Transcript Quick Overview of Bioinformatics

Quick Overview of Bioinformatics
NCBI
Chuong Huynh
NIH/NLM/NCBI
New Delhi, India
September 28, 2004
[email protected]
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
• http://www.bisti.nih.gov/
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– “Computational Biology: the development and application of dataanalytical and theoretical methods, mathematical modeling and
computational simulation techniques to the study of biological,
behavioral, and social systems.”
Useful/Necessary Bioinformatics Skills
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• 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)
Bioinformatics Flow Chart (0)
1a. Sequencing
1b. Analysis of nucleic acid seq.
2. Analysis of protein seq.
3. Molecular structure prediction
6. Gene & Protein expression data
7. Drug
screening
Ab initio drug design OR
Drug compound screening in
database of molecules
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4. molecular interaction
8. Genetic variability
5. Metabolic and regulatory networks
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
5. Metabolic and regulatory networks
Protein-protein interaction
Protein-ligand interaction
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4. molecular interaction
3D modeling;
DNA, RNA, protein, lipid/carbohydrate
Bioinformatics Flow Chart (2)
6. Gene & Protein expression data
7. Drug
screening
Ab initio drug design OR
Drug compound screening in
database of molecules
8. Genetic variability
-EST
-DNA chip/microarray
a) Lead compound binds tightly to binding site of target protein
b) Lead optimization – lead compound modified to be nontoxic,
few side effects, target deliverable
Drug molecules designed to be complementary to binding
Sites with physiochemical and steric restrictions.
-Now investigated at the genome scale
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-SNP, SAGE
Genome Sequencing
Strategy
Clone by clone vs whole genome shotgun
Libraries
Subcloning; generate small insert libraries
Sequencing
Assembly
Closure
Release
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Annotation
•Most genome will be sequenced and can be sequenced;
few problem are unsolvable.
Assembly: Process of taking raw single-pass reads into
contiguous •Problem
consensus
sequence
(Phred/Phrap)
lies
in understanding
what you have:
Closure: Process of ordering and merging consensus
•Gene
finding
sequences into a
singleprediction/gene
contiguous sequence
•Annotation
-DNA features (repeats/similarities)
-Gene finding
Release
to the public e.g. EMBL or GenBank
-Peptidedata
features
-Initial role assignment
-Others- regulatory regions
Sequencing
Genomic DNA
Shearing/Sonication
Subclone and Sequence
Small DNA fragments
1.0-2.0kb
Clone Library
pUC18
DNA sequencing
Random clones
Shotgun reads
Assembly
Contigs
Finishing read
Both strands coverage;
Gap filled
Complete sequence
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Finishing
Annotation of eukaryotic genomes
Genomic DNA
ab initio gene
prediction
transcription
Unprocessed RNA
RNA processing
Mature mRNA
Gm3
AAAAAAA
translation
Nascent polypeptide
Comparative gene
prediction
folding
Active enzyme
Function
Reactant A
Product B
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Functional
identification
Annotation
•
•
•
•
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Predict protein
Extract ORFs
Remove errors
Compare with database of ‘known
function proteins’
• Provide transitive annotations
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
• Expressed sequences are there in part and
represent a very very powerful key.
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– 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?
Interpreting data from many sources
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Genomics and Tropical Diseases
How Can Genomics Contribute to
the Control of Tropical Diseases?
Challenges and Opportunities
The Role of Bioinformatics
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Strategic emphases for research
http://www.who.int/tdr/grants/strategic-emphases/default.htm
WHO/TDR Genomics and World Health Report 2002
Why Pathogen Genomics?
B. Bloom (1995) A microbial minimalist. Nature 378:236
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“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.”
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
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– 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
Doing Successful Science in the new millennium
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• 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
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
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• Sulphonamides: Inhibition of the p-aminobenzoic
acid pathway not lethal for growth in laboratory
but severely attenuate the capacity to cause
disease
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
• Pathogen genomics and data mining for
the discovery of new drug targets
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– Signature tagged mutagenesis (STM)
– Transposon site hybridization (TraSH)
Fosmidomycin
• September
1999: a basic
• 1st semester
2001: Results of
Phase I clinical
trials
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science
breakthrough
(data mining
through
bioinformatics
identify new
targets for
chemotherapy of
malaria)
Fosmidomycin example - lesson
• A lesson to take home: 1½ years
from data mining and laboratory
research to phase II, proof-ofprinciple 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
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• Identification of vaccine candidates through
analysis of surface antigens and epitopes
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.)
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• 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
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
– EMB network nodes, FIOCRUZ (Brazil), SANBI
(South Africa), CECALCULA (Venezuela), ICGEB
(Trieste and New Delhi)
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• Individual accounts for remote access and
data processing can be open at highperformance computer facilities and regional
centers
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.:
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– Asia-Pacific Advanced Network Consortium
(APAN) http://www.th.apan.net/
– MIMCom Malaria Research Resources
http://www.nlm.nih.gov/mimcom/about.html
International Training Course on Bioinformatics and Computational
Biology Applied to Genome Studies (Train-the-trainers Workshop)
May 21-June 15, 2001 FIOCRUZ, Brazil
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TDR Regional Training Centers & Regional Training Courses on
Bioinformatics Applied to Tropical Diseases
• Africa
– SANBI, Cape Town, South Africa
• Course: Jan 20-Feb 02, 2002; Mar 19-Apr 4, 2003; Feb 215, 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 516, 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 26Aug6, 2004
Training Course on Bioinformatics and Functional
Genomics Applied to Insect Vectors of Human Diseases
At the
Center for Bioinformatics and Applied Genomics (CBAG)
and Center for Vector and Vector-Borne Diseases (CVVD),
Faculty of Science, Mahidol University,
Bangkok, Thailand
January 17-28, 2005
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Training Course on Functional Genomics of Insect
Vectors of Human Diseases
African Center for Training in Functional Genomics of
Insect Vectors of Human Diseases
(AFRO VECTGEN)
At the Malaria Research and Training Center (MRTC),
Bamako, Mali
Dec 1-16, 2004
Beginning Bioinformatics Books
NCBI
• 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. O’Reilly.
• 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
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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