1. The iPlant Collaborative
Community Cyberinfrastructure for Life Science
Tools and Services Workshop
Intro to RNA-Seq with the Tuxedo Suite

2. Experiment Overview
Goals
Determine differential expression abundance of transcripts in between a WT and mutant organism

3. RNA-Seq Overview Basic concept
Image source:

4. Experiment Overview Example experiment

5. 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

6. 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

7. Getting a feel for the data
FASTQ format

8. Now what? 1 Bioinformatician 1 1 1 11 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

9. Papers and Background
Read these first!

10. Tuxedo Workflow Differential expression
*TopHat and Cufflinks require a sequenced genome
Differential expression

11. No reference genome?
Resources

12. Standards for RNA Suggestions before you sequence

13. Command line version 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
Your RNA-Seq Data

14. Discovery Environment
Tophat (bowtie)
Cufflinks
Cuffmerge
Cuffdiff
CummeRbund
Using a GUI
Your Data
iPlant Data Store
FASTQ
Discovery Environment Atmosphere

15. Moving your data in
Complete documentation

16. iDrop Desktop
Easy to use!

17. Discovery Environment
Easy to use!

18. Decompress your data
Know what files you have

19. Remove barcodes? Demultiplexing and adapter trimming
Image from:
Pre-process sequences if needed (e.g., Sabre for de-multiplexing reads, and Scythe for removing primer/adapter sequences)

20. Quality Control FastQC

21. Quality Control 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

22. Quality Control Per sequence quality BAD GOOD
Fail: most frequently observed mean quality is below 20 (1% error rate)

23. Quality Control Fail: error if any of the sequences have zero length.
Sequence length distribution
GOOD
Fail: error if any of the sequences have zero length.

24. Quality Control Overrepresented sequences Potentially BAD
Fail: module will issue an error if any sequence is found to represent more than 1% of the total

25. TopHat
Maps reads to reference genome

26. TopHat
Maps reads to reference genome
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, STAR,OLego, Stampy, Novoalign, etc.

27. TopHat Maps reads to reference genome
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

28. IGV
Visualize mapped reads

29. Cufflinks
Assemble transcripts

30. Cufflinks Hint: Provide a mask file (gtf/gff)
Assemble transcripts
Hint: 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

31. Cufflinks - Cufflinks User Manual Assemble 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

32. Cufflinks
Assemble transcripts

33. Cuffmerge Assemble transcriptome from RABT and Cufflinks
Cuffmerge is a meta-assembler; Assembly of Cufflinks transcripts /
Reference based assembly

34. Cuffdiff
Determine sample differences

35. Cuffdiff Determine sample differences
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

36. Changes in fragment counts ≠ changes in expression
Cuffdiff
Determine sample differences
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.

37. Cuffdiff Determine sample differences 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.)

38. Cuffdiff Determine sample differences
Example filtered Cuffdiff results generated in the Discovery Environment.

39. Cuffdiff Determine sample differences
Example filtered Cuffdiff results generated in the Discovery Environment.

40. Cuffdiff
Density plot

41. Cuffdiff
Scatter plot

42. Cuffdiff
Volcano plot

43. CummeRbund
Using R in Atmosphere (tomorrow)

44. Keep asking: ask.iplantcollabortive.org

45. The iPlant Collaborative is funded by a grant from the National Science Foundation Plant Cyberinfrastructure Program (#DBI ).