[](https://github.com/lh3/minimap2/releases)
[](https://anaconda.org/bioconda/minimap2)
[](https://pypi.python.org/pypi/mappy)
[](https://github.com/lh3/minimap2/actions)
## Getting Started
```sh
git clone https://github.com/lh3/minimap2
cd minimap2 && make
# long sequences against a reference genome
./minimap2 -a test/MT-human.fa test/MT-orang.fa > test.sam
# create an index first and then map
./minimap2 -x map-ont -d MT-human-ont.mmi test/MT-human.fa
./minimap2 -a MT-human-ont.mmi test/MT-orang.fa > test.sam
# use presets (no test data)
./minimap2 -ax map-pb ref.fa pacbio.fq.gz > aln.sam # PacBio CLR genomic reads
./minimap2 -ax map-ont ref.fa ont.fq.gz > aln.sam # Oxford Nanopore genomic reads
./minimap2 -ax map-hifi ref.fa pacbio-ccs.fq.gz > aln.sam # PacBio HiFi/CCS genomic reads (v2.19 or later)
./minimap2 -ax lr:hq ref.fa ont-Q20.fq.gz > aln.sam # Nanopore Q20 genomic reads (v2.27 or later)
./minimap2 -ax sr ref.fa read1.fa read2.fa > aln.sam # short genomic paired-end reads
./minimap2 -ax splice ref.fa rna-reads.fa > aln.sam # spliced long reads (strand unknown)
./minimap2 -ax splice -uf -k14 ref.fa reads.fa > aln.sam # noisy Nanopore Direct RNA-seq
./minimap2 -ax splice:hq -uf ref.fa query.fa > aln.sam # Final PacBio Iso-seq or traditional cDNA
./minimap2 -ax splice --junc-bed anno.bed12 ref.fa query.fa > aln.sam # prioritize on annotated junctions
./minimap2 -cx asm5 asm1.fa asm2.fa > aln.paf # intra-species asm-to-asm alignment
./minimap2 -x ava-pb reads.fa reads.fa > overlaps.paf # PacBio read overlap
./minimap2 -x ava-ont reads.fa reads.fa > overlaps.paf # Nanopore read overlap
# man page for detailed command line options
man ./minimap2.1
```
## Table of Contents
- [Getting Started](#started)
- [Users' Guide](#uguide)
- [Installation](#install)
- [General usage](#general)
- [Use cases](#cases)
- [Map long noisy genomic reads](#map-long-genomic)
- [Map long mRNA/cDNA reads](#map-long-splice)
- [Find overlaps between long reads](#long-overlap)
- [Map short accurate genomic reads](#short-genomic)
- [Full genome/assembly alignment](#full-genome)
- [Advanced features](#advanced)
- [Working with >65535 CIGAR operations](#long-cigar)
- [The cs optional tag](#cs)
- [Working with the PAF format](#paftools)
- [Algorithm overview](#algo)
- [Getting help](#help)
- [Citing minimap2](#cite)
- [Developers' Guide](#dguide)
- [Limitations](#limit)
## Users' Guide
Minimap2 is a versatile sequence alignment program that aligns DNA or mRNA
sequences against a large reference database. Typical use cases include: (1)
mapping PacBio or Oxford Nanopore genomic reads to the human genome; (2)
finding overlaps between long reads with error rate up to ~15%; (3)
splice-aware alignment of PacBio Iso-Seq or Nanopore cDNA or Direct RNA reads
against a reference genome; (4) aligning Illumina single- or paired-end reads;
(5) assembly-to-assembly alignment; (6) full-genome alignment between two
closely related species with divergence below ~15%.
For ~10kb noisy reads sequences, minimap2 is tens of times faster than
mainstream long-read mappers such as BLASR, BWA-MEM, NGMLR and GMAP. It is more
accurate on simulated long reads and produces biologically meaningful alignment
ready for downstream analyses. For >100bp Illumina short reads, minimap2 is
three times as fast as BWA-MEM and Bowtie2, and as accurate on simulated data.
Detailed evaluations are available from the [minimap2 paper][doi] or the
[preprint][preprint].
### Installation
Minimap2 is optimized for x86-64 CPUs. You can acquire precompiled binaries from
the [release page][release] with:
```sh
curl -L https://github.com/lh3/minimap2/releases/download/v2.28/minimap2-2.28_x64-linux.tar.bz2 | tar -jxvf -
./minimap2-2.28_x64-linux/minimap2
```
If you want to compile from the source, you need to have a C compiler, GNU make
and zlib development files installed. Then type `make` in the source code
directory to compile. If you see compilation errors, try `make sse2only=1`
to disable SSE4 code, which will make minimap2 slightly slower.
Minimap2 also works with ARM CPUs supporting the NEON instruction sets. To
compile for 32 bit ARM architectures (such as ARMv7), use `make arm_neon=1`. To
compile for for 64 bit ARM architectures (such as ARMv8), use `make arm_neon=1
aarch64=1`.
Minimap2 can use [SIMD Everywhere (SIMDe)][simde] library for porting
implementation to the different SIMD instruction sets. To compile using SIMDe,
use `make -f Makefile.simde`. To compile for ARM CPUs, use `Makefile.simde`
with the ARM related command lines given above.
### General usage
Without any options, minimap2 takes a reference database and a query sequence
file as input and produce approximate mapping, without base-level alignment
(i.e. coordinates are only approximate and no CIGAR in output), in the [PAF format][paf]:
```sh
minimap2 ref.fa query.fq > approx-mapping.paf
```
You can ask minimap2 to generate CIGAR at the `cg` tag of PAF with:
```sh
minimap2 -c ref.fa query.fq > alignment.paf
```
or to output alignments in the [SAM format][sam]:
```sh
minimap2 -a ref.fa query.fq > alignment.sam
```
Minimap2 seamlessly works with gzip'd FASTA and FASTQ formats as input. You
don't need to convert between FASTA and FASTQ or decompress gzip'd files first.
For the human reference genome, minimap2 takes a few minutes to generate a
minimizer index for the reference before mapping. To reduce indexing time, you
can optionally save the index with option **-d** and replace the reference
sequence file with the index file on the minimap2 command line:
```sh
minimap2 -d ref.mmi ref.fa # indexing
minimap2 -a ref.mmi reads.fq > alignment.sam # alignment
```
***Importantly***, it should be noted that once you build the index, indexing
parameters such as **-k**, **-w**, **-H** and **-I** can't be changed during
mapping. If you are running minimap2 for different data types, you will
probably need to keep multiple indexes generated with different parameters.
This makes minimap2 different from BWA which always uses the same index
regardless of query data types.
### Use cases
Minimap2 uses the same base algorithm for all applications. However, due to the
different data types it supports (e.g. short vs long reads; DNA vs mRNA reads),
minimap2 needs to be tuned for optimal performance and accuracy. It is usually
recommended to choose a preset with option **-x**, which sets multiple
parameters at the same time. The default setting is the same as `map-ont`.
#### Map long noisy genomic reads
```sh
minimap2 -ax map-pb ref.fa pacbio-reads.fq > aln.sam # for PacBio CLR reads
minimap2 -ax map-ont ref.fa ont-reads.fq > aln.sam # for Oxford Nanopore reads
minimap2 -ax map-iclr ref.fa iclr-reads.fq > aln.sam # for Illumina Complete Long Reads
```
The difference between `map-pb` and `map-ont` is that `map-pb` uses
homopolymer-compressed (HPC) minimizers as seeds, while `map-ont` uses ordinary
minimizers as seeds. Empirical evaluation suggests HPC minimizers improve
performance and sensitivity when aligning PacBio CLR reads, but hurt when aligning
Nanopore reads. `map-iclr` uses an adjusted alignment scoring matrix that
accounts for the low overall error rate in the reads, with transversion errors
being less frequent than transitions.
#### Map long mRNA/cDNA reads
```sh
minimap2 -ax splice:hq -uf ref.fa iso-seq.fq > aln.sam # PacBio Iso-seq/traditional cDNA
minimap2 -ax splice ref.fa nanopore-cdna.fa > aln.sam # Nanopore 2D cDNA-seq
minimap2 -ax splice -uf -k14 ref.fa direct-rna.fq > aln.sam # Nanopore Direct RNA-seq
minimap2 -ax splice --splice-flank=no SIRV.fa SIRV-seq.fa # mapping against SIRV control
```
There are different long-read RNA-seq technologies, including tranditional
full-length cDNA, EST, PacBio Iso-seq, Nanopore 2D cDNA-seq and Direct RNA-seq.
They produce data of varying quality and properties. By default, `-x splice`
assumes the read orientation relative to the transcript strand is unknown. It
tries two rounds of alignment to infer the orientation and write the strand to
the `ts` SAM/PAF tag if possible. For Iso-seq, Direct RNA-seq and tranditional
full-length cDNAs, it would be desired to apply `-u f` to force minimap2 to
consider the forward transcript strand only. This speeds up alignment with
slight improvement to accuracy. For noisy Nanopore Direct RNA-seq reads, it is
recommended to use a smaller k-mer size for increased sensitivity to the first
or the last exons.
Minimap2 rates an alignment by the score of the max-scoring sub-segment,
*excluding* introns, and marks the best alignment as primary in SAM. When a
spliced gene also has unspliced pseudogenes, minimap2 does not intentionally
prefer spliced alignment, though in practice it more often marks the spliced
alignment as the primary. By default, minimap2 outputs up to five secondary
alignments (i.e. likely pseudogenes in the context of RNA-seq mapping). This
can be tuned with option **-N**.
For long RNA-seq reads, minimap2 may produce chimeric alignments potentially
caused by gene fusions/structural variations or by an intron longer than the
max intron length **-G** (200k by default). For now, it is not recommended to
apply an excessively large **-G** as this slows down minimap2 and sometimes
leads to false alignments.
It is worth noting that by default `-x splice` prefers GT[A/G]..[C/T]AG
over GT[C/T]..[A/G]AG, and then over other splicing signals. Considering
one additional base improves the junction accuracy for noisy reads, but
reduces the accuracy when aligning against the widely used SIRV control data.
This is because SIRV does not honor the evolutionarily conservative splicing
signal. If you are studying SIRV, you may apply `--splice-flank=no` to let
minimap2 only model GT..AG, ignoring the additional base.
Since v2.17, minimap2 can optionally take annotated genes as input and
prioritize on annotated splice junctions. To use this feature, you can
```sh
paftools.js gff2bed anno.gff > anno.bed
minimap2 -ax splice --junc-bed anno.bed ref.fa query.fa > aln.sam
```
Here, `anno.gff` is the gene annotation in the GTF or GFF3 format (`gff2bed`
automatically tests the format). The output of `gff2bed` is in the 12-column
BED format, or the BED12 format. With the `--junc-bed` option, minimap2 adds a
bonus score (tuned by `--junc-bonus`) if an aligned junction matches a junction
in the annotation. Option `--junc-bed` also takes 5-column BED, including the
strand field. In this case, each line indicates an oriented junction.
#### Find overlaps between long reads
```sh
minimap2 -x ava-pb reads.fq reads.fq > ovlp.paf # PacBio CLR read overlap
minimap2 -x ava-ont reads.fq reads.fq > ovlp.paf # Oxford Nanopore read overlap
```
Similarly, `ava-pb` uses HPC minimizers while `ava-ont` uses ordinary
minimizers. It is usually not recommended to perform base-level alignment in
the overlapping mode because it is slow and may produce false positive
overlaps. However, if performance is not a concern, you may try to add `-a` or
`-c` anyway.
#### Map short accurate genomic reads
```sh
minimap2 -ax sr ref.fa reads-se.fq > aln.sam # single-end alignment
minimap2 -ax sr ref.fa read1.fq read2.fq > aln.sam # paired-end alignment
minimap2 -ax sr ref.fa reads-interleaved.fq > aln.sam # paired-end alignment
```
When two read files are specified, minimap2 reads from each file in turn and
merge them into an interleaved stream internally. Two reads are considered to
be paired if they are adjacent in the input stream and have the same name (with
the `/[0-9]` suffix trimmed if present). Single- and paired-end reads can be
mixed.
Minimap2 does not work well with short spliced reads. There are many capable
RNA-seq mappers for short reads.
#### Full genome/assembly alignment
```sh
minimap2 -ax asm5 ref.fa asm.fa > aln.sam # assembly to assembly/ref alignment
```
For cross-species full-genome alignment, the scoring system needs to be tuned
according to the sequence divergence.
### Advanced features
#### Working with >65535 CIGAR operations
Due to a design flaw, BAM does not work with CIGAR strings with >65535
operations (SAM and CRAM work). However, for ultra-long nanopore reads minimap2
may align ~1% of read bases with long CIGARs beyond the capability of BAM. If
you convert such SAM/CRAM to BAM, Picard and recent samtools will throw an
error and abort. Older samtools and other tools may create corrupted BAM.
To avoid this issue, you can add option `-L` at the minimap2 command line.
This option moves a long CIGAR to the `CG` tag and leaves a fully clipped CIGAR
at the SAM CIGAR column. Current tools that don't read CIGAR (e.g. merging and
sorting) still work with such BAM records; tools that read CIGAR will
effectively ignore these records. It has been decided that future tools
will seamlessly recognize long-cigar records generated by option `-L`.
**TL;DR**: if you work with ultra-long reads and use tools that only process
BAM files, please add option `-L`.
#### The cs optional tag
The `cs` SAM/PAF tag encodes bases at mismatches and INDELs. It matches regular
expression `/(:[0-9]+|\*[a-z][a-z]|[=\+\-][A-Za-z]+)+/`. Like CIGAR, `cs`
consists of series of operations. Each leading character specifies the
operation; the following sequence is the one involved in the operation.
The `cs` tag is enabled by command line option `--cs`. The following alignment,
for example:
```txt
CGATCGATAAATAGAGTAG---GAATAGCA
|||||| |||||||||| |||| |||
CGATCG---AATAGAGTAGGTCGAATtGCA
```
is represented as `:6-ata:10+gtc:4*at:3`, where `:[0-9]+` represents an
identical block, `-ata` represents a deletion, `+gtc` an insertion and `*at`
indicates reference base `a` is substituted with a query base `t`. It is
similar to the `MD` SAM tag but is standalone and easier to parse.
If `--cs=long` is used, the `cs` string also contains identical sequences in
the alignment. The above example will become
`=CGATCG-ata=AATAGAGTAG+gtc=GAAT*at=GCA`. The long form of `cs` encodes both
reference and query sequences in one string. The `cs` tag also encodes intron
positions and splicing signals (see the [minimap2 manpage][manpage-cs] for
details).
#### Working with the PAF format
Minimap2 also comes with a (java)script [paftools.js](misc/paftools.js) that
processes alignments in the PAF format. It calls variants from
assembly-to-reference alignment, lifts over BED files based on alignment,
converts between formats and provides utilities for various evaluations. For
details, please see [misc/README.md](misc/README.md).
### Algorithm overview
In the following, minimap2 command line options have a dash ahead and are
highlighted in bold. The description may help to tune minimap2 parameters.
1. Read **-I** [=*4G*] reference bases, extract (**-k**,**-w**)-minimizers and
index them in a hash table.
2. Read **-K** [=*200M*] query bases. For each query sequence, do step 3
through 7:
3. For each (**-k**,**-w**)-minimizer on the query, check against the reference
index. If a reference minimizer is not among the top **-f** [=*2e-4*] most
frequent, collect its the occurrences in the reference, which are called
*seeds*.
4. Sort seeds by position in the reference. Chain them with dynamic
programming. Each chain represents a potential mapping. For read
overlapping, report all chains and then go to step 8. For reference mapping,
do step 5 through 7:
5. Let *P* be the set of primary mappings, which is an empty set initially. For
each chain from the best to the worst according to their chaining scores: if
on the query, the chain overlaps with a chain in *P* by **--mask-level**
[=*0.5*] or higher fraction of the shorter chain, mark the chain as
*secondary* to the chain in *P*; otherwise, add the chain to *P*.
6. Retain all primary mappings. Also retain up to **-N** [=*5*] top secondary
mappings if their chaining scores are higher than **-p** [=*0.8*] of their
corresponding primary mappings.
7. If alignment is requested, filter out an internal seed if it potentially
leads to both a long insertion and a long deletion. Extend from the
left-most seed. Perform global alignments between internal seeds. Split the
chain if the accumulative score along the global alignment drops by **-z**
[=*400*], disregarding long gaps. Extend from the right-most seed. Output
chains and their alignments.
8. If there are more query sequences in the input, go to step 2 until no more
queries are left.
9. If there are more reference sequences, reopen the query file from the start
and go to step 1; otherwise stop.
### Getting help
Manpage [minimap2.1][manpage] provides detailed description of minimap2
command line options and optional tags. The [FAQ](FAQ.md) page answers several
frequently asked questions. If you encounter bugs or have further questions or
requests, you can raise an issue at the [issue page][issue]. There is not a
specific mailing list for the time being.
### Citing minimap2
If you use minimap2 in your work, please cite:
> Li, H. (2018). Minimap2: pairwise alignment for nucleotide sequences.
> *Bioinformatics*, **34**:3094-3100. [doi:10.1093/bioinformatics/bty191][doi]
and/or:
> Li, H. (2021). New strategies to improve minimap2 alignment accuracy.
> *Bioinformatics*, **37**:4572-4574. [doi:10.1093/bioinformatics/btab705][doi2]
## Developers' Guide
Minimap2 is not only a command line tool, but also a programming library.
It provides C APIs to build/load index and to align sequences against the
index. File [example.c](example.c) demonstrates typical uses of C APIs. Header
file [minimap.h](minimap.h) gives more detailed API documentation. Minimap2
aims to keep APIs in this header stable. File [mmpriv.h](mmpriv.h) contains
additional private APIs which may be subjected to changes frequently.
This repository also provides Python bindings to a subset of C APIs. File
[python/README.rst](python/README.rst) gives the full documentation;
[python/minimap2.py](python/minimap2.py) shows an example. This Python
extension, mappy, is also [available from PyPI][mappypypi] via `pip install
mappy` or [from BioConda][mappyconda] via `conda install -c bioconda mappy`.
## Limitations
* Minimap2 may produce suboptimal alignments through long low-complexity
regions where seed positions may be suboptimal. This should not be a big
concern because even the optimal alignment may be wrong in such regions.
* Minimap2 requires SSE2 instructions on x86 CPUs or NEON on ARM CPUs. It is
possible to add non-SIMD support, but it would make minimap2 slower by
several times.
* Minimap2 does not work with a single query or database sequence ~2
billion bases or longer (2,147,483,647 to be exact). The total length of all
sequences can well exceed this threshold.
* Minimap2 often misses small exons.
[paf]: https://github.com/lh3/miniasm/blob/master/PAF.md
[sam]: https://samtools.github.io/hts-specs/SAMv1.pdf
[minimap]: https://github.com/lh3/minimap
[smartdenovo]: https://github.com/ruanjue/smartdenovo
[longislnd]: https://www.ncbi.nlm.nih.gov/pubmed/27667791
[gaba]: https://github.com/ocxtal/libgaba
[ksw2]: https://github.com/lh3/ksw2
[preprint]: https://arxiv.org/abs/1708.01492
[release]: https://github.com/lh3/minimap2/releases
[mappypypi]: https://pypi.python.org/pypi/mappy
[mappyconda]: https://anaconda.org/bioconda/mappy
[issue]: https://github.com/lh3/minimap2/issues
[k8]: https://github.com/attractivechaos/k8
[manpage]: https://lh3.github.io/minimap2/minimap2.html
[manpage-cs]: https://lh3.github.io/minimap2/minimap2.html#10
[doi]: https://doi.org/10.1093/bioinformatics/bty191
[doi2]: https://doi.org/10.1093/bioinformatics/btab705
[simde]: https://github.com/nemequ/simde
[unimap]: https://github.com/lh3/unimap