Talk for the ECCB'14 workshop: Analysis of differential isoform usage by RNA-seq: statistical methodologies and open software - Strasbourg, 7th September 2014

1. Identification, annotation and visualisation of
extreme changes in splicing with SwitchSeq
Mar Gonzàlez-Porta
Functional Genomics team

2. Outline
• The extent of transcriptome diversity
• Applications:
• Improving the existing annotation
• Detecting extreme changes in splicing with SwitchSeq
• Evaluating the impact of splicing at the protein level

3. Outline
• The extent of transcriptome diversity
• Applications:
• Improving the existing annotation
• Detecting extreme changes in splicing with SwitchSeq
• Evaluating the impact of splicing at the protein level
Gonzàlez-Porta, Frankish, Rung, Harrow & Brazma.
Genome Biology 14, R70 (2013)

4. What is the extent of transcriptome diversity?
~95% of human genes have more than one splice form expressed
[Pan et al 2008; Wang et al 2008; Djebali and Davis et al 2012 (ENCODE)]
What are the expression levels for the different transcripts from a given gene
in a given sample?

5. Methodology
2 different datasets: 46 samples
Illumina Body Map
ENCODE
5 cell lines
PE (50bp x 2)
• 3 tools for transcript expression estimation
MISO, Cufflinks, MMSEQ
• 2 mapping strategies
TopHat (genome), Bowtie (transcriptome)
GAII
16 tissues
PE (50bp x 2)
HiSeq 2000

7. Most protein coding genes express one dominant transcript
TopHat + MISO

8. Most protein coding genes express one dominant transcript
The evaluation of different methods led to
TopHat + MISO
a consistent outcome

9. Outline
• The extent of transcriptome diversity
• Applications:
• Improving the existing annotation
• Detecting extreme changes in splicing with SwitchSeq
• Evaluating the impact of splicing at the protein level

10. Outline
• The extent of transcriptome diversity
• Applications:
• Improving the existing annotation
• Detecting extreme changes in splicing with SwitchSeq
• Evaluating the impact of splicing at the protein level

11. Major transcripts do not always contain the longest CDS

12. Major transcripts do not always code for proteins

13. Outline
• The extent of transcriptome diversity
• Applications:
• Improving the existing annotation
• Detecting extreme changes in splicing with SwitchSeq
• Evaluating the impact of splicing at the protein level

14. On the concept of switch event
Switch event (SE)

15. Detecting switch events with SwitchSeq
Goal: detect changes in major transcripts across conditions
https://github.com/mgonzalezporta/SwitchSeq

16. Detecting switch events with SwitchSeq
INPUT
• Annotation [switchseq
–t
get_data]
• Transcript expression levels:
- Focus on differentially spliced genes (e.g. MMDIFF, DEXSeq…)
recommended
- Any matrix will do

17. Detecting switch events with SwitchSeq
OUTPUT
• Self-contained html (+txt, JSON)
• High resolution plots

18. Example use case (I): switch events in cancer
45 matched samples
PE (100bp x 2)
HiSeq 2000
Context: the CAGEKID project (ICGC), for the
genomic, transcriptomic and epigenetic
characterisation of kidney cancer (ccRCC).
The transcriptome is broadly altered in ccRCC
~40% of the expressed genes are differentially spliced (n = 7,842)
Big and recurrent changes in splicing are rare
~25% of the differentially spliced genes undergo switch events (n = 3,943)
Scelo*, Riazalhosseini*, Greger* et al.
Nature Communications (in press).

19. Example use case (I): switch events in cancer
PPP2R4
Ensembl: switch between two PC transcripts
APPRIS: principal transcript in N, but not in T
EMBOSS Needle + UniPDB: <35% protein overlap

20. Example use case (I): switch events in cancer
SRSF6
Ensembl: switch from PC to NMD
APPRIS: principal transcript in N, but not in T

21. Example use case (II): switch events across human tissues
27 human tissues (171 samples)
PE (100bp x 2)
HiSeq 2000 + HiSeq 2500
all SE
2-fold dominant SE
5-fold dominant SE
Fagerberg et al.
Molecular & Cellular Proteomics (2013)
Context: E-MTAB-1733
% differentially spliced genes
0 4 8 12 1 FPKM 5 FPKM 10 FPKM

22. Example use case (II): switch events across human tissues
CLTB
Ensembl: switch between two PC transcripts
APPRIS: principal transcript in both conditions
EMBOSS Needle: 92.1% protein overlap

23. Outline
• The extent of transcriptome diversity
• Applications:
• Improving the existing annotation
• Detecting extreme changes in splicing with SwitchSeq
• Evaluating the impact of splicing at the protein level

24. Can changes in splicing be recapitulated at the protein level?
Most of the efforts aimed at detecting proteins from alternatively spliced
transcripts, rather than validating changes in splicing
[e.g. Blakeley et al. 2010; Ezkurdia et al. 2012; Leoni et al. 2011]
Context:
• Control vs PRPF8* KD Cal51 cells (human)
• RNA-seq + MS data
Integration of RNA-seq
+ SWATH-MS data
11/17 switch events could be
recapitulated
*core spliceosomal factor

25. Outline
• Most protein coding genes express one dominant transcript
• Major transcript predictions can be used to improve the exiting annotation
• SwitchSeq enables the study of extreme changes in splicing
• Splicing changes can be recapitulated at the protein level

26. Acknowledgements
Supervisor
Alvis Brazma
Thesis commitee John Marioni, Jan Korbel, Simon Tavaré
Yansheng Liu
Collaborators
Everyone Wolfgang Huber, Nicholas Luscombe, Roderic Guigó,
who provided Sushma-Nagaraja Grellscheidand and the Functional Genomics
feedback team