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CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction

机译:CompaRNA:用于对RNA二级结构预测的自动化方法进行连续基准测试的服务器

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We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.
机译:我们提供了一种连续的基准测试方法,用于评估在CompaRNA Web服务器中实现的RNA二级结构预测方法。截至2012年10月3日,已经通过蛋白质数据库每周发布的RNA序列/结构评估了28种单序列和13种比较方法的性能。我们还提供了从RNAstrand数据库衍生的RNA 2D结构上生成的静态基准。两种数据集上的基准都有助于洞察RNA二级结构预测方法在不同大小,不同结构类型的RNA上的相对性能。根据我们的测试,平均而言,通过比较方法获得的最准确的预测由CentroidAlifold,MXScarna,RNAalifold和TurboFold生成。平均而言,单序列分析获得的最准确的预测由CentroidFold,ContextFold和IPknot生成。如果有同源RNA序列的比对,最好的比较方法通常会胜过最好的单序列方法。本文介绍了截至2012年10月3日的基准测试结果,而在线排名则不断更新。我们将很高兴在CompaRNA基准的新版本中包括新的预测方法和新的准确性度量。

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