Accurately and rapidly distinguishing long noncoding RNAs (lncRNAs) from transcripts is prerequisite for exploring their biological functions. In recent years, many computational methods have been developed to predict lncRNAs from transcripts, but there is no systematic review on these computational methods. In this review, we introduce databases and features involved in the development of computational prediction models, and subsequently summarize existing state-of-the-art computational methods, including methods based on binary classifiers, deep learning and ensemble learning. However, a user-friendly way of employing existing state-of-the-art computational methods is in demand. Therefore, we develop a Python package ezLncPred, which provides a pragmatic command line implementation to utilize nine state-of-the-art lncRNA prediction methods. Finally, we discuss challenges of lncRNA prediction and future directions.

中文翻译：

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系统评价预测长非编码 RNA 的计算方法

准确快速地区分长链非编码 RNA (lncRNA) 与转录本是探索其生物学功能的先决条件。近年来，已经开发了许多计算方法来从转录本中预测 lncRNA，但没有对这些计算方法进行系统评价。在这篇综述中，我们介绍了计算预测模型开发所涉及的数据库和特征，随后总结了现有的最先进的计算方法，包括基于二元分类器、深度学习和集成学习的方法。然而，需要一种使用现有最先进计算方法的用户友好方式。因此，我们开发了一个 Python 包 ezLncPred，它提供了一个实用的命令行实现来利用九种最先进的 lncRNA 预测方法。