Aligners

An aligner is a type of analysis software that matches short sequences, or reads, stored in FASTQ to a reference genome or transcriptome. STAR and HISAT2 are representative aligners for RNA-seq. Their output is stored in SAM/BAM, a file containing alignment information.

FASTQ -> alignment(STAR/HISAT2, etc.) -> SAM/BAM -> count/assembly/variant/fusion analysis

A DNA-seq aligner can generally assume that a read came from a continuous region of the genome. An RNA-seq read, however, comes from mature mRNA and can cross the joined boundary between exons, called a splice junction. From the genome’s perspective, the front of the read may need to align to exon 1 and the back to exon 2, thousands of base pairs away.

RNA-seq therefore usually requires a spliced aligner.

read:       AAAAAAA|GGGGGGG
genome:     exon 1 |---- intron ----| exon 2
alignment:  AAAAAAA                 GGGGGGG

At a glance

Tool	Released / introduced	Main use	Key characteristic
STAR	2012/2013	bulk RNA-seq, fusion, large cohorts	Very fast spliced alignment, high memory use
HISAT2	2015 beta, 2019 paper	bulk RNA-seq, low memory, graph index	Hierarchical Graph FM index, can incorporate SNP and transcript information
TopHat / TopHat2	2009 / 2013	Historically important RNA-seq aligner	Bowtie-based exon-first method, now generally a legacy tool
Subread / Subjunc	2013	expression counting, junction detection	Seed-and-vote strategy, same package ecosystem as featureCounts
Bowtie / Bowtie2	2009 / 2012	Unspliced alignment of short reads	BWT/FM-index based, unsuitable for RNA-seq spliced alignment by itself
BWA	2009	DNA-seq short-read alignment	One of the standard BWT-based DNA aligners
minimap2	2018	long reads, long RNA/cDNA, genome alignment	Minimizer based, important for long-read RNA-seq

Why RNA-seq needs its own aligners

RNA-seq alignment has four main challenges.

Problem	Explanation
splice junction	One read can span two or more discontinuous positions in the genome
multi-mapping	The same read can align to multiple positions because of paralogs, pseudogenes, or repeats
sequencing error / variant	A read may not exactly match the reference because of mismatches, indels, or SNPs
transcript ambiguity	Isoforms share exons, making transcript-level assignment ambiguous

Splice junctions in particular are the key distinction between DNA-seq and RNA-seq aligners.

DNA-seq aligner: match a read to a continuous region of the genome
RNA-seq spliced aligner: split a read into exon segments and match them to discontinuous regions of the genome

Algorithmic background

Seed and extend

Most modern aligners do not align an entire read from the beginning with dynamic programming. They first find short pieces of the read, called seeds, quickly in the reference, then extend the alignment around those candidate positions.

read:      ACTGACCTGAACT...
seed:      ACTGAC
candidate: a position where ACTGAC was found somewhere in the genome
extend:    extend around it while evaluating mismatches, indels, and splices

This method connects to older ideas used by tools such as BLAST. The difference is the use of data structures such as suffix arrays, FM-indexes, and minimizer indexes to search an entire reference genome quickly.

BWT / FM-index

These are important concepts in the Bowtie, BWA, and HISAT families. The Burrows-Wheeler Transform (BWT) and FM-index store a large genome string in compressed form while still enabling fast searches for short queries.

Advantages:

Advantage	Explanation
Low memory	Keeps the index for whole-genome searches relatively small
Fast exact and near-exact search	Well suited to searching for short-read seeds
Strong for short-read DNA/RNA alignment	A core data structure of the Illumina short-read era

The disadvantage is that they do not directly solve the problem of a read aligning to discontinuous regions of the reference, as in splicing. TopHat therefore ran Bowtie first and inferred junctions from the remaining reads, while HISAT and HISAT2 extended the FM-index structure for RNA-seq.

Suffix array

STAR makes extensive use of an uncompressed suffix array rather than a compressed FM-index. This makes seed searches very fast, but loading a human genome index requires substantial memory.

A key term in the STAR paper is Maximal Mappable Prefix (MMP). STAR starts at the front of a read and finds the longest prefix that can align to the reference. If a splice or mismatch interrupts it, STAR searches for another seed in the remaining portion.

read:       exonA_part | exonB_part
MMP 1:      exonA_part
MMP 2:                   exonB_part
stitching:  connect the two seeds through a splice junction to form one alignment

Aligners commonly used for RNA-seq

STAR

STAR (Spliced Transcripts Alignment to a Reference) is an RNA-seq aligner whose paper was published online in 2012 and appeared in Bioinformatics in 2013. It was developed to process large transcriptome datasets such as ENCODE quickly.

Core algorithm:

Item	Details
index	uncompressed suffix array
seed search	sequential maximum mappable seed search, Maximal Mappable Prefix (MMP)
splice handling	creates split alignments through seed clustering, stitching, and scoring
annotation use	GTF splice junctions can be included during index generation to improve sensitivity
output	supports sorted BAM, splice junction tables, chimeric junctions, and other outputs

STAR’s major advantage is speed. Its paper reported much faster performance than other RNA-seq aligners of the time, and it remains widely used in bulk RNA-seq pipelines. It is often selected for these situations:

Situation	Why STAR fits
Human/mouse bulk RNA-seq	A well-validated default choice
Processing many samples	Very fast when given CPU resources
Fusion transcript discovery	Can use chimeric alignment output
Novel splice junction discovery	Can find junctions without annotation

Cautions:

Caution	Explanation
Memory use	A human genome index may require tens of GB of RAM
Parameter effects	Multi-mapping, chimeric alignment, and splice-overhang settings change results
Very short reads	Junctions are harder to detect reliably and depend more on annotation

In practice, GTF is usually included when building the index.

STAR \
  --runThreadN 8 \
  --runMode genomeGenerate \
  --genomeDir star_index \
  --genomeFastaFiles genome.fa \
  --sjdbGTFfile annotation.gtf \
  --sjdbOverhang 100

--sjdbOverhang is usually set to read length - 1. For example, use 100 for a 101 bp read.

HISAT2

HISAT2 is the successor to HISAT. A beta release appeared in 2015, and the HISAT2 paper covering graph-based genome alignment and genotyping was published in Nature Biotechnology in 2019.

The core of HISAT2 is its Hierarchical Graph FM index (HGFM).

Item	Details
index	global FM/GFM index plus many local FM/GFM indexes
graph support	can include SNP, haplotype, and transcript information in the index
memory	can align to the human genome with much less memory than STAR
RNA-seq support	splice-aware alignment, can use known splice-site and exon information

HISAT2 has a smaller memory burden than STAR. It is a good choice when RNA-seq alignment must run on an ordinary server or personal workstation.

hisat2 \
  -p 8 \
  --dta \
  -x genome_index \
  -1 sample_R1.fastq.gz \
  -2 sample_R2.fastq.gz \
  -S sample.sam

--dta reports alignments in a form convenient for downstream transcript assemblers such as StringTie.

Advantages:

Advantage	Explanation
Low memory	Relatively manageable even in desktop-class environments
Fast	Strong short-read processing through an FM-index
Graph index	Can incorporate known SNPs and variants into the reference
Works well with StringTie	Often presented as the HISAT2 + StringTie + Ballgown pipeline

Cautions:

Caution	Explanation
Fusion analysis	Less common than tools based on STAR chimeric output
Preparing a graph index	Indexes including SNPs or transcripts can complicate preparation and file management
Parameters require understanding	Use of known and novel splice sites and --dta depends on the purpose

TopHat / TopHat2

TopHat, released in 2009, was an early RNA-seq spliced aligner. It first used Bowtie to align reads that mapped continuously to the genome, then used the remaining reads to find splice junctions.

1. First align unspliced reads with Bowtie
2. Collect unaligned reads and infer exon islands and junction candidates
3. Perform spliced alignment including the junctions

TopHat is historically important. The combination of TopHat and Cufflinks was widely used in the “Tuxedo pipeline” during the early years of RNA-seq analysis. For new analyses today, STAR or HISAT2 is generally recommended instead.

Item	TopHat
Advantage	Popularized RNA-seq spliced alignment
Limitation	Slow and inefficient for modern read lengths and large datasets
Current role	Legacy tool, mainly encountered when reproducing older papers or maintaining old pipelines

TopHat2 improved handling of insertions, deletions, fusion transcripts, and other situations. Even so, modern new analyses generally use HISAT2 or STAR instead of TopHat2.

Subread / Subjunc

Subread, released in 2013, uses a seed-and-vote strategy. It extracts multiple seeds from a read, maps them to the reference, and selects the genomic location supported by the greatest number of seeds.

Tool	Explanation
Subread	Used for genomic read alignment and RNA-seq expression analysis
Subjunc	A tool in the Subread family specialized for RNA-seq junction detection
featureCounts	A read-counting tool in the same package, very commonly used to create RNA-seq count matrices

Subread itself is mentioned less often than STAR or HISAT2 as an RNA-seq aligner, but the Subread package is extremely common in RNA-seq analysis because of featureCounts.

Pseudoalignment and quasi-mapping: are they aligners?

Tools such as Salmon and kallisto also appear frequently in RNA-seq expression estimation. They differ from genome aligners in the traditional sense.

Method	Representative tools	Output	Characteristic
genome alignment	STAR, HISAT2	BAM/SAM	Records where each read aligns in the genome
transcriptome quantification	Salmon, kallisto	transcript/gene abundance table	Quickly calculates compatibility with a transcriptome

kallisto uses pseudoalignment, while Salmon uses quasi-mapping or selective alignment. Instead of generating a complete base-level alignment for every read, these methods quickly determine which transcripts are compatible with a read and estimate abundance.

Salmon and kallisto are therefore very fast and practical when the goal is gene or transcript expression quantification. A genome aligner is needed for these other goals:

Goal	Why genome alignment is needed
Novel splice junctions	Must directly inspect exon-exon connections on the genome
Fusion transcripts	Requires chimeric alignments spanning distant loci or chromosomes
RNA editing / variant candidates	Requires reference coordinates and base-level mismatches
IGV visualization	Requires BAM for inspection in a genome browser

Aligners outside RNA-seq

Bowtie / Bowtie2

Bowtie is a BWT-based short-read aligner released in 2009. Its speed and low memory use made it important in early NGS analysis.

Bowtie focused on rapidly aligning short reads to a reference and was close to ungapped alignment by default. Bowtie2, released in 2012, improved handling of gapped alignments and longer reads.

Bowtie and Bowtie2 do not directly handle splice junctions in RNA-seq. They instead serve supporting roles such as:

Use	Explanation
TopHat’s internal aligner	TopHat constructs spliced alignments on top of Bowtie/Bowtie2
Transcriptome alignment	Aligning reads to a transcript FASTA in which splicing is already represented
QC / contamination checks	Rapid mapping to rRNA, adapters, microbial sequences, and similar references

BWA

BWA (Burrows-Wheeler Aligner) is a DNA-seq short-read aligner released in 2009. It includes the BWA-backtrack, BWA-SW, and BWA-MEM families, and BWA-MEM was a standard choice for WGS and WES analysis for many years.

Item	BWA
Main use	DNA-seq, WGS, WES, targeted sequencing
index	BWT based
Role in RNA-seq	Unsuitable as the default aligner for ordinary mRNA RNA-seq because it is not a spliced aligner

BWA can align RNA-seq reads that map to a continuous region of the genome, but it cannot naturally handle reads crossing exon-exon junctions.

minimap2

minimap2 is a general-purpose pairwise aligner released in 2018. It supports some short-read use cases but is especially strong for long reads and alignment between large sequences.

Situation	Why minimap2 is often used
Oxford Nanopore / PacBio long reads	Handles long reads and high error rates
Long-read RNA-seq	Spliced alignment of full-length cDNA and direct RNA reads
Assembly to reference	Aligns contigs or an assembly to a reference
Genome-to-genome comparison	Fast alignment between large sequences

minimap2 uses minimizers as seeds. It selects representative k-mers from the read and reference to find candidate positions quickly, then creates the final alignment through chaining and alignment extension.

For short-read Illumina bulk RNA-seq, STAR and HISAT2 are usually more familiar choices. minimap2 appears very frequently in long-read transcriptome analysis.

GMAP / GSNAP / MapSplice

These tools were important aligners that addressed RNA-seq and spliced alignment before or around the same time as STAR and HISAT2.

Tool	Characteristic
GMAP	A long-established splice-aware mapper for aligning cDNA/EST to a genome
GSNAP	A short-read aligner in the GMAP family, supporting SNP-tolerant alignment and splicing
MapSplice	An RNA-seq aligner focused on splice-junction discovery

They are not the most common choices for new bulk RNA-seq analyses today, but they can still appear in result files or methods descriptions from older papers and particular pipelines.

Which aligner should you choose?

Goal	Recommended choice	Reason
Ordinary human/mouse bulk RNA-seq	STAR or HISAT2	The most widely validated spliced aligners
Plenty of RAM and fast processing required	STAR	Fast and well supported by downstream tools
Limited RAM	HISAT2	Low memory use
Transcript assembly with StringTie	HISAT2 --dta or STAR	Produces BAM files convenient for assemblers
Fusion transcript discovery	STAR + Arriba/STAR-Fusion, etc.	Strong chimeric-alignment ecosystem
Simple expression quantification	Also consider Salmon/kallisto	Fast abundance estimates without BAM
Long-read RNA/cDNA	minimap2	Strong for spliced alignment of long reads
Short-read DNA-seq WGS/WES	BWA-MEM/BWA-MEM2 family	Use a DNA aligner because there is no splicing
Long-read DNA-seq	minimap2	Strong for Nanopore/PacBio long-read alignment

For practice, begin with either STAR or HISAT2 to create a BAM file and inspect the alignments directly in IGV. Seeing how reads cross exon-exon junctions makes the concept of RNA-seq alignment much quicker to grasp.

What to inspect in the results

Whichever aligner you use, you will usually inspect BAM/SAM in the end. The important items are:

Item	Meaning
mapping rate	Proportion of all reads that aligned to the reference
uniquely mapped reads	Reads confidently aligned to only one location
multi-mapped reads	Reads that align similarly well to several locations
splice junction support	Number of reads crossing a junction
MAPQ	Mapping quality representing alignment confidence
CIGAR	Encodes matches, insertions, deletions, splice skips, and other operations

N is an especially important CIGAR character in RNA-seq BAM. It means that a long region of the reference was skipped, which usually represents an intron in RNA-seq.

50M1000N50M

This read aligned its first 50 bp to one exon, skipped 1,000 bp of the reference, and aligned its last 50 bp to the next exon.

Practical cautions

1. Match the genome FASTA and GTF versions

STAR and HISAT2 can both use annotations. The genome build, chromosome names, and release version of the genome FASTA and GTF must match.

Good: GRCh38 FASTA + GENCODE v46 GRCh38 GTF
Risky: GRCh37 FASTA + GRCh38 GTF
Risky: FASTA uses chr1, GTF uses 1

2. Record alignment parameters with the results

RNA-seq alignment is highly sensitive to parameters. At minimum, record the following in a paper or notebook:

aligner: STAR 2.7.x
genome: GRCh38
annotation: GENCODE v46
key options: --sjdbOverhang 100, --outSAMtype BAM SortedByCoordinate

3. Be careful with multi-mapping reads

In gene families, pseudogenes, and repetitive regions, a read may align to several places. Aligners differ in how they report multi-mapping reads, and counting tools differ in how they handle them.

4. A “fast aligner” is not always the better answer

The right choice depends on the purpose. If you need only expression quantification, Salmon or kallisto may be more practical. If you want to inspect fusions or novel junctions, you need spliced genome alignment. If you want to examine variant candidates, base-level alignment and post-processing matter.

Cheat sheet

The default choice for short-read RNA-seq is STAR or HISAT2.
STAR is fast and feature-rich but uses substantial memory.
HISAT2 offers low memory use and a graph FM index.
TopHat is historically important but generally a legacy choice for new analyses.
Think of Bowtie and BWA as DNA or unspliced short-read aligners.
minimap2 is important for long-read RNA/cDNA and genome alignment.
Salmon and kallisto estimate transcript abundance rather than serving as traditional aligners.

References and sources

• Dobin A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15-21 (2013). DOI: 10.1093/bioinformatics/bts635
• Kim D. et al. HISAT: a fast spliced aligner with low memory requirements. Nature Methods 12, 357-360 (2015). DOI: 10.1038/nmeth.3317
• Kim D. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology 37, 907-915 (2019). DOI: 10.1038/s41587-019-0201-4
• Trapnell C., Pachter L., Salzberg S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9), 1105-1111 (2009). DOI: 10.1093/bioinformatics/btp120
• Kim D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology 14, R36 (2013). DOI: 10.1186/gb-2013-14-4-r36
• Langmead B. et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10, R25 (2009). DOI: 10.1186/gb-2009-10-3-r25
• Langmead B., Salzberg S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357-359 (2012). DOI: 10.1038/nmeth.1923
• Li H., Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25(14), 1754-1760 (2009). DOI: 10.1093/bioinformatics/btp324
• Liao Y., Smyth G. K., Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Research 41(10), e108 (2013). DOI: 10.1093/nar/gkt214
• Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34(18), 3094-3100 (2018). DOI: 10.1093/bioinformatics/bty191
• HISAT2 official documentation: https://daehwankimlab.github.io/hisat2/
• STAR source and manual: https://github.com/alexdobin/STAR
• minimap2 source and manual: https://github.com/lh3/minimap2