An overview of Bioinformatics. Cell and Central Dogma.

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Presentation transcript:

An overview of Bioinformatics

Cell and Central Dogma

Source: “Post-genome Informatics” by M Kanehisa

Deduction and Analogy

Biological System (Organism) Reductionistic Synthetic Approach (Experiments) (Bioinformatics) Building Blocks (Genes/Molecules) Source: “Post-genome Informatics” by M Kanehisa

Principles Known Physics Chemistry Biology Matter Compound Organism Elementary Elements Genes Particles Yes Yes No Source: “Post-genome Informatics” by M Kanehisa

Searching and learning problems in biology Source: “Post-genome Informatics” by M Kanehisa

Sequence Comparison: Algorithms and Approaches

Homology Search New sequence Similar sequences Expert knowledge Sequence interpretation Sequence database (Primary data) retrieval Source: “Post-genome Informatics” by M Kanehisa

Pairwise sequence alignment by dynamic programming Needleman Wunsch alogrithm Source: “Post-genome Informatics” by M Kanehisa

Database Search for Similar Sequences

Web Lab

Motif

Source: “Introduction to Protein Structure” by Branden & Tooze

Web Lab

Motif Search New sequenceExpert knowledge Sequence interpretation Sequence database (Primary data) Motif library (Empirical rules) inference Source: “Post-genome Informatics” by M Kanehisa

Introduction to Structural Biology Structural Biology

Source: “Introduction to Protein Structure” by Branden & Tooze

Web Lab

Genome Project

Web Lab

Genome Sequencing and Genome Annotation

A general model of the structure of genomic sequences Source: “Bioinformatics” by D W Mount

Microarray

Joe Sutliff for Science 291 p1224 (2001) What kind of solution Genomics can provide with ?  High Throughput Gene Discovery

 165 genes are up-regulated in 75% tumors (MAPK pathway, APC, promotion of mitosis; 69 unknown)  170 genes are down-regulated in 65% tumors (hepatocyte-specific gene products, retinoid metabolism; 75 unknown)  Hierarchical Clustering  K-means  Self Organization Map  Support Vector  Single Value Decomposition

Gene Expression andTranscriptome

Web Lab

Proteomicsand Functional Genomics

Source: “Post-genome Informatics” by M Kanehisa

Web Lab

Integrative Genomics

Network of physical interactions between nuclear proteins

Attributes of generic network structures

Virtual Cell Living Cell Perturbation Environmental change Gene disruption Gene overexpression Dynamic Response Changes in: Gene expression profiles, Etc. Biological Knowledge Molecular and Cellular Biology,Biochemistry, Genetics, etc Basic Principles Practical Applications Complete Genome Sequences Source: “Post-genome Informatics” by M Kanehisa

Take Home Message  Define the biological problem.  Why is bioinformatics important ? A synthesis approach.  Prediction is a dangerous game. Always try your best to validate in the bench side.  The devil is in the detail. Always try different bioinformatic tools and databases.  Your knowledge rests on your own practice.

Reference Books you will find useful: Bioinformatics -sequence and genome analysis by D W Mount Introduction to Bioinformatics by A M Lesk Post-genome Informatics by M Kanehisa

Evolution of molecular biology databases Database category Data contentExamples 1. Literature databaseBibliographic citationsMEDLINE(1971) On-line journals 2. Factual DatabaseNucleic acid sequencesGenBank(1982) Amino acid sequencesEMBL(1982) 3D molecular structuresDDBJ(1984) SWISS_PROT(1986) PDB(1971) 3. Knowledge baseMotif librariesPROSITE(1988) Molecular classificationSCOP(1994) Biochemical pathwaysKEGG(1995)