Title

A method for clustering of miRNA sequences using fragmented programming

Authors

Anatoly Ivashchenko*, Anna Pyrkova & Raigul Niyazova

Affiliation

Computer Science Laboratory, Al-Farabi Kazakh National University, Almaty - 050038, Kazakhstan

Email

a_ivashchenko@mail.ru;  *Corresponding author

Article Type

Prediction Model

Date

Received January 05, 2016; Revised January 07 2016; Accepted January 07, 2016; Published January 31, 2016

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.

Citation

Ivashchenko et al. Bioinformation 12(1): 15-18 (2016)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.