MicroRNA Transcription Start Site Prediction with Multi-objective Feature Selection

Article Type

Research Article

Publication Title

Statistical Applications in Genetics and Molecular Biology

Abstract

MicroRNAs (miRNAs) are non-coding, short (21-23nt) regulators of protein-coding genes that are generally transcribed first into primary miRNA (pri-miR), followed by the generation of precursor miRNA (pre-miR). This finally leads to the production of the mature miRNA. A large amount of information is available on the pre-A nd mature miRNAs. However, very little is known about the pri-miRs, due to a lack of knowledge about their transcription start sites (TSSs). Based on the genomic loci, miRNAs can be categorized into two types Â-intragenic (intra-miR) and intergenic (inter-miR). While it is already an established fact that intra-miRs are commonly transcribed in conjunction with their host genes, the transcription machinery of inter-miRs is poorly understood. Although it is assumed that miRNA promoters are similar in structure to gene promoters, since both are transcribed by RNA polymerase II (Pol II), computational validations exhibit poor performance of gene promoter prediction methods on miRNAs. In this paper, we concentrate on the problem of TSS prediction for miRNAs. The present study begins with the identification of positive and negative promoter samples from recently published data stemming from RNA-sequencing studies. From these samples of experimentally validated miRNA TSSs, a number of standard sequence features are extracted. Furthermore, to account for potential footprints related to promoter regulation by CpG dinucleotide targeted DNA methylation, a number of novel features are defined. We develop a support vector machine (SVM) with RBF kernel for the prediction of miRNA TSSs trained on human miRNA promoters. A novel feature reduction technique based on archived multi-objective simulated annealing (AMOSA) identifies the final set of features. The resulting model trained on miRNA promoters shows improved performance over the one trained on protein-coding gene promoters in terms of classification accuracy, sensitivity and specificity. Results are also reported for a completely independent biologically validated test set. In a part of the investigation, the proposed approach is used to predict protein-coding gene TSSs. It shows a significantly improved performance when compared to previously published gene TSS prediction methods.

DOI

10.2202/1544-6115.1743

Publication Date

1-1-2020

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