Introduction to Bioinformatics

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Introduction to Bioinformatics

• Amandeep S. Sidhu
• Data Mining in Bioinformatics (Week 1)

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Outline

• What is Bioinformatics?
• Biological Data
• Data Sources
• What Bioinformatics can do?
• Challenges in understanding Genetic Data
• Central Paradigm
• Genomics
• Proteomics
• Genome to Life Program
• Whats Next?

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What is 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.
• NIH working definition
  (2000)

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What is Bioinformatics/ Computational Biology?

• Bioinformatics collection and storage of
  biological information
• Computational biology development of algorithms
  and statistical models to analyze biological data
• Bioinformatics/Computational Biology will be
  interchanged

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What is Bioinformatics?
Informatics Computer Science Computer
Engineering Information Science
Biology Other Natural Sciences
Bioinformatics
Mathematics Statistics
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Biological Data

• Genomes
• DNA Sequences of A, T, C, G
• Annotated with function, interesting features
• Proteins
• Amino Acid Sequences
• Sequences of 20 letters
• Annotated with structure, function, etc.

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Biological Data

• Gene Expression
• Dynamic behavior of genes
• Protein Expression
• Dynamic behavior of proteins
• Structural Features
• RNA and proteins

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Data Sources

• Information stored in the genetic code (DNA) -
  GDB
• protein sequences and 3D structures -PDB
• experimental results from various sources
• patient statistics
• scientific literature

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Data Sources

• Important part of research in bioinformatics is
  the development of methods for
• data storage
• data retrieval
• data analysis

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What can Bioinformatics do?

• Analyze genetic and molecular sequences
• Look for patterns, similarities, matches
• Identify structures
• Store derived information
• Large databases of genetic information

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Challenges in understanding Genetic Data

• Genetic information is redundant
• Structural information is redundant
• Genes and proteins are meta-stable
• Single genes have multiple functions
• Genes are one dimensional but function depends on
  three-dimensional structure

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Central Paradigm

• Central Dogma (Discussed Earlier)
• Molecules
• Structure
• Function
• Processes
• Mechanism
• Specificity
• Regulation

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Genomics

• Accumulation of gene data
• sequences
• structures,
• function-related
• not hypothesis-driven discovery science
• Hypothesis formed later and tested in silico

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Proteomics

• Structure determination (modelling)
• Prediction of structure from sequence
• secondary structure
• homology modelling, threading
• ab initio 3D prediction
• Analysis of 3D structure
• structure comparison/ alignment
• prediction of function from structure
• molecular mechanics/ molecular dynamics
• prediction of molecular interactions, docking

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Genomes to Life Program

• The Natural Complexity of Biological Molecules
  sets stage for two challenges for DoEs Genome to
  Life (GTL) Program
• Collect Enormous Amounts of Data about groups of
  biological molecules and protein machines
  expressed and assembled in different cell types
  and varying conditions.
• Much deeper and complex challenge is deriving
  underlying theoretical and principles of biology.
  High Performance Computing linked to biology via
  detailed knowledge of protein structures and
  interactions.

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Reference

• Glossary Provided
• Dictionary Bains, William (2004) Biotechnology
  from A to Z. 3rd Edition. Oxford University
  Press.

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Whats Next?

• Bioinformatics Databases
• Database Searching
• Bioinformatics Tools