Comparison of circular RNA prediction tools

By T. B. Hansen, M. Venø, C. Damgaard, Jørgen Kjems. Nucleic Acids Research, 2015

364 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f6accc4bc290c6870cc0385b397eed7001e034f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Linda Szabo
2. Steven P. Barrett
3. Julia Salzman

In Molecular Biology

1. Linda Szabo
2. Steven P. Barrett
3. Julia Salzman

Citations in the corpus

• The bioinformatics toolbox for circRNA discovery and analysis (2020, methodology): “Based on comparison results, MapSplice is more reliable but timeconsuming [78, 87].”
• Detecting circular RNAs: bioinformatic and experimental challenges (2016, methodology): “With little agreement between the predictions of the different circRNA detection algorithms 41,58 , and in the absence of a gold standard that can be used to assess the ccuracy of the predictions from these algorithms, current hypotheses on the regulation and function of cir-cRNA are based on a…”
• Past, present, and future of circRNAs (2019, neutral): “Nowadays, there are a number of pipelines for annotating and quantifying circRNAs (reviewed by Hansen et al , 2016; Zeng et al , 2017; and compared by Hansen, 2018) as well as databases for circRNA reference (reviewed by Xu, 2017).”
• Diverse alternative back-splicing and alternative splicing landscape of circular RNAs (2016, neutral): “Our previously reported pipeline, CIRCexplorer (Zhang et al. 2014), has been reported as one of the best pipelines for circRNA annotation by identifying back-splice junctions (Hansen et al. 2016).”
• Circular RNAs: analysis, expression and potential functions (2016, background): “A recent article compared the performance of several such published algorithms, finding dramatic differences between sensitivity and specificity (Hansen et al., 2015).”
• CSCD: a database for cancer-specific circular RNAs (2017, methodology): “To identify cancer-specific circRNAs (CS-circRNAs), four popular algorithms with high performance (32): CIRI2 (31,33), find circ (5), circRNA finder (34) and Circexplorer (35,36) were utilized for detecting the back-splice junction sites of circRNAs.”