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Bioinformatics ppt | PPTX
BASICS
OF
BIOINFORMATICS
By
Sai Tharun.G
&
Rachna
Bioinformatics
MASCOT Search
UNIprot Search
What is bioinformatics?
IT is an interdisciplinary field that develops methods
and software tools for
understanding biological data. As
an interdisciplinary field of science, bioinformatics
combines computer science, statistics, mathematics,
and engineering to analyze and
interpret biological data
Why Bioinformatics is necessary?
The need for bioinformatics has arisen from the
recent explosion of publicly available genomic
information, such as resulting from the Human
Genome Project.
 Gain a better understanding of gene analysis,
taxonomy, & evolution.
 To work efficiently on the rational drug designs
and reduce the time taken for the development
of drug manually
Goals of Bioinformatics
To uncover the wealth of Biological information
hidden in the mass of sequence, structure, literature
and biological data.
 It is being used now and in the foreseeable future
in the areas of molecular medicine.
 It has environmental benefits in identifying waste
and clean up bacteria.
In agriculture, it can be used to produce high yield,
low maintenance crops.
Where Bioinformatics helps?
 In Experimental Molecular Biology
 In Genetics and Genomics
 In generating Biological Data
 Analysis of gene and protein expression
 Comparison of genomic data
 Understanding of evolutionary aspect of Evolution
 Understanding biological pathways and networks in System
Biology
 In Simulation & Modeling of DNA, RNA & Protein
Bio Informatics key areas
Structural Bioinoformatics
 Prediction of structure from sequence
◦ secondary structure
◦ homology modelling, threading
◦ ab initio 3D prediction
 Analysis of 3D structure
◦ structure comparison/ alignment
◦ prediction of function from structure
◦ molecular mechanics/ molecular dynamics
◦ prediction of molecular interactions, docking
 Structure databases (RCSB)
List of Database
 DNA Data Bank of Japan (National Institute of Genetics)
 EMBL (European Bioinformatics Institute)
 GenBank (National Center for Biotechnology Information)
 UniProt Universal Pesource (EBI, Swiss Institute of
Bioinformatics, PIR)
 Swiss-Prot Protein Knowledgebase (Swiss Institute of
Bioinformatics)
 National Center for Biotechnology Information (NCBI)
NIM,USA
MASCOT Search
Simple MS – molecular weight of peptide
mixture.
 MS/MS (Tandem MS) – sequence
structural information by recording the
fragment ion spectrum of peptide.
PURPOSE OF MS:
 Elemental composition.
Masses of particles of molecules.
Identify unknown compounds.
Isotopic Composition.
 Mascot is a software package from Matrix Science (www.matrixscience.com) that
interprets mass spectral data into protein identities.
 It uses mass spectrometry data to identify proteins from primary sequence
databases.
 The experimental mass values are then compared with calculated peptide mass by
applying cleavage rules to the entries in a comprehensive primary sequence
database.
 If unknown protein is present, we will get precise entry otherwise pull out those
entries which exhibit the closest homology(related species).
Algorithm used..
Program MASCOT is based on the MOWSE
algorithm; this program also evaluates a
possibility of random matching of experimental
and theoretical peptide masses.
Two Mascot Choices
Matrix Sciences offers two choice for users:
 A free, open access web-based system for
occasional (1-10) queries.
 A locally installed version for heavy use or
highthroughput MS (100’s queries/day)
MASCOT Home Page
PEPTIDE MASS FINGERPRINT
Parameters used in database searching
Database searched
 Taxonomy
Enzyme
Missed cleavages
 Fixed versus variable modifications (PTMs)
SCORING SCHEMES
 PROBABILITY BASED SCORING
 Mascot incorporates a probability based implementation of the Mowse
algorithm
 The total score is the absolute probability that the observed match is a
random event.
Advantages :
 Different types of matching (peptide masses and fragment ions) can be
combined in a single search.
 Scores from different searches and on different databases can be
compared.
 Search parameters can be optimised more readily by iteration.
Thank you

Bioinformatics ppt

  • 1.
  • 2.
  • 3.
    What is bioinformatics? ITis an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data
  • 5.
    Why Bioinformatics isnecessary? The need for bioinformatics has arisen from the recent explosion of publicly available genomic information, such as resulting from the Human Genome Project.  Gain a better understanding of gene analysis, taxonomy, & evolution.  To work efficiently on the rational drug designs and reduce the time taken for the development of drug manually
  • 6.
    Goals of Bioinformatics Touncover the wealth of Biological information hidden in the mass of sequence, structure, literature and biological data.  It is being used now and in the foreseeable future in the areas of molecular medicine.  It has environmental benefits in identifying waste and clean up bacteria. In agriculture, it can be used to produce high yield, low maintenance crops.
  • 7.
    Where Bioinformatics helps? In Experimental Molecular Biology  In Genetics and Genomics  In generating Biological Data  Analysis of gene and protein expression  Comparison of genomic data  Understanding of evolutionary aspect of Evolution  Understanding biological pathways and networks in System Biology  In Simulation & Modeling of DNA, RNA & Protein
  • 8.
  • 9.
    Structural Bioinoformatics  Predictionof structure from sequence ◦ secondary structure ◦ homology modelling, threading ◦ ab initio 3D prediction  Analysis of 3D structure ◦ structure comparison/ alignment ◦ prediction of function from structure ◦ molecular mechanics/ molecular dynamics ◦ prediction of molecular interactions, docking  Structure databases (RCSB)
  • 10.
    List of Database DNA Data Bank of Japan (National Institute of Genetics)  EMBL (European Bioinformatics Institute)  GenBank (National Center for Biotechnology Information)  UniProt Universal Pesource (EBI, Swiss Institute of Bioinformatics, PIR)  Swiss-Prot Protein Knowledgebase (Swiss Institute of Bioinformatics)  National Center for Biotechnology Information (NCBI) NIM,USA
  • 11.
    MASCOT Search Simple MS– molecular weight of peptide mixture.  MS/MS (Tandem MS) – sequence structural information by recording the fragment ion spectrum of peptide.
  • 12.
    PURPOSE OF MS: Elemental composition. Masses of particles of molecules. Identify unknown compounds. Isotopic Composition.
  • 13.
     Mascot isa software package from Matrix Science (www.matrixscience.com) that interprets mass spectral data into protein identities.  It uses mass spectrometry data to identify proteins from primary sequence databases.  The experimental mass values are then compared with calculated peptide mass by applying cleavage rules to the entries in a comprehensive primary sequence database.  If unknown protein is present, we will get precise entry otherwise pull out those entries which exhibit the closest homology(related species).
  • 15.
    Algorithm used.. Program MASCOTis based on the MOWSE algorithm; this program also evaluates a possibility of random matching of experimental and theoretical peptide masses.
  • 16.
    Two Mascot Choices MatrixSciences offers two choice for users:  A free, open access web-based system for occasional (1-10) queries.  A locally installed version for heavy use or highthroughput MS (100’s queries/day)
  • 17.
  • 18.
  • 20.
    Parameters used indatabase searching Database searched  Taxonomy Enzyme Missed cleavages  Fixed versus variable modifications (PTMs)
  • 21.
    SCORING SCHEMES  PROBABILITYBASED SCORING  Mascot incorporates a probability based implementation of the Mowse algorithm  The total score is the absolute probability that the observed match is a random event. Advantages :  Different types of matching (peptide masses and fragment ions) can be combined in a single search.  Scores from different searches and on different databases can be compared.  Search parameters can be optimised more readily by iteration.
  • 23.