LBSizeCleav: improved support vector machine (SVM)-based prediction of Dicer cleavage sites using loop/bulge length

• Published in: BMC bioinformatics Vol. 17; no. 1; p. 487
• Main Authors: Bao, Yu, Hayashida, Morihiro, Akutsu, Tatsuya
• Format: Journal Article
• Language: English
• Published: London BioMed Central 25.11.2016; BioMed Central Ltd; Springer Nature B.V
• Subjects: Algorithms; Base Sequence; Bayes Theorem; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; DEAD-box RNA Helicases; Humans; Life Sciences; Machine learning; Methods; Microarrays; MicroRNA; MicroRNAs - genetics; Nucleic Acid Conformation; Physiological aspects; Research Article; Ribonuclease III - metabolism; RNA Precursors - genetics; RNA Precursors - metabolism; RNA sequencing; Sequence analysis (methods); Software; Support Vector Machine; Technology application; Dicer cleavage site; Loop/bulge length; Support vector machine
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-016-1353-6

Background Dicer 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. Result In 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. Conclusion We 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.