Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6359
Title: MicroRNA categorization using sequence motifs and k-mers
Authors: Yousef, Malik
Khalifa, Waleed
Acar, İlhan Erkin
Allmer, Jens
Acar, İlhan Erkin
Allmer, Jens
Izmir Institute of Technology. Molecular Biology and Genetics
Keywords: Machine learning
MicroRNAs
MiRNA categorization
Sequence motifs
Differentiate miRNAs among species
Issue Date: Mar-2017
Publisher: BioMed Central Ltd.
Source: Yousef, M., Khalifa, W., Acar, İ. E., and Allmer, J. (2017). MicroRNA categorization using sequence motifs and k-mers. BMC Bioinformatics, 18(1). doi:10.1186/s12859-017-1584-1
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.
URI: http://doi.org/10.1186/s12859-017-1584-1
http://hdl.handle.net/11147/6359
ISSN: 1471-2105
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
6359.pdfMakale773.31 kBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

SCOPUSTM   
Citations

13
checked on Jun 19, 2021

WEB OF SCIENCETM
Citations

13
checked on Jun 19, 2021

Page view(s)

2
checked on Jun 19, 2021

Download(s)

2
checked on Jun 19, 2021

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.