A convNet based multi label microRNA sub cellular location predictor, by incorporating k-mer positional encoding

Abstract

MicroRNAs are special RNA sequences containing 22 nucleotides and are capable of regulating almost 60% of highly complex mammalian transcriptome. Presently, there exists very limited approaches capable of visualizing miRNA locations inside cell to reveal the hidden pathways, and mechanisms behind miRNA functionality, transport, and biogenesis. State-of-the-art miRNA sub-cellular location prediction MIRLocatar approach makes use of sequence to sequence model along with pre-train k-mer embeddings. Existing pre-train k-mer embedding generation methodologies focus on the extraction of semantics of k-mers. In RNA sequences, rather than semantics, positional information of nucleotides is more important because distinct positions of four basic nucleotides actually define the functionality of RNA molecules. Considering the dynamicity and importance of nucleotides positions, instead of learning representation on the basis of k-mers semantics, we propose a novel kmerRP2vec feature representation approach that fuses positional information of k-mers to randomly initialized neural k-mer embeddings. Effectiveness of proposed feature representation approach is evaluated with two deep learning based convolutional neural network CNN and recurrent neural network RNN methodologies using 8 evaluation measures. Experimental results on a public benchmark miRNAsubloc dataset prove that proposed kmerRP2vec approach along with a simple CNN model outperforms state-of-the-art MirLocator approach with a significant margin of 18% and 19% in terms of precision and recall.

Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction

“…Emergence of diverse metathesauruses including RNALocate [ 34 ], Ensembl [ 35 ], and ENCODE [ 36 ] has opened new horizons for the large scale determination of sub-cellular localizations of different non-coding RNAs through computational methodologies [ 37 , 38 ]. To date, several long non-coding RNAs (lncRNAs) [ 39 , 40 , 41 ], miRNAs [ 42 , 43 ], and messenger RNAs (mRNAs) [ 42 ] sub-cellular localization prediction approaches have been presented.…”

“…Considering the downfalls of experimental approaches, there is a lack of computational circRNA sub-cellular localization approaches, and there is also the fact that sub-cellular localization prediction approaches developed for other ncRNAs [ 38 ] cannot be applied to determine the sub-cellular localization of circRNAs due to the differentiation of biological structure, distribution of residues, and sequence length. The paper in hand develops a computational framework (Circ-LocNet) capable of performing a large scale sub-cellular analysis of a variety of circRNAs.…”