HISAT2 RNA-seq Alignment

HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts 2) is the fast, memory-efficient RNA-seq aligner that uses a hierarchical FM-index. It's the right choice when STAR's RAM cost is too high or when you want a balance of speed and accuracy. The 2026 reality: STAR is the gold standard for novel-junction discovery; HISAT2 is the fast standard for known-junction discovery and low-RAM environments.

When to use

• Bulk RNA-seq alignment to a reference transcriptome/genome.
• Small-genome RNA-seq where STAR's index cost is too high.
• 3' tag-seq (e.g., Lexogen QuantSeq) with appropriate flag settings.
• Long RNA-seq (>250 bp reads, e.g., ONT cDNA).

When NOT to use

• De novo transcript assembly → use STAR (better novel junction discovery).
• DNA-seq variant calling → use bwa-mem or bwa-mem2.
• Long reads → use minimap2 with -ax splice.

Prerequisites

• hisat2 ≥ 2.2
• samtools ≥ 1.19
• Reference FASTA + HISAT2 index
• For best sensitivity: known splice sites from a GTF file

Core workflow

1. Extract splice sites and exons from a GTF to build a sensitive index.
2. Build the HISAT2 index (hisat2-build or hisat2-build-s with known splice sites).
3. Align with hisat2 --dta for downstream transcriptome assembly (StringTie / Cufflinks) or --no-spliced-alignment for general use.
4. Sort and index the BAM with samtools.

Code patterns

Extract splice sites and exons from a GTF

hisat2_extract_splice_sites.py genes.gtf > splice_sites.txt
hisat2_extract_exons.py genes.gtf > exons.txt

Build a sensitive index (with known splice sites)

hisat2-build -p 16 --ss splice_sites.txt --exon exons.txt reference/genome.fa genome_hs2

Build a basic index (no annotation, faster)

hisat2-build -p 16 reference/genome.fa genome_hs2

Paired-end alignment (typical bulk RNA-seq)

hisat2 -p 16 --dta -x genome_hs2 \
    -1 reads_R1.fq.gz -2 reads_R2.fq.gz \
    --rg-id sample1 --rg SM:sample1 --rg PL:ILLUMINA --rg LB:lib1 \
    -S sample1.sam
samtools sort -@ 8 -o sample1.bam sample1.sam
samtools index sample1.bam
rm sample1.sam

--dta (downstream-transcriptome-assembly) is required if you'll run StringTie; it reports alignments tailored to transcript assembly.

Single-end alignment

hisat2 -p 16 --dta -x genome_hs2 -U reads.fq.gz --rg-id s1 --rg SM:s1 |
  samtools sort -@ 8 -o s1.bam -

rRNA-aware alignment (filter rRNA first, or use --un-gz to drop)

hisat2 -p 16 --dta --un-gz unmapped.fq.gz -x genome_hs2 -1 R1.fq -2 R2.fq |
  samtools sort -@ 8 -o s.bam -

--un-gz writes reads that didn't align (often rRNA or contaminant) for downstream QC.

Strand-specific libraries (dUTP / Ligation)

HISAT2 doesn't natively set XS tags; the convention is to use featureCounts or StringTie to infer strand from spliced alignments. For library prep-specific strand:

# Ligation protocol (Illumina TruSeq stranded)
# HISAT2 reports XS:A:+ / XS:A:- via the spliced alignment orientation
# featureCounts -s 2 (reverse stranded) will pick this up

Long-read cDNA (ONT / PacBio Iso-Seq)

hisat2 -p 16 --dta -x genome_hs2 -U iso_seq.fq.gz --no-temp-splicesite |
  samtools sort -@ 8 -o iso.bam -

nf-core integration

nf-core/rnaseq defaults to STAR. To use HISAT2, pass --aligner hisat2 on the CLI.

Common pitfalls

• Forgetting --dta for transcriptome assembly. StringTie/Cufflinks require the special scoring.
• Building an index without splice sites. The basic index works but is less sensitive for novel junctions.
• Mixing up --rna-strandness (legacy flag removed in 2.2). In HISAT2 2.2+, use downstream tools (featureCounts -s) to handle strand.
• Not using --rg-id. featureCounts and StringTie need read groups for multi-sample merges.
• STAR's memory cost is too high → HISAT2 with low sensitivity. If STAR fails on RAM, HISAT2 with the basic index will also miss junctions. Consider subsampling reads or using a known-splice-site index.

Validation

• samtools flagstat s.bam — high mapping rate expected (≥80% for human bulk RNA-seq).
• samtools view -c -f 2 s.bam — properly-paired count.
• samtools view s.bam | grep -c 'N:M:' — spliced alignment count (look for N in CIGAR).
• samtools view s.bam | awk '$6 ~ /N/' | wc -l — spliced reads.
• rRNA fraction should be < 10% for poly-A selected, < 25% for ribo-depleted.

Open alternatives

References

• HISAT2 paper: Kim et al. 2019, Nature Methods — 10.1038/s41587-019-0201-4
• HISAT2 manual: http://daehwankimlab.github.io/hisat2/manual/
• Companion: ors-bioinformatics-sequence-star-alignment, ors-bioinformatics-sequence-bwa-alignment.

Changelog

• 1.0.0 (2026-06-10): Initial adaptation by Pradyumna Jayaram from bio-hisat2-alignment (bioSkills-main/read-alignment/hisat2-alignment)."