Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)
Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM)
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DOI：
10.1093/bioinformatics/btr427
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发表时间：
2011-09-15
期刊：
影响因子：
5.8
通讯作者：
Pierce, Eric A.
中科院分区：
文献类型：
--
作者：
Grant, Gregory R.;Farkas, Michael H.;Pierce, Eric A.
动机：高通量测序中的一项关键任务是将数百万个短读段与参考基因组进行比对。由于RNA剪接，RNA测序（RNA-Seq）的比对特别复杂。许多RNA-Seq算法是可用的，并且声称在检测剪接点的同时以高准确度和效率对齐读数。RNA-Seq数据本质上是离散的;因此，通过合理的基因模型和比较度量，可以模拟RNA-Seq数据以达到足够的准确性，以实现比对算法的有意义的基准测试。严格比较所有可行的已发表的RNA-Seq算法的练习尚未执行previous.Results：我们开发了一个RNA-Seq模拟器，该模拟器模拟了RNA比对的主要障碍，包括选择性剪接，插入，缺失，取代，测序错误和内含子信号。我们用这个模拟器来衡量的准确性和鲁棒性的基础和结的水平可用的算法。此外，我们使用逆转录-聚合酶链反应（RT-PCR）和桑格测序来验证算法检测小鼠视网膜RNA-Seq数据中新转录本特征（如新外显子和可变剪接）的能力。一个管道的基础上BLAT的开发，以探讨这个问题的既定工具的性能，并比较它最近开发的方法。这个管道，RNA-Seq统一映射器（RUM），执行最佳的当前比对，并提供准确性，速度和可用性的有利组合。
Motivation: A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously.Results: We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability.