1. Introduction to RNA-Seq and Transcriptome Analysis
Bacterial Genome Assembly | Victor Jongeneel
Introduction to RNA-Seq and Transcriptome Analysis
Hands – on activities (Fun with UNIX!)
PowerPoint: Jessica Kirkpatrick and Casey Hanson
RNA-Seq Lab | Jessica Kirkpatrick | 2015

2. 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.

3. Cufflinks does reference-based transcriptome assembly
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

4. Premise
1. Procedure:
Run 1: Allow TopHat to select splice junctions and proceed through the steps without giving the software any information about known genes/gene models.
Run 2: 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.
Question: Is there a difference in the results if the Tuxedo Suite is run 2 different ways?

5. Premise VS

6. Input data RNA-Seq: 100 bp, single end data Genome & gene information:
sample
replicate #
fastq name
# reads
control
Replicate 1
thrombin_control.fastq
10,953
experiment
thrombin_expt.fastq
12,027
Genome & gene information:
name
description
chr22.fa
Fasta file with the sequence of chromosome 22 from the human genome (hg19 – UCSC) (reference genome)
genes-chr22.gtf
GTF file with gene annotation, known genes (hg19 – UCSC)

7. Sign in to Galaxy
Go to Click on the button Sign in using your classroom ID and password

8. How Galaxy works with the biocluster
MAY WANT TO DELETE THIS
Biocluster
Signing up -
Usage and cost -
Christopher Fields

9. Rename the History

10. Accessing the input files
The data are located in the following directory:
/home/classroom/rnaseq-mayo/
The rnaseq-mayo directory contains an input_data folder as well as a results folder.
(Note “~” is a symbol in UNIX paths referring to your home directory).
$ mkdir rnaseq-mayo
# Make a working directory in your home directory.
$ cp /home/classroom/rnaseq-mayo/input_data/* ~/rnaseq-mayo/
# Copy data to your working directory.
$ qsub -I -q classroom -l nodes=1;ppn=4
# Login to a “classroom” computer on the cluster with 4 processors and in an interactive mode.

11. Getting data into Galaxy (Method 4)
Click on the “Shared Data” pulldown menu Click on “Published Histories”

12. Getting data
Click on the “Workshop FASTQs”

13. Getting data
Click on the “Import History” on the top, towards the right

14. Getting data

15. Now your current history is the imported history, called “imported: RNA-Seq Chr 22 Data” In the top right corner of the history panel is a wheel, click on that wheel

16. Getting data
The pulldown menu that is revealed when you click on the wheel has many options that are worth exploring… Right now we are interested in the “Copy Datasets” option Basically, we want to copy the data we have in this imported history to our previously created “RNA-Seq workshop” history

17. Getting data into Galaxy (Method 4)
For your “Source History”, select the imported one and for your “Destination History”, select the RNA-Seq workshop Select all the datasets that you want to copy to the “RNA-Seq workshop” history Click on “Copy History Items”

18. Getting data

19. A glimpse at the input data
FASTA
chr22.fa
GTF
genes-chr22.gtf
FASTQ
thrombin_expt.fastq
thrombin_control.fastq

20. RNA-Seq Lab | Jessica Kirkpatrick | 2015
RUN 1: Alignment
RNA-Seq Lab | Jessica Kirkpatrick | 2015

21. Aligning reads using TopHat
We are not going to provide any genic structure information. TopHat will find splice junctions on its own.

22. Aligning reads using TopHat
Always read the instructions before running software
In the left tools panel search for tophat2
Click on tophat2, this will result in the central panel showing you all the options for tophat2
Remember you need the quality values in your fastq to be phred 33, or Sanger scores

23. Aligning reads using TopHat2
Run 1:
No genic structure information (i.e. no GTF file)
TopHat2 will find splice junctions on its own
Run this on experimental & control data.
Run 2:
Genic structure information will be used
Run this on experimental data.

24. Alignment with Tophat2: Run 1
In the left tools panel search for tophat2
Click on tophat2, this will result in the central panel showing you all the options for tophat2
Remember you need the quality values in your fastq to be phred 33, or Sanger scores
RNA-Seq Lab | Jessica Kirkpatrick | 2015

25. Alignment with Tophat2: Run 1
RNA-Seq Lab | Jessica Kirkpatrick | 2015

26. Alignment with Tophat2: Run 1
Ask about split-segment
RNA-Seq Lab | Jessica Kirkpatrick | 2015

27. Alignment with Tophat2: Run 1
Click “Execute” once you have made all the selections.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

28. Alignment with Tophat2: Run 1
Now we want to start a new tophat2 run for another fastq file in the RNA-Seq workshop history
RNA-Seq Lab | Jessica Kirkpatrick | 2015

29. Alignment with Tophat2: Run 1
Now we want to start a new tophat2 run for the control fastq file in the RNA-Seq workshop history
Since this is “re run”, all the parameters should be the same; this makes it easy to replicate runs, and easy to go back and check run parameters.
Always re-label new files immediately with names that makes sense to you, by clicking on the pencil and changing attributes
RNA-Seq Lab | Jessica Kirkpatrick | 2015

30. Rename Files
On Galaxy its important to rename your files to something meaningful

31. Evaluating alignment: Run 1
How many reads DID NOT align to the reference genome chr22?

32. Run 2: Informed Alignment.
Run 2: Informed Alignment
RNA-Seq Lab | Jessica Kirkpatrick | 2015

33. Aligning reads using TopHat2
Run 1:
No genic structure information (i.e. no GTF file)
TopHat2 will find splice junctions on its own
Run this on experimental and control data
Run 2:
Genic structure information will be used
Run this on experimental data only

34. Alignment with Tophat2: Run 2
Now we want to start a new informed tophat2 run
RNA-Seq Lab | Jessica Kirkpatrick | 2015

35. Aligning reads using gene information
Click “Execute” once you have changed the selections shown above.

36. Rename Files
Rename your files and make sure they are distinct from the last dataset

37. Evaluating alignment: Run 2

38. Comparison of alignments
sample #
fastq name
# reads
Unmapped Reads
Run 1
Informed run (Run 2)
control
thrombin_control.txt
10,953
101
27*
experimental
thrombin_expt.txt
12,027
147 39
Conclusions
* We will not do an informed run on the control data in class. The results of such a run are given.
There are fewer unmapped reads with the informed alignment, or Run 2 (i.e. when we use the known genes, and known splice sites)! TopHat’s prediction of splice junctions is not working very well for this dataset. (This is likely due to the low number of reads in our dataset)

39. Finding Differentially Expressed Genes.
Finding Differentially Expressed Genes
RNA-Seq Lab | Jessica Kirkpatrick | 2015

40. Tuxedo suite (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

41. Assembling transcripts using Cufflinks
Run Cufflinks to obtain newly assembled gene transcripts from the aligned RNA-Seq reads.
There is no need to conduct this step for the informed alignment (Run 2) because the locations of known genes are known already.

42. Cufflinks: Expt data
Click “Execute” once you have made all the selections.

43. Cufflinks: Control data
Now we want to start a new cufflinks run for the control dataset
RNA-Seq Lab | Jessica Kirkpatrick | 2015

44. Cufflinks: Control data
Now we want to start a new cufflinks run for the control dataset
Since this is “re run”, all the parameters should be the same; this makes it easy to replicate runs, and easy to go back and check run parameters.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

45. Merging transcripts sets using Cuffmerge
Run Cuffmerge in order to merge the assembled transcripts from control and experimental samples. The output of this will be your transcriptome.
There is no need to conduct this step for the informed alignment

46. Differential gene expression using Cuffdiff
For Run 1 (uninformed) lets find out how many differentially expressed (DE) genes are present
We need a gene (.gtf) file and both the alignment (.bam) files (control and experimental)
We could use Cuffdiff on the informed alignments (run 2) as well, but we normally recommend using htseqcount and edgeR instead

47. Differential gene expression using Cuffdiff
Once you have set your specifications, hit execute
This results in many output files
See the “Outputs” description below the Cuffdiff page for more details
We are interested in the differential expressions of genes
Look at the last column and count the number of yes’s.

48. Visualization Using IGV.
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.
RNA-Seq Lab | Jessica Kirkpatrick | 2015

49. Download data Lets compare alignments and GTFs
Download 6 files to your computer
thrombin_expt_accepted_hits
thrombin_expt_inform_accepted_hits
Cuffmerge results
genes-chr22.fa
Index files for both alignment files

50. Start IGV and load data Load Genome
1. Within IGV, click the FILE tab on the menu bar.
2. Click the ‘Load Genome from Server’ option.
3. In the browser window, search for “human”, and select
the hg19 version
Load Other Files
2. Click the ‘Load from File’ option.
3. Select the files below (one at a time or use the
ctrl key to make multiple selections).
ctrl_accepted_hits.bam
ctrl_genes_accepted_hits.bam
expt_accepted_hits.bam
expt_genes_accepted_hits.bam
first-cuffmerge_merged.gtf
genes-chr22.gtf

51. Visualization with IGV
Your browser window should look similar to the picture below:

52. 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?

53. Visualization with IGV
Looks like the new transcriptome (first-cuffmerge_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.

54. Today we did the following:
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.

55. 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: