Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers

Abstract

Background: Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs (miRNAs) play a key role in the modulation of protein quantity. MicroRNAs are used throughout all kingdoms and have been shown to be exploited by viruses to modulate their host environment. Since the experimental detection of miRNAs is difficult, computational methods have been developed. Many such tools employ machine learning for pre-miRNA detection, and many features for miRNA parameterization have been proposed. To train machine learning models, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs from one species as positive data versus another species' pre-miRNAs as negative examples based on sequence motifs and k-mers. Here, we introduce the additional usage of information-theoretic (IT) features. Results: Pre-miRNAs from one species were used as positive and another species' pre-miRNAs as negative training data for machine learning. The categorization capability of IT and k-mer features was investigated. Both feature sets and their combinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-mer based method. However, for obtaining a high performance, a sufficiently large phylogenetic distance between the species and sufficiently high number of pre-miRNAs in the training set is required. To examine the contribution of the IT and k-mer features, an information gain-based feature ranking was performed. Although the top 3 are IT features, 80% of the top 100 features are k-mers. The comparison of all three individual approaches (motifs, IT, and k-mers) shows that the distinction of species based on their pre-miRNAs k-mers are sufficient. Conclusions: IT sequence feature extraction enables the distinction among species and is less computationally expensive than motif calculations. However, since IT features need larger amounts of data to have enough statistics for producing highly accurate results, future categorization into species can be effectively done using k-mers only. The biological reasoning for this is the existence of a codon bias between species which can, at least, be observed in exonic miRNAs. Future work in this direction will be the ab initio detection of pre-miRNA. In addition, prediction of pre-miRNA from RNA-seq can be done.

Species Categorization via MicroRNAs - Based on 3’UTR Target Sites using Sequence Features

“…There have been accounts of cross-kingdom regulation via miRNAs and we were able to reject some of them (Bağcı and Allmer, 2016), but on the other hand cross-kingdom regulation may occur in tightly coupled systems like viruses or intracellular parasites and their hosts (Saçar, Bağcı and Allmer, 2014;Saçar Demirci, Bağcı and Allmer, 2016). Machine learning models allowing the differentiation of miRNA targets among species add another line of evidence for the investigation of cross-kingdom regulation and we suggest that both miRNAs should fit the host species machine model (Malik Yousef, Nigatu, et al, 2017) as well as the targeting model (this study) to consider the regulation for experimental follow-up studies.…”

GrpClassifierEC: a novel classification approach based on the ensemble clustering space

“…The source of this data is miRbase [18]. Part of the data we have used has was from other different studies [19][20][21], including our study [16].…”