1. NCBI Quick Overview of Bioinformatics Chuong Huynh NIH/NLM/NCBI New Delhi, India September 28, 2004 huynh@ncbi.nlm.nih.gov

2. NCBI 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/

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

5. NCBI Bioinformatics Flow Chart (1) 1a. Sequencing 1b. Analysis of nucleic acid seq. -Base calling -Physical mapping -Fragment assembly - gene finding -Multiple seq alignment  evolutionary tree Stretch of DNA coding for protein; Analysis of noncoding region of genome 2. Analysis of protein seq. 3. Molecular structure prediction3D modeling; DNA, RNA, protein, lipid/carbohydrate Sequence relationship 4. molecular interaction Protein-protein interaction Protein-ligand interaction 5. Metabolic and regulatory networks

6. NCBI Bioinformatics Flow Chart (2) 6. Gene & Protein expression data 7. Drug screening -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 Ab initio drug design OR Drug compound screening in database of molecules 8. Genetic variability Drug molecules designed to be complementary to binding Sites with physiochemical and steric restrictions. -Now investigated at the genome scale -SNP, SAGE

7. NCBI Genome Sequencing Libraries Sequencing Release Assembly Annotation Closure Strategy Most genome will be sequenced and can be sequenced; few problem are unsolvable. Clone by clone vs whole genome shotgun Problem lies in understanding what you have: Gene prediction/gene finding Annotation Subcloning; generate small insert librariesAssembly: Process of taking raw single-pass reads into contiguous consensus sequence (Phred/Phrap) Assembly Libraries Strategy Sequencing Closure: Process of ordering and merging consensus sequences into a single contiguous sequence Closure Annotation -DNA features (repeats/similarities) -Gene finding -Peptide features -Initial role assignment -Others- regulatory regions Release Release data to the public e.g. EMBL or GenBank

8. NCBI Complete sequence Shotgun reads Contigs Genomic DNA Shearing/Sonication Subclone and Sequence Assembly Finishing Finishing read Sequencing Small DNA fragments 1.0-2.0kb Clone Library pUC18 DNA sequencing Random clones Both strands coverage; Gap filled

9. NCBI Annotation of eukaryotic genomes transcription RNA processing translation AAAAAAA Genomic DNA Unprocessed RNA Mature mRNA Nascent polypeptide folding Reactant A Product B Function Active enzyme ab initio gene prediction Comparative gene prediction Functional identification Gm 3

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

11. NCBI Positional Cloning

12. NCBI Positional Candidate Cloning

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

14. NCBI 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. NCBI Interpreting data from many sources

16. NCBI 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 http://www.who.int/tdr/grants/strategic-emphases/default.htm WHO/TDR Genomics and World Health Report 2002

17. NCBI 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 378:236

18. NCBI 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. NCBI 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. NCBI The Bigger Picture (Malaria)

21. NCBI 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. NCBI 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. NCBI 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. NCBI 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. NCBI 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. NCBI 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. NCBI 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) Relatively Modest Hardware Needs and Technical Support

28. NCBI 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 Relatively Modest Hardware Needs and Technical Support

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

30. NCBI 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. NCBI Beginning Bioinformatics Books Baxevanis & Ouellette 2001. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins 2 nd 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

32. NCBI Course Schedule Comments and Suggestions Take out your course schedule.

33. NCBI The Challenge What is expected of you?

34. NCBI Extra Slides

35. NCBI Gabon Thailand mean FCT (range) mean PCT(range) 7-day cure (n) 28-day cure (n) 24 h (0-48) 46 h (24-48) 10/10 7/9 (1 lost to FU) 45 h (8-80) 44 h (16-80) 10/10 2/10 Results FCT: Fever clearance time PCT: Parasite clearance time Fosmidomycin

36. NCBI 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 Fosmidomycin

37. NCBI Fosmidomycin has intrinsic antimalarial activity - i.e. proof of concept established 2 nd 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 Fosmidomycin / Next Steps