PCirc: random forest-based plant circRNA identification software.
PCirc: random forest-based plant circRNA identification software.
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PCirc：基于随机森林的植物circRNA识别软件
DOI：
10.1186/s12859-020-03944-1
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发表时间：
2021-01-06
影响因子：
3
通讯作者：
Li G
中科院分区：
文献类型：
--
作者：
Yin S;Tian X;Zhang J;Sun P;Li G
研究背景环状RNA（Circular RNA）是一类新型的具有闭环结构的RNA。越来越多的circRNA在植物和动物中被发现，最近的研究表明circRNA在基因调控中起着重要作用。因此，从越来越多的RNA-seq数据中识别circRNA非常重要。然而，传统的circRNA识别方法具有局限性。近年来，新兴的机器学习技术为识别动物中的circRNA提供了一种很好的方法。然而，利用这些特征来鉴定植物circRNA是不可行的，因为植物circRNA序列的特征不同于动物circRNA序列的特征。例如，植物中含有极其丰富的剪接信号和转座因子，而它们在水稻中的序列保守性远低于哺乳动物。为了解决这些问题，更好地识别植物中的circRNA，迫切需要基于植物circRNA.ResultsIn这项研究中，我们建立了一个软件程序PCirc使用机器学习方法预测植物circRNA从RNA-seq数据的特点，使用机器学习的circRNA识别软件。首先，我们从水稻circRNA和lncRNA数据中提取了不同的特征，包括开放阅读框架，k-mer的数量和剪接接头序列编码。其次，我们通过随机森林算法训练了一个机器学习模型，并在训练集中进行了十次交叉验证。第三，我们根据准确度、精确度和F1得分来评估我们的分类，模型测试数据的所有得分都在0.99以上。第四，我们通过其他植物测试对我们的模型进行了测试，并获得了良好的结果，准确度得分在0.8以上。最后，我们将构建的机器学习模型和使用的编程脚本打包到本地运行的环状RNA预测软件Pcirc（ https://github.com/Lilab-SNNU/Pcirc 结论基于水稻circRNA和lncRNA数据，利用随机森林算法构建了植物circRNA识别的机器学习模型，该模型也可应用于拟南芥和玉米等植物circRNA识别。同时，在模型构建完成后，将构建的机器学习模型和本研究中使用的编程脚本打包成本地化的circRNA预测软件Pcirc，方便植物circRNA研究者使用。
BackgroundCircular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on the characteristics of plant circRNAs.ResultsIn this study, we built a software program named PCirc using a machine learning method to predict plant circRNAs from RNA-seq data. First, we extracted different features, including open reading frames, numbers of k-mers, and splicing junction sequence coding, from rice circRNA and lncRNA data. Second, we trained a machine learning model by the random forest algorithm with tenfold cross-validation in the training set. Third, we evaluated our classification according to accuracy, precision, and F1 score, and all scores on the model test data were above 0.99. Fourth, we tested our model by other plant tests, and obtained good results, with accuracy scores above 0.8. Finally, we packaged the machine learning model built and the programming script used into a locally run circular RNA prediction software, Pcirc ( https://github.com/Lilab-SNNU/Pcirc ).ConclusionBased on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such asArabidopsis thalianaand maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.
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影响因子：
5.6
作者：
Wang Y;Yang M;Wei S;Qin F;Zhao H;Suo B
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DOI：
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