HISAT specifications


Unique identifier OMICS_07225
Name Hierarchical Indexing for Spliced Alignment of Transcripts
Alternative name HISAT2
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS
Computer skills Advanced
Version 0.1.5
Stability Beta
Maintained Yes


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HISAT article

HISAT citations

PMCID: 5935908

[…] reads were generated upon sequencing. the count per million (cpm) method was utilized for filtering low counts/noise by noiseq. the clean reads were mapped to the reference genome using the hisat [14]/ bowtie2 tool [15]. the fragments per kilobase of transcript per million mapped reads (fpkm) method was utilized to calculate the expression levels. a false detection rate (fdr) ≤ 0.001 […]

PMCID: 5908970

[…] were sequenced. rna-seq data were deposited in the ncbi sra database under accession number srp120765. the clean data were mapped to the genome of f. graminearum (king et al., 2015) by using hisat2 (kim et al., 2015) with default parameters. the differentially expressed genes (log2fc > 1 and fdr < 0.05) were carried out based on the read counts by edgerun with tmm normalization […]

PMCID: 5858502

[…] was used for library construction. sequencing was performed on prepared library 30. all the generated raw sequencing reads were filtered to get clean reads stored as fastq format 31. bowtie2 and hisat were used to map clean reads to reference gene and genome respectively 32, 33. gene expression level (fpkm) is quantified by rsem 34. noiseq method was used to screen out differentially […]

PMCID: 5895686

[…] protocol of zhong et al. (2011) and sequenced using the illumina nextseq 500 to obtain single-end 75 bp reads. reads were aligned to the 12x.2 version of the grapevine reference genome pn40024 using hisat2 (kim et al. 2015) and the transcriptome was assembled using stringtie (pertea et al. 2015) with the cribi functional annotation (vitulo et al. 2014). transcribed genes were analyzed by fitting […]

PMCID: 5799976

[…] number srp117710., the high-quality paired-end rna-seq reads from each library were aligned to the wheat reference genome (version tgacv1, http://plants.ensembl.org/triticum_aestivum/) [32] using hisat2 with parameters “--phred33 --max-intronlen 7000 -k 10 -t -p 4 --no-unal --ignore-quals --rdg 3,1 --rfg 3,1 --score-min l,0,-0.19”, and alignments with no more than one mismatch were retained […]

HISAT institution(s)
Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
HISAT funding source(s)
This work was supported in part by the National Human Genome Research Institute (US National Institutes of Health) under grants R01-HG006102 and R01-HG006677 to S.L.S.

HISAT review

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Arup Ghosh

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Hisat is one of the best and fastest RNA-seq data aligner available till date. Mouse transcriptome data with ~30 million reads took only 30mins for alignment. There are a lot other alignment-free alternatives available now but for getting insight about spliced variants Hisat is the best tool to use.

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