Summary of accuracy measures on the positive and mixed datasets.

<p>Summary of accuracy measures on the positive and mixed datasets.</p

A comprehensive overview and evaluation of circular RNA detection tools - Fig 3

<p>Coverage between circRNA detection methods on <b>(a)</b> HeLa and <b>(b)</b> Hs68 RNase R–treated data. For a pair of methods (i, j), the number of candidates detected by each method and the common candidates between them are calculated, then the proportion of common candidates for each method can be further computed and depicted. Cells within the same column reflect proportions of candidates detected by a specific method (column name) covered by other methods (row names) while cells within the same row show the proportions of candidates detected by other methods (column names) covered by a specific method (row name). CE, CIRCexplorer; CF, circRNA_finder; circRNA, circular RNA; FC, find_circ; MS, MapSplice; SG, Segemehl; NCLS, NCLScan; PF, PTESFinder; RNase R, exonuclease that digests linear RNAs but preserves circRNAs; UB, UROBORUS.</p

Overview of circRNA candidates detected on the background dataset.

<p>Overview of circRNA candidates detected on the background dataset.</p

Comparison of circRNA candidates detected with and without RNase R treatment.

<p>Comparison of circRNA candidates detected with and without RNase R treatment.</p

Overview of circRNA detection tools evaluated.

<p>Overview of circRNA detection tools evaluated.</p

Number of experimentally verified circRNAs detected by each method.

<p>A total of 282 experimentally verified circRNAs were manually compiled from 17 studies, and the number of circRNAs rediscovered by each method on HeLa and Hs68 RNase R–treated samples was computed. CircRNAs with ≥1 and ≥2 supporting reads are shown in light blue and deep blue color, respectively. GE1: candidates with greater than or equal to 1 supporting reads; GE2: candidates with greater than or equal to 2 supporting reads. circRNA, circular RNA; RNase R, exonuclease that digests linear RNAs but preserves circRNAs.</p

Comparison of performance on the synthetic positive and mixed datasets in terms of sensitivity and precision rate.

<p>After filtering for candidates with ≥2 supporting reads, the number of candidates and true positives detected by each method were computed, then precision and sensitivity rate for each method were depicted.</p

A comprehensive overview and evaluation of circular RNA detection tools - Fig 6

<p><b>(a)</b> Computational cost for each method on metrics of runtime, <b>(b)</b> memory consumption, and <b>(c)</b> physical disk space usage. While HeLa_RNaseR+ and HeLa_RNaseR− datasets are moderately sized RNA-Seq datasets, Hs68_RNaseR+ and Hs68_RNaseR− are examples of datasets with deep sequencing depth. Note: The analyses were run on an Ubuntu 10.04 server with two Intel® Xeon® E5530 Central Processing Units and 102 gigabytes of RAM. The running time presented was based on at most 3 threads allocated for each tool.</p

Precision-recall Curve for the 11 circular RNA detection tools on synthetic positive and mixed datasets.

<p>Counts of candidates and true positives were calculated at increasing thresholds for the number of supporting reads, and the precision-recall results for each tool were further computed and depicted in the figures above. <b>(a)</b> Positive dataset (Inset: precision above 0.99 was detailed). <b>(b)</b> Mixed dataset (Inset: precision above 0.97 was detailed).</p

Additional file 1: of Refine gene functional similarity network based on interaction networks

CoreComplexes.xls is the referenced complex downloaded from the CORUM database. (XLS 1637 kb