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    BIOINFORMATICS-basic

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    BIOINFORMATICS-basic

    Uploaded by

    asmaarshad2626
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    ppt

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    ppt

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    BIOINFORMATICS-basic

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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
    What is Bioinformatics?

    Source: http://ccb.wustl.edu/
    Why is bioinformatics in demand?
     Very 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


    What skills are needed?
     Well-grounded in one of the following areas:
     Computer science
     Molecular biology
     Statistics

     Working knowledge and appreciation in the others!


    Brief history of bioinformatics: Databases
    • The first biological database - Protein Identification Resource
    was established in 1972 by Margaret Dayhoff
    • Dayhoff and co-workers organized the proteins into families
    and superfamilies based on degree of sequence similarity
    • Idea of sequence alignment was introduced as well as special
    tables that reflected the frequency of changes observed in the
    sequences of a group of closely related proteins
    • Currently there are several huge Protein Banks : SwissProt,
    PIR International, etc.
    • The first DNA database was established in 1979. Currently
    there are several powerful databases: GenBank, EMBL, DDBJ,
    etc.
    Brief history of bioinformatics: other
    important steps
    • Development of sequence retrieval methods (1970-80s)
    • Development of principles of sequence alignment (1980s)
    • Prediction of RNA secondary structure (1980s)
    • Prediction of protein secondary structure and 3D (1980-90s)
    • The FASTA and BLAST methods for DB search (1980-90s)
    • Prediction of genes (1990s)
    • Studies of complete genome sequences (late 1990s –2000s)
    Where Can You Learn More?
     ISCB: http://www.iscb.org/
     NBCI: http://ncbi.nlm.nih.gov/
     http://www.bioinformatics.org/
     Journals (Journal of Computational Biology,
    Bioinformatics, BMC Bioinformatics,…)
     Conferences (ISMB, RECOMB, PSB, InCoB,…)
    Data Mining & Bioinformatics : Why?
     Many biological processes are not well-understood
     Biological knowledge is highly complex, imprecise, descriptive, and
    experimental
     Biological data is abundant and information-rich
     Genomics & proteomics data (sequences), microarray and protein-
    arrays, protein database (PDB), bio-testing data
     Huge data banks, rich literature, openly accessible
     Largest and richest scientific data sets in the world
     Mining: gain biological insight (data/information  knowledge)
     Mining for correlations, linkages between disease and gene sequences,
    protein networks, classification, clustering, outliers, ...
     Find correlations among linkages in literature and heterogeneous
    databases
    Algorithms Used in Bioinformatics
     Comparing sequences: Comparing large numbers of long sequences, allow
    insertion/deletion/mutations of symbols
     Constructing evolutionary (phylogenetic) trees: Comparing seq. of diff.
    organisms, & build trees based on their degree of similarity (evolution)
     Detecting patterns in sequences
    ◦ Search for genes in DNA or subcomponents of a seq. of amino acids
     Determining 3D structures from sequences
    ◦ E.g., infer RNA shape from seq. & protein shape from amino acid seq.
     Inferring cell regulation:
    ◦ Cell modeling from experimental (say, microarray) data
     Determining protein function and metabolic pathways: Interpret human
    annotations for protein function and develop graph db that can be queried
     Assembling DNA fragments (provided by sequencing machines)
     Using script languages: script on the Web to analyze data and applications

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