STAR is shown to have high accuracy and outperforms other aligners by more than a factor of 50 in mapping speed, but it is memory intensive requiring around 30GB to run one mammalian sample. The algorithm achieves this highly efficient mapping by performing a two-step process:

  1. Seed searching: for every read that STAR aligns, STAR will search for the longest sequence that exactly matches one or more locations on the reference genome. These longest matching sequences are called the Maximal Mappable Prefixes (MMPs). The different parts of the read that are mapped separately are called ‘seeds’. So the first MMP that is mapped to the genome is called seed1.

  2. Clustering, stitching and scoring: The separate seeds are stitched together to create a complete read by first clustering the seeds together based on proximity to a set of ‘anchor’ seeds, or seeds that are not multi-mapping.

    Then the seeds are stitched together based on the best alignment for the read (scoring based on mismatches, indels, gaps, etc.).

STAR uses a Genome index to map reads to the genome.


Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England), 29(1), 15–21.

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