Prediction of RNA Secondary Structure from Random Sequences using ZEM

The biological role of many RNA crucially depends on their structure. The in depth understanding of the secondary structure of RNA would provide a better insight in to their functionality. Predicting secondary structure of RNA is the most important factor in determining its 3d structure and functions. This work proposes a model for exploring the features of a number of RNA sequences simultaneously so that comparison of sequences can be made and relevant sequences can be identified. The proposed model accepts RNA sequences in any valid biological file format. For each given sequence, required number of random sequences are generated. The generated sequences should have the same base composition as that of original sequence. ZEM (Zuker?s Energy Minimization) Algorithm finds the biologically correct structure of each RNA sequence and its corresponding free energy value. The proposed prototype enables to experiment with a number of RNA sequences and to study their features so that biologically relevant inferences can be made. An important area where it finds application is in the design of pharmaceutical products.