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COMPUTATIONAL ANALYSIS OF MULTILEVEL
OMICS DATA FOR THE ELUCIDATION OF
MOLECULAR MECHANISMS OF CANCER
Presented by
Azeez Ayomide Fatai
Supervisor: Junaid Gamieldien
Note: You only have 10-15 minutes maximum, so I suggest presenting only an
introduction + section 2
INTRODUCTION
Pre-genomic era
Post-genomic era
Cloning genes at the site
of proviral integration
Functional assays
Positional cloning
Tools in clinic
MammaPrint
Oncotype DX
Breast cancer profiling
test (HOXB13/IL17RB)
Cancer genomics
project & Databases
TCGA
ICGC
High-throughput technologies
WES and NGS
DNA methylation
Genomic hybrization
Copy number alteration
Gene expression profiling
DNA methylation
Simultaneous study on a
cohort of samples
Underlying mechanisms
Prognostic and predictive
biomarkers
Target identification
Breakdown of my study
1. Network-based identification of candidate cancer genes
• Identification of functionally relevant genes in copy
number regions
• Co-expression and transcriptional analysis
2. Identification of differentially expressed miRNAs and
their target genes in the GBM network
3. Identification of prognostic miRNAs for progression-free
survival prediction
4. Identification of prognostic protein coding transcripts?
genes for progression-free survival prediction
5. Pathway-based and machine learning based feature
selection (describe more completely)
Identification of differentially
expressed miRNAs and their targets
in the GBM network
INTRODUCTION
• Discuss the aims and objectives and the
rationale of this section here
• State your hypothesis
Flowchart for miRNA analysis in GBM
Materials and Methods
• Add a slide that gives specific details of the
method used to identify differentially expressed
miRNAs (and WHY they were chosen)
• R modules
• Underlying statistical tests
• p-value cutoffs
• fold-change cutoffs (if any)
• Describe the samples – numbers, classes, etc
• etc
Differentially expressed miRNAs between tumour and non-neoplastic brain
samples
Is there any way to rank these and then list only the ‘best’?
Also, be careful to explain what the red text is highlighting
Convert the underxpressed fold change as follows: -1/foldchange
- that will make 0.1 = -10 fold change for example
…continues
Produce a better layout if possible – Also highlight any known cancer related miRNAs and genes
Any known important genes that
you can point out to the audience?
Very important: stress that the agreement between miRNA and mRNA expression direction
illustrate that the experimental data (and conclusions) are trustworthy
Underexpressed miRNA-overexpressed gene network
Highlight any known cancer related miRNAs and genes.
Also, are there any miRNAs that appear to be regulatory ‘hubs’ based on
number of genes they interact with? If so, point them out.
Overexpressed miRNA-underexpressed gene network
Pathways enriched with miRNA target genes
Discussion
• What did you learn from this section?
• Find anything important?
• Eg. is there any disregulated miRNA that looks
like it plays dominant major role?
• Can it be a drug target?
• Is there any gene that can be a drug target?
• Etc
Conclusions
• Biological take home message (e.g. miRNAmRNA networks play a role in GBM… etc)
• Mention what you took from this chapter into
the next chapters and just give a BRIEF verbal
description of the predictive features you
found (just to show again that this is just part
of a bigger study)
Acknowledgements
• Your university that sponsors your PhD
• Anyone other than me that helped you with
data or analysis or tips/clues even in the
smallest way
• Etc