Introduction to RNA-Seq& Transcriptome Analysis
Transcript of Introduction to RNA-Seq& Transcriptome Analysis
Introduction to RNA-Seq & Transcriptome Analysis
Jenny Drnevich Zadeh
PowerPoint by Pei-Chen Peng
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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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Trapnell et al., Nature Protocols, March 2012
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
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Premise
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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Question: Is there a difference in our results if the Tuxedo Suit is run two different ways?
sample replicate # fastq name # reads
control Replicate 1 thrombin_control.txt 10,953
experimental Replicate 1 thrombin_expt.txt 12,027
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: 100 bp, single end data
Genome & gene information
Input Data
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Step 1A: Logging into Galaxy
Go to: http://galaxy.illinois.edu/galaxy
Click Enter
Click Login
Input your login credentials.
Click Login.
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Step 1B: Galaxy Start Screen
The resulting screen should look like the figure below:
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Step 2A: Accessing Input Files
At the top of the page, click Shared Data.
Then click 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 Import
You should see an imported history like the following.
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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.
.
Run 1A: de novo Alignment
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Step 3A: Align Reads de novo Using TopHat
At the top right of the page, click the search box :
Type TopHat
Select TopHat under NGS: RNA Analysis
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Step 3B: Align Reads de novo Using TopHat
You should a page similar to the one below. We will run TopHat 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 TopHat
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 TopHat
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. Do you want to supply your own junction data: No
7. Use coverage-based search for junctions: Yes (--coverage-search)
8. Maximum intron length that may be found during coverage search: 500000
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Step 3E: Align Reads de novo Using TopHat
The rest of the page contains parameters.
We will change the following parameters:
9. Use Microexon Search: No 10. Do Fusion Search: No11. Set Bowtie2 settings: No12. Specify read group: No
Click Execute when you have set the parameters.
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Step 3F: Align Reads de novo Using TopHat
You will see confirmation in the Main Pane denoting which tracks have been added to run.
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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.
Step 3G: Align Reads de novo Using TopHatWe want to run TopHat for the control dataset now.
Navigate to the TopHat page again.
This time use 1: thrombin_control.fastq for RNA-Seq FASTQ file.
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Step 3H: Align Reads de novo Using TopHat2
Configure the parameters as before (below) and click execute:
1. Library Type: FR Unstranded2. Minimum Intron Length: 703. Maximum Intron Length: 5000004. Maximum number of alignment to be allowed: 205. Number of mismatches allowed in each segment alignments for
reads mapped independently : 26. Do you want to supply your own junction data: No7. Use coverage-based search for junctions: Yes (--coverage-search)8. Maximum intron length that may be found during coverage search:
5000009. Use Microexon Search: No 10. Do Fusion Search: No11. Set Bowtie2 settings: No12. 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: Tophat_on_data4_and data2: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 attributes.
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. expt_align_summary6. expt_insertions7. expt_deletions8. expt_splice_junctions
10. ctrl_align_summary11. ctrl_insertions12. ctrl_deletions13. ctrl_splice_junctions14. ctrl_accepted_hits
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Step 4C: Renaming Files
Fill in required fields:
Number of comment lines: 0
Strand column (click box & select) : 5
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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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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.
.
Run 1B: Informed Alignment
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Step 6A: Informed Align Reads Using TopHat
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 TopHat
Keep the same parameters as before, but change the following:
1. Do you want to supply your own junction data: Yes
2. Use Gene Annotation Model: Use a gene annotation from history
3. Gene Model Annotations:3: genes-chr22.gtf
4. Use Raw Junctions: No5. Only look for supplied junctions: No
Click Execute.
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Step 6C: Informed Align Reads Using TopHat
This should generate tracks 15 through 19.
Rename the tracks the following:
15. expt-genes_align_summary16. expt-genes_insertions17. expt-genes_deletions18. expt-genes_splice_junctions19. expt-genes_accepted_hits
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Step 6D: Informed Align Reads Using TopHat
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 TopHat
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Keep the same parameters as before, but change the following:
1. Do you want to supply your own junction data: Yes
2. Use Gene Annotation Model: Use a gene annotation from history
3. Gene Model Annotations:3: genes-chr22.gtf
4. Use Raw Junctions: No5. Only look for supplied junctions: No
Click Execute.
Step 6F: Informed Align Reads Using TopHat
This should generate tracks 15 through 19.
Rename the tracks the following:
20. ctrl-genes_align_summary21. ctrl-genes_insertions22. ctrl-genes_deletions23. ctrl-genes_splice_junctions24. 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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sample # fastq name # readsUnmapped Reads
de novo Informed
experimental thrombin_expt.txt 11,679 147 39
control thrombin_control.txt 10,619 101 27
Step 8: Comparison of Alignments
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.)
Conclusions
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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).
Finding Differentially Expressed Genes
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Trapnell et al., Nature Protocols, March 2012
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
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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. Apply length correction: No Length Correction at all (use raw counts)
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. Apply length correction: No Length Correction at all (use raw counts)
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 – 29 are the results of the experimental Cufflinks run.
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Tracks 30 – 34 are the results of the control Cufflinks run.
We will merge the assembled transcripts from the control and experimental samples next using Cuffmerge.
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
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For GTF file, choose track 27: Cufflinks on data 9, which are the assembled transcripts run on the experimental accepted hits (track 9) of the de novo assembly.
Press Ctrl and choose track 32: Cufflinks on data 14, which are the assembled transcripts run on the control accepted hits (track 14) of the de novo assembly.
Choose No for the other parameters and click Execute.
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 35 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 45:
You should see output like the following:
Count the number of "yes" answers in the significant column (column 14) as you scroll down.
There should be 2. 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 linksOnline resources for RNA-Seq analysis questions –
² http://www.biostars.org/ - Biostar (Bioinformatics explained)
² http://seqanswers.com/ - SEQanswers (the next generation sequencing community)
² Most tools have a dedicated lists
Information about the various parts of the Tuxedo suite is available here -http://ccb.jhu.edu/software.shtml
Genome Browsers tutorials –
² http://www.broadinstitute.org/igv/QuickStart/ - IGV tutorials
² http://www.openhelix.com/ucsc/ - UCSC browser tutorials
(openhelix is a great place for tutorials, UIUC has a campus-wide subscription)
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Contact us at:[email protected]
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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.
.
Visualization Using IGV
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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]/04_Transcriptomics/results
Step 9: Start IGV
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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
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.
Files to Load
genes-chr22.fa
ctrl_accepted_hits.bam
expt_accepted_hits.bam
merged.gtf
Step 10A: Visualization With IGV
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Your browser window should look similar to the picture below:
Step 10B: Visualization With IGV
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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?
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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