Abstract
Clustering of miRNA sequences is an important problem in molecular genetics associated cellular biology. Thousands of such sequences are known today through advancement in sophisticated molecular tools, sequencing techniques, computational resources and rule based mathematical models. Analysis of such large-scale miRNA sequences for inferring patterns towards deducing cellular function is a great challenge in modern molecular biology. Therefore, it is of interest to develop mathematical models specific for miRNA sequences. The process is to group (cluster) such miRNA sequences using well-defined known features. We describe a method for clustering of miRNA sequences using fragmented programming. Subsequently, we illustrated the utility of the model using a dendrogram (a tree diagram) for publically known A.thaliana miRNA nucleotide sequences towards the inference of observed conserved patterns.