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

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

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

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

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. 2 metrics: # of mapped reads and # of significantly different identified genes b. Compare new transcriptome to known genes. RNA-Seq Lab | Jessica Kirkpatrick | 2015

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

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

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

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 MAY WANT TO DELETE THIS

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

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

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

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

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

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

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

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

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

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

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

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: 500000 Maximum number of alignment to be allowed: 20 RNA-Seq Lab | Jessica Kirkpatrick | 2015

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: 500000 RNA-Seq Lab | Jessica Kirkpatrick | 2015

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

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

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

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

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: 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 RNA-Seq Lab | Jessica Kirkpatrick | 2015

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

RNA-Seq Lab | Jessica Kirkpatrick | 2015 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 Genome Browsers tutorials – http://www.broadinstitute.org/igv/QuickStart/ - IGV tutorials http://www.openhelix.com/ucsc/ - UCSC browser tutorials (openhelix is a great place for tutorials, UIUC has a campus-wide subscription) Contact us at: hpcbiohelp@illinois.edu hpcbiotraining@illinois.edu RNA-Seq Lab | Jessica Kirkpatrick | 2015

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

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

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

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

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? RNA-Seq Lab | Jessica Kirkpatrick | 2015

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