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                          BIOINFORMATICS

 

 

 

 

 

INTRODUCTION
 

 

     The term bioinformatics and computational biology are often used interchangeably; however, bioinformatics more properly refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Computational biology, on the other hand, refers to hypothesis driven investigation of a specific biological problem using computers, carried out with experimental or simulated data 0, with the primary goal of discovery and the advancement of biological knowledge. A similar distinction is made by national institute of health in their working definitions of bioinformatics and computational biology and technique driven research in bioinformatics. Bioinformatics is also often specified as an applied subfield of the more general discipline of biomedical informatics. Major research efforts in the field include sequence alignment, genre finding, genome assembly, protein structure alignment, protein structure prediction, prediction of gene expression and protein- protein interaction, and the modeling of evolution. A common thread in projects in bioinformatics and computational biology is the use of mathematical tools to extract useful information from data produced by high throughput biological techniques such as genome sequencing. A representive problem in bioinformatics is the assembly of high quality genome sequences from fragmentary ‘shotgun’ DNA sequencing. Other common problems include the study of gene regulating using data from microarrays or mass spectrometry. Bioinformatics at the basic level deals with biological information like data collection and storage, data searching and retrieval, analysis and predicting patterns. Fredj tekaia suggested the following definitions of bioinformatics; ‘the mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acids sequences and related information’.

 

 

 

HISTORY

 

 

     The first comprehensive collection of amino acid sequence was complied in the ‘atlas of protein sequence and structure’ by the national biomedical research foundation. This collection was edited by Margaret o dayhoff 1965 to 1978. Dayhoff and coworkers also made notable contribution to the comparison of amino acid sequences by developing computer software for detecting distantly related sequences, inferring evolutionary relationship, etc. the European molecular biology laboratory established their data library in 1980 to collect, organize and distribute nucleotide sequences data and related information. This function is now performed by the European bioinformatics institute (EBI), hinxton, U.K. during early 1980s. The national center for bioinformatics information was established in U.S.A. NCBI serves as a primary information data bank and provider for information. Some time later, DNA data bank was established by Japan. The national biomedical research foundation established the protein information resource (PIR) in 1984. PIR helps researchers in the identification and interpretation of proteins sequences information. All these data banks operate in close collaboration and regularly exchange data. The databanks serve as an important resource to all researchers interested in the biological phenomenons. Particularly molecular aspects of biological science.

 

 

     The management and analysis of the rapidly accumulating sequence data required new computer software and statically methods. This attracted scientists from computer science and mathematics to the fast emerging field of bioinformatics. As a result a variety of methods and tools have been developed that facilitate management, utilization and dissemination of biological information this is one of major advantage of bioinformatics.

 

 

 

 

IMPORTANCE

 

 

 

  • Data acquisition; high through put biology requires automatic capture and conversion of biological data to symbolic digital representation without human intervenition.-incresing efficiency of current DNA sequencing systems will require base calling algorithms that provide increased accuracy increased read lengths and confidence estimations.
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  • Assembly of sequence coatings; large scale sequencing projects require sequences assembly algorithms. Bio informatics will help in generating coatings becoz software’s have been developed.
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  • Predicting functional domains in genomic sequences; advances in sequencing technology are expected to make the acquisition of large amount of anonymous sequences, witch can be converted to its functions. It is likely that experimental determination of coding region will be necessary to use computational methods to find all the axons in genomic sequences. Computational approaches to recognize functional sites based on certain assumptions have been generated.
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  • Sequences alignment; once the sequences of nucleotides is obtained, it I compared to all the known sequences to see it any instructive can be detected. Finding a similar sequence of known function can often be helpful in elucidating the function of a new cloned gene. For example. A quarter of positionally cloned human genes were found to have matches.

 

 

CHALLENGES

 

 

  • Data management; the physical mapping of genomes and large scale sequencing projects require storage of information. In laboratory conditions data of a few 100 megabytes to nearly 100 gigabytes can be stored. Information technology talk in term of terabytes, pet bytes, and Exabyte. It is stated that all the words ever spoken by human beings amount to about five Exabyte.
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  • Research is also needed for new and improved analytical methods. These methods are essential for turning molecular and structural data in to biological knowledge.
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  • The searching of biological databases via the WWW is becoming increasingly difficult. There are differences ib databases structure and nomenclature that hider research efforts and standardizations have met with much resistance.
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  • The development of various strategies is surf WWW and reaching a consensus on cloning uniform definitions and adopting uniform platform and technologies is another challenge in the area of bioinformatics.

 

 

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