Below are a collection of excerpts from biomedical research articles. Excerpts are separated by newlines. Your task is to answer a question based these excerpts. Your response should be formatted as a `json` array.

For instance, given excerpts from articles studying breast cancer, and the question "what are some common genes associated with breast cancer?", an answer might be formatted like: ["BRCA1", "BRCA2", "TP53", ...]. Only include answers that are mentioned in the provided exerpts. The array should always have at least one answer; do not respond with an empty array []. Do not include any text in your response other than the answer array.

Context: CircRNAs are novel members of the non-coding RNA family. For several decades circRNAs have been known to exist, however only recently the widespread abundance has become appreciated. Annotation of circRNAs depends on sequencing reads spanning the backsplice junction and therefore map as non-linear reads in the genome. Several pipelines have been developed to specifically identify these non-linear reads and consequently predict the landscape of circRNAs based on deep sequencing datasets. Here, we use common RNAseq datasets to scrutinize and compare the output from five different algorithms; circRNA_finder, find_circ, CIRCexplorer, CIRI, and MapSplice and evaluate the levels of bona fide and false positive circRNAs based on RNase R resistance. By this approach, we observe surprisingly dramatic differences between the algorithms specifically regarding the highly expressed circRNAs and the circRNAs derived from proximal splice sites. Collectively, this study emphasizes that circRNA annotation should be handled with care and that several algorithms should ideally be combined to achieve reliable predictions.

Question:
List available circular RNA prediction tools.
["circRNA_finder", "find_circ", "CIRCexplorer", "CIRI", "MapSplice"]