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Map with minimap2 (version 2.28+galaxy0)
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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 preprint.

General usage

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:

*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

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. Emperical evaluation suggests HPC minimizers improve performance and sensitivity when aligning PacBio reads, but hurt when aligning Nanopore reads.

Map long mRNA/cDNA reads

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.

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.

Find overlaps between long reads

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

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

For cross-species full-genome alignment, the scoring system needs to be tuned according to the sequence divergence.

Self-homology map creation

A self-homology map is created by mapping a genome (e.g. that of E. coli) against itself. When this option is used the same FASTA file should be used for reference and for the (single ended mode) query.

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.

TD;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:

||||||   ||||||||||   |||| |||

is represented as :6-ata:10+gtc:4*at:3, where :[0-9]+ represents an identical block, -ata represents a deltion, +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.

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.


  • 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.