1. Bio-Medical Informatics
Instructor : Hanif Yaghoobi
Website: site webydo.com
My personal Mail:

2. About this Course Activities during the semester 5 score: 1)Home Works
2) MATLAB exercises
Your Final Projects 3 score
Final Exam 12 score

4. Shortliffe
“ Medical informatics is the rapidly developing scientific field that deals with resources, devices and formalized methods for optimizing the storage, retrieval and management of biomedical information for problem solving and decision making”
Edward Shortliffe, MD, PhD
1995

15. Organisms Classified into two types:
Eukaryotes: contain a membrane-bound nucleus and organelles (plants, animals, fungi,…)
Prokaryotes: lack a true membrane-bound nucleus and organelles (single-celled, includes bacteria)
Not all single celled organisms are prokaryotes!

16. Cells 60 trillion cells 320 cell types
Complex system enclosed in a membrane
Organisms are unicellular (bacteria, baker’s yeast) or multicellular
Humans:
60 trillion cells
320 cell types
Example Animal Cell
biology_intro.htm

17. DNA Basics – cont.
DNA in Eukaryotes is organized in chromosomes.

18. Chromosomes
In eukaryotes, nucleus contains one or several double stranded DNA molecules orgainized as chromosomes
Humans:
22 Pairs of autosomes
1 pair sex chromosomes
Human Karyotype
Session8/Session8.html

20. What is DNA? DNA: Deoxyribonucleic Acid
Single stranded molecule (oligomer, polynucleotide) chain of nucleotides
4 different nucleotides:
Adenosine (A)
Cytosine (C)
Guanine (G)
Thymine (T)

21. Nucleotide Bases Purines (A and G) Pyrimidines (C and T)
Difference is in base structure
Image Source: biology_intro.htm

22. DNA

25. The Central DogmaProtein Synthesis
Transcription
Translation
Cell Function
Genome
Transcriptome
Proteome
Gene Expression
Level

28. Genome chromosomal DNA of an organism
number of chromosomes and genome size varies quite significantly from one organism to another
Genome size and number of genes does not necessarily determine organism complexity

29. Genome Comparison ORGANISM CHROMOSOMES GENOME SIZE GENES
Homo sapiens (Humans) 23
3,200,000,000
~ 30,000
Mus musculus
(Mouse) 20
, 2600,000,000
~30,000
Drosophila melanogaster (Fruit Fly) 4
180,000,000
~18,000
Saccharomyces cerevisiae (Yeast) 16
14,000,000
~6,000
Zea mays (Corn) 10
2,400,000,000
???

31. DNA Basics – cont.
The DNA in each chromosome can be read as a discrete signal to {a,t,c,g}. (For example: atgatcccaaatggaca…)

32. DNA Basics – cont.
In genes (protein-coding region), during the construction of proteins by amino acids, these nucleotides (letters) are read as triplets (codons). Every codon signals one amino acid for the protein synthesis (there are 20 aa).

33. DNA Basics – cont.
There are 6 ways of translating DNA signal to codons signal, called the reading frames (3 * 2 directions).
…CATTGCCAGT…

34. DNA Basics – Cont. …CATTGCCAGT… Start: ATG Stop: TAA, TGA, TAG Exon
gene
Exon
Intron

35. Understanding Genome Sequences
~3,289,000,000 characters:
aattgtgctctgcaaattatgatagtgatctgtatttactacgtgcatat attttgggccagtgaatttttttctaagctaatatagttatttggacttt tgacatgactttgtgtttaattaaaacaaaaaaagaaattgcagaagtgt tgtaagcttgtaaaaaaattcaaacaatgcagacaaatgtgtctcgcagt cttccactcagtatcatttttgtttgtaccttatcagaaatgtttctatg tacaagtctttaaaatcatttcgaacttgctttgtccactgagtatatta tggacatcttttcatggcaggacatatagatgtgttaatggcattaaaaa taaaacaaaaaactgattcggccgggtacggtggctcacgcctgtaatcc cagcactttgggagatcgaggagggaggatcacctgaggtcaggagttac agacatggagaaaccccgtctctactaaaaatacaaaattagcctggcgt ggtggcgcatgcctgtaatcccagctactcgggaggctgaggcaggagaa tcgcttgaacccgggagcggaggttgcggtgagccgagatcgcaccgttg cactccagcctgggcgacagagcgaaactgtctcaaacaaacaaacaaaa aaacctgatacatggtatgggaagtacattgtttaaacaatgcatggaga tttaggttgtttccagtttttactggcacagatacggcaatgaatataat tttatgtatacattcatacaaatatatcggtggaaaattcctagaagtgg aatggctgggtcagtgggcattcatattgagaaattggaaggatgttgtc aaactctgcaaatcagagtattttagtcttaacctctcttcttcacaccc ttttccttggaagaaagctaaatttagacttttaaacacaaaactccatt ttgagacccctgaaaatctgggttcaaagtgtttgaaaattaaagcagag gctttaatttgtacttatttaggtataatttgtactttaaagttgttcca . . .
Goal:
Identify components encoded in the DNA sequence

36. Open Reading Frame
ATGCTCAGCGTGACCTCA CAGCGTTAA
M L S V T S Q R STP
Protein-encoding DNA sequence consists of a sequence of 3 letter codons
Starts with the START codon (ATG)
Ends with a STOP codon (TAA, TAG, or TGA)

37. Finding Open Reading Frames
ATGCTCAGCGTGACCTCA CAGCGTTAA
M L S V T S Q R STP
Try all possible starting points
3 possible offsets
2 possible strands
Simple algorithm finds all ORFs in a genome
Many of these are spurious (are not real genes)
How do we focus on the real ones?

38. Using Additional Genomes
Basic premise
“What is important is conserved”
Evolution = Variation + Selection
Variation is random
Selection reflects function
Idea:
Instead of studying a single genome, compare related genomes
A real open reading frame will be conserved

39. Phylogentic Tree of Yeasts
S. cerevisiae
S. paradoxus
S. mikatae
S. bayanus
C. glabrata
S. castellii
K. lactis
A. gossypii
K. waltii
D. hansenii
C. albicans
Y. lipolytica
N. crassa
M. graminearum
M. grisea
A. nidulans
S. pombe
~10M years
Kellis et al, Nature 2003

40. Evolution of Open Reading Frame
S. cerevisiae
S. paradoxus
S. mikatae
S. bayanus
ATGCTCAGCGTGACCTCA . . .
ATGCTCAGCGTGACATCA . . .
ATGCTCAGGGTGACA--A . . .
ATGCTCAGG---ACA--A . . .
Conserved
positions
Frame shift
changes interpretation of downstream seq
Variable
positions
A deletion

41. Examples Spurious ORF Confirmed ORF Frame shift ATG not conserved
Variable
Frame shift
Spurious ORF
ATG not conserved
Confirmed ORF
Greedy algorithm to find conserved ORFs surprisingly effective (> 99% accuracy) on verified yeast data
Sequencing error
[Kellis et al, Nature 2003]

42. Defining Conservation
Conserved
Variable
Naïve approach
Consensus between all species
Problem:
Rough grained
Ignores distances between species
Ignores the tree topology
Goal:
More sensitive and robust methods A A C G T A C C A
% conserv
100 33 55 55

43. Bioinformatics – an area of emerging knowledge
Each cell of the body contains the whole DNA of the individual (about 40,000 genes in the human genome, each of them comprising from 50 to a mln base pairs – A,T,C or G)
The Main Dogma in Genetics: DNA->RNA->proteins
Transcription: DNA (about 5%) -> mRNA
DNA -> pre-RNA -> splicing -> mRNA (only the exons)
Translation: mRNA -> proteins
Proteins make cells alive and specialised (e.g. blue eyes)
Genome -> proteome
N.Kasabov, 2003

44. Bioinformatics
The area of Science that is concerned with the development and applications of methods, tools and systems for storing and processing of biological information to facilitate knowledge discovery.
Interdisciplinary: Information and computer science, Molecular Biology, Biochemistry, Genetics, Physics, Chemistry, Health and Medicine, Mathematics and Statistics, Engineering, Social Sciences.
Biology, Medicine -- Information Science --> IT, Clinics, Pharmacy,
I____________________I
Links to Health informatics, Clinical DSS, Pharmaceutical Industry
N.Kasabov, 2003

45. Bioinformatics: challenging problems for computer and information sciences
Discovering patterns (features) from DNA and RNA sequences (e.g. genes, promoters, RBS binding sites, splice junctions)
Analysis of gene expression data and predicting protein abundance
Discovering of gene networks – genes that are co-regulated over time
Protein discovery and protein function analysis
Predicting the development of an organism from its DNA code (?)
Modeling the full development (metabolic processes) of a cell (?)
Implications: health; social,…
N.Kasabov, 2003

46. Problems in Computational Modeling for Bioinformatics
Abundance of genome data, RNA data, protein data and metabolic pathway data is now available (see and this is just the beginning of computational modeling in Bioinformatics
Complex interactions:
between proteins, genes, DNA code,
between the genome and the environment
much yet to to be discovered
Stability and repetitiveness: Genes are relatively stable carriers of information.
Many sources of uncertainty:
Alternative splicing
Mutation in genes caused by: ionising radiation (e.g. X-rays); chemical contamination, replication errors, viruses that insert genes into host cells, aging processes, etc.
Mutated genes express differently and cause the production of different proteins
It is extremely difficult to model dynamic, evolving processes
N.Kasabov, 2003

47. Bioinformatics Important Challenges
Transcription
Translation
Protein Function
Protein 3D Structure
Gene
Predication
Gene Function

48. Public Data Base Protein sequence KMLSLLMARTYW DNA sequence Microarray
Transcription
Translation
Protein sequence
KMLSLLMARTYW
DNA
sequence
{A,T,C,G}
Microarray
Gene Expression
Level

49. Gene Expression
Gene Expression Level: The amount of mRNA copies are in the cell for each Gene.
Over expressed
Under expressed 49

50. What are the Advantages?
Microarray
What can it be used for?
How does it work?
What are the Advantages?
An Example Application
Current version
I the past Biologist have followed a one gene one experiments philosophy. Today with new technology of microarrays scientist can investigate the expression of several genes at once using this high throughput method. This provides the potential to monitor thousands of genes at the same time.
Hybridization is the fundamental concept of how this works.
Impacting many fields like genomics, drug discovery, and toxicological research.
Currently cost is high. Demand, competition and time will bring down the costs.
Most used for RNA expression levels.
Get differences between oligonucleotides and cDNA methods.
Arrays with 5,000 to 10,000 genes are common.
The challenge is in
The main use is for comparison of expression analysis.
oligonucleotides method;
Photolithography is used to construct arrays with high information content onto glass slides. The slides are turned upside down over the hybridization chamber so that fluorescently tagged nucleic acids can hybridized. The laser if focused through the back of the glass to the target solution. The fluorescence emission is collected by a lens and passed through filters to the detector. The array may be moved or the laser or both.
Used to understand under what conditions and to what level the gene is expressed.
cDNA methods;
Gene-specific polynucleotides are arrayed on the matrix. The matrix is probed by fluorescently tagged cDNA made from the expressed RNA.
Printing Methods;
Robot used to place material on slide.
Contact method.
Non-contact, capillary tube, ink-jet being tried.
The DNA is cross-linked to the matrix by UV light to fix it. Some of the DNA is changed to single-strand by heat or alkali.
RNA is used as template to make cDNA by reverse transcription using fluorescence labeled nucleotides.

51. Microarrays can be used for:
Comparison of transcription levels between two cells
Examples:
Comparison between:
Cells from a young mouse vs cell from an old mouse
Drug efficacy:
Treated cells vs untreated cells

52. Based on hybridizationG A C U G A
mRNA
How it works:
Based on hybridization U G A C A C T G ▀
A =
C ≡
T =
G ≡ ▀ U G A C
A =
C ≡
T =
A ≡ ▀ U G A
A =
C ≡
T =
A ≡ ▀

53. Probes and the printing process
Head
slides (100)
Microtiter
Plates

54. Pins Print Head Printing Methods;
Robot used to place material on slide.
Contact method.
Non-contact, capillary tube, ink-jet being tried.
The DNA is cross-linked to the matrix by UV light to fix it. Some of the DNA is changed to single-strand by heat or alkali.

56. The printing tips is like a foutain tip pen
The printing tips is like a foutain tip pen. They are machined in a way that promotes capillary action. Liquid is drawn into the tip by putting it into DNA solution in a 96-well plate. The drops are also formed by capillary action, and the size of the drops are determined partly by the sharpness of the point. Printing is performed by lowered the tip very close the surface of the slide.

58. Print Head with Pins

60. Microarray Technology
23/2/2008

61. pseudo-colour image sample (labelled) probe (on chip)
[image from Jeremy Buhler]

62. Experimental design Track what’s on the chip
which spot corresponds to which gene
Duplicate experimental spots
reproducibility
Controls
DNAs spotted on glass
positive probe (induced or repressed)
negative probe (bacterial genes on human chip)
oligos on glass or synthesised on chip (Affymetrix)
point mutants (hybridisation plus/minus)

63. Images from scanner Resolution
standard 10m [currently, max 5m]
100m spot on chip = 10 pixels in diameter
Image format
TIFF (tagged image file format) 16 bit (65’536 levels of grey)
1cm x 1cm image at 16 bit = 2Mb (uncompressed)
other formats exist e.g.. SCN (used at Stanford University)
Separate image for each fluorescent sample
channel 1, channel 2, etc.

64. Images in analysis software
The two 16-bit images (cy3, cy5) are compressed into 8-bit images
Goal : display fluorescence intensities for both wavelengths using a 24-bit RGB overlay image
RGB image :
Blue values (B) are set to 0
Red values (R) are used for cy5 intensities
Green values (G) are used for cy3 intensities
Qualitative representation of results

65. Images : examples Pseudo-color overlay cy3 cy5 Spot color
Signal strength
Gene expression
yellow
Control = perturbed
unchanged
red
Control < perturbed
induced
green
Control > perturbed
repressed

66. Data : DNA Microarray assay gene 1 gene 2 gene 3
It is far from trivial to predict gene expression from the sequence code alone.
The current availability of microarray measurements of thousands of gene expression levels during the course of an experiment or after the knockout of a gene provides a wealth of complementary information that may be exploited to unravel the complex interplay between genes.
23/2/2008

67. Data Required: Gene Expression Matrix1 2 g2 g3 1. g4
23/2/2008

68. Data Required: Gene Expression Matrix1 2 g2 g3 1. g4 a1 a2 a3 a4 g1 3 1 g2 2 g3 1. g4
Time serious
Snap Shot
23/2/2008

78. World Health Organization