1. Introductory RNA-Seq Transcriptome Profiling

2. Tutorial on the web….
environment/de-002-characterizing-differential-expression-rna-seq- tuxedo

3. 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 genom
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.

4. Where is the Sample Data?

5. 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.

6. 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?

7. 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.

8. 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.

9. 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

10. 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:

11. RNA-Seq Data …Now What? @SRR070570.4 HWUSI-EAS455:3:1:1:1096 length=41
CAAGGCCCGGGAACGAATTCACCGCCGTATGGCTGACCGGC +
@SRR HWUSI-EAS455:3:1:2:1592 length=41
GAGGCGTTGACGGGAAAAGGGATATTAGCTCAGCTGAATCT
@SRR HWUSI-EAS455:3:1:2:869 length=41
TGCCAGTAGTCATATGCTTGTCTCAAAGATTAAGCCATGCA
@SRR HWUSI-EAS455:3:1:4:1075 length=41
CAGTAGTTGAGCTCCATGCGAAATAGACTAGTTGGTACCAC
@SRR HWUSI-EAS455:3:1:5:238 length=41
AAAAGGGTAAAAGCTCGTTTGATTCTTATTTTCAGTACGAA
@SRR HWUSI-EAS455:3:1:5:1871 length=41
GTCATATGCTTGTCTCAAAGATTAAGCCATGCATGTGTAAG
@SRR HWUSI-EAS455:3:1:5:1981 length=41
GAACAACAAAACCTATCCTTAACGGGATGGTACTCACTTTC
…Now What?

12. Bioinformagician @SRR070570.4 HWUSI-EAS455:3:1:1:1096 length=41
CAAGGCCCGGGAACGAATTCACCGCCGTATGGCTGACCGGC +
@SRR HWUSI-EAS455:3:1:2:1592 length=41
GAGGCGTTGACGGGAAAAGGGATATTAGCTCAGCTGAATCT
@SRR HWUSI-EAS455:3:1:2:869 length=41
TGCCAGTAGTCATATGCTTGTCTCAAAGATTAAGCCATGCA
@SRR HWUSI-EAS455:3:1:4:1075 length=41
CAGTAGTTGAGCTCCATGCGAAATAGACTAGTTGGTACCAC
@SRR HWUSI-EAS455:3:1:5:238 length=41
AAAAGGGTAAAAGCTCGTTTGATTCTTATTTTCAGTACGAA
@SRR HWUSI-EAS455:3:1:5:1871 length=41
GTCATATGCTTGTCTCAAAGATTAAGCCATGCATGTGTAAG
@SRR HWUSI-EAS455:3:1:5:1981 length=41
GAACAACAAAACCTATCCTTAACGGGATGGTACTCACTTTC
Bioinformagician

14. Your transformed RNA-Seq Data
Your RNA-Seq Data
$ tophat -p 8 -G genes.gtf -o C1_R1_thout genome C1_R1_1.fq C1_R1_2.fq
$ tophat -p 8 -G genes.gtf -o C1_R2_thout genome C1_R2_1.fq C1_R2_2.fq
$ tophat -p 8 -G genes.gtf -o C1_R3_thout genome C1_R3_1.fq C1_R3_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R1_thout genome C2_R1_1.fq C1_R1_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R2_thout genome C2_R2_1.fq C1_R2_2.fq
$ tophat -p 8 -G genes.gtf -o C2_R3_thout genome C2_R3_1.fq C1_R3_2.fq
$ cufflinks -p 8 -o C1_R1_clout C1_R1_thout/accepted_hits.bam
$ cufflinks -p 8 -o C1_R2_clout C1_R2_thout/accepted_hits.bam
$ cufflinks -p 8 -o C1_R3_clout C1_R3_thout/accepted_hits.bam
$ cufflinks -p 8 -o C2_R1_clout C2_R1_thout/accepted_hits.bam
$ cufflinks -p 8 -o C2_R2_clout C2_R2_thout/accepted_hits.bam
$ cufflinks -p 8 -o C2_R3_clout C2_R3_thout/accepted_hits.bam
$ cuffmerge -g genes.gtf -s genome.fa -p 8 assemblies.txt
$ cuffdiff -o diff_out -b genome.fa -p 8 –L C1,C2 -u merged_asm/merged.gtf \
./C1_R1_thout/accepted_hits.bam,./C1_R2_thout/accepted_hits.bam,\
./C1_R3_thout/accepted_hits.bam \./C2_R1_thout/accepted_hits.bam,\
./C2_R3_thout/accepted_hits.bam,./C2_R2_thout/accepted_hits.bam
Your transformed RNA-Seq Data

15. RNA-Seq Analysis Workflow
Tophat (bowtie)
Cufflinks
Cuffmerge
Cuffdiff
CummeRbund
Your Data
iPlant Data Store
FASTQ
Discovery Environment Atmosphere
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.

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

18. 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

19. Examining Data Quality with FastQC

20. Examining Data Quality with FastQC

21. 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

22. 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 GSNAP, BWA, MAQ, 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.

23. RNA-seq Sample Read Statistics
Genome alignments from TopHat were saved as BAM files, the binary version of SAM (samtools.sourceforge.net/).
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.

24. 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

25. 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

26. 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

27. Examining Differential Gene Expression
Introducing CuffDiff with replicates

28. 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.

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

30. 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