Abstract
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.
Highlights
Circular RNAs were recently discovered to be widespread, abundant, expressed across species, and implicated in several diseases
To assess the quality of the predictions provided by the webcircRNA web server, we evaluated the performance of each of the three classifiers on independent test sets
We addressed classifying protein coding gene (PCG) and long non-coding RNA
Summary
Circular RNAs (circRNAs) were recently discovered to be widespread, abundant, expressed across species, and implicated in several diseases. They are created by non-linear backsplicing between a splice donor and an upstream splice acceptor, and evidence is emerging for them playing functional roles as microRNA (miRNA) sponges [1,2] and in regulation of gene splicing and transcription [3]. Of the 92,375 human circRNAs in the circBase database (v0.1) [6] arise from protein-coding genes (PCGs). The number of discovered circRNAs has been rapidly increasing in recent years due to the development of new high-throughput sequencing technologies, and circBase contains more than. CircRNAs are expressed in a cell/tissue-specific manner [2]; for example, 16,017 are expressed in stem cells, and they are especially prominent during embryonic development [7]
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