Authors:
Kübra Narcı
1
;
Hasan Oğul
2
and
Mahinur Akkaya
1
Affiliations:
1
Middle East Technical University, Turkey
;
2
Başkent University, Turkey
Keyword(s):
MicroRNA, Sequence Clustering, Clustering Algorithms, Pair-wise Sequence Comparison Sequence Similarity.
Related
Ontology
Subjects/Areas/Topics:
Bioinformatics
;
Biomedical Engineering
;
Pattern Recognition, Clustering and Classification
;
Sequence Analysis
Abstract:
MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation. Altogether, understanding integrative and co-operative activities in gene regulation is conjugated with identification of miRNA families. In current applications, the identification of such groups of miRNAs is only investigated by the projections of their expression patterns and so along with their functional relations. Considering the fact that the miRNA regulation is mediated through its mature sequence by the recognition of the target mRNA sequences in the RISC (RNA-induced silencing complex) binding regions, we argue here that relevant miRNA groups can be obtained by de novo clustering them solely based on their sequence information, by a sequence clustering approach. In this way, a new study can be guided by a set of previously annotated miRNA groups without any preliminary experimentation or literature evidence. In this report, we presents the results of a computational study that considers only ma
ture miRNA sequences to obtain relevant miRNA clusters using various machine learning methods employed with different sequence representation schemes. Both statistical and biological evaluations encourages the use this approach in silico assessment of functional miRNA groups.
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