1. Introduction to Bioinformatics February 13, 2017
Dr. ir. Perry Moerland
Bioinformatics Laboratory
Academic Medical Center
Graduate School ‘Bioinformatics’

2. Aim of course
Get acquainted with the basic principles and algorithms of commonly used bioinformatics tools
Gain sufficient theoretical knowledge and practical skills to be able to apply bioinformatics adequately in your own work

3. Topics (I) Possibilities and limitations of public biological DBs
Statistical concepts for ‘omics data analysis
DNA microarray analysis
Proteomics data analysis
Metabolomics data analysis
Pathways and networks
Genetical genomics
Capita selecta: DNA methylation array analysis, sample misannotation, …

4. Topics (I) Possibilities and limitations of public biological DBs
Statistical concepts for ‘omics data analysis
DNA microarray analysis
Proteomics data analysis
Metabolomics data analysis
Pathways and networks
Genetical genomics
Capita selecta

5. Topics (II) Methods for the analysis of data
generated with high-throughput
technologies
Microarrays
Mass spectrometry
Next generation sequencing
Course: Bioinformatics
Sequence Analysis

6. What has been left out Almost anything sequence-based Phylogenetics
Construction of evolutionary trees
Image from Florian Markowetz’s blog:

7. What has been left out Almost anything sequence-based Phylogenetics
Construction of evolutionary trees
Modeling of intra-tumour heterogeneity
Source: Florian Markowetz’s blog:

8. What has been left out Almost anything sequence-based Phylogenetics
Construction of evolutionary trees
Modeling of intra-tumour heterogeneity
Comparative genomics
Protein modeling, protein docking
Systems biology
Information management
Programming
e-Science
Multi-omic approaches, exception: eQTL

9. Related AMC Graduate School courses
Computing in R
Unix
e-Science (Big Data)
Bioinformatics Sequence Analysis
Systems Medicine
Practical Biostatistics
Advanced Biostatistics
Genetic Epidemiology
BioSB Research School:
Pattern Recognition (Machine Learning)
DNA Technology
Mass Spectrometry, Proteomics and Protein Research

10. Possibilities and limitations of public biological databases
Most high-throughput data is publicly available
Often enforced by journals
Possibilities
Limitations
Errors in databases
GPL11012 (Gene Expression Omnibus)

11. Possibilities and limitations of public biological databases
Most high-throughput data is publicly available
Often enforced by journals
Possibilities
Limitations
Errors in databases
GPL11012 (Gene Expression Omnibus)
Zeeberg et al., BMC Bioinformatics. 5:

12. Possibilities and limitations of public biological databases
Most high-throughput data is publicly available
Often enforced by journals
Possibilities
Limitations
Errors in databases
GPL11012 (Gene Expression Omnibus)

13. Statistical concepts for ‘omics data analysis
High-dimensional data
10,000s of genes, transcript variants, proteins, metabolites
100,000s of single nucleotide polymorphisms, epigenetic markers
In general, much less samples: ~100s
Experimental design
Quality control
Pre-processing: normalization
Differential expression: statistical tests, multiple testing
Unsupervised: clustering
Supervised: classification, prediction
Widely applicable: next-generation sequence analysis, for example

14. ‘Omics technologies Microarrays mRNA Single nucleotide polymorphisms
Methylation
Transcription factor binding
Chromosal aberrations – aCGH (comparative genomic hybridization)
Mass spectrometry
Proteins: identification
Metabolites: pre-processing

15. Pathways and networks activated pathways
Interorgan coordination of the murine adaptive response
to fasting :
5 tissues
5 timepoints
5 mice per timepoint
Hakvoort et al., J Biol Chem, 286(18): , 2011

16. lipid steroid carbohydrates metabolism amino acid FoxOs cell turnover
transcriptional network
FASTING CHALLENGE
‘to serve and protect’
metabolic regulators
central controller
lipid
steroid
carbohydrates
amino acid
metabolism
cell turnover
immune response
ox. stress defense
cMyc
Sp1
p53
EGF
AP-1
HNF4α
FoxOs
NRs

17. Genetical genomics
Locus SNP X modulates expression of gene Y = Expression quantitative trait locus (eQTL)
SNP X
TFIIB
TFIIE
TFIIH IN R
TFIID
TFIIF
RNA polymerase II
TFIIA
TBP
proximal
promoter
core
distal promoter/
enhancer
TF binding sites
„DNA-looping“
TATA
TF binding
sites
Expression gene Y
Gene Y
Genotype SNP X
Source: Michiel Adriaens

18. Bioinformatics Laboratory
Department of Clinical Epidemiology, Biostatistics and Bioinformatics
You are welcome if you need bioinformatics expertise
The earlier, the better!
wiki.bioinformaticslaboratory.nl

19. Practical things Certificate Other things
Attend all sessions (half a day can be skipped, ask for possibility
for self-study)
Active participation
Other things
Lunch is not included
Coffee, tea, … is available at the machines (with your AMC badge)
Slides and exercises will be made available on
under ‘Education’

20. Schedule