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Quick Overview of Bioinformatics

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Title: Quick Overview of Bioinformatics


1
Quick Overview of Bioinformatics
  • Chuong Huynh
  • NIH/NLM/NCBI
  • Bangkok, Thailand
  • July 9, 2002
  • huynh_at_ncbi.nlm.nih.gov

2
What is bioinformatics? - Definition
  • My definition bringing biological themes to
    computers
  • BISTIC Bioinformatics Definition Research,
    development, or application of computational
    tools and approaches for expanding the use of
    biological, medical, behavioral or health data,
    including those to acquire, store, organize,
    archive, analyze, or visualize such data
  • BISTIC Computational Biology Definition --
    Computational Biology the development and
    application of data-analytical and theoretical
    methods, mathematical modeling and computational
    simulation techniques to the study of biological,
    behavioral, and social systems.
  • http//grants2.nih.gov/grants/bistic/bistic.cfm

3
Useful/Necessary Bioinformatics Skills
  • Strong background in some aspect of molecular
    biology!!!
  • Ability to communicate biological questions
    comprehensibly to computer scientists
  • Thorough comprehension of the problem in the
    bioinformatics field
  • Statistics (association studies, clustering,
    sampling)
  • Ability to filter, parse, and munge data and
    determine the relationships between the data sets
  • Mathematics (e.g. algorithm development)
  • Engineering (e.g. robotics)
  • Good knowledge of a few molecular biology
    software packages (molecular modeling / sequence
    analysis)
  • Command line computing environment (Linux/Unix
    knowledge)
  • Data administration (esp. relational database
    concept) and Computer Programming
    Skills/Experience (C/C, Sybase, Java, Oracle)
    and Scripting Language Knowledge (Perl and
    perhaps Phython)

4
Bioinformatics Flow Chart (0)
1a. Sequencing
6. Gene Protein expression data
1b. Analysis of nucleic acid seq.
7. Drug screening
2. Analysis of protein seq.
Ab initio drug design OR Drug compound screening
in database of molecules
3. Molecular structure prediction
4. molecular interaction
8. Genetic variability
5. Metabolic and regulatory networks
5
Bioinformatics Flow Chart (1)
1a. Sequencing
  • Base calling
  • Physical mapping
  • Fragment assembly

1b. Analysis of nucleic acid seq.
  • -gene finding
  • Multiple seq alignment
  • ? evolutionary tree

Stretch of DNA coding for protein Analysis of
noncoding region of genome
2. Analysis of protein seq.
Sequence relationship
3. Molecular structure prediction
3D modeling DNA, RNA, protein, lipid/carbohydrate
Protein-protein interaction Protein-ligand
interaction
4. molecular interaction
5. Metabolic and regulatory networks
6
Bioinformatics Flow Chart (2)
6. Gene Protein expression data
  • EST
  • DNA chip/microarray

7. Drug screening
  • Lead compound binds tightly to binding site of
    target protein
  • Lead optimization lead compound modified to be
    nontoxic,
  • few side effects, target deliverable

Ab initio drug design OR Drug compound screening
in database of molecules
Drug molecules designed to be complementary to
binding Sites with physiochemical and steric
restrictions.
  • Now investigated at the genome scale
  • SNP, SAGE

8. Genetic variability
7
Sequencing
Genomic DNA
Shearing/Sonication
Subclone and Sequence
Shotgun reads
Assembly
Contigs
Finishing read
Finishing
Complete sequence
8
Annotation of eukaryotic genomes
Genomic DNA
ab initio gene prediction
transcription
Unprocessed RNA
RNA processing
Mature mRNA
AAAAAAA
Gm3
Comparative gene prediction
translation
Nascent polypeptide
folding
Active enzyme
Functional identification
Reactant A
Product B
Function
9
Annotation
  • Predict protein
  • Extract ORFs
  • Remove errors
  • Compare with database of known function
    proteins
  • Provide transitive annotations

10
The new information is always partial
  • Complete Eukaryotic Genomes
  • Ongoing Eukaryotic
  • Prokaryotic Ongoing
  • Published
  • Even a complete genome is only partially
    understood

11
Why not use the genome sequence once its ready?
  • Finding exons
  • 30 overprediction
  • 20 not found at all
  • Comparison systems rely on EST sequences which
    themselves contain large error rates
  • Others are looking through partial data
  • Once the genome is done when?
  • Expressed sequences are there in part and
    represent a very very powerful key.

12
Interpreting data from many sources
13
Genomics and Tropical Diseases
  • How Can Genomics Contribute tothe Control of
    Tropical Diseases?
  • Challenges and Opportunities
  • The Role of Bioinformatics

14
Why Pathogen Genomics?
  • The power and cost-effectiveness of modern
    genome sequencing technology mean that complete
    genome sequences of 25 of the major bacterial and
    parasitic pathogens could be available within
    five years. For about 100 million dollars (), we
    could buy the sequence of every virulence
    determinant, every protein antigen and every drug
    target.

B. Bloom (1995) A microbial minimalist. Nature
378236
15
Genomics and Drug Development for Tropical
Diseases Challenges
  • Knowledge limitations
  • A large proportion of pathogen genes have unknown
    function
  • Heavy investment in genomics is done by the
    commercial sector and therefore not widely
    available
  • Emphasis and priorities
  • Genomes of non-pathogenic model organisms (S.
    cerevisiae, D. melanogaster, C. elegans, A.
    thaliana)
  • Genomes of pathogens that affect individuals in
    developed countries
  • Neglected diseases ? neglected pathogens

16
Doing Successful Science in the new millennium
  • Huge increase in available biological information
  • Classic paradigm of molecular biology now is
    altering rapidly to genomics
  • Understanding of the new paradigms concerns more
    than just bench biology
  • Discovery requires large scale systems and broad
    collaborations, Global problems
  • Funding comes in large amounts at group level, no
    longer a single laboratory or institution effort.
  • Accountable output

17
The Bigger Picture (Malaria)
18
Genomics Approach to Drug Development
Opportunities
  • Classical laboratory assays aim at targets in
    which mutation is lethal to the pathogen
  • Valuable targets can be missed
  • Sulphonamides Inhibition of the p-aminobenzoic
    acid pathway not lethal for growth in laboratory
    but severely attenuate the capacity to cause
    disease

19
Genomics Approach to Drug Development
Opportunities
  • New approaches for the identification of gene
    products specifically involved in the disease
    process may uncover further drug targets
  • Signature tagged mutagenesis (STM)
  • Transposon site hybridization (TraSH)
  • Pathogen genomics and data mining for the
    discovery of new drug targets

20
Fosmidomycin
  • September 1999 a basic science breakthrough
    (data mining through bioinformatics identify new
    targets for chemotherapy of malaria)
  • 1st semester 2001 Results of Phase I clinical
    trials

21
Fosmidomycin example - lesson
  • A lesson to take home 1½ years from data mining
    and laboratory research to phase II,
    proof-of-principle clinical trials

22
Bioinformatics Opportunities in Health Research
and Development
  • New drug research and development
  • Identification of novel drug/vaccine targets
  • Structural predictions
  • Tapping into biodiversity
  • Reconstruction of metabolic pathways
  • Systems biology
  • Identification of vaccine candidates through
    analysis of surface antigens and epitopes

23
A Window of Opportunity for Disease Endemic
Countries
  • Bioinformatics is an extremely important tool,
    with relevance to studying pathogenic organisms
  • Pathogens of interest to DECs already being
    sequenced (e.g. P. falciparum, T. cruzi, T.
    brucei, Leishmania sp.)
  • Computational biology is people-intensive, less
    affected by infrastructure, economics, etc than
    other areas of biological research
  • Critical mass issues less critical a
    world-wide community is within reach

24
Relatively Modest Hardware Needs and Technical
Support
  • Linux operating system permits use of the
    personal computer as a powerful workstation
  • Vast repository of public domain software for
    computational biology
  • Individual accounts for remote access and data
    processing can be open at high-performance
    computer facilities and regional centers
  • EMB network nodes, FIOCRUZ (Brazil), SANBI (South
    Africa), CECALCULA (Venezuela), ICGEB (Trieste
    and New Delhi)

25
Relatively Modest Hardware Needs and Technical
Support
  • Powerful searches using public websites
  • NCBI, EMB nodes, Sanger Center, Expasy/SwissProt,
    KEGG database
  • High-speed internet access is becoming more and
    more available in disease endemic countries
    through regional and international support, e.g.
  • Asia-Pacific Advanced Network Consortium (APAN)
    http//www.th.apan.net/
  • MIMCom Malaria Research Resources
    http//www.nlm.nih.gov/mimcom/about.html

26
TDR Regional Training Centers Courses in
Bioinformatics
International Training Course on Bioinformatics
and Computational Biology Applied to Genome
Studies(Train-the-trainers Workshop)May 21-June
15, 2001 FIOCRUZ, Brazil
  • Africa
  • SANBI, Cape Town, South Africa
  • Course 20/Jan-02/Feb/2002
  • South America
  • USP, São Paulo, Brazil
  • Course 18/Feb-02/Mar/2002
  • Southeast Asia
  • ICGEB, New Delhi, India
  • Course 26/April-09/May/2002
  • Mahidol University, Bangkok, Thailand
  • Course 09-23/Jul/2002

27
Beginning Bioinformatics Books
  • Baxevanis Ouellette 2001. Bioinformatics A
    Practical Guide to the Analysis of Genes and
    Proteins 2nd Edition. John Wiley Publishing.
  • Gibas Jambeck 2001. Developing Bioinformatics
    Computer Skills. OReilly.
  • Mount 2001. Bioinformatics

28
Course Schedule
Take out your course schedule.
  • Comments and Suggestions

29
The Challenge
What is expected of you?
30
Extra Slides
31
Genome Sequencing - Review
Strategy
Strategy
Libraries
Libraries
Sequencing
Sequencing
Assembly
Assembly
Closure
Closure
Annotation
Annotation
Release
Release
32
Positional Cloning
33
Positional Candidate Cloning
34
Fosmidomycin
Results
FCT Fever clearance time PCT Parasite
clearance time
35
Fosmidomycin
  • Objective To determine proof of concept by
    evaluating the efficacy of fosmidomycin in
    uncomplicated P. falciparum malaria
  • Study sites Africa (Gabon), Asia (Thailand)
  • Patients Adult uncomplicated P. falciparum
    malaria
  • Regimen 1200 mg q 8 h for 7 days
  • Primary endpoint Cure rate at day 7
  • Secondary endpoint Cure rate at day 28, fever
    clearance time, parasite clearance time

36
Fosmidomycin / Next Steps
  • Fosmidomycin has intrinsic antimalarial activity
    - i.e. proof of concept established
  • 2nd antimalarial drug with short half-life
  • Potential use in drug combinations
  • Not good enough to use on its own
  • Do more chemistry to improve PK
  • A lesson to take home 1½ years from data mining
    and laboratory research to phase II,
    proof-of-principle clinical trials
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