1. Quick Overview of Bioinformatics
Chuong Huynh
NIH/NLM/NCBI
New Delhi, India
September 28, 2004

2. 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.”
BISTIC definition of bioinformatics:
- Bioinformatics: 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
- 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.
Bioinformatics applies principles of information sciences and technologies to make the vast, diverse, and complex life sciences data more understandable and useful. Computational biology uses mathematical and computational approaches to address theoretical and experimental questions in biology. Although bioinformatics and computational are distinct, there is also significant overlap and activity at their interface.
What is Bioinformatics?
Bioinformatics is an integration of mathematical, statistical and computer methods to analyze biological, biochemical and biophysical data.
-- old url

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)
More of a list of industry bioinformatics core requirement
Molecular evolution, physical chemistry (kinetics, thermodynamics), statistics and probability; database design and implementation; algorithm development; molecular biology laboratory methods
computer science and genomics expertise for analysis of small- and large-scale informatics problems;
the ability to filter information and extract possible relationships between data sets;
database administration and programming skills (e.g., Sybase, CORBA, PERL, Java, C++, C);
the ability to frame biological questions in a manner understandable to computer scientists (enabling the design of effective tools); and
a thorough understanding of the problems addressed in the bioinformatics field.

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.
3. Molecular structure prediction
Ab initio drug design OR
Drug compound screening in
database of molecules
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
This is more of a drug discovery perspective
Gene discovery require annotating genes
Many of the concepts here will be discussed through this course.
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. Genome Sequencing Clone by clone vs whole genome shotgun
Strategy
Strategy
Clone by clone vs whole genome shotgun
Libraries
Libraries
Subcloning; generate small insert libraries
Sequencing
Sequencing
Most genome will be sequenced and can be sequenced;
few problem are unsolvable.
Assembly
Assembly
Assembly: Process of taking raw single-pass reads into contiguous consensus sequence (Phred/Phrap)
Problem lies in understanding what you have:
Gene prediction/gene finding
Annotation
Closure
Closure
Closure: Process of ordering and merging consensus sequences into a single contiguous sequence
Finishing = Assembly + Closure step
Annotation
Annotation
DNA features (repeats/similarities)
Gene finding
Peptide features
Initial role assignment
Others- regulatory regions
Release
Release
Release data to the public e.g. EMBL or GenBank

8. Both strands coverage;
Sequencing
Genomic DNA
Shearing/Sonication
Small DNA fragments kb
Clone Library pUC18
Subclone and Sequence
DNA sequencing
Random clones
Shotgun reads
Assembly
Contigs
Finishing read
Take large piece of usually genomic DNA and break it up into smaller piece - use restriction enzyme in smaller projects at the beginning of sequencing; for sequencing project use shearing or sonication; the longer you sonicate the smaller the pieces you get; put in bacterial vector – subclone – sequencing in random; choose random clones, then sequence both ends M13, pUC; try to assemble these pieces together and reform the original sequence again; fill in gaps called finishing; how big piece of DNA do you start off with? Depends on how much confidence you have in putting those pieces together; remember the average read lengths is about 600 bp
Slide provided from Daniel Lawson
Both strands coverage;
Gap filled
Finishing
Complete sequence

9. Annotation of eukaryotic genomes
Genomic DNA
ab initio gene prediction
transcription
Unprocessed RNA
RNA processing
Mature mRNA
Gm3
AAAAAAA
Comparative gene prediction
translation
Nascent polypeptide
folding
Prokaryotic world would ignore some of this steps; based on what you are interested in;
Ab initio – black magic? – whether you trust a gene finder will depend on your knowledge of underlying
Comparative another organism to your known DNA – interesting from a DNA to DNA (mouse/human; C elegans C. brissae; P falciparum/vivax comparison)
Functional identification something the community wants.
Annotation:
Extract ORFs
Predict protein
Remove errors
Compare with database of ‘known function proteins’
Provide transitive annotations
Active enzyme
Functional identification
Function
Reactant A
Product B

10. Annotation Predict protein Extract ORFs Remove errors
Compare with database of ‘known function proteins’
Provide transitive annotations

11. Positional Cloning

12. Positional Candidate Cloning

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

14. 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.

15. Interpreting data from many sources

16. Genomics and Tropical Diseases
How Can Genomics Contribute to the Control of Tropical Diseases?
Challenges and Opportunities
The Role of Bioinformatics
Strategic emphases for research
WHO/TDR Genomics and World Health Report 2002

17. B. Bloom (1995) A microbial minimalist. Nature 378:236
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.”
From a slide from Carlos Morel
B. Bloom (1995) A microbial minimalist. Nature 378:236

18. 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

19. 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

20. The Bigger Picture (Malaria)

21. 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

22. 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

23. 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

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

25. 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

26. 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

27. 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)

28. 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)
MIMCom Malaria Research Resources

29. SANBI, Cape Town, South Africa
International Training Course on Bioinformatics and Computational Biology Applied to Genome Studies (Train-the-trainers Workshop) May 21-June 15, 2001 FIOCRUZ, Brazil
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 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

30. 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
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

31. Beginning Bioinformatics Books
Baxevanis & Ouellette Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 2nd Edition. John Wiley Publishing.
Gibas & Jambeck 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

32. The Challenge
What is expected of you?

33. Comments and Suggestions
Course Schedule
Take out your course schedule.
Comments and Suggestions