tetano
Editor, Senior Moderator
J Integr Bioinform
. 2021 Mar 16.
doi: 10.1515/jib-2020-0047. Online ahead of print.
Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection
Y?lmaz Mehmet Demirci[SUP] 1 [/SUP], M??erref Duygu Sa?ar Demirci[SUP] 2 [/SUP]
Affiliations
Abstract
Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.
Keywords: SARS-CoV-2; circRNA; gene regulation; machine learning; miRNA.
. 2021 Mar 16.
doi: 10.1515/jib-2020-0047. Online ahead of print.
Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection
Y?lmaz Mehmet Demirci[SUP] 1 [/SUP], M??erref Duygu Sa?ar Demirci[SUP] 2 [/SUP]
Affiliations
- PMID: 33725751
- DOI: 10.1515/jib-2020-0047
Abstract
Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.
Keywords: SARS-CoV-2; circRNA; gene regulation; machine learning; miRNA.