WebCircRNA: Classifying the Circular RNA Potential of Coding and Noncoding RNA.

TY - JOUR

T1 - WebCircRNA

T2 - Classifying the Circular RNA Potential of Coding and Noncoding RNA.

AU - Pan, Xiaoyong

AU - Xiong, Kai

AU - Anthon, Christian

AU - Hyttel, Poul

AU - Freude, Kristine

AU - Jensen, Lars Juhl

AU - Gorodkin, Jan

PY - 2018/11/6

Y1 - 2018/11/6

N2 - Circular RNAs (circRNAs) are increasingly recognized to play crucial roles in post-transcriptional gene regulation including functioning as microRNA (miRNA) sponges or as wide-spread regulators, for example in stem cell differentiation. It is therefore highly relevant to identify if a transcript of interest can also function as a circRNA. Here, we present a user-friendly web server that predicts if coding and noncoding RNAs have circRNA isoforms and whether circRNAs are expressed in stem cells. The predictions are made by random forest models using sequence-derived features as input. The output scores are converted to fractiles, which are used to assess the circRNA and stem cell potential. The performances of the three models are reported as the area under the receiver operating characteristic (ROC) curve and are 0.82 for coding genes, 0.89 for long noncoding RNAs (lncRNAs) and 0.72 for stem cell expression. We present WebCircRNA for quick evaluation of human genes and transcripts for their circRNA potential, which can be essential in several contexts.

AB - Circular RNAs (circRNAs) are increasingly recognized to play crucial roles in post-transcriptional gene regulation including functioning as microRNA (miRNA) sponges or as wide-spread regulators, for example in stem cell differentiation. It is therefore highly relevant to identify if a transcript of interest can also function as a circRNA. Here, we present a user-friendly web server that predicts if coding and noncoding RNAs have circRNA isoforms and whether circRNAs are expressed in stem cells. The predictions are made by random forest models using sequence-derived features as input. The output scores are converted to fractiles, which are used to assess the circRNA and stem cell potential. The performances of the three models are reported as the area under the receiver operating characteristic (ROC) curve and are 0.82 for coding genes, 0.89 for long noncoding RNAs (lncRNAs) and 0.72 for stem cell expression. We present WebCircRNA for quick evaluation of human genes and transcripts for their circRNA potential, which can be essential in several contexts.

KW - Circular RNA

KW - Noncoding RNA

KW - Random forest

U2 - 10.3390/genes9110536

DO - 10.3390/genes9110536

M3 - Journal article

C2 - 30404245

SN - 2073-4425

VL - 9

JO - Genes

JF - Genes

IS - 11

M1 - 536

ER -