Mirana Ramialison Developmental Systems Biology Laboratory

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Mirana Ramialison Developmental Systems Biology Laboratory
“I have my list of differentially
expressed genes, now what?”
Mirana Ramialison
!
Developmental Systems Biology Laboratory
!
Australian Regenerative Medicine Institute
!
#UQwinterSchool
Outline
1. Background
2. Biological significance of differentially expressed
genes (DEGs)
3. Pathway and Ontology enrichment: tips and
challenges
4. Identifying the key drivers
5. Concluding remarks
Outline
1. Background !
2. Biological significance of differentially expressed
genes (DEGs)
3. Pathway and Ontology enrichment: tips and
challenges
4. Identifying the key drivers
5. Concluding remarks
Biological
question
Bioinformatics
workflow
based on existing
bioinformatics tools
Designing
new tools
Henrich, Ramialison et al., Bioinformatics 2005
Biological
questions
Synexpression groups:!
1- How common are they?
2- What is their role?
3- How are they formed?
Biological
questions
Bioinformatics
workflow
based on existing
bioinformatics tools
Synexpression groups:!
1- How common are they? => clustering algorithm
2- What is their role?
3- How are they formed?
Biological
question
Bioinformatics
workflow
based on existing
bioinformatics tools
Synexpression groups:!
1- How common are they? => clustering algorithm
=> gene ontology enrichment
2- What is their role?
3- How are they formed?
Biological
question
Bioinformatics
workflow
Designing
new tools
based on existing
bioinformatics tools
Synexpression groups:!
1- How common are they? => clustering algorithm
=> gene ontology enrichment
2- What is their role?
3- How are they formed?
=> motif discovery tool
Ramialison et al., Development 2012
Outline
1. Background
2. Biological significance of differentially
expressed genes (DEGs)!
3. Pathway and Ontology enrichment: tips and
challenges
4. Identifying the key drivers
5. Concluding remarks
Biological
question
Bioinformatics
workflow
Designing
new tools
based on existing
bioinformatics tools
“I
“I have
have my
my list
list of
of differentially
differentially
expressed
expressed genes,
genes, now
now what?”
what?”
A (not so) hypothetical conversation
-
“I have my list of differentially expressed
genes, now what?”
!
-
“ok, why did you do the experiment?”
!
-
“because it’s fashionable and we got
funding and it will increase the impact of
my publication”
Biological significance of DEGs
ASSUMPTION: Differences between 2 (or more)
biological conditions can be explained by changes
in gene expression!
Conditions: time points, gain of function (gene overexpression), loss-of-function (gene knock-down/knock-out),
treatment (drug, challenges, stress etc…)
Gene expression: qPCR arrays, large scale in situ
hybridisation, microarrays, RNA-seq
Biological significance of DEGs
What is the molecular scenario (a.k.a “story”)
which can explain the differences between the
conditions? !
Challenge: The outcome of the analysis is just a
list of genes, with fold-changes and p-values:
“Is the most differentially expressed gene (in
terms of fold change) the most important
gene explaining my condition?”
“Or is it the one with the lowest p-value”?
Gene networks organised in
functional pathways
Perturbation =>
Transcriptional changes in
the gene network
DEG enriched in subsets of
the gene network are
indicative of which
pathways are changed in a
specific condition.
PATHWAY ENRICHMENT!
!
KEY DRIVERS!
not necessarily the most
differentially expressed gene
Outline
1. Background
2. Biological significance of differentially expressed
genes (DEGs)
3. Pathway and Ontology enrichment: tips and
challenges!
4. Identifying the key drivers
5. Concluding remarks
pathway enrichment
Differentially expressed genes
pathway enrichment
Sample of Web Tools using DEGs as input!
https://david.ncifcrf.gov/
http://pantherdb.org/
http://gostat.wehi.edu.au/
http://apps.cytoscape.org/apps/bingo
http://cbl-gorilla.cs.technion.ac.il/
http://software.broadinstitute.org/gsea/index.jsp
http://www.ingenuity.com/products/ipa [commercial product]
http://ipscience.thomsonreuters.com/product/metacore/ [commercial product]
!
The power of orthogonal approaches as tools differ in:!
database contents (some have manually curated sets)
algorithms and statistical approaches used to calculate pathway enrichment
Not all pathways are known
Differentially expressed genes
http://geneontology.org/page/go-enrichment-analysis
Lucia Poggi
GO Visualisation tools
Use GO term IDs as input
http://revigo.irb.hr/
!
!
!
!
!
!
GO Visualisation tools
http://wencke.github.io/
Gonzalo del Monte Nieto
Outline
1. Background
2. Biological significance of differentially expressed
genes (DEGs)
3. Pathway and Ontology enrichment: tips and
challenges
4. Identifying the key drivers!
5. Concluding remarks
Jeannette Hallab
No clear hierarchy
blue,orange=coherent
yellow=non-coherent
(grey=direc6onofregula6onunknown)
LPS
LPS:12of13regulatedtargetsare‘coherent’
->LPSac;va;oncouldcausethispa?ern
STAT3:Only4of7targetsare‘coherent’
->STAT3ac;va;onprobablynotbehindthispa?ern
Stuart Archer, using IPA
No network at all
Searching for common upstream regulator (transcription factor)
?
?
RNA-seq gene 1
?
?
RNA-seq gene 2
? ?
RNA-seq gene 3
Searching for shared DNA-binding sites
regulatory sequences of
DEGs
random sequences
DNA motif discovery tool
http://rsat.sb-roscoff.fr/
http://meme-suite.org/tools/meme-chip
https://trawler.erc.monash.edu.au/
AIM:Todeterminetheextenttowhichthegeneis
cri5caltotranscriptomicchangesinstress
Iden5fica5onofGeneX’sTranscriptome:
WT
GENEKO
RNAExtrac5on
RNASequencing
Analysis
Geneexpressioninstress
inWT
Geneexpressioninstress
inKO
Kasia Gajewska
Geneontology:
cellularstressresponse
DEGs
WT
70genes
207genes
2genes
219genes
KO
RREB1
Modulatesp53transcrip2onin
responsetoDNAdamagetoregulate
apoptosis.
NFAT1
Inducescelldeathpathways
77genes
223genes
SP1
Astressinducedtranscrip2onfactor
Kasia Gajewska
Outline
1. Background
2. Biological significance of differentially expressed
genes (DEGs)
3. Pathway and Ontology enrichment: tips and
challenges
4. Identifying the key drivers
5. Concluding remarks
Biological
question
Bioinformatics
workflow
Designing
new tools
Design and interpretation of the bioinformatics analysis is
driven by the biology
Separating up-regulated from down-regulated genes?
Power of orthogonal approaches
Running the bioinformatics workflows is the fun part! DIY!
Allocate sufficient time to work around the tools and to
explore all the options
Acknowledgements
Mark Drvodelic - Markus Tondl - Michael Eichenlaub - Henry Chiu !
Nathalia Tan - @ramialison_lab - Louis Dang - Jeannette Hallab - Julian Stolper - Lauren Bottrell -
Monash Platforms
MHTP Genomics Facility
FishCore
FlowCore
eResearch
Bioinformatics Platform
!
Ramaciotti Center for Gene Regulation (Sydney)
Collaborators!
ARMI/Monash: Kasia Gajewska, David Jans, Stuart Archer, Monash Bioinformatics platform
National: Gonzalo del Monte Nieto, Richard Harvey (VCCRI)
International: Jochen Wittbrodt, Lucia Poggi, Heidelberg University

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