GenScan is bioinformatics software. Its mainsail function is to acquire a DNA sequence and find the open reading frames (a sequence of DNA that could potentially encode a protein) that accord to genes. GenScan was formulated by Prof. Chris Burg who is currently working on his thesis. This program is not only used to detect genes in a sequenced set of DNA, it can also be used to determine a specific sequence using measures of the percentage of C+G content. As a matter of fact there are mainly four profiles for a particular species in a parameter file and each profile is equivalent to C+G%.
There are mainly two types of structure predictions; one is for proteins and the other is for genes. For the understanding of gene prediction and analysis terms one should know what actually genes are. Genes are biological unit of heredity; they are located at distinct position (locus) on a specific chromosome. Basically genes are of two types.
Coding genes or Exons: a sequence of DNA that carries instructions to perform specific function or translated into protein. For example, there are globin genes that imparts instructions for the manufacturing of hemoglobin (which carry oxygen throughout the body) protein. As humans contains 50,000 different genes that work collectively in intricate way to perform different functions.
Non-coding genes or Introns: Usually do not contain any instruction for a function. Non-coding genes never translated into proteins.
Structure of Gene
In a count to regions that overtly code for a protein mostly genes are comprised of regulatory regions. Given below are the regions that all genes have:
Promoter region: it is a regulatory region of gene, which controls the initiation of RNA transcription.
Enhancers: they enhance the transcription.
Introns: are non-coding regions and these regions are transcribed but do not translate into proteins.
Exons: are coding regions and these regions are translated into proteins.
GenScan: Gene Structure Prediction
Now for the complete structure prediction of gene by using computational advances is to find out the location and function of gene. The main problem is to separate and define the exon-inton boundaries of a gene.
Two approaches followed by gene prediction:
Statistical patterns identification: This approach of gene prediction uses all-purpose knowledge about gene structure i.e. statistics and rules. Knowledge of gene structure as discussed earlier includes promoter region (where transcription initiates), start and end sequences of intron and exon etc.
Sequence similarity comparison: As similarity is based on evolution, either our sequence is homologous or not. This approach is based on similarity which takes advantage on the fact that if the sequence is similar it will have same function. But the structure of gene cannot be predicted accurately based on sequence information alone.
For the large scale analysis of gene the typical strategy is to completely inactivate each gene or over express it. In each case resulting phenotype may not be informative. The loss of many proteins is lethal and this tells us the protein is essential but it does not tell us what actually protein does. After the prediction of gene structure we can investigate its function, expression level, diseases, mutations, etc. By using this information we can cure different diseases.
Why to use GenScan?
• It can identify disease severity.
• It can help children and adults to live a long, healthy life.
• It can help people concentrate on precautionary measures against numerous, serious debilities.
• It can predict gene structure to investigate its function, expression level, disease, mutation.
• It can prevent the disease by delay of occurrence of disease.
Bioinformatics software developed to solve same problems
GENSCAN (Burge 1997)
FGENESH (Solovyev 1997)
HMMgene (Krogh 1997)
GENIE (Kulp 1996)
GENMARK (Borodovsky & McIninch 1993)
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