A method for clustering of miRNA sequences using fragmented programming

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 su...

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Detalles Bibliográficos
Autores principales: Ivashchenko, Anatoly, Pyrkova, Anna, Niyazova, Raigul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2016
Materias:
Prediction Model
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857460/
https://www.ncbi.nlm.nih.gov/pubmed/27212839
http://dx.doi.org/10.6026/97320630012015
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author Ivashchenko, Anatoly
Pyrkova, Anna
Niyazova, Raigul
author_facet Ivashchenko, Anatoly
Pyrkova, Anna
Niyazova, Raigul
author_sort Ivashchenko, Anatoly
collection PubMed
description 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
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spelling pubmed-48574602016-05-20 A method for clustering of miRNA sequences using fragmented programming Ivashchenko, Anatoly Pyrkova, Anna Niyazova, Raigul Bioinformation Prediction Model 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 Biomedical Informatics 2016-01-31 /pmc/articles/PMC4857460/ /pubmed/27212839 http://dx.doi.org/10.6026/97320630012015 Text en © 2016 Biomedical Informatics 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.
spellingShingle Prediction Model
Ivashchenko, Anatoly
Pyrkova, Anna
Niyazova, Raigul
A method for clustering of miRNA sequences using fragmented programming
title A method for clustering of miRNA sequences using fragmented programming
title_full A method for clustering of miRNA sequences using fragmented programming
title_fullStr A method for clustering of miRNA sequences using fragmented programming
title_full_unstemmed A method for clustering of miRNA sequences using fragmented programming
title_short A method for clustering of miRNA sequences using fragmented programming
title_sort method for clustering of mirna sequences using fragmented programming
topic Prediction Model
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857460/
https://www.ncbi.nlm.nih.gov/pubmed/27212839
http://dx.doi.org/10.6026/97320630012015
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