Annotation and differential analysis of alternative …...Annotation and differential analysis of...

Annotation and differential analysis of alternative splicing using

de novo assembly of RNAseq data

Clara Benoit-Pilven Team ERABLE, LBBE, Lyon Team GENDEV, CRNL, Lyon

______

31 March 2017

Alternative SplicingIntroduction 1

exonintron

gene

transcription

pre-mRNA

mRNA

splicing

protein

translation

Alternative Splicing1

exonintron

gene

transcription

pre-mRNA

mRNA

alternative splicing

protein

translation

Introduction


exonintron

gene

transcription

pre-mRNA

mRNA


protein

translation

Introduction

PrematureSTOP Codon

degradation by NMD (Nonsense Mediated Decay)


exonintron

gene

transcription

pre-mRNA

mRNA


protein

translation

à Concerns more than 95% of multi-exons human genes

à Deregulation of AS involved in many diseases (like cancer)

Introduction

PrématureSTOP Codon

dégradation par le NMD

(Nonsense Mediated Decay)

Assembly-first and Mapping first approaches 2

adapted from Martin et Wang, Nat Rev Genet 2011

RNA-seq reads

Mapping-first approach

Align readsto the

genome

Alternative splicing event

Introduction

Assembly-first and Mapping first approaches


RNA-seq reads

Assembly-first approach

De-novo assembly

Align readsto the

genome

Aligncontigs to

the genome


Introduction


2

Assembly-first and Mapping first approaches


RNA-seq reads

Assembly-first approach

De-novo assembly

Align readsto the

genome

Aligncontigs to

the genome


Introduction


2

Local methods :- MISO- DEXSeq- MATS

Global methods :- Cufflinks- String-Tie- Flip-Flop- Scripture

Local methods :- KisSplice

Global methods :- Trinity- Trans-ABySS

- OASES

3

How much the predictions of these two approaches overlap ?

Introduction

INTERSECTION

Mapping-first

Assembly-first

Identify pros and cons of assembly-first and mapping-first methods

à Comparison done on alternative skipped exon (ASE) events only

Introduction

INTERSECTION

Mapping-first

Assembly-first



3

à Comparison done on alternative skipped exon (ASE) events only

à Public dataset (ENCODE) from neuroblastoma SK-N-SH cell line with or without retinoic acid (RA) treatment


Sk-n-sh cell lineSK-N-SH

RA treatmentduring 2 days

Differenciated Sk-n-sh cell lineSK-N-SH RA

Introduction

INTERSECTION

Mapping-first

Assembly-first


3

FasterDB RNAseq pipelineMethods 4

Mapped reads

Reads

Mapping(TopHat2)

A BS

FasterDB RNAseq pipelineMethods

Mapped reads

Splicing event

Reads

Mapping(TopHat2)

Identification and annotation of splicing events

A BS

A BS

4


Mapped reads

Splicing event

Reads

Mapping(TopHat2)


A BS

A BSA BS

150

10 10

Quantification

Quantified event

4


Mapped reads

Splicing event

Significant event

Reads

Mapping(TopHat2)


A BS

A BS

A BS

150 / 50

10 / 100

Condition 1 Condition 2

10 / 100

A BS

150

10 10

Quantification

Differential analysis (KissDE)

Quantified event

4


Mapped reads

Splicing event

Significant event

Reads

Mapping(TopHat2)


A BS

A BS

A BS

150 / 50

10 / 100


10 / 100

A BS

150

10 10

Quantification


Quantified event

Condition 1

Ψ1 = 10

10 + 150= 6%

Condition 2

Ψ2 = 100

100 + 50= 67%

ΔΨ = Ψ2 - Ψ1 = 61 %

4

Differential analysis with KissDEl Count regression with negative binomial distribution l Generalized linear model (GLM)

Methods 5

mean gene expression

contribution of variant i

interaction termcontribution

of condition j

Lopez-Maestre, NAR, 2016


l Target hypothesis

l Likelihood ratio test l Multiple test correction (Benjamini-hochberg procedure)

Methods 5




of condition j



l Target hypothesis

l Likelihood ratio test l Multiple test correction (Benjamini-hochberg procedure)l Significant variants : P-value adjusted < 0,05 et ΔΨ ≥ 10%

Methods 5




of condition j


KisSplice pipelineMethods 6

Assembly, events identification and quantification (KisSplice)

Bubble in thede Bruijn Graph

Reads

http://kissplice.prabi.fr/

Assembly with KisSplice7

ATTCAATGGTAGCTATCTAT

AGTTGTATTCATAGCTATCTGTATTCATAGCTATCTATTA

CAATGGTAGCTATCTATTACTTCATAGCTATCTATTACCA

TTGTATTCAATGGTAGCTAT

Gene structure and sequence : …GTATTCA TAGCTAT…ATGG

Methods

7





…GTATTCA TAGCTAT…ATGGGene structure and sequence :

Methods

Assembly with KisSplice

7

ATTCAATGGTAGCTATCTATATTC

TTCATCAA

CAAT...

GTATTCATAGCTATCTATTAGTAT

TATTATTC

TTCA...

List all k-mers for a chosen value of k

…GTATTCA TAGCTAT…ATGG





Methods

Gene structure and sequence :


7

Construct the de Bruijn graphfor that set of k-mers


ATTC TTCA AGCT

TCAT CATA ATAG

TAGC

TCAA CAAT AATG ATGG TGGT GGTA GTAG

TATTGTAT GCTA CTAT

Methods







TTCATCAA

CAAT...


TATTATTC

TTCA...



ATTC TTCA AGCT

TCAT CATA ATAG

TAGC


TATTGTAT GCTA CTAT

7


Methods








TTCATCAA

CAAT...


TATTATTC

TTCA...



7

ATTC TTCA AGCT

TCAT CATA ATAG

TAGC


TATTGTAT GCTA CTAT


Methods








TTCATCAA

CAAT...


TATTATTC

TTCA...



All bubbles don’t come fromalternative splicing events

8

→ Sequencing errors : filter on edges relative minimum coverage (default : 5 %)

low expression high expression

Methods

8


→ Repeats : filter on maximum number of ‟branching” nodes (default : 5)

R1

R2

Methods



8

R1

R2

R4

R3

R5

R6

R7

R8

Methods


→ Repeats : filter on maximum number of ‟branching” nodes (default : 5)



KisSplice pipelineMethods 9



Reads

A BS

Mapping (STAR)

Mapped event


KisSplice pipelineMethods



Reads

A BS

Mapping (STAR)

Mapped event A BS

150

10 10

Annotated and quantified event

Event annotation (Kiss2RefGenome)


9

KisSplice2RefGenomeMethods 10

Exonskipping

Number ofalignment

blocks

3

2

SS SS SS SS

KisSplice2RefGenomeMethods 10

Exonskipping

Number ofalignment

blocks

3

2

SS SS SS SS

SS SS SS

Alternativedonor

2

2

SS SS SS

Alternativeacceptor

2

2

Intronretention

1

2

SS SS

> threshold

Multipleexon skipping

>3

2

Number ofalignment

blocks

SS SS SS SSSS SS

Deletion1

2≤ threshold

Insertion2

1

KisSplice pipelineMethods



Reads

A BS

Mapping (STAR)

Mapped event

Significant eventA BS

150 / 50

10 / 100


10 / 100

A BS

150

10 10


Annotated and quantified event

Event annotation (Kiss2RefGenome)

Condition 1

Ψ1 = 10

10 + 150= 6%

Condition 2

Ψ2 = 100

100 + 50= 67%

ΔΨ = Ψ2 - Ψ1 = 61 %


11

12

Compared pipelines

Methods


Strength of this comparison :

- Similar rules for annotation and quantification

- Same statistical package to carry out the differential analysis

Comparison done at 2 levels:

- Annotation - Differential analysis

13

9 91830 577 4 676

Results

KisSpliceAssembly-first

approach

FaRLineMapping-first

approach

Comparison at the annotation level

Mapping-first approach finds manyunfrequent variants

expr

essio

n of

the

min

oriso

form

Results

FaRLineonly

KisSpliceonly

common


approach


approach

10

1000

FaRLine only Common KisSplice only

9 91830 577 4 676

13

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1000

FaRLine only Common KisSplice onlyFaRLineonly

KisSpliceonly

common

expr

essio

n of

the

min

oriso

form

The overlap between methods increases whenunfrequent variants are filtered out

Results


approach

5 7821 637 2 384


approach

Unfrequent variant = less than 5 reads orrelative abundance < 10 %

14

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10

1000

FaRLine only Common KisSplice onlyFaRLineonly

KisSpliceonly

common

expr

essio

n of

the

min

oriso

form

Strong candidates are however missed by each method

Results


approach


approach

Unfrequent variant = less than 5 reads orrelative abundance < 10 %

14

5 7821 637 2 384

Some events are systematically missed by one approach

Results


approach


approach

Repeats

Complexevents

Not annotated

Paralogs 65

1 620 338

762

15

Results

Events found only by KisSplice16

E8’

97

29 48

HIRA

→ New event

E8 E9E8’E8 E9

SK-N-SH RA

ψ = 0,28

Results


E8’

97

29 48

HIRA

→ New event

E8 E9E8’E8 E9

SK-N-SH RA

ψ = 0,28

- new alternative exon- new flanking exon- new association of exons

Results 17

Reference annotation

Incomplete annotation :exon not present in the annotation

Rich annotation :too many exons annotated

Annotations

Event

Results 17

Reference annotation

Rich annotation :too many exons annotated

Annotations

Event

How to choose the flanking exons ?

- the closest exons in the annotation- the most common flanking exons in the annotation (MISO)- all the possible flanking exons present in the annotation- all the possible flanking exons defined by the reads in the

RNAseq samples analyzed

Incomplete annotation :exon not present in the annotation

Results


E8’

97

29 48

HIRA

→ New event

E8 E9E8’E8 E9

SK-N-SH RA

ψ = 0,28


RASA4 and RASA4B

→ Recent paralogs

Results

Events found only by KisSplice

168

329 412E18/E17

E17 E20E18

E17 E20

SK-N-SHψ = 0,69

18

E8’

97

29 48

HIRA

→ New event

E8 E9E8’E8 E9

SK-N-SH RA

ψ = 0,28


Results

Events found only by FaRLine

→ Exons overlapping repeats

RAB5C164

29 77

ALU E1 E5E2

E1 E5

SK-N-SH RA

ψ = 0,24

19

Results

RAB5C

Events found only by FaRLine

164

29 77

ALU E1 E5E2

E1 E5

SK-N-SH RA

ψ = 0,24

→ Complex eventsRPAIN

Both

FaRLineonly

E4 E5 E6 E7

19

→ Exons overlapping repeats

Results

Annotation summary

18

→ Mapping-first approach is stronger for - rare variants - and exonised repeats (like ALU).

→ Assembly-first approach is stronger for - novel variants

- and recent paralogs.

Should I care about these differences ? Does it have an impact on my differential analysis ?

Results

Comparison after differential analysis


approach


approach

19

587287 522

Results



approach


approach

ΔΨ (K

isSpl

ice)

ΔΨ (FaRLine)

y = −0.0061 + 0.99 ⋅ x , r2 = 0.944

−0.8

−0.4

0.0

0.4

−0.8 −0.4 0.0 0.4∆Ψ for the mapping−first method

19

587287 522

Complex events

Results



approach


approach

ΔΨ (K

isSpl

ice)

ΔΨ (FaRLine)

y = −0.0061 + 0.99 ⋅ x , r2 = 0.944

−0.8

−0.4

0.0

0.4

−0.8 −0.4 0.0 0.4∆Ψ for the mapping−first method

19

587226 25261 270

Inherited fromthe annotation

Differences due to complex

events

Inherited fromthe annotation

Complex events

Results

Comparison to others methods (I)

4231

997

124 155

MISO -FaRLine -KisSplice

MISO -FaRLine

MISOMISO - KisSplice

20

Results

Comparison to others methods (I)20

5 2282 191 279

FaRLine MISO

4231

997

124 155

MISO -FaRLine -KisSplice

MISO -FaRLine

MISOMISO - KisSplice

Results

Comparison to others methods (II)

FaRLine

1 359 1036 060

Cufflinks

Mappingevent

Cufflinkstranscript

GTF2I

SK-N-SH

E12 E15E13

E12 E15

21

Results

Comparison to others methods (II)

KisSplice Trinity

3 855 9724 311

FaRLine

1 359 1036 060

Cufflinks

Mappingevent

Cufflinkstranscript

GTF2I

SK-N-SH

E12 E15E13

E12 E15

Trinitytranscript

KisSpliceevent

RFWD2

SK-N-SH

E8 E10E9

E8 E10

21

ConclusionAnnotating alternative splicing with a single approach leads to

missing a large number of candidates.These candidates cannot be neglected, since many of them are

differentially regulated across conditions.

Conclusion & perspectives 22

ConclusionAnnotating alternative splicing with a single approach leads to

missing a large number of candidates.These candidates cannot be neglected, since many of them are

differentially regulated across conditions.

We advocate for the use of a combination of both mapping-first and assembly-first approaches for annotation and differential

analysis of alternative splicing from RNA-seq data.

Conclusion & perspectives

Mapping-first

Assembly-first

UNION INTERSECTION

Mapping-first

Assembly-first

22

http://kissplice.prabi.fr/sknsh/http://biorxiv.org/content/early/2016/09/12/074807

Perspectives

Third generation sequencing to annotate splicing events

Conclusion & perspectives 23

è Long reads

è High error rateè Low throughtput

Advantage :

But :

AcknowledgmentsVincent LacroixLeandro Lima

Emilie ChautardCamille Marchet

Gustavo SacomotoDidier Auboeuf Cyril Bourgeois Amandine Rey

Marie-Pierre LambertLouis DulaurierSophie Terrone

Jean-Baptiste Claude

Thank you for your attention !

Comparison all vs all

KisSplice

FaRLine

MISO

CufflinksTrinity

Readmapping(TopHat)

Eventidentification & annotation

QuantificationDifferential

analysis(KissDE)

Knownannotations

New annotations

AnnotatedES event

Exon 1 Exon 2 Exon 3Intron 1 Intron 2

Exon 1 Exon 3Intron 1 Intron 2Exon 2

Junctionreads

2 reads 1 read

2 reads

2 reads

FasterDB RNAseq pipeline

KisSplice pipeline

Reads assembly & Event identification &

Quantification (KisSplice)

Event annotation(Kiss2RefGenome)

Differentialanalysis(KissDE)

Event mapping

(STAR)

ES event found by KisSplice(‘‘Bubble’’)

BA

S

Condition 1

Condition 2Exon(s)

ES event mapped onthe reference genome

Exon Intron

A

A S

Intron Exon Exon

B

B

PSI (Ψ) : Percent Spliced In

ΔΨ = Ψ1 - Ψ2

Ψ1 =inclusion1 + exclusion1

inclusion1 Ψ2 =inclusion2 + exclusion2

inclusion2

• Quantification : exon skipping example

A S B A S B

AS1 + SB1inclusion1 = 2

AS2 + SB2inclusion2 = 2


Event containing ALU elements

àMapping-first using a reference transcriptome

à Assembly-first

àMapping-first without using a reference transcriptome

Number of skipping of ALU exon annotated

TopHat with option --transcriptome-index

KisSplice

TopHat run default option

àMapping-first using a reference transcriptome

à Assembly-first

àMapping-first without using a reference transcriptome

TopHat with option --transcriptome-index

Results 18

Complex events

Annotation and differential analysis of alternative …...Annotation and differential analysis of...

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