1. Introduction to Bioinformatics
Spring 2002
Adapted from Irit Orr Course at WIS

2. What Is Bioinformatics? A Marriage Between Biology and Computers!

3. What Is Bioinformatics
What Is Bioinformatics? (My Correction) A Marriage Between Molecular Biology and Computer Science!

4. What Is Bioinformatics?
Bioinformatics is the field of science in which biology, computer science, and information technology merge into a single discipline.
Bioinformatics is the science of managing and analyzing biological data using advanced computing techniques.

5. What Is Bioinformatics?
Bioinformatics ultimate goal, (as is described by an expert), is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned.

6. What Is Bioinformatics? The Definition From Bioplanet
Bioinformatics is the application of computer technology to the management of biological information.
Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied to gene-based drug discovery and development.

7. What Is Bioinformatics?
The need for Bioinformatics capabilities has been precipitated by the explosion of publicly available genomic information resulting from the Human Genome Project.
The science of Bioinformatics, is essential to the use of genomic information in understanding human diseases and in the identification of new molecular targets for drug discovery.

8. What Is Bioinformatics? The Definition From Whatis.Com
Bioinformatics is the science of developing computer databases and algorithms for the purpose of speeding up and enhancing biological research.
Bioinformatics is being used most noticeably in the Human Genome Project, the effort to identify the 80,000 genes in human DNA .

9. What Is Bioinformatics? The Definition From BitsJournal
Two simple statements seem to suffice in order to create our definition of bioinformatics and make it reflect the future of the life sciences:
Bioinformatics is “a combination of Computer Science, Information Technology and Genetics used to determine and analyze genetic information.”

10. What Is Bioinformatics? The Definition From BitsJournal
The highlight is on the words determine and analyze. These are expressions which involve very real human elements.

11. What Is Bioinformatics? The Definition From BitsJournal
Bioinformatics is the future of the Life Sciences.

12. What Is Done in Bioinformatics?
Analysis and interpretation of various types of biological data including: nucleotide and amino acid sequences, protein domains, and protein structures.

13. What Is Done in Bioinformatics?
Development of new algorithms and statistics with which to assess biological information, such as relationships among members of large data sets.
Development and implementation of tools that enable efficient access and management of different types of information, such as various databases, integrated mapping information.

14. Why Use Bioinformatics?
The explosive growth in the amount of biological information necessitates the use of computers for cataloging and retrieval of this information.
A more global perspective in experimental design. As we move from the one scientist-one gene/protein/disease paradigm of the past, to a consideration of whole organisms, we gain opportunities for new, more general insights into health and disease.

15. Why Use Bioinformatics?
Data-mining - the process by which testable hypotheses are generated regarding the function or structure of a gene or protein of interest by identifying similar sequences in better characterized organisms.
For example, new insight into the molecular basis of a disease may come from investigating the function of ortholog of the disease gene in other organisms.
Equally exciting is the potential for uncovering phylogenetic relationships and evolutionary patterns.

16. Examples of Biology Data for Bioinformatics ?
Whole Genome Annotations
Experimental data involving thousands of Genes simultaneously
DNA Chips, MicroArray, and Expression Arrays Analyses
Comparative Genomics (Analyses between Species and Strains )
Proteomics ('Proteome' of an Organism, 2D gels, Mass Spec, 3D structure )

17. Examples of Biology Data for Bioinformatics ?
Medical applications: Genetic Disease (SNPs)
Pharmaceutical and Biotech Industry (Drug design)
Agricultural applications
(Plants resistance to various types of diseases) 

18. Molecular Biology Information DNA
Raw DNA Sequence
Coding or Not coding?
Parse into genes?
4 bases: AGCT
~1 K in a gene,
~2 M in genome
atggcaattaaaattggtatcaatggttttggtcgtatcggccgtatcgtattccgtgcagcacaacaccgtgatgacattgaagttgtaggtattaacgacttaatcgacgttgaatacatggcttatatgttgaaatatgattcaactcacggtcgtttcgacggcactgttgaagtgaaagatggtaacttagtggttaatggtaaaactatccgtgtaactgcagaacgtgatcca

19. Molecular Biology Information Protein
20 letter alphabet
ACDEFGHIKLMNPQRSTVWY
But not BJOUXZ
Strings of ~300 aa in an average protein
(I.g bacteria),
~200 aa in a protein domain
LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQNLVIMGKKTWFSILNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTSTSHVEGKQNAVIMGKKTWFSIISLIAALAVDRVIGMENAMPWNLPADLAWFKRNTLDKPVIMGRHTWESITAFLWAQDRNGLIGKDGHLPWHLPDDLHYFRAQTVGKIMVVGRRTYESF

20. Molecular Biology Information Macromolecular Structure
DNA, RNA, Protein

21. Molecular Biology Information
Whole Genomes
Viruses, Prokaryotes,
Yeast, Fly, Arabidopsis,
Mouse, Human

22. Molecular Biology Information Other Integrative Data
Information to understand whole genomes:
Metabolic Pathways
traditional biochemistry
Regulatory Networks
Whole Organisms Phylogeny
Environments, Habitats, ecology
The Literature (MEDLINE)

23. Molecular Biology Information Redundancy and Multiplicity
Different Sequences Have the Same Structure.
One Organism has many similar genes.
Single Gene May Have Multiple Functions.
Genomic Sequence Redundancy due to the Genetic Code.

24. Exponential Growth of Data Matched by
Development of Computer Technology
• CPU vs Diskspace & Net
As important as the increase in computer
speed has been, the ability to store large
amounts of information on computers is even more crucial and need special attention as well.

25. Bioinformatics Tasks (Several Suggestions)
Finding the genes in the DNA sequences of various organisms.
Prediction of promoters and other regulatory regions.

26. Bioinformatics Tasks (Several Suggestions)
Developing methods to predict the structure of structural RNA sequences.
Developing methods to predict the structure and/or function of newly discovered proteins.
Clustering protein sequences into families of related sequences and the development of protein models.

27. Bioinformatics Tasks (Several Suggestions)
Aligning similar proteins and generating phylogenetic trees to examine evolutionary relationships.
The process of evolution has produced DNA sequences that encode proteins with very specific functions.
It is possible to predict the three-dimensional structure of a protein using algorithms that have been derived from our knowledge of physics, chemistry and most importantly, from the analysis of other proteins with similar amino acid sequences.

28. Top 10 Future Challenges for Bioinformatics (At a Bioinformatics Conference Last Fall)
Precise, predictive model of transcription initiation and termination: ability to predict where and when transcription will occur in a genome.
Precise, predictive model of RNA splicing /alternative splicing: ability to predict the splicing pattern of any primary transcript in any tissue.

29. Top 10 Future Challenges for Bioinformatics (At a Bioinformatics Conference Last Fall)
Precise, quantitative models of signal transduction pathways: ability to predict cellular responses to external stimuli

30. Top 10 Future Challenges for Bioinformatics (At a Bioinformatics Conference Last Fall)
Determining effective protein:DNA, protein:RNA and protein:protein recognition codes
Accurate ab initio protein structure prediction

31. Top 10 Future Challenges for Bioinformatics (At a Bioinformatics Conference Last Fall)
Rational design of small molecule inhibitors of proteins.

32. Top 10 Future Challenges for Bioinformatics (At a Bioinformatics Conference Last Fall)
Mechanistic understanding of protein evolution: understanding exactly how new protein functions evolve.
Mechanistic understanding of speciation: molecular details of how speciation occurs.

33. Top 10 Future Challenges for Bioinformatics (At a Bioinformatics Conference Last Fall)
Continued development of effective gene ontologies - systematic ways to describe the functions of any gene or protein.
Education: development of appropriate bioinformatics curricula for secondary, undergraduate and graduate education.