Identification, annotation and visualisation of extreme changes in splicing with SwitchSeq

Mar Gonzàlez-Porta

Functional Genomics team

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

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)

What is the extent of transcriptome diversity?

~95% of human genes have more than one splice form expressed

What are the expression levels for the different transcripts from a given gene in a given sample?

[Pan et al 2008; Wang et al 2008; Djebali and Davis et al 2012 (ENCODE)]

Methodology

•  3 tools for transcript expression estimation MISO, Cufflinks, MMSEQ

•  2 mapping strategies TopHat (genome), Bowtie (transcriptome)

5 cell lines PE (50bp x 2)

GAII

ENCODE Illumina Body Map

16 tissues PE (50bp x 2) HiSeq 2000

2 different datasets: 46 samples

Most protein coding genes express one dominant transcript

TopHat + MISO

Most protein coding genes express one dominant transcript

TopHat + MISO

The evaluation of different methods led to

a consistent outcome

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

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

Major transcripts do not always contain the longest CDS

Major transcripts do not always code for proteins

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

On the concept of switch event

Switch event (SE)

Detecting switch events with SwitchSeq

https://github.com/mgonzalezporta/SwitchSeq

Goal: detect changes in major transcripts across conditions

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

Detecting switch events with SwitchSeq

OUTPUT

•  Self-contained html (+txt, JSON)

•  High resolution plots

Example use case (I): switch events in cancer

45 matched samples PE (100bp x 2)

HiSeq 2000

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)

Context: the CAGEKID project (ICGC), for the genomic, transcriptomic and epigenetic characterisation of kidney cancer (ccRCC).

Scelo*, Riazalhosseini*, Greger* et al. Nature Communications (in press).

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

Example use case (I): switch events in cancer

SRSF6

Ensembl: switch from PC to NMD APPRIS: principal transcript in N, but not in T

Example use case (II): switch events across human tissues

27 human tissues (171 samples) PE (100bp x 2)

HiSeq 2000 + HiSeq 2500

Fagerberg et al. Molecular & Cellular Proteomics (2013)

Context: E-MTAB-1733 %

diff

eren

tially

splic

ed g

enes

04

812

1 FPKM 5 FPKM 10 FPKM

all SE2-fold dominant SE5-fold dominant SE

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

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

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

*core spliceosomal factor

Integration of RNA-seq + SWATH-MS data

11/17 switch events could be recapitulated

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

Acknowledgements

Thesis commitee John Marioni, Jan Korbel, Simon Tavaré

Everyone Wolfgang Huber, Nicholas Luscombe, Roderic Guigó,

who provided Sushma-Nagaraja Grellscheidand and the Functional Genomics

feedback team

Yansheng Liu

Collaborators

Supervisor Alvis Brazma