AStrap: identification of alternative splicing from transcript sequences without a reference genome
AStrap: identification of alternative splicing from transcript sequences without a reference genome
复制标题
AStrap：在没有参考基因组的情况下从转录序列中鉴定选择性剪接
DOI：
10.1093/bioinformatics/bty1008
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发表时间：
2019-08-01
期刊：
影响因子：
5.8
通讯作者：
Wu, Xiaohui
中科院分区：
文献类型：
--
作者：
Ji, Guoli;Ye, Wenbin;Wu, Xiaohui
选择性剪接 (AS) 是一种成熟的增加转录组和蛋白质组多样性的机制，然而，在没有可用参考基因组的情况下检测 AS 事件并区分生物体中的 AS 类型仍然具有挑战性。我们开发了一种名为 AStrap 的从头方法，用于在不使用参考基因组的情况下进行 AS 分析。 AStrap 通过转录序列的广泛成对比对来识别 AS 事件，并通过集成 500 多个组装特征的机器学习模型来预测 AS 类型。我们使用从水稻和人类参考基因组中收集的 AS 事件以及来自 Amborella trichopoda 的单分子实时测序数据来评估 AStrap。结果表明，AStrap 可以识别更多的 AS 事件，其准确度与竞争方法相当或更高。 AStrap 还具有预测 AS 类型的独特功能，对于不同物种，其总体准确率接近 0.87。使用不同参数、样本量和机器学习模型对不同物种对 AStrap 进行广泛评估也证明了 AStrap 的稳健性和灵活性。 AStrap 可能是对遗传资源有限的非模型生物体中 AS 研究的社区的一个有价值的补充。可用性和实施 AStrap 可在 https://github.com/BMILAB/AStrap 下载。补充信息补充数据可在生物信息学在线获取。
Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of similar to 0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources.Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap.Supplementary informationSupplementary data are available at Bioinformatics online.