The Sequence Structures of Human MicroRNA Molecules and Their Implications

Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana, United States of America.
PLoS ONE (Impact Factor: 3.53). 01/2013; 8(1):e54215. DOI: 10.1371/journal.pone.0054215
Source: PubMed

ABSTRACT The count of the nucleotides in a cloned, short genomic sequence has become an important criterion to annotate such a sequence as a miRNA molecule. While the majority of human mature miRNA sequences consist of 22 nucleotides, there exists discrepancy in the characteristic lengths of the miRNA sequences. There is also a lack of systematic studies on such length distribution and on the biological factors that are related to or may affect this length. In this paper, we intend to fill this gap by investigating the sequence structure of human miRNA molecules using statistics tools. We demonstrate that the traditional discrete probability distributions do not model the length distribution of the human mature miRNAs well, and we obtain the statistical distribution model with a decent fit. We observe that the four nucleotide bases in a miRNA sequence are not randomly distributed, implying that possible structural patterns such as dinucleotide (trinucleotide or higher order) may exist. Furthermore, we study the relationships of this length distribution to multiple important factors such as evolutionary conservation, tumorigenesis, the length of precursor loop structures, and the number of predicted targets. The association between the miRNA sequence length and the distributions of target site counts in corresponding predicted genes is also presented. This study results in several novel findings worthy of further investigation that include: (1) rapid evolution introduces variation to the miRNA sequence length distribution; (2) miRNAs with extreme sequence lengths are unlikely to be cancer-related; and (3) the miRNA sequence length is positively correlated to the precursor length and the number of predicted target genes.

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Available from: Ruofei Du, Feb 18, 2015
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