HISAT: Hierarchical Indexing for Spliced Alignment of Transcripts

Abstract

HISAT is a new, highly efficient system for alignment of sequences from RNA sequencing experiments that achieves dramatically faster performance than previous methods. HISAT uses a new indexing scheme, hierarchical indexing, which is based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index. Hierarchical indexing employs two types of indexes for alignment: (1) a whole-genome FM index to anchor each alignment, and (2) numerous local FM indexes for very rapid extensions of these alignments. HISATs hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ~64,000 bp. The algorithm includes several customized alignment strategies specifically designed for mapping RNA-seq reads across multiple exons. In tests on a variety of real and simulated data sets, we show that HISAT is the fastest system currently available, approximately 50 times faster than TopHat2 and 12 times faster than GSNAP, with equal or better accuracy than any other method. Despite its very large number of indexes, HISAT requires only 4.3 Gigabytes of memory to align reads to the human genome. HISAT supports genomes of any size, including those larger than 4 billion bases. HISAT is available as free, open-source software from http://www.ccb.jhu.edu/software/hisat.