1. Introductory RNA-seq Transcriptome Profiling of the hy5 mutation in Arabidopsis thaliana

2. Before we start: Align sequence reads to the reference genome
The most time-consuming part of the analysis is doing the alignments of the reads (in Sanger fastq format) for all replicates against the reference genome.
Make sure everyone has gotten the four replicates loaded into the new Tophat implementation that accepts
multiple fastq files and runs them serially (TopHat-1.4.1) at the beginning of the lecture. This takes the most time but will finish for most
people while you do the lecture.

3. RNA-seq in the Discovery Environment
Overview: This training module is designed to provide a hands on experience in using RNA-Seq for transcriptome profiling.
Question:
How well is the annotated transcriptome represented in RNA-seq data in Arabidopsis WT and hy5 genetic backgrounds?
How can we compare gene expression levels in the two samples?

4. Scientific Objective
LONG HYPOCOTYL 5 (HY5) is a basic leucine zipper transcription factor (TF).
Mutations in the HY5 gene cause aberrant phenotypes in Arabidopsis morphology, pigmentation and hormonal response.
We will use RNA-seq to compare the transcriptomes of seedlings from WT and hy5 genetic backgrounds to identify HY5-regulated genes.

5. Samples
Experimental data downloaded from the NCBI Short Read Archive (GEO:GSM and GEO:GSM613466)
Two replicates each of RNA-seq runs for Wild-type and hy5 mutant seedlings.

6. Specific Objectives By the end of this module, you should
Be more familiar with the DE user interface
Understand the starting data for RNA-seq analysis
Be able to align short sequence reads with a reference genome in the DE
Be able to analyze differential gene expression in the DE
Be able to use DE text manipulation tools to explore the gene expression data

7. Quick Summary Differential Expression: CuffDiff
Download Reads from SRA
Align to Genome: TopHat
Find Differentially Expressed genes
Export Reads to FASTQ
View Alignments: IGV

8. Pre-Configured: Getting the RNA-seq Data
Import SRA data from NCBI SRA
Extract FASTQ files from the downloaded SRA archives
These steps are pre-done to make the work-flow fit into the module time allocation.
Spend a moment explaining the provenance (ie getting the data from NCBI, SRA-lite format)
Explain that the fastq dumper rescales the quality scores to the Sanger convention for fastq
Let them know we did this for them in advance

9. RNA-Seq Conceptual Overview
This is a quick visual overview of transcriptome profiling via RNA-seq. It does not go into comparisons but we cover that
with CuffDiff later.
Image source:

10. RNA-Seq Workflow Overview
Explain reference-sequence based NGS read alignments.
Explain that we are skipping the cufflinks step because the Arabidopsis transcriptome is so well annotated that we can use the TAIR gene models as our refernce transcripts for CuffDiff

11. Step 1: Align Reads to the Genome
Align the four FASTQ files to Arabidopsis genome using TopHat
They will have done this part by now.

12. It uses the BOWTIE aligner internally.
TopHat
TopHat is one of many applications for aligning short sequence reads to a reference genome.
It uses the BOWTIE aligner internally.
Other alternatives are BWA, MAQ, TopHat, Stampy, Novoalign, etc.
Emphasize that the TopHat aligner is one of many choices. Let them know that others are available in the DE and they can also integrate their own if they want to.

13. RNA-seq Sample Read Statistics
Genome alignments from TopHat were saved as BAM files, the binary version of SAM ().
Reads retained by TopHat are shown below
Sequence run
WT-1
WT-2
hy5-1
hy5-2
Reads
10,866,702
10,276,268
13,410,011
12,471,462
Seq. (Mbase)
445.5
421.3
549.8
511.3
These are the read counts generated by TopHat as part of its alignment analysis.
This is a modestly sized data set by NGS standard; good time to mention scalability, Data Store, etc.

14. Prepare BAM files for viewing
Index BAM files using SAMtools
This is done for them.

15. Using IGV in Atmosphere
We already Launched an instance of NGS Viewers in Atmosphere
Use VNClient to connect to your remote desktop
We will just show them the slides. Launching an Atmosphere instance is out of scope for this module.
Explain that we will cover Atmosphere later in the day/workshop.

16. Pre-configured VM for NGS Viewers

17. Integrated Genomics Viewer (IGV)
The Integrative Genomics Viewer (IGV) is a high-performance visualization tool for interactive exploration of large, integrated genomic datasets. It supports a wide variety of data types, including array-based and next-generation sequence data, and genomic annotations.
IGV: Make sure you know how to run IGV yourself. Work the example. Play with configuring tracks.
You don’t NEED to run IGV in Atmosphere. If that product is flaking out, show users how to do the same thing on their OWN desktop!
Use IGV to inspect outputs from TopHat

18. Explain this figure:
The gene on the left is differentially expressed (down-regulated in hy5).
Compare to gene on right that is not differentially expressed in the two samples.
ATG44120 (12S seed storage protein) significantly down-regulated in hy5 mutant
Background (> 9-fold p=0). Compare to gene on right lacking differential expression

19. Other Ways to View Alignment Data WIG->Ensembl
Explain that we can also export to popular browsers like Ensembl and UCSC by using the Bam->Wig converter.

20. RNA-Seq Workflow Overview
Explain that we are skipping the cufflinks step because the Arabidopsis transcriptome is so well annotated that we can use the TAIR gene models as our refernce transcripts for CuffDiff

21. CuffDiff
CuffLinks is a program that assembles aligned RNA-Seq reads into transcripts, estimates their abundances, and tests for differential expression and regulation transcriptome-wide.
CuffDiff is a program within CuffLinks that compares transcript abundance between samples
Explain that we are skipping the cufflinks step because the Arabidopsis transcriptome is so well annotated that we can use the TAIR gene models as our refernce transcripts for CuffDiff

22. Examining Differential Gene Expression
Introducing CuffDiff with replicates

23. Examining the Gene Expression Data
Explain that there are various text manipulation tools integrated into the DE (grep, cut, awk etc) for very configurable modular analysis
Of the tabular output data from CuffDiff. Then segue into the Filter_CuffDiff_Results App, which consolidates some of these steps.

24. Differentially expressed genes
Filter CuffDiff results for up or down-regulated gene expression in hy5 seedlings

25. Differentially expressed genes
Example filtered CuffDiff results generated with the Filter_CuffDiff_Results to
Select genes with minimum two-fold expression difference
Select genes with significant differential expression (q <= 0.05)
Add gene descriptions