title: MicroRNA categorization using sequence motifs and k-mers.
abstract: BACKGROUND
  Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Many features have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decision-making process, we aimed to establish a means to differentiate pre-miRNAs from different species.
  
  
  RESULTS
  To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values.
  
  
  CONCLUSIONS
  We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.
authors.affiliation: Community Information Systems, Zefat Academic College, Zefat, 13206, Israel. malik.yousef@gmail.com.
authors.forename: Malik
authors.initials: M
authors.lastname: Yousef
authors.affiliation: Computer Science, The College of Sakhnin, Sakhnin, 30810, Israel.
authors.forename: Waleed
authors.initials: W
authors.lastname: Khalifa
authors.affiliation: Biotechnology, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey.
authors.forename: İlhan Erkin
authors.initials: İE
authors.lastname: Acar
authors.affiliation: Molecular Biology and Genetics, Izmir Institute of Technology, 35430, Urla, Izmir, Turkey. jens@allmer.de.|Bionia Incorporated, IZTEKGEB A8, 35430, Urla, Izmir, Turkey. jens@allmer.de.
authors.forename: Jens
authors.identifier: http://orcid.org/0000-0002-2164-7335
authors.initials: J
authors.lastname: Allmer
mesh_terms: D000818:Animals; D001483:Base Sequence; D007887:Fabaceae / Q000145:classification / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry / Q000235:genetics / Q000378:metabolism*; D010802:Phylogeny; D012322:RNA Precursors / Q000235:genetics / Q000378:metabolism
chemical_list: D035683:MicroRNAs; D012322:RNA Precursors
keywords: Differentiate miRNAs among species; Machine learning; Pre-microRNA; Sequence motifs; k-mer; miRNA categorization; microRNA