Optimizing RNA-Seq Mapping with STAR
Optimizing RNA-Seq Mapping with STAR
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DOI：
10.1007/978-1-4939-3572-7_13
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发表时间：
2016-01-01
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影响因子：
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通讯作者：
Gingeras, Thomas R.
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文献类型：
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作者：
Dobin, Alexander;Gingeras, Thomas R.
高通量测序技术的最新进展使得通过产生数亿个代表转录RNA分子片段的短读段来探测细胞转录组成为可能。RNA-seq数据分析中的第一个也是最关键的任务是将读数映射到参考基因组。星星（剪接转录物与参考比对）是一种RNA-seq mapper，可以以超快的速度进行高度准确的剪接序列比对。星星对齐算法可以通过许多用户定义的参数进行控制。在这里，我们描述了最重要的星星选项和参数，以及实现最大映射精度和速度的最佳实践。
Recent advances in high-throughput sequencing technology made it possible to probe the cell transcriptomes by generating hundreds of millions of short reads which represent the fragments of the transcribed RNA molecules. The first and the most crucial task in the RNA-seq data analysis is mapping of the reads to the reference genome. STAR (Spliced Transcripts Alignment to a Reference) is an RNA-seq mapper that performs highly accurate spliced sequence alignment at an ultrafast speed. STAR alignment algorithm can be controlled by many user-defined parameters. Here, we describe the most important STAR options and parameters, as well as best practices for achieving the maximum mapping accuracy and speed.