Slide for 2014 Summer Camp in ISS AS

1. BIO-INFO-R-MATICS
劉佳鑫
中研院統計科學研究所
中研院國際研究生生物資訊學程

2. HI! STORY~~~C.S.
Atanasoff-Berry
(first electronic digital computer )
FORTRAN
1939
1954
1955
First disk storage (IBM)
1963
BASIC
1969
UNIX OS
1970
SD RAM
1972
C
1981~85
DOS, Windows, C++
1994
JAVA
2007

3. MAY, 1951
Rosalind Elsie Franklin
Photo No. 51

4. HI! STORY~~~BIOLOGY
1953
1939
Computer
science

5. HI! STORY~~~BIOLOGY
1953
Structure of DNA
A = T
C≣G
1939
Computer
science

6. HI! STORY~~~BIOLOGY
The theories of Heredity
Chromosome, gene, protein
1953
Structure of DNA
1944
DNA was the agent
responsible for transferring
genetic information
1822
1884
1943
1859 Darwinian Evolution
On the Origin of Species
1890
Sir Ronald Aylmer Fisher
neo-Darwinian synthesis
population genetics
ANOVA
The Design of Experiments
1962
Statistics !!
1990
2003
Human Genome Project 2008
2012
1000 Genome Project
1939
Computer
science
Lamarckian Evolution
1801

7. DATE! DATE! DATE!
Biology Statistics Computer
Genetics
Evolution
Cell biology
Molecule biology
Biochemistry
Medical science
Bioinformatics

8. Bioinformatics
Biology

9. BIOINFOMATICIAN
5 levels
Level 1: Analyze data on websites ……
Level 2: Install software and conduct it !
Level 3: Programming (C/R/JAVA/PERL/Paython……)
Level 4: Write scripts of known algorithms, combine ,and maintain them
Level 5: Develop new algorithm to solve question in biology
1
2 3
4
5

10. LEVEL 1
Paste
Submit

11. LEVEL 1

12. LEVEL 2
Installation guide

13. DATA SCIENCE

15. Visualization
Parsing

16. LEVEL 3

17. LEVEL 4
seed=1234,
sample.size=500,
resample.number=1000,
alpha=0.05
original.sample<-runif(sample.size, min=0, max=1)
resample.results<-data.frame("Run.Number"=NULL,"mean"=NULL)
for(counter in 1:resample.number){
temp<-sample(original.sample, size=length(original.sample), replace = TRUE)
temp.mean<-mean(temp)
temp.table.row<-data.frame("Run.Number"=counter,"mean"=temp.mean)
resample.results<-rbind(resample.results,temp.table.row)
}
resample.results<-resample.results[with(resample.results, order(mean)), ]
lowerCI.row<-resample.number*alpha/2
upplerCI.row<-resample.number*(1-(alpha/2))
median.row<-resample.number/2
median<-resample.results$mean[median.row]
lowerCI<-resample.results$mean[lowerCI.row]
upperCI<-resample.results$mean[upplerCI.row]
median.run<-resample.results$Run.Number[median.row]
lowerCI.run<-resample.results$Run.Number[lowerCI.row]
upperCI.run<-resample.results$Run.Number[upplerCI.row]
mc.table<-data.frame("median"=NULL,"lowerCI"=NULL,"upperCI"=NULL)
values<-data.frame(median,lowerCI,upperCI)
runs<-as.numeric(data.frame(median.run,lowerCI.run,upperCI.run))
mc.table<-rbind(mc.table,values)
mc.table<-rbind(mc.table,runs)
Monte Carlo simulation
Hidden Markov model
Simulation annealing
Bayesian analysis
.
.
.
.

18. LEVEL 5

19. 周易-形上形下
 形而上者謂之道形而下者謂之器
 「形」: 天象地形
 「道」: 天象地形上存在的抽象原理(Metaphysics)
 「器」: 天地變化、陰陽交感下所生的具體事物
 道器不相離，如有天地，太極之理

20. QUESTION
lysC
E.coli
lysine
glucose lysine
lysine
lysine
lysine
lysine
lysine
lysine
lysine
lysine
lysine
lysine
lysine lysine

21. QUESTION
 Less than 10 % of human genome with know(?) functions
• Only 1% code for protein
 Identify functional elements in the Human genome
• Genetic approach
• Evolutionary approach
• Biochemical approach
AIM

22. GENETIC
 Rely on sequence alterations
 To establish the biological relevance of a DNA segment

23. GENETIC
 Association ~~~~
• Pearson
• Spearman
• Logistic
 Genome-Wide Association Studies (GWAS)
 NHGRI GWAS Catalog

24. GENETICS

25. EVOLUTIONARY
 Comparative genomics
 only 5% of mammalian genomes are under strong evolutionary
constraint across multiple species (e.g., human, mouse, and dog)
Multiple alignment technology

26. BIOCHEMICAL
 Detect biochemical activity
 Encyclopedia of DNA Elements (ENCODE) Project
 50% of nucleotides in the human genome are readily recognizable
as repeat elements.

27. ANOTHER TOPIC IN
BIOINFORMATICS
 The most popular bio-industry in human history

28. ANOTHER TOPIC IN
BIOINFORMATICS
 Enzyme kinetics

29. ANOTHER TOPIC IN
BIOINFORMATICS
 Metabolic control analysis
wiki

30. BIOLOGICAL NETWORKS
Metabolism network
KEGG
Signal transduction network
2008, Science Signaling
Protein
interaction
network
2004, NRG, Barabasi

31. BIOLOGICAL NETWORKS ANALYS I S
 Metabolic control analysis
 Flux balance analysis
 Sensitivity analysis
 Network property analysis
 Differential equation
 Partial differential equation
 Linear programming
 Genetic algorithm

32. TAKE HOME MESSAGE
 History
 Components of bioinformatics
 Levels of involving bioinformatics
 Some topics in bioinformatics
 Keep thinking……
 Google!