HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning RNA sequencing reads. It employs a hierarchical indexing strategy based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, using a global FM index to anchor alignments and numerous local FM indexes for rapid extensions. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each covering ~64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system available, with equal or better accuracy than other methods, and requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases. The authors designed HISAT to be a fast and scalable computational tool for RNA-seq analysis, addressing the time-consuming bottleneck of read alignment. HISAT's hierarchical indexing and specific alignment strategies enable it to handle different types of reads, including those spanning multiple exons or with short anchors, more efficiently than other methods. In comparisons with leading spliced-alignment programs, HISAT demonstrated superior speed and accuracy, making it a valuable tool for RNA-seq data analysis.HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning RNA sequencing reads. It employs a hierarchical indexing strategy based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, using a global FM index to anchor alignments and numerous local FM indexes for rapid extensions. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each covering ~64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system available, with equal or better accuracy than other methods, and requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases. The authors designed HISAT to be a fast and scalable computational tool for RNA-seq analysis, addressing the time-consuming bottleneck of read alignment. HISAT's hierarchical indexing and specific alignment strategies enable it to handle different types of reads, including those spanning multiple exons or with short anchors, more efficiently than other methods. In comparisons with leading spliced-alignment programs, HISAT demonstrated superior speed and accuracy, making it a valuable tool for RNA-seq data analysis.