A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life.
A comprehensive benchmark of RNA-RNA interaction prediction tools for all domains of life.
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DOI：
10.1093/bioinformatics/btw728
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发表时间：
2017-04-01
期刊：
影响因子：
--
通讯作者：
Gardner PP
中科院分区：
文献类型：
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作者：
Umu SU;Gardner PP
本研究的目的是评估RNA-RNA相互作用预测工具在生命所有领域的性能。最小自由能（MFE）和比对方法构成了目前大部分的RNA相互作用预测算法。包括最终预测结合能的可及性（即RNAup、IntaRNA和RNAplex）的MFE工具在所有数据集中具有比其他方法更好的真阳性率（TPR）和高阳性预测值（PPV）。它们还可以区分几乎一半的本地交互和背景交互。与基于可及性的方法相比，包括内部结合能对其模型和比对方法的影响的算法似乎具有高TPR但相对低的相关PPV。我们在Github（github.com/UCanCompBio/RNA_Interactions_Benchmark）上分享了我们的包装脚本和数据集。所有参数均记录在案，供个人使用。 补充数据可在Bioinformatics在线获得。
The aim of this study is to assess the performance of RNA–RNA interaction prediction tools for all domains of life. Minimum free energy (MFE) and alignment methods constitute most of the current RNA interaction prediction algorithms. The MFE tools that include accessibility (i.e. RNAup, IntaRNA and RNAplex) to the final predicted binding energy have better true positive rates (TPRs) with a high positive predictive values (PPVs) in all datasets than other methods. They can also differentiate almost half of the native interactions from background. The algorithms that include effects of internal binding energies to their model and alignment methods seem to have high TPR but relatively low associated PPV compared to accessibility based methods. We shared our wrapper scripts and datasets at Github (github.com/UCanCompBio/RNA_Interactions_Benchmark). All parameters are documented for personal use. Supplementary data are available at Bioinformatics online.