1. Introduction to RNA-Seq
Transcriptome profiling with iPlant
Jason Williams
iPlant / Cold Spring Harbor Laboratory

2. RNA-Seq in the Discovery Environment
This training module is designed to demonstrate a workflow in the iPlant Discovery Environment using RNA-Seq for transcriptome profiling.
Question:
How can we compare gene expression levels using RNA-Seq data in Arabidopsis WT and hy5 genetic backgrounds?

3. Scientific Objective
LONG HYPOCOTYL 5 (HY5) is a basic leucine zipper transcription factor (TF).
Mutations cause aberrant phenotypes in Arabidopsis morphology, pigmentation and hormonal response.
We will use RNA-Seq to compare WT and hy5 to identify HY5-regulated genes.
Source:

4. Sample Dataset
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.

5. 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:

6. 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.

7. 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?

8. 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?

9. RNA-Seq Data - FastQ

10. 1 1 1
@SRR 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 1 1
Bioinformatician

11. *Graphics taken from these publications

12. The Tuxedo Protocol
*TopHat and Cufflinks require a sequenced genome

13. The Tuxedo Protocol

14. Most of RNA-Seq happens before analysis
ENCODE Project RNA-Seq Standards

15. Your RNA-Seq Data Your transformed 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

16. 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. www.iplantc.org/ds2 Moving your data in
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.

18. Moving your data in iDrop Desktop – Java Program
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.
iDrop Desktop – Java Program

19. Moving your data in iCommands
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.
iCommands

20. The iPlant Discovery Environment

21. Data preparation
Decompress your data?

22. Data preparation
Decompress your data?

23. Data preparation
Pre-process sequences if needed (e.g., Sabre for de-multiplexing reads, and Scythe for removing primer/adapter sequences)
Image from:

24. FASTQC – Quality Control
Data preparation
FASTQC – Quality Control

25. Per Base Sequence Quality
Data preparation
Per Base Sequence Quality
BAD GOOD
The central red line is the median value
The yellow box represents the inter-quartile range (25-75%)
The upper and lower whiskers represent the 10% and 90% points
The blue line represents the mean quality

26. Data preparation Per Sequence Quality Scores
BAD GOOD
Fail: most frequently observed mean quality is below 20 (1% error rate)

27. Data preparation Per Base N Content
BAD GOOD
Fail: any position shows an N content of >20%.

28. The iPlant Discovery Environment
Sequence Length Distribution
GOOD
Fail: error if any of the sequences have zero length.

29. The iPlant Discovery Environment
Overrepresented Sequences
BAD
Fail: module will issue an error if any sequence is found to represent
more than 1% of the total

30. Tophat 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

31. 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, OLego, 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.

32. TopHat
TopHat has a number of parameters and options, and their default values are tuned for processing mammalian RNA-Seq reads. If you would like to use TopHat for another class of organism, we recommend setting some of the parameters with more strict, conservative values than their defaults. Usually, setting the maximum intron size to 4 or 5 Kb is sufficient to discover most junctions while keeping the number of false positives low. - TopHat User Manual
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.

33. TopHat outputs in IGV

34. Assembling the Transcripts
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

35. Assembling the Transcripts
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

36. Assembling the Transcripts
Provide a mask file (gtf/gff)
Tells Cufflinks to ignore all reads that could have come from transcripts in this GTF file.
Annotated rRNA, mitochondrial transcripts other abundant transcripts you wish to ignore.
- Cufflinks User Manual
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

37. Assembling the Transcripts
1) transcripts.gtf
This GTF file contains Cufflinks' assembled isoforms. The first 7 columns are standard GTF, and the last column contains attributes, some of which are also standardized ("gene_id", and "transcript_id"). There one GTF record per row, and each record represents either a transcript or an exon within a transcript.
2) isoforms.fpkm_tracking
This file contains the estimated isoform-level expression values (FPKM).
3) genes.fpkm_tracking
This file contains the estimated gene-level expression values (FPKM).
- Cufflinks User Manual
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

38. Assembling the Transcripts
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
- Cufflinks User Manual

39. Merging the Transcriptomes
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
Cuffmerge is a meta-assembler; Assembly of Cufflinks transcripts /
Reference based assembly

40. Comparing wild-type to hy5 transcriptomes
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

41. Comparing wild-type to hy5 transcriptomes
Cuffdiff evaluates variation in read counts for each gene across the replicates
this estimate is used to calculate significance of expression changes
Cuffdiff can identify genes that are differentially spliced or differentially regulated
via promoter switching. Isoforms of a gene that have the same TSS are grouped
Detection rate of differentially expressed genes/transcripts is strongly dependent on sequencing depth

42. Comparing wild-type to hy5 transcriptomes
Changes in fragment counts ≠ changes in expression
True expression is estimated by the sum of the length-normalized isoform read counts so the entire transcript must be taken into account.

43. Cuffdiff Results 1. FPKM tracking files
Cuffdiff calculates the FPKM of each transcript, primary transcript, and gene in each sample. Primary transcript and gene FPKMs are computed by summing the FPKMs of transcripts in each primary transcript group or gene group.
(tss_groups.fpkm_tracking tracks summed FPKM of transcripts sharing tss_ids)
2) Count tracking files
Estimate of the number of fragments that originated from each transcript, primary transcript, and gene in each sample.
3) Read group tracking files
Expression and fragment count for each transcript, primary transcript, and gene in each replicate.
4) Differential expression tests
Tab delimited file lists the results of differential expression testing between samples for spliced transcripts, primary transcripts, genes, and coding sequences.
Plus several other outputs (diff splicing, CDS, promoter, etc.)

44. Differentially expressed genes
Example filtered Cuffdiff results generated in the Discovery Environment.

45. Differentially expressed transcripts
Example filtered Cuffdiff results generated in the Discovery Environment.

46. Density Plot

47. Scatter Plot

48. Volcano Plot

49. Expression Plots
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