Bioinformatics and Computational Biology

Bioinformatics collection and storage of biological information derives knowledge from computer analysis of biological data Computational biology development of algorithms and statistical models to analyze biological data

Why bioinformatics is critical? Few people adequately trained in both biology and computer science Genome sequencing, microarrays etc. lead to large amounts of data to be analyzed Leads to important discoveries Saves time and money

Why is the relationship between Computer Science and Biology is essential? Three main reasons- First, massive amounts of data have to be stored, analyzed and made accessible Second, the nature of the data is often such that a computational statistical method is necessary. This applies in particular to the information on the building plans of proteins and spatial organization of their expression in the cell encoded by the DNA. Third, there is a strong analogy between the DNA sequence and a computer program

Key Areas/Scope of Bioinformatics Organizing biological knowledge in database Analysing sequence data Structural Bioinformatics Pharmacological relevance (Population genetics)

1. Organizing biological knowledge in database Genbank/Organized DNA sequences - NCBI, EMBL Protein sequence databank and its structure and functional characteristics. For example, SWISSPROT contains verified protein sequences and more annotations describing the function of a protein Literature database – PUBMED, MEDLINE

2. Analysing sequence data Establish the correct order of sequence contigs Find the translation and transcription initiation sites, find promoter sites, define open reading frames (ORF) Find splice sites, introns, exons Translate the DNA sequence into a protein sequence Compare the DNA sequence to known protein sequences in order to verify exons etc with homologous sequences. Multiple sequence alignments Studying evolutionary aspects, by the construction of phylogenetic trees Determining active site residues, and residues specific for subfamilies Predicting protein–protein interactions Analysing single nucleotide polymorphism to hunt for genetic sources of diseases.

3. Structural Bioinformatics This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. multiple sequence alignment, secondary structure, 3D structure can be predicted with an accuracy above 70 %.

4. Pharmacological relevance Drug targets in infectious organisms can be revealed by whole genome comparisons of infectious and non–infectious organisms. The analysis of single nucleotide polymorphisms reveals genes potentially responsible for genetic diseases. Prediction and analysis of protein 3D structure is used to develop drugs and understand drug resistance. Patient databases with genetic profiles, e.g. for cardiovascular diseases, diabetes, cancer, etc. may play an important role in the future for individual health care, by integrating personal genetic profile (population genetics) into diagnosis.

Genomic Browsers National Center for Biotechnology information (NCBI) (http://ncbi.nlm.nih.gov) Ensembl Genome Browser (http://www.ensembl.org) UCSC Genome Browser (http://genome.ucsc.edu/) WormBase (http://www.wormbase.org/) AceDB (http://www.acedb.org/) FlyBase (http://flybase.bio.indiana.edu/)

Protein databses SWISS-PROT/TrEMBL curated protein sequences http://www.expasy.ch/sprot InterPro: Protein families and domains http://www.ebi.ac.uk/interpro EXProt: proteins with experimentally verified functions http://www.cmbi.nl/exprot Protein Information Resource (PIR) http://pir.georgetown.edu/

NCBI

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NCBI text search of a protein

Abstract finding by NCBI

Nucleotide search of a typical gene

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FASTA format

FASTA: FASTA format is a text-based format for representing either nucleic acid sequences or protein sequences, in which base pairs or protein residues are represented using single letter codes.