Quick Overview of Bioinformatics

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

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.” • http://www.bisti.nih.gov/

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. 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 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. Subcloning; generate small insert libraries • DNA features (repeats/similarities) • Gene finding • Peptide features • Initial role assignment • Others- regulatory regions Closure: Process of ordering and merging consensus sequences into a single contiguous sequence 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 • Most genome will be sequenced and can be sequenced; • few problem are unsolvable. Clone by clone vs whole genome shotgun Release data to the public e.g. EMBL or GenBank Genome Sequencing Strategy Strategy Libraries Libraries Sequencing Sequencing Assembly Assembly Closure Closure Annotation Annotation Release Release

8. 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

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 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 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

17. 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. 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) • 1stsemester2001: 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) http://www.th.apan.net/ • MIMCom Malaria Research Resources http://www.nlm.nih.gov/mimcom/about.html

29. 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

30. 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

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

33. The Challenge What is expected of you?