Quick Overview of Bioinformatics

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Quick Overview of Bioinformatics

• Chuong Huynh
• NIH/NLM/NCBI
• New Delhi, India
• September 28, 2004
• huynh_at_ncbi.nlm.nih.gov

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What is bioinformatics? - Definition

• My definition bringing biological themes to
  computers
• Peter Elkin Primer on Medical Genomics Part V
  Bioinformatics
• Bioinformatics is the discipline that develops
  and applies informatics to the field of molecular
  biology.
• 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//www.bisti.nih.gov/

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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)

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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
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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
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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
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Genome Sequencing
Strategy
Strategy
Libraries
Libraries
Sequencing
Sequencing
Assembly
Assembly
Closure
Closure
Annotation
Annotation
Release
Release
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Sequencing
Genomic DNA
Shearing/Sonication
Small DNA fragments 1.0-2.0kb
Clone LibrarypUC18
Subclone and Sequence
DNA sequencing Random clones
Shotgun reads
Assembly
Contigs
Finishing read
Both strands coverage Gap filled
Finishing
Complete sequence
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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
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Annotation

• Predict protein
• Extract ORFs
• Remove errors
• Compare with database of known function
  proteins
• Provide transitive annotations

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Positional Cloning
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Positional Candidate Cloning
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The new information is always partial

• Complete Eukaryotic Genomes
• Ongoing Eukaryotic
• Prokaryotic Ongoing
• Published
• Even a complete genome is only partially
  understood

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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.

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Interpreting data from many sources
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Genomics and Tropical Diseases

• How Can Genomics Contribute tothe Control of
  Tropical Diseases?
• Challenges and Opportunities
• The Role of Bioinformatics

Strategic emphases for research
http//www.who.int/tdr/grants/strategic-emphases/
default.htm WHO/TDR Genomics and World Health
Report 2002
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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
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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

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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

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The Bigger Picture (Malaria)
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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

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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

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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

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Fosmidomycin example - lesson

• 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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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

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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

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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)

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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

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TDR Regional Training Centers Regional Training
Courses on Bioinformatics Applied to Tropical
Diseases
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 Jan 20-Feb 02, 2002 Mar 19-Apr 4, 2003
  Feb 2-15, 2004 (with NBN series)
• Univ of Ibadan, Ibadan, Nigeria
• Course May 26-Jun 07, 2003
• South America
• USP, São Paulo, Brazil
• Course Feb 18-March 02, 2002 July 17-19, 2003
  July 5-16, 2004
• Southeast Asia
• ICGEB, New Delhi, India
• Course Apr 26-May 09, 2002 Sep 22-Oct 06, 2003
  Sept 28-Oct 11, 2004
• Mahidol University, Bangkok, Thailand
• Course Jul 09-23, 2002 Sep 29-Oct 10, 2003
  July 26-Aug6, 2004

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Training Course on Bioinformatics and Functional
Genomics Applied to Insect Vectors of Human
DiseasesAt the Center for Bioinformatics and
Applied Genomics (CBAG) and Center for Vector and
Vector-Borne Diseases (CVVD), Faculty of
Science, Mahidol University, Bangkok,
ThailandJanuary 17-28, 2005
Training Course on Functional Genomics of Insect
Vectors of Human DiseasesAfrican Center for
Training in Functional Genomics of Insect Vectors
of Human Diseases (AFRO VECTGEN) At the Malaria
Research and Training Center (MRTC), Bamako,
MaliDec 1-16, 2004
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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.
• Bioinformatics Genome Sequence Analysis Mount
  2001
• Bioinformatics For Dummies Claverie Notredame
  2003
• Bioinformatics and Functional Genomics Pesvner
  2003
• Introduction to Bioinformatics Lesk 2002
• Fundamental Concepts of Bioinformatics Krane
  Raymer 2003
• Beginning Perl for Bioinformatics Tisdall 2002
• Primer of Genome Science Gibson Muse 2002

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The Challenge
What is expected of you?
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Course Schedule
Take out your course schedule.

• Comments and Suggestions

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Extra Slides
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Fosmidomycin
Results
FCT Fever clearance time PCT Parasite
clearance time
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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

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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