Genome Sequences analysis using HMM in Biological Databases

There are variety of problems exist in molecular biology. The sequenced genomes generally give information about to understand the underlying mechanisms of various biological functions in cells. However, as data is available enormously, it is really difficult to analyze them without the help of computational methods. In order to extract meaningful information from the data, we need computational techniques to biological sequence analysis. Due to digitization, the biological databases are continuously improved and updated. In other hand many Biological Databases are available which are classified as primary, secondary and composite type. While we do sequence analysis, we analyze in terms of functional or structural analysis. The hidden data certain time could not reflect in databases which may have vital role in analyzing biological results. The HMM (Hidden Markov Model) is one of the application area of artificial intelligence technique to model data analysis features. This research work here focuses on modeling DNA sequencing error with HMM by identifying hidden data from various depository of molecular databases which will help in re-sequencing genome data.