute.
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Step 6C: Informed Align Reads Using TopHat2
This should generate tracks 15 through 19.
Rename the tracks the following:
15.
16.
17.
18.
19.
expt-genes_align_summary
expt-genes_insertions
expt-genes_deletions
expt-genes_splice_junctions
expt-genes_accepted_hits
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Step 6D: Informed Align Reads Using TopHat2
We want to re-run the analysis for the control
group, but using a gene-model annotation this
time.
Instead of repeating the previous steps, we can
save some time by clicking on the update icon on
track 14: ctrl_accepted_hits.
Click on track 14.
Click the update icon.
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Step 6E: Informed Align Reads Using TopHat2
Keep the same parameters as before, but change the following:
1.
2.
3.
4.
5.
Use Own Junctions: Yes
Use Gene Annotation Model: Yes
Gene Model Annotations: 3: genes-chr22.gtf
Use Raw Junctions: No
Only look for supplied junctions: No
Click Execute.
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Step 6F: Informed Align Reads Using TopHat2
This should generate tracks 15 through 19.
Rename the tracks the following:
20.
21.
22.
23.
24.
ctrl-genes_align_summary
ctrl-genes_insertions
ctrl-genes_deletions
ctrl-genes_splice_junctions
ctrl-genes_accepted_hits
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Step 7A: Evaluating Informed Alignment
Click the eye icon 15: expt-genes_align_summary
You should see the results on the screen, like below :
In the experimental group, 39 reads were not aligned.
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Step 7B: Evaluating Informed Alignment
Click the eye icon 20: ctrl-genes_align_summary
You should see the results on the screen, like below :
In the control group, 27 reads were not aligned.
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Step 8: Comparison of Alignments
Unmapped Reads
sample #
fastq name
# reads
de novo
Informed
control
thrombin_control.txt
10,953
101
27
experimental
thrombin_expt.txt
12,027
163
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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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Reminder: Cufflinks
The Cufflinks package has 4 components,
the 2 major ones are listed below –
Cufflinks does reference-based
transcriptome assembly
Cuffdiff does statistical analysis and
identifies differentially expressed
transcripts in a simple pairwise comparison,
and a series of pairwise comparisons in a
time-course experiment
Trapnell et al., Nature Protocols, March 2012
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Step 9A: 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 because
we have the locations of known genes already
Type Cufflinks into the search box.
Click on Cufflinks under NGS: RNA Analysis.
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Step 9B: Assemble Transcripts using Cufflinks
Choose 9: expt_accepted_hits for the BAM file.
Use the default parameters for everything except
change the following:
1. Use effective length correction: No
Ensure your parameters match up with the figure on
the right.
Click Execute.
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Step 9C: Assemble Transcripts using Cufflinks
Go back to Cufflinks.
This time choose 14: ctrl_accepted_hits for the BAM file.
Use the default parameters for everything except change
the following:
1.
Use effective length correction: No
Ensure your parameters match up with the figure on the
right.
Click Execute.
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Step 9D: Assemble Transcripts using Cufflinks
Tracks 25 – 27 are the results of
the experimental Cufflinks run.
Tracks 29 – 31 are the results of
the control Cufflinks run.
We will merge the assembled transcripts from the control and experimental
samples next using Cuffmerge.
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Step 10A: Merge Transcripts Using CuffMerge
In the search box, type Cuffmerge
Click Cuffmerge under NGS: RNA Analysis.
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Step 10B: Merge Transcripts Using CuffMerge
For GTF file, choose track 27, which are
the assembled transcripts run on the
experimental accepted hits (track 9) of
the de novo assembly.
Click Add new Additional GTF Input Files.
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Step 10B: Merge Transcripts Using CuffMerge
For GTF file, choose track 27, which are
the assembled transcripts run on the
experimental accepted hits (track 9) of
the de novo assembly.
Click Add new Additional GTF Input Files.
For the next GTF file, choose track 31,
which are the assembled transcipts run
on the control accepted hits (track 14) of
the de novo assembly.
Choose No for the other parameters and
click Execute.
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Step 11A: Differential Gene Expression
For the de novo assembly, lets find out how many differentially
expressed (DE) genes are present. We will use Cuffdiff to do this.
To do this, we need a GTF file and a BAM file for both the control and
experimental assemblies.
We could use Cuffdiff on the informed alignments, as well, but we
normally recommend using htseqcount and edgeR instead.
Type Cuffdiff into the search and click its link:
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Step 11B: Differential Gene Expression
Choose track 33 for the Transcripts.
Under Condition 1:
Name: Control
Add replicate: 14: ctrl_accepted_hits
Under Condition 2:
Name: Experimental
Add replicate: 9: expt_accepted_hits
Accept the default parameters and click
Execute.
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Step 11C: Differential Gene Expression
When done, click the eye icon on track 47:
You should see output like the following:
Count the number of "yes" answers in the significant column as you
scroll down.
There should be 3. These are the DE genes.
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Conclusion
We did the following today
Use the Tuxedo Suite to:
1. Align RNA-Seq reads using TopHat (splice-aware aligner).
2. Perform reference-based transcriptome assembly with CuffLinks.
3. Obtain a new transcriptome using CuffLinks & CuffMerge.
4. Use CuffDiff to obtain a list of differentially expressed genes.
5. Report a list of significantly expressed genes.
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Useful links
Online resources for RNA-Seq analysis questions –

http://www.biostars.org/ - Biostar (Bioinformatics explained)

http://seqanswers.com/ - SEQanswers (the next generation sequencing community)

Most tools have a dedicated lists
Information about the various parts of the Tuxedo suite is available here http://ccb.jhu.edu/software.shtml
Contact us at:
Genome Browsers tutorials –
[email protected]

http://www.broadinstitute.org/igv/QuickStart/ - IGV tutorials

http://www.openhelix.com/ucsc/ - UCSC browser tutorials
[email protected]
(openhelix is a great place for tutorials, UIUC has a campus-wide subscription)
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Extra Material
IGV
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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 [course_directory]/05_Transcriptomics/results
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 chr22.fa (genome).
Graphical Instruction: Load Other Files
Files to Load
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).
expt_accepted_hits.bam
4.
Perform Steps 1-3 for the files to the right.
merged.gtf
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genes-chr22.f
ctrl_accepted_hits.bam
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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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