Optimizing RNA-Seq mapping with STAR
A Dobin, TR Gingeras - Data mining techniques for the life sciences, 2016 - Springer
Data mining techniques for the life sciences, 2016•Springer
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 …
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 …
Abstract
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.
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