I have my list of differentially expressed genes, now what?
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1 I have my list of differentially expressed genes, now what? Mirana Ramialison! Developmental Systems Biology Laboratory! Australian Regenerative Medicine Institute! #UQwinterSchool
2 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
3 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
4 Biological question Bioinformatics workflow Designing new tools based on existing bioinformatics tools
5 Henrich, Ramialison et al., Bioinformatics 2005
6 Biological questions Synexpression groups:! 1- How common are they? 2- What is their role? 3- How are they formed?
7 Biological questions Bioinformatics workflow based on existing bioinformatics tools Synexpression groups:! 1- How common are they? 2- What is their role? 3- How are they formed? => clustering algorithm
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9 Biological question Bioinformatics workflow based on existing bioinformatics tools Synexpression groups:! 1- How common are they? 2- What is their role? 3- How are they formed? => clustering algorithm => gene ontology enrichment
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11 Biological question Bioinformatics workflow Designing new tools based on existing bioinformatics tools Synexpression groups:! 1- How common are they? 2- What is their role? 3- How are they formed? => clustering algorithm => gene ontology enrichment => motif discovery tool
12 Ramialison et al., Development 2012
13 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
14 Biological question Bioinformatics workflow Designing new tools based on existing bioinformatics tools I have my list of differentially expressed genes, now what?
15 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
16 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
17 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?
18 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
19 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
20 pathway enrichment Differentially expressed genes
21 pathway enrichment Sample of Web Tools using DEGs as input!! [commercial product] [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
22 Not all pathways are known Differentially expressed genes
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31 Lucia Poggi
32 GO Visualisation tools Use GO term IDs as input
33 GO Visualisation tools Gonzalo del Monte Nieto
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35 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
36 Jeannette Hallab
37 No clear hierarchy blue, orange = coherent yellow = non-coherent (grey = direc6on of regula6on unknown) LPS LPS: 12 of 13 regulated targets are coherent -> LPS ac;va;on could cause this pa?ern STAT3: Only 4 of 7 targets are coherent -> STAT3 ac;va;on probably not behind this pa?ern Stuart Archer, using IPA
38 No network at all Searching for common upstream regulator (transcription factor)?? RNA-seq gene 1?? RNA-seq gene 2?? RNA-seq gene 3
39 Searching for shared DNA-binding sites regulatory sequences of DEGs random sequences DNA motif discovery tool
40 AIM: To determine the extent to which the gene is cri5cal to transcriptomic changes in stress Iden5fica5on of Gene X s Transcriptome: WT GENE KO RNA Extrac5on RNA Sequencing Analysis Gene expression in stress in WT Gene expression in stress in KO Kasia Gajewska
41 DEGs Gene ontology: cellular stress response WT 2 genes 219 genes 70 genes 207 genes RREB1 Modulates p53 transcrip2on in response to DNA damage to regulate apoptosis. NFAT1 KO 77 genes 223 genes Induces cell death pathways SP1 A stress induced transcrip2on factor Kasia Gajewska
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43 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
44 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
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46 Acknowledgements Mark Drvodelic - Markus Tondl - Michael Eichenlaub - Henry Chiu! Nathalia Tan - 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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