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Role of Computer and Information Science in
Biology Presented By Dr G. P. S.
Raghava Co-ordinator, Bioinformatic Centre,
IMTECH, Chandigarh, India Visiting Professor,
Pohang Univ. of Science Technology, Republic of
Korea Email raghava_at_imtech.res.in Web
http//www.imtech.res.in/raghava/
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Major Applications Challenges

• Introduction to Biology
• Genome Annotation Gene Prediction
• Analysis and Comparison of Sequences
• Protein Structure Prediction
• DNA Chip (Microarray) technology
• Proteomics Analysis of 2D gel
• Fingerprinting Technique
• Drug development
• Computer-Aided Vaccine Design

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Hierarchy in Biology Atoms Molecules Macromolecule
s Organelles Cells Tissues Organs Organ
Systems Individual Organisms Populations Communiti
es Ecosystems Biosphere
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Animal cell
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Human Chromosomes
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Genes are linearly arranged along chromosomes
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Chromosomes and DNA
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DNA can be simplified to a string of four letters
GATTACA
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(RT)
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Sequence to StructureIts a matter of
dimensions!

• 1D Nucleic acid sequence
• AGT-TTC-CCA-GGG
• 1D Protein sequence
• Met-Ala-Gly-Lys-His
• M A G K H
• 3D Spatial arrangement of atoms

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Genome Annotation

• The Process of Adding Biology Information and
• Predictions to a Sequenced Genome Framework

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Importance of Sequence Comparison

• Protein Structure Prediction
• Similar sequence have similar structure
  function
• Phylogenetic Tree
• Homology based protein structure prediction
• Genome Annotation
• Homology based gene prediction
• Function assignment evolutionary studies
• Searching drug targets
• Searching sequence present or absent across
  genomes

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Protein Sequence Alignment and Database Searching

• Alignment of Two Sequences (Pair-wise Alignment)
• The Scoring Schemes or Weight Matrices
• Techniques of Alignments
• DOTPLOT
• Multiple Sequence Alignment (Alignment of gt 2
  Sequences)
• Extending Dynamic Programming to more sequences
• Progressive Alignment (Tree or Hierarchical
  Methods)
• Iterative Techniques
• Stochastic Algorithms (SA, GA, HMM)
• Non Stochastic Algorithms
• Database Scanning
• FASTA, BLAST, PSIBLAST, ISS
• Alignment of Whole Genomes
• MUMmer (Maximal Unique Match)

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Alignment of Two Sequences

• Dealing Gaps in Pair-wise Alignment
• Sequence Comparison without Gaps
• Slide Windos method to got maximum score
• ALGAWDE
• ALATWDE
• Total score 11001115 (PID) (5100)/7
• Sequence with variable length should use dynamic
  programming
• Sequence Comparison with Gaps
• Insertion and deletion is common
• Slide Window method fails
• Generate all possible alignment
• 100 residue alignment require gt 1075

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Alternate Dot Matrix PlotDiagnoal shows
align/identical regions
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Dynamic Programming

• Dynamic Programming allow Optimal Alignment
  between two sequences
• Allow Insertion and Deletion or Alignment with
  gaps
• Needlman and Wunsh Algorithm (1970) for global
  alignment
• Smith Waterman Algorithm (1981) for local
  alignment
• Important Steps
• Create DOTPLOT between two sequences
• Compute SUM matrix
• Trace Optimal Path

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Alignment of Multiple Sequences

• Extending Dynamic Programming to more sequences
• Dynamic programming can be extended for more than
  two
• In practice it requires CPU and Memory (Murata et
  al 1985)
• MSA, Limited only up to 8-10 sequences (1989)
• DCA (Divide and Conquer Stoye et al., 1997),
  20-25 sequences
• OMA (Optimal Multiple Alignment Reinert et al.,
  2000)
• COSA (Althaus et al., 2002)
• Progressive or Tree or Hierarchical Methods
  (CLUSTAL-W)
• Practical approach for multiple alignment
• Compare all sequences pair wise
• Perform cluster analysis
• Generate a hierarchy for alignment
• first aligning the most similar pair of sequences
• Align alignment with next similar alignment or
  sequence

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Database scanning

• Basic principles of Database searching
• Search query sequence against all sequence in
  database
• Calculate score and select top sequences
• Dynamic programming is best
• Approximation Algorithms
• FASTA
• Fast sequence search
• Based on dotplot
• Identify identical words (k-tuples)
• Search significant diagonals
• Use PAM 250 for further refinement
• Dynamic programming for narrow region

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Principles of FASTA Algorithms
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Database Scanning or Fold Recognition

• Concept of PSIBLAST
• Perform the BLAST search (gap handling)
• GeneImprove the sensivity of BLAST
• rate the position-specific score matrix
• Use PSSM for next round of search
• Intermediate Sequence Search
• Search query against protein database
• Generate multiple alignment or profile
• Use profile to search against PDB

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Comparison of Whole Genomes

• MUMmer (Salzberg group, 1999, 2002)
• Pair-wise sequence alignment of genomes
• Assume that sequences are closely related
• Allow to detect repeats, inverse repeats, SNP
• Domain inserted/deleted
• Identify the exact matches
• How it works
• Identify the maximal unique match (MUM) in two
  genomes
• As two genome are similar so larger MUM will be
  there
• Sort the matches found in MUM and extract longest
  set of possible matches that occurs in same order
  (Ordered MUM)
• Suffix tree was used to identify MUM
• Close the gaps by SNPs, large inserts
• Align region between MUMs by Smith-Waterman

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Protein Structure Prediction

• Experimental Techniques
• X-ray Crystallography
• NMR
• Limitations of Current Experimental Techniques
• Protein DataBank (PDB) -gt 24000 protein
  structures
• SwissProt -gt 100,000 proteins
• Non-Redudant (NR) -gt 1,000,000 proteins
• Importance of Structure Prediction
• Fill gap between known sequence and structures
• Protein Engg. To alter function of a protein
• Rational Drug Design

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Protein Structures

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Techniques of Structure Prediction

• Computer simulation based on energy calculation
• Based on physio-chemical principles
• Thermodynamic equilibrium with a minimum free
  energy
• Global minimum free energy of protein surface
• Knowledge Based approaches
• Homology Based Approach
• Threading Protein Sequence
• Hierarchical Methods

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Energy Minimization Techniques

• Energy Minimization based methods in their pure
  form, make no priori assumptions and attempt to
  locate global minma.
• Static Minimization Methods
• Classical many potential-potential can be
  construted
• Assume that atoms in protein is in static form
• Problems(large number of variables minima and
  validity of potentials)
• Dynamical Minimization Methods
• Motions of atoms also considered
• Monte Carlo simulation (stochastics in nature,
  time is not cosider)
• Molecular Dynamics (time, quantum mechanical,
  classical equ.)
• Limitations
• large number of degree of freedom,CPU power not
  adequate
• Interaction potential is not good enough to model

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Knowledge Based Approaches

• Homology Modelling
• Need homologues of known protein structure
• Backbone modelling
• Side chain modelling
• Fail in absence of homology
• Threading Based Methods
• New way of fold recognition
• Sequence is tried to fit in known structures
• Motif recognition
• Loop Side chain modelling
• Fail in absence of known example

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Hierarcial Methods

• Intermidiate structures are predicted, instead of
  predicting tertiary structure of protein from
  amino acids sequence
• Prediction of backbone structure
• Secondary structure (helix, sheet,coil)
• Beta Turn Prediction
• Super-secondary structure
• Tertiary structure prediction
• Limitation
• Accuracy is only 75-80
• Only three state prediction

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excitation
scanning
cDNA clones (probes)
laser 1
laser 2
PCR product amplification purification
emission
printing
mRNA target)
overlay images and normalise
0.1nl/spot
Hybridise target to microarray
microarray
analysis
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• Major Applications
• Identification of differentially expressed genes
  in diseased tissues (in presence of drug)
• Classification of differentially expressed
  (genes) or clustering/ grouping of genes having
  similar behaviour in different conditions
• Use expression profile of known disease to
  diagnosis and classify of unknown genes

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Terms/Jargons

• Stanford/cDNA chip
• one slide/experiment
• one spot
• 1 gene gt one spot or few spots(replica)
• control control spots
• control two fluorescent dyes (Cy3/Cy5)

• Affymetrix/oligo chip
• one chip/experiment
• one probe/feature/cell
• 1 gene gt many probes (2025 mers)
• control match and mismatch cells.

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Images examples
Spot colour Signal strength Gene expression
yellow Control perturbed unchanged
red Control lt perturbed induced
green Control gt perturbed repressed
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Processing of images

• Addressing or gridding
• Assigning coordinates to each of the spots
• Segmentation
• Classification of pixels either as foreground or
  as background
• Intensity determination for each spot
• Foreground fluorescence intensity pairs (R, G)
• Background intensities
• Quality measures

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Management of Microarray Data

• Magnitude of Data
• Experiments
• 50 000 genes in human
• 320 cell types
• 2000 compunds
• 3 times points
• 2 concentrations
• 2 replicates
• Data Volume
• 41011 data-points
• 1015 1 petaB of Data

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Management of Microarray Data

• Major Issues
• Large volume of microarray data in last few years
• Storage and efficient access
• Comparison and integration of data
• Problem of data access and exchange
• Data scattered around Internet
• Supplementary material of publications
• Difficult for user to access relivent data
• Problems with existing databases
• Diverse purpose
• Developed for specific purpose

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Management of Microarray Data

• Specific Database
• Platform (eg.Stanford MA Database SMD)
• Organism (Yeast MA global viewer)
• Project (Life cycle database of Drosophila)
• Problem with Supplement and MA databases
• Lack of direct access
• Quality not checked
• No standard format
• Incomplete data

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Pre-processed cDNA Gene Expression Data

• On p genes for n slides p is O(10,000), n is
  O(10-100), but growing,

Slides
slide 1 slide 2 slide 3 slide 4 slide 5 1
0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49
0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10
0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.
06 1.06 1.35 1.09 -1.09 ...
Genes
Gene expression level of gene 5 in slide 4

Log2( Red intensity / Green intensity)
These values are conventionally displayed on a
red (gt0) yellow (0) green (lt0) scale.
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Analysis of Microarray Data

• Analysis of images
• Preprocessing of gene expression data
• Normalization of data
• Subtraction of Background Noise
• Global/local Normalization
• House keeping genes (or same gene)
• Expression in ratio (test/references) in log
• Differential Gene expression
• Repeats and calculate significance (t-test)
• Significance of fold used statistical method
• Clustering
• Supervised/Unsupervised (Hierarchical, K-means,
  SOM)
• Prediction or Supervised Machine Learnning (SVM)

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Normalization Techniques

• Global normalization
• Divide channel value by means
• Control spots
• Common spots in both channels
• House keeping genes
• Ratio of intensity of same gene in two channel is
  used for correction
• Iterative linear regression
• Parametric nonlinear nomalization
• log(CY3/CY5) vs log(CY5))
• Fitted log ratio observed log ratio
• General Non Linear Normalization
• LOESS
• curve between log(R/G) vs log(sqrt(R.G))

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Classification

• Task assign objects to classes (groups) on the
  basis of measurements made on the objects
• Unsupervised classes unknown, want to discover
  them from the data (cluster analysis)
• Supervised classes are predefined, want to use
  a (training or learning) set of labeled objects
  to form a classifier for classification of future
  observations

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Issues in Clustering

• Pre-processing (Image analysis and Normalization)
• Which genes (variables) are used
• Which samples are used
• Which distance measure is used
• Which algorithm is applied
• How to decide the number of clusters K

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Unsupervised Learnning

• Hierarchical clustering merging two branches at
  the time until all vari-ables
• (genes) are in one tree. it does not answer the
  question of how
• many gene clusters there are?
• K-mean clustering assuming there are K clusters.
  what if this assump-tion
• is incorrect?
• Model-based clustering the number of clusters is
  determined dynami-cally
• could be one of the most promising methods

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Supervised Analysis

• Fishers linear discriminant analysis
• Quadratic discriminant analysis
• Logistic regression (a linear discriminant
  analysis)
• Neural networks
• Support vector machine

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Traditional Proteomics

• 1D gel electrophoresis (SDS-PAGE)
• 2D gel electrophoresis
• Protein Chips
• Chips coated with proteins/Antibodies
• large scale version of ELISA
• Mass Spectrometry
• MALDI Mass fingerprinting
• Electrospray and tandem mass spectrometry
• Sequencing of Peptides (N-gtC)
• Matching in Genome/Proteome Databases

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Overview of 2D Gel

• SDS-PAGE Isoelectric focusing (IEF)
• Gene Expression Studies
• Medical Applications
• Sample Experiments
• Capturing and Analyzing Data
• Image Acquistion
• Image Sizing Orientation
• Spot Identification
• Matching and Analysis

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Comparision/Matcing of Gel Images

• Compare 2 gel images
• Set X and y axis
• Overlap matching spots
• Compare intensity of spots
• Scan against database
• Compare query gel with all gels
• Calculate similarity score
• Sort based on score

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Differential Proteomics Fingerprints of Disease
Phenotypic Changes

• Differential protein expression
• Protein nitration patterns
• Altered phosporylation
• Altered glycosylation profiles

• Utility
• Target discovery
• Disease pathways
• Disease biomarkers

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Fingerprinting Technique

• What is fingerprinting
• It is technique to create specific pattern for a
  given organism/person
• To compare pattern of query and target object
• To create Phylogenetic tree/classification based
  on pattern
• Type of Fingerprinting
• DNA Fingerprinting
• Mass/peptide fingerprinting
• Properties based (Toxicity, classification)
• Domain/conserved pattern fingerprinting
• Common Applications
• Paternity and Maternity
• Criminal Identification and Forensics
• Personal Identification
• Classification/Identification of organisms
• Classification of cells

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Fingerprinting Techniques Principles
Applications

• What is fingerprinting
• Type of Fingerprinting
• Common Applications
• Role of Computer in DNA Fingerprinting
• Searching Restriction Enzymes
• Searching VNTRs
• Computation of size of DNA fragments
• Optimization of gels
• Comparison of patterns
• Creation of Phylogenetic tree

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Drug Design

• History of Drug/Vaccine development
• Plants or Natural Product
• Plant and Natural products were source for
  medical substance
• Example foxglove used to treat congestive heart
  failure
• Foxglove contain digitalis and cardiotonic
  glycoside
• Identification of active component
• Accidental Observations
• Penicillin is one good example
• Alexander Fleming observed the effect of mold
• Mold(Penicillium) produce substance penicillin
• Discovery of penicillin lead to large scale
  screening
• Soil micoorganism were grown and tested
• Streptomycin, neomycin, gentamicin, tetracyclines
  etc.

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Drug Design

• Chemical Modification of Known Drugs
• Drug improvement by chemical modification
• Pencillin G -gt Methicillin morphine-gtnalorphine
• Receptor Based drug design
• Receptor is the target (usually a protein)
• Drug molecule binds to cause biological effects
• It is also called lock and key system
• Structure determination of receptor is important
• Ligand-based drug design
• Search a lead ocompound or active ligand
• Structure of ligand guide the drug design process

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Drug Design based on Bioinformatics Tools

• Detect the Molecular Bases for Disease
• Detection of drug binding site
• Tailor drug to bind at that site
• Protein modeling techniques
• Traditional Method (brute force testing)
• Rational drug design techniques
• Screen likely compounds built
• Modeling large number of compounds (automated)
• Application of Artificial intelligence
• Limitation of known structures

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Important Points in Drug Design based on
Bioinformatics Tools

• Application of Genome
• 3 billion bases pair
• 30,000 unique genes
• Any gene may be a potential drug target
• 500 unique target
• Their may be 10 to 100 variants at each target
  gene
• 1.4 million SNP
• 10200 potential small molecules

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Concept of Drug and Vaccine

• Concept of Drug
• Kill invaders of foreign pathogens
• Inhibit the growth of pathogens
• Concept of Vaccine
• Generate memory cells
• Trained immune system to face various existing
  disease agents

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VACCINES

• A. SUCCESS STORY
• COMPLETE ERADICATION OF SMALLPOX
• WHO PREDICTION ERADICATION OF PARALYTIC
• POLIO THROUGHOUT THE WORLD BY YEAR 2003
• SIGNIFICANT REDUCTION OF INCIDENCE OF DISEASES
• DIPTHERIA, MEASLES, MUMPS, PERTUSSIS, RUBELLA,
• POLIOMYELITIS, TETANUS
• B.NEED OF AN HOUR
• 1) SEARCH FOR NONAVAILABILE EFFECTIVE VACCINES
  FOR
• DISEASES LIKE
• MALARIA, TUBERCULOSIS AND AIDS
• 2) IMPROVEMENT IN SAFETY AND EFFICACY OF PRESENT
• VACCINES
• 3) LOW COST
• 4) EFFICIENT DELIVERY TO NEEDY
• 5) REDUCTION OF ADVERSE SIDE EFFECTS

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Computer Aided Vaccine Design

• Whole Organism of Pathogen
• Consists more than 4000 genes and proteins
• Genomes have millions base pair
• Target antigen to recognise pathogen
• Search vaccine target (essential and non-self)
• Consists of amino acid sequence (e.g.
  A-V-L-G-Y-R-G-C-T )
• Search antigenic region (peptide of length 9
  amino acids)

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Major steps of endogenous antigen processing
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Computer Aided Vaccine Design

• Problem of Pattern Recognition
• ATGGTRDAR Epitope
• LMRGTCAAY Non-epitope
• RTTGTRAWR Epitope
• EMGGTCAAY Non-epitope
• ATGGTRKAR Epitope
• GTCVGYATT Epitope
• Commonly used techniques
• Statistical (Motif and Matrix)
• AI Techniques

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Why computational tools are required for
prediction.
200 aa proteins
Chopped to overlapping peptides of 9 amino acids
Bioinformatics Tools
192 peptides
10-20 predicted peptides
invitro or invivo experiments for detecting which
snippets of protein will spark an immune response.
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Thanks