Comparison of circular RNA prediction tools.
Comparison of circular RNA prediction tools.
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DOI：
10.1093/nar/gkv1458
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发表时间：
2016-04-07
影响因子：
14.9
通讯作者：
Kjems J
中科院分区：
文献类型：
--
作者：
Hansen TB;Venø MT;Damgaard CK;Kjems J
CircRNA是非编码RNA家族的新成员。几十年来，人们一直知道circRNA的存在，但直到最近才认识到它的广泛存在。circRNA的注释取决于跨越反向剪接点的测序读段，因此在基因组中映射为非线性读段。已经开发了几个管道来特异性地识别这些非线性读段，并因此基于深度测序数据集预测circRNA的景观。在这里，我们使用常见的RNAseq数据集来仔细检查和比较五种不同算法的输出; circRNA_finder，find_circ，CIRCexplorer，CIRI和MapSplice，并基于RNase R抗性评估真实和假阳性circRNA的水平。通过这种方法，我们观察到特别是关于高度表达的circRNA和来自近端剪接位点的circRNA的算法之间令人惊讶的显著差异。总的来说，这项研究强调，circRNA注释应谨慎处理，理想情况下应结合几种算法来实现可靠的预测。
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.