LBSizeCleav: improved support vector machine SVM-based prediction of Dicer cleavage sites using loop-bulge lengthReport as inadecuate




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BMC Bioinformatics

, 17:487

Sequence analysis methods

Abstract

BackgroundDicer is necessary for the process of mature microRNA miRNA formation because the Dicer enzyme cleaves pre-miRNA correctly to generate miRNA with correct seed regions. Nonetheless, the mechanism underlying the selection of a Dicer cleavage site is still not fully understood. To date, several studies have been conducted to solve this problem, for example, a recent discovery indicates that the loop-bulge structure plays a central role in the selection of Dicer cleavage sites. In accordance with this breakthrough, a support vector machine SVM-based method called PHDCleav was developed to predict Dicer cleavage sites which outperforms other methods based on random forest and naive Bayes. PHDCleav, however, tests only whether a position in the shift window belongs to a loop-bulge structure.

ResultIn this paper, we used the length of loop-bulge structures in addition to their presence or absence to develop an improved method, LBSizeCleav, for predicting Dicer cleavage sites. To evaluate our method, we used 810 empirically validated sequences of human pre-miRNAs and performed fivefold cross-validation. In both 5p and 3p arms of pre-miRNAs, LBSizeCleav showed greater prediction accuracy than PHDCleav did. This result suggests that the length of loop-bulge structures is useful for prediction of Dicer cleavage sites.

ConclusionWe developed a novel algorithm for feature space mapping based on the length of a loop-bulge for predicting Dicer cleavage sites. The better performance of our method indicates the usefulness of the length of loop-bulge structures for such predictions.

KeywordsDicer cleavage site Support vector machine Loop-bulge length AbbreviationsDGCR8DiGeorge syndrome chromosomal region 8

dsRNDDouble-stranded RNA-binding domain

FNfalse negative

FPFalse positive

miRNAMicroRNA

PAZPIWI, AGO, and Zwille domain

RISCRNA-induced silencing complex

SVMSupport Vector Machine

TNTrue negative

TPTrue positive

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Author: Yu Bao - Morihiro Hayashida - Tatsuya Akutsu

Source: https://link.springer.com/



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