1. Introduction to RNA-Seq & Transcriptome Analysis
Bacterial Genome Assembly | Victor Jongeneel
Introduction to RNA-Seq & Transcriptome Analysis
Jessica Kirkpatrick
PowerPoint by Casey Hanson
RNA-Seq Lab | Jessica Kirkpatrick | 2015

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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

3. RNA-Seq Lab | Jessica Kirkpatrick | 2015
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
RNA-Seq Lab | Jessica Kirkpatrick | 2015

4. RNA-Seq Lab | Jessica Kirkpatrick | 2015v
Pipeline Overview
RNA-Seq Lab | Jessica Kirkpatrick | 2015

5. Bacterial Genome Assembly | Victor Jongeneel
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 metrics: # of mapped reads and # of significantly different identified genes
b. Compare new transcriptome to known genes.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

6. Bacterial Genome Assembly | Victor Jongeneel
Input Data
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 | Jessica Kirkpatrick | 2015

7. Accessing the IGB Biocluster
RNA-Seq Lab | Jessica Kirkpatrick | 2015

8. Step 1A: Sign into Illinois Galaxy
Open Chrome and go to Click Login and enter your Biocluster username and password.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

9. Step 1B: How Galaxy works with the Biocluster
Biocluster Signing up - Usage and cost -
MAY WANT TO DELETE THIS

10. RNA-Seq Lab | Jessica Kirkpatrick | 2015
Step 1C: Interface
You should see a workspace similar to the one below:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

11. Step 1B: Changing History Name
Click on Unnamed History in the History Pane on the left side : Type RNA – Seq workshop and press Enter.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

12. Step 2A: Accessing Input Files
At the top of the page, click Shared Data. Then click Publish Histories.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

13. Step 2B: Accessing Input Files
Bacterial Genome Assembly | Victor Jongeneel
Step 2B: Accessing Input Files
Click RNA-Seq_Chr_22 Data You should see this page. Click Import History.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

14. Step 2C: Accessing Input Files
Click start_using_this_history You should see an imported history like the following.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

15. Step 2D: Accessing Input Files
Click the gear icon at the top of the History pane. Click Copy Datasets.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

16. 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 the Copy History Items button.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

17. 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 :
RNA-Seq Lab | Jessica Kirkpatrick | 2015

18. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

19. 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
RNA-Seq Lab | Jessica Kirkpatrick | 2015

20. 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:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

21. Step 3C: Align Reads de novo Using TopHat2
The rest of the page contains parameters.
We will change the following parameters:
Library Type: FR Unstranded
Minimum Intron Length: 70
Maximum Intron Length:
Maximum number of alignment to be allowed: 20
RNA-Seq Lab | Jessica Kirkpatrick | 2015

22. Step 3C: Align Reads de novo Using TopHat2
Bacterial Genome Assembly | Victor Jongeneel
Step 3C: Align Reads de novo Using TopHat2
The rest of the page contains parameters.
We will change the following parameters:
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:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

23. Step 3E: Align Reads de novo Using TopHat2
The rest of the page contains parameters.
We will change the following parameters:
Use Microexon Search: No
Do Fusion Search: No
Set Bowtie2 settings: No
Specify read group: No
Click Execute when you have set the parameters.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

24. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

25. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

26. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

27. Step 3I: Align Reads de novo Using TopHat2
Configure the parameters as before (below) and click execute:
Library Type: FR Unstranded
Minimum Intron Length: 70
Maximum Intron Length:
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:
Use Microexon Search: No
Do Fusion Search: No
Set Bowtie2 settings: No
Specify read group: No
RNA-Seq Lab | Jessica Kirkpatrick | 2015

28. RNA-Seq Lab | Jessica Kirkpatrick | 2015
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
RNA-Seq Lab | Jessica Kirkpatrick | 2015

29. RNA-Seq Lab | Jessica Kirkpatrick | 2015
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:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

30. RNA-Seq Lab | Jessica Kirkpatrick | 2015
Step 4C: Renaming Files
In this manner, rename the following tracks with the respective names:
expt_align_summary
expt_insertions
expt_deletions
expt_splice_junctions
ctrl_align_summary
ctrl_insertions
ctrl_deletions
ctrl_splice_junctions
ctrl_accepted_hits
RNA-Seq Lab | Jessica Kirkpatrick | 2015

31. 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, 148 reads were not aligned.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

32. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

33. 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 | Jessica Kirkpatrick | 2015

34. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

35. Step 6B: Informed Align Reads Using TopHat2
Keep the same parameters as before, but change the following:
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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

36. Step 6C: Informed Align Reads Using TopHat2
This should generate tracks 15 through 19.
Rename the tracks the following:
expt-genes_align_summary
expt-genes_insertions
expt-genes_deletions
expt-genes_splice_junctions
expt-genes_accepted_hits
RNA-Seq Lab | Jessica Kirkpatrick | 2015

37. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

38. Step 6E: Informed Align Reads Using TopHat2
Keep the same parameters as before, but change the following:
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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

39. Step 6F: Informed Align Reads Using TopHat2
This should generate tracks 15 through 19.
Rename the tracks the following:
ctrl-genes_align_summary
ctrl-genes_insertions
ctrl-genes_deletions
ctrl-genes_splice_junctions
ctrl-genes_accepted_hits
RNA-Seq Lab | Jessica Kirkpatrick | 2015

40. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

41. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

42. Step 8: Comparison of Alignments
sample #
fastq name
# reads
Unmapped Reads
de novo
Informed
control
thrombin_control.txt
10,953
101 27
experimental
thrombin_expt.txt
12,027
163 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 | Jessica Kirkpatrick | 2015

43. 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 | Jessica Kirkpatrick | 2015

44. RNA-Seq Lab | Jessica Kirkpatrick | 2015
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
RNA-Seq Lab | Jessica Kirkpatrick | 2015

45. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

46. 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:
Use effective length correction: No
Ensure your parameters match up with the figure on the right.
Click Execute.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

47. 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:
Use effective length correction: No
Ensure your parameters match up with the figure on the right.
Click Execute.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

48. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

49. Step 10A: Merge Transcripts Using CuffMerge
In the search box, type Cuffmerge Click Cuffmerge under NGS: RNA Analysis.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

50. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

51. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

52. 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:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

53. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

54. 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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

55. Bacterial Genome Assembly | Victor Jongeneel
Conclusion
We did the following today
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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

56. RNA-Seq Lab | Jessica Kirkpatrick | 2015
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:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

57. RNA-Seq Lab | Jessica Kirkpatrick | 2015
Extra Material
IGV
RNA-Seq Lab | Jessica Kirkpatrick | 2015

58. 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 | Jessica Kirkpatrick | 2015

59. RNA-Seq Lab | Jessica Kirkpatrick | 2015
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
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.f
ctrl_accepted_hits.bam
expt_accepted_hits.bam
merged.gtf
RNA-Seq Lab | Jessica Kirkpatrick | 2015

60. Step 10A: Visualization With IGV
Your browser window should look similar to the picture below:
RNA-Seq Lab | Jessica Kirkpatrick | 2015

61. 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 | Jessica Kirkpatrick | 2015

62. 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 | Jessica Kirkpatrick | 2015