Sequence Alignment Algorithms

1
Sequence Alignment Algorithms Application to
Bioinformatics Tool Development

• Dr. S. Parthasarathy
• Reader and Head
• Department of Bioinformatics
• Bharathidasan University
• Tiruchirappalli 620 024
• (E-mail partha_at_cnld.bdu.ac.in)

2
Plan

• Introduction to Bioinformatics
• Sequence alignment algorithms
• Global alignment Needleman - Wunsch algorithm
• Local alignment Smith Waterman algorithm
• Predict Fold to a protein
  sequence
• Methodology
• Algorithm, Coding Tool Development
• Benchmarking
• Conclusions

PredictFold
3
Introduction

• Why do we need Bioinformatics?
• What is Bioinformatics?
• Where is Bioinformatics used?

4
Why?

• Biological Data Explosion
• How did Biological Data Explosion happen?
• Sequence Databases are HUGE than the Structure
  Databases
• Why so?

5
Introduction Biological Data Genome Projects

• Latest Revolution
• On 26 June, 2000 - Announcement of completion of
  the draft of the Human Genome
• Genetic Code of Human Life is Cracked by
  Scientists
• Human Genome contains 3.2 x 109 bps
• Unit of (Genome) sequence length
• bps (base pairs)
• Mbps (Mega base pairs) 106 bps
• Gbps (Giga base pairs) 109 bps
• huge (human genome equivalent) 3.2 Gbps
• Unit of Genetic distance
• centiMorgan (cM) - arbitrary unit Named for
  Thomas Hunt Morgan
• (e.g. 1 cM 0.01 recombinant frequency)

6
Introduction Biological Data Genome Projects
16 February 2001
15 February 2001
7
Biological Data Recombinant DNA Technology

• Old Revolution
• 1940 Role of DNA as the genetic material was
  confirmed
• 1953 Discovery of DNA structure by James Watson
  Francis Crick
• 1966 Establishment of the Genetic Code
• 1967 DNA ligase was isolated (join two
  strands of DNA together)
• Molecular Glue
• 1970 Isolation of Restriction enzyme
  Molecular Scissors
• 1972 Recombinant DNA molecules were generated
  at Stanford
• University, USA
• 1973 Joining DNA fragments to the plasmid
  pSC101 isolated from
• E.Coli. They could replicate when
  introduced into E.Coli.
• The discoveries of 1972 1973 triggered off
  the biggest scientific revolution Genetic
  Engineering

8
Biological Data explosion

• GenBank, NCBI, USA
• 44 Gbps of DNA 40 Million Sequences (upto
  2004)
• GenBank, National Center for Biotechnology
  Information, USA
• Protein Data Bank (PDB), RCSB, USA
• 29,000 structures (2004)
• PDB, Research Collaboratory for Structural
  Bioinformatics, USA
• QUALITY of Data - HIGH
• Experimental error in modern genomic sequencing
  is extremely low
• QUANTITY of Data - HUGE
• With Recombinant DNA technology genomic
  sequencing, size of sequence data bases is
  increasing very rapidly
• SEQUENCE Versus STRUCTURE Databases
• Sequence Databases are HUGE than Structure
  Databases
• Leads to Bioinformatics

9
What?

• What is Bioinformatics?
• Define Bioinformatics

10
Bioinformatics - Definition
F(i,j) max F(i-1, j-1)s(xi,yj), F(i-1, j)
d, F(i, j-1) d.
Bioinformat ics
atcggcatgcatcagtcatgcaactg
PEPTIDESE QSEDITPEP
Bioinformatics is an integration of mathematical,
statistical and computer methods to analyze
biological data. We use computer programs to
make inference from the biological data, to make
connections among them and to derive useful and
interesting predictions. The marriage of biology
and computer science has created a new field
called Bioinformatics. - Arthur M. Lesk
11
Biology Basic Definitions

• Cell - It is the building block of living
  organisms
• Eukaryotic Cells or organisms have the nucleus
  separated from the cytoplasm by a nuclear
  membrane and the genetic material borne on a
  number of chromosomes consisting of DNA and
  Protein
• Chromosome
• The physical basis of heredity. Deeply staining
• rod-like structures present with the nuclei of
  eukaryotes
• Contains DNA and protein arranged in compact
  manner
• Replicate identically during cell division
• Same number of chromosomes present in cells of a
  particular species (e.g. Human 22, X and Y)

12
GenomeBasic Definitions

• Genome
• A complete set of chromosomes inherited from one
  parent
• Gene
• One of the units of inherited material carried on
  by chromosomes. They are arranged in a linear
  fashion on DNAs. Each represents one character,
  which is recognized by its effect on the
  individual bearing the gene in its cells. There
  are many thousand genes in each nucleus.
• DNA (Deoxyribo Nucleic Acid)
• DNA is made up of FOUR bases
• a t g c adenine, thymine, guanine,
  cytosine
• Protein
• Protein is made up of TWENTY different amino
  acids
• A T G C ... Alanine, Threonine, Glycine,
  Cysteine,

13
Central Dogma
CCTGAGCCAACTATTGATGAA
CCUGAGCCAACUAUUGAUGAA
PEPTIDE
14
Genome DataHuman Model Organisms

• Most mapping and sequencing technologies were
  developed from studies of simpler non-human
  organisms
• Non-Human/Model organisms
• Bacterium Escherichia Coli - 4.6 Mbp
• Yeast Saccharomyces Cerevisiae - 12.1 Mbp
• Fruit Fly Drosophila melanogaster - 180.0 Mbp
• Roundworm C. elegans - 95.5 Mbp
• Laboratory Mouse Mus musculus - 3.0 Gbp
• Human more complex genome
• Human Homo sapiens - 3.2 Gbp

15
Genome DataHuman (Homo Sapiens)

• Genome 1
• Chromosomes 23
• Genes / DNAs 30,000
• Nucleotides 3.2 x 109 bps

16
Bioinformatics in Genome Research

• Data Collection and Interpretation
• Collecting and Storing Data
• Sequence generated by genome research will be
  used as primary information source for human
  biology and medicine
• The vast amount of data produced will first need
  to be collected, stored and distributed
• Interpretation of Data
• Recognizing where genes begin and end
• Searching a database for a particular DNA
  sequence may uncover these homologous sequences
  in a known gene from a model organism, revealing
  insights into the function of the corresponding
  human gene

17
Understanding Gene Function

• Correct protein function depends on the 3-D or
  folded structure the protein assumes in
  biological environments
• Understanding protein structure will be essential
  in determining gene function

Gene Protein Function
Structure
18
Where?

• Where is Bioinformatics used?
• What are the uses of Bioinformatics?
• Applications of Bioinformatics

19
Bioinformatics Tasks

• Sequence Analysis (Protein sequences)
• Similarity Homology
• pairwise local/global alignment
• GCG Seqlab Seqweb
• Scoring Matrices - PAM, BLOSUM
• Database Search
• BLAST, FASTA
• Multiple alignment
• ClustalW, PRINTS, BLOCKS
• Secondary Structure Prediction (from Sequence)
• Proteins ?-Helix, ß-Sheet, Turn or coil
• Protein Folding

20
Bioinformatics Tasks

• Structure analysis Experimental Determination
• X-ray crystallography 3 dimensional coordinates
  Structure
• Nuclear Magnetic Resonance (NMR)
• PDB Protein Data Bank
• RasMol Molecular Viewing Software
• High-throughput crystallographic structure
  determination
• High flux synchrotron radiation sources (data
  collection)
• Multiple anomalous diffraction method (data
  interpretation)
• Bioinformatics - Structure Prediction
• Homology Modelling InsightII, SwissPDBViewer,
  Biosuite
• ab initio method - Monte Carlo Simulation
• Protein Structure Classification
• SCOP - Structural Classification Of Proteins
• CATH - Class, Architecture, Topology, Homologous
  superfamily
• FSSP - Fold Classification based on Structure-
  Structure alignment
• of Proteins obtained by DALI
  (Distance-matrix
• ALIgnment)

21
Bioinformatics Tasks

• Protein Engineering
• Mutations
• Alter particular amino acid/base for desired
  effect
• Site directed mutagenesis
• Identify the potential sites where we can do
  alterations
• Applications
• Agricultural Genetically Modified Plants,
  Vegetables, GM Food
• Pharmaceutical Molecular Modelling base Drug
  Design
• Medical Gene Therapy
• DNA Bending
• Application to Genomes
• (Ref M.G.Munteanu, K.Vlahovicek,
  S.Parthasarathy, I.Simon and S.Pongor, Rod Models
  of DNA Sequence-dependent anisotropic elastic
  modelling of local phenomena, Trends in
  Biochemical Sciences, 23 (1998) 341-347)

22
Bioinformatics TasksGenomics Proteomics

• Genomics is the study of the structure, content,
  evolution and functions of genes in genomes
• Aims of Genomics
• To establish an integrated web based database and
  research interface
• To assemble Physical,Genetic and Cytological maps
  of the Genome
• To identify and annotate the complete set of
  genes encoded within a genome
• To provide the resources for comparison with
  other genomes

23
Proteomics Proteome

• Proteome is the complete collection of proteins
  in a cell/tissue/organism at a particular time.
  Unlike genomes, which are stable over the life
  time of the organism, proteomes change rapidly as
  each cell response to its changing environment
  and produces new proteins and at different
  amounts.
• Genome is a more stable entity. An organism has
  only one genome but many proteomes.
• For an organism, there may be
• one body wide proteome,
• about 200 tissue proteomes
• about a trillion (1012) individual cell
  proteomes.

24

Proteomics Definition

• The study of proteomes that includes determining
  the 3D shapes of proteins, their roles inside
  cells, the molecules with which they interact,
  and defining which proteins are present and how
  much of each is present at a given time.

25

Proteomics Applications

• To correlate proteins on the basis of their
  expression profiles.
• To observe patterns in protein synthesis and this
  observed pattern changes can be used as an
  indicator of the state of cell and its gene
  expression.
• To characterize bacterial pathogens and to
  develop novel antimicrobials.
• To identify regions of the bacterial genome that
  encode pathogenic determinants.
• To develop drugs and in toxicology Structural
  Proteomics
• Proteomics as a tool for plant genetics and
  breeding

26
Systems Biology

• Systems Biology is a new perspective and emerging
  field for research in the post-genomic era.
• It aims at system level understanding of
  biological systems.
• It studies whole cells/tissues/organisms not by a
  traditional reductionists approach but by
  holistic means in a reiterative attempt to model
  the complete cell/tissue/organism.
• It is an integrated and interacting network of
  genes, proteins and biochemical reactions which
  give rise to life.

27
Systems Biology
28
Sequence Alignment Algorithms

• Similarity and Homology
• Sequence Comparison - Issues
• Types of alignments
• Algorithms Used

29
Sequence similarity and homology

• Nature is a tinkerer and not an inventor. New
  sequences are adapted from pre-existing sequences
  rather than invented de novo . There exists
  significant similarity between a new sequence and
  already known sequences. Fortunate for
  computational sequence analysis
• Similarity Measurement of resemblance and
  differences, independent of the source of
  resemblance.
• Homology The sequences and the organisms in
  which they occur are descended from a common
  ancestor.
• If two related sequences are homologous, then we
  can transfer information about structure and/or
  function, by homology.

30
3-D Structure and Homology

• 3-D structure patterns (motifs) of proteins are
  much more evolutionarily conserved than amino
  acid sequences - This type of Homology search
  could prove more fruitful
• Particular motifs may serve similar functions in
  several different proteins, information that
  would be valuable in genome analysis
• Only a few protein motifs can be recognised at
  the sequence level
• Development of more analytic capabilities to
  facilitate grouping protein sequences into motif
  families will make homology searches more useful

31
Sequence ComparisonIssues

• Types of alignment
• Global end to end matching
  (Needleman-Wunsch)
• Local portions or subsequences matching
  (Smith-Waterman)
• Scoring system used to rank alignments
• PAM BLOSUM matrices
• Algorithms used to find optimal (or good) scoring
  alignments
• Heuristic
• Dynamic Programming
• Hidden Markov Model (HMM)
• Statistical methods used to evaluate the
  significance of an alignment score
• Z- score, P- value and E- value

32
Substitution Matrices

• PAM (Point Accepted Mutation)
• BLOSUM (BLOcks SUbstitution Matrix)

40
90
Close
62
Default
250
Distant
500
30
33
Types of Algorithms

• Heuristic
• A heuristic is an algorithm that will yield
  reasonable results, even if it is not provably
  optimal or lacks even a performance guarantee.
• In most cases, heuristic methods can be very
  fast, but they make additional assumptions and
  will miss the best match for some sequence pairs.
• Dynamic Programming
• The algorithm for finding optimal alignments
  given an additive alignment score dynamically
• (We are going to discuss about it soon.)
• These type of algorithms are guaranteed to
  find the optimal scoring alignment or set of
  alignments.
• HMM - Based on Probability Theory very
  versatile.

34
Global AlignmentNeedleman-Wunsch Algorithm

• Formula
• F(i-1,j-1)
  s(xi,yj) D
• F(i, j) max F(i-1 , j) - d
  H
• F(i , j-1) - d
  V

F(i-1,j-1) D F(i,j-1) V
F(i-1,j) H F(i,j)
35
Global AlignmentNeedleman-Wunsch Algorithm

• Gap penalties
• Linear score f(g) - gd
• Affine score f(g) - d (g-1) e
• d gap open penalty e gap extend penalty
• g gap length
• Trace back
• Take the value in the bottom right corner and
  trace back till the end. (i.e. align end end
  always).
• Algorithm complexity
• It takes O(nm) time and O(nm) memory, where n and
  m are the lengths of the sequences.

36
Local AlignmentSmith-Waterman Algorithm

• Same as Global alignment algorithm with
• TWO differences.
• F(i,j) to take 0 (zero), if all other options
  have value less than 0.
• Alignment can end anywhere in the matrix.
• Take the highest value of F(i,j) over the whole
• matrix and start trace back from there.

37
Local AlignmentSmith-Waterman Algorithm

• Formula
• F(i-1,j-1) S(xi,yj)
  D
• F(i, j) max F(i-1 , j) - d
  H
• F(i , j-1) - d
  V
• 0 (if all other
  value is lt 0)

F(i-1,j-1) D V F(i,j-1)
F(i-1,j) H F(i,j)
38
Web based server development

• Design the web page to get the data
• Use cgi-bin or Perl script to parse the submitted
  data
• Invoke the corresponding program to get the
  appropriate results
• Send the results either by e-mail or to the web
  page directly

39
Application to Bioinformatics Tool Development

• To predict a fold to protein sequence

PredictFold
40
To predict a fold to protein sequence
PredictFold

• To predict possible folds for a given protein
  sequence, whose structure is not known
• To develop a fold recognition technique / tool
  that is sensitive in detecting folds of given
  protein sequences in the twilight zone (sequences
  sharing less than 25 identity)
• Application of the fold recognition strategy to
  genomic annotation

41
Twilight Zone sequencesExampleCytochrome
Sequences

• 256b
• gt256BA CYTOCHROME B562 (OXIDIZED) - CHAIN A
  ADLEDNMETLNDNLKVIEKADNAAQVKDALTKMRAAALDAQKATPPKLED
  KSPDSPEMKD FRHGFDILVGQIDDALKLANEGKVKEAQAAAEQLKTTRN
  AYHQKYR
• gt256BB CYTOCHROME B562 (OXIDIZED) - CHAIN B
  ADLEDNMETLNDNLKVIEKADNAAQVKDALTKMRAAALDAQKATPPKLED
  KSPDSPEMKD FRHGFDILVGQIDDALKLANEGKVKEAQAAAEQLKTTRN
  AYHQKYR
• 2ccy
• gt2CCYA CYTOCHROME C(PRIME) - CHAIN A
  QQSKPEDLLKLRQGLMQTLKSQWVPIAGFAAGKADLPADAAQRAENMAMV
  AKLAPIGWAK GTEALPNGETKPEAFGSKSAEFLEGWKALATESTKLAAA
  AKAGPDALKAQAAATGKVCKA CHEEFKQD
• gt2CCYB CYTOCHROME C(PRIME) - CHAIN B
  QQSKPEDLLKLRQGLMQTLKSQWVPIAGFAAGKADLPADAAQRAENMAMV
  AKLAPIGWAK GTEALPNGETKPEAFGSKSAEFLEGWKALATESTKLAAA
  AKAGPDALKAQAAATGKVCKA CHEEFKQD

42
ExampleSequences similarity

• lalign output
• for
• 256b 2ccy
• follows

43
ExampleCytochrome Structures
256b
CYTOCHROME STRUCTURES (seq. similarity 24)
2ccy
44
Goals

• Exploration of suitable fold recognition
  techniques that are sensitive in detecting
  similar folds despite low sequence similarity
• Identification of functional motifs in proteins
  at sequence (1D) and structure (3D) level
• Development of a protocol that aid in the rapid
  classification and annotation of genomic data
  based on functional motifs

45
Methodology

• Reduction of 3D-structure to 1D-environment
  string. Environment at each residue position is a
  function of local secondary structure and extent
  of exposure to the solvent (based on 3D-1D
  profile method developed by Eisenberg et al.,
  1991).
• Extract residue environment profiles of the
  available protein structures.
• A scoring matrix is generated from a library of
  profiles. Each matrix element is the information
  value of a residue in the given environment.
• A library of environment strings is created for
  the available protein fold structures.
• The probe sequence is queried against this
  library to look for best matches.

46
Workflow
47
Residue Environments
_Helix
Partially buried
_Exposed
_Coil
Strand_
Buried_
48
Residue Environments

• The residue environments are described by
• the area (A) of the residue buried in the protein
• the fraction (f) of side-chain area that is
  covered by polar atoms (O and N)
• the local secondary structure

49
Residue Environments

• CLASS Area (A) Å2 FRACTION (f)
• BURIED 1 (B1) A gt 114 f lt 0.45
• BURIED 2 (B2) 0.45 lt f lt 0.58
• BURIED 3 (B3) f gt 0.58
• PARTIAL 1 (P1) 40 lt A lt 114 f lt 0.67
• PARTIAL 2 (P2) f gt 0.67
• EXPOSED (E0) A lt 40 f gt 0.67

50
Residue Environment classes

• We have 6 classes based on the extend of exposure
  to solvent
• We have 3 classes based on secondary structure
  Alpha Helix(A), Beta Sheet (B) Coil(C)
• Total 6 x 3 18 environments
• B1A,B1B,B1C, B2A,B2B,B2C, B3A,B3B,B3C
  P1A,P1B,P1C, P2A,P2B,P2C, E0A,E0B,E0C.
• For example
• B1A - Buried 1 Alpha Helix
• P2B - Partially Buried 2 Beta Sheet
• E0C - Exposed 0 Coil

51
Scoring Table

• The scoring table used in this case is a 20 x 18
  matrix, constructed from a statistical analysis
  of the profile library (consisting of 1200
  protein structures) provided by PROFILES_3D
  module of Insight II (Accelrys Inc.)
• The scores Sij are calculated using the formula
• Sij ln P(i j) / Pi x 100
• where P(i j) is the probability of
  finding residue i in the environment j and Pi
  is the overall probability of finding residue i
  in any environment.

52
Scoring Table

• The scoring table contains measure of the
  compatibility of the 20 amino acids with the 18
  environmental classes.
• The individual matrix elements are propensities
  (information values) for the amino acid residues.
  

53
Scoring Table
54
Fold Library
1565 Functional forms
Scan PDB to identify all the structures having
these folds
Identify a representative structure with
resolution 2.5Å or better
Quality of the structure (Occupancy, R-Factor,
Stereochemistry)
968 Chains
55
DALI / FSSP Fold Library

• DALI http//www.ebi.ac.uk/dali
• Touring protein fold space with DALI/FSSP. Lisa
  Holm and Chris Sander, Nucleic Acid Research,
  (1998), 26, 316-319
• Mapping the Protein Universe, Lisa Holm and
  Chris Sander, Science, (1996), 273, 595-602

56
Sequence ComparisonDetails

• Type of Alignment
• Local - portions or subsequences matching
• Smith-Waterman Algorithm
• Scoring Table 3D-1D matrix
• Algorithm used Dynamic Programming
• Alignment Score Z- Score

57
Local AlignmentSmith-Waterman Algorithm

• Formula
• F(i-1,j-1) S(xi,yj)
  D
• F(i, j) max F(i-1 , j) - d
  H
• F(i , j-1) - d
  V
• 0 (if all other
  value is lt 0)

F(i-1,j-1) D V F(i,j-1)
F(i-1,j) H F(i,j)
58
Gap Penalties

• Gap penalties
• Linear score f(g) - gd
• Affine score f(g) - d (g-1) e
• d gap open penalty e gap extend penalty
• g gap length
• Gap penalty values used are
• d 500
• e 50

59
Local Alignment

• Trace back
• Alignment can end anywhere in the matrix
• Take the highest value of F(i,j) over the whole
  matrix and start trace back from there.
• Algorithm complexity
• It takes O(nm) time and O(nm) memory, where n and
  m are the lengths of the sequences.

60
Significance of an Alignment Score

• Statistical methods used to evaluate the
  significance of an alignment score
• Z-score, P-value and E-value
• Significance of Score
• Z- score (score mean)/std. dev
• Measures how unusual our original match is.
• Z ? 5 are significant.
• P- value measures probability that the alignment
  is no better than random. (Z and P depends on the
  distribution of the scores)
• P ? 10-100 exact match.
• E- value is the expected number of sequences that
  give the same Z- score or better. (E P x size
  of the database)
• E ? 0.02 sequences probably homologous

61
Benchmarking

• All 968 proteins in the fold library were
  profiled on each of the other members
• A histogram indicating the rank and the number of
  sequences which got the self score as the
  highest, is shown in Figure.

62
Benchmarking
63
Benchmarking

• Report
• 797 retain the self as the highest score
• 63 report the self to have the second highest
  score
• There were about 100 proteins that have ranks
  between 5 and 100.
• Limitations
• Prediction is restricted to the 968 folds in the
  library
• The algorithm is insensitive to partially folded
  sequences
• Specific to globular proteins and not for
  membrane proteins
• Sequences that fold in the presence of cofactors
  and ligands are not accounted for

64
Web based server development

• Design the web page to get the data
• Use cgi-bin or Perl script to parse the submitted
  data
• Invoke the corresponding program to get the
  appropriate results
• Send the results either by e-mail or to the web
  page directly
• Prepare a user manual to describe the salient
  features of the server

65
Conclusions

• PredictFold A program to predict possible folds
  for a new protein sequence based on the 3D-1D
  profile method
• Benchmarking results show the reliability of the
  method
• There are lot of scopes for further improvements

66
Future Directions

• To update the fold library by including more
  known folds
• To use the predicted secondary structure
  information of the given sequence also
• To optimise the source code for efficient
  handling of genome sequences, automatically
• To combine results from other algorithms ORF,
  HMM, etc. to detect remote homologs
• To develop maintain a web-based sever for fold
  recognition

67
BT versus IT

• Bioinformatics including Biotechnology (BT)
  requires lot of Information Technology (IT)
  skills for Genomic annotation projects
• Bioinformatics is one of the potential areas for
  IT professionals also
• Genome Projects will be the next huge task for IT
  industries (like the Y2K problem in the past)
• BT will take on IT soon in the near future

68
Conclusions

• Developing Web based Bioinformatics tools
• Develop/modify useful algorithms
• Generate computer source codes
• Create/Maintain Web based server
• Using existing Web based tools efficiently
• Ethical issues
• Bioethics Biosafety Ensure always that any
  bioinformatics tool harmful to environment
  society has neither been developed nor been used
  by you
• Cloning of human, Terminator technology, GM Food,
  etc.

69
References (latest)

• Arthur M. Lesk, Introduction to Bioinformatics,
  Oxford University Press, New Delhi (2003).
• D. Higgins and W. Taylor (Eds), Bioinformatics-
  Sequence structure and databanks, Oxford
  University Press, New Delhi (2000).
• R.Durbin, S.R.Eddy, A.Krogh and G.Mitchison,
  Biological Sequence Analysis, Cambridge Univ.
  Press, Cambridge, UK (1998).
• A. Baxevanis and B.F. Ouellette, Bioinformatics
  A Practical Guide to the Analysis of Genes and
  Proteins, (Third Edition) Wiley-Interscience,
  Hoboken, NJ (2005).
• G.Gibson and S.V.Muse, A Primer of Genome
  Science, Sinauer Associates, USA (2002).
• N. C. Jones and P. A. Pevzner, An Introduction to
  Bioinformatics Algorithms, Ane Books, New Delhi
  (2005).
• Michael S. Waterman, Introduction to
  computational Biology, Chapman Hall, (1995).
• J. A. Clasel and M. P. Deutscher (Eds),
  Introduction to Biophysical Methods for Protein
  and Nucleic Acid Research, Academic press, New
  York (1995).
• D.S. T.Nicholl, An Introduction to Genetic
  Engineering, (Second Edition) Cambrdige Univ.
  Press, UK (2002).

70
References

• 3D-1D Profile method
• J.U.Bowie, E.Luthy D.Eisenberg, Science, 253,
  164-170 (1991).
• Ostensible Recognition of Folds (ORF) method
• Rajeev Aurora and George D.Rose, Proc. Natl.
  Acad. Sci. (USA), 95(6), 2818-2823 (1998).
• Superfamily Hidden Markov Model (SHMM) method
• A.Krogh, M.Brown, IS.Mian, K.Sjolander and
  D.Haussler, J. Mol. Biol. 235(5), 1501-31 (1994).

71
ImportantBioinformatics Resources

• Databases Tools
• NCBI, NIH - www.ncbi.nlm.nih.gov
• EMBL, EBI - www.ebi.ac.uk
• ExPasy, Swiss - www.expasy.org
• DDBJ - www.ddbj.nig.ac.jp
• PDB - www.rcsb.org/pdb
• Software
• Accelrys - www.accelrys.com/products
• GCG, Insight II, Cerius II, Discovery Studio
• TCS - www.atc.tcs.co.in/biosuite/
• BIOSUITE
• Jalaja Technologies - www.jalaja.com
• GENOCLUSTER

72

• Thank You