115 lines
4.9 KiB
Markdown
115 lines
4.9 KiB
Markdown
## Getting Started
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```sh
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git clone https://github.com/lh3/minimap2
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cd minimap2 && make
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# long reads against a reference genome
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./minimap2 -ax map10k test/MT-human.fa test/MT-orang.fa > test.sam
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# create an index first and then map
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./minimap2 -x map10k -d MT-human.mmi test/MT-human.fa
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./minimap2 -ax map10k MT-human.mmi test/MT-orang.fa > test.sam
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# long-read overlap (no test data)
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./minimap2 -x ava-pb your-reads.fa your-reads.fa > overlaps.paf
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# man page
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man ./minimap2.1
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```
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## Introduction
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Minimap2 is a fast sequence mapping and alignment program that can find
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overlaps between long noisy reads, or map long reads or their assemblies to a
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reference genome optionally with detailed alignment (i.e. CIGAR). At present,
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it works efficiently with query sequences from a few kilobases to ~100
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megabases in length at an error rate ~15%. Minimap2 outputs in the [PAF][paf] or
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the [SAM format][sam]. On limited test data sets, minimap2 is over 20 times
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faster than most other long-read aligners. It will replace BWA-MEM for long
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reads and contig alignment.
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Minimap2 is the successor of [minimap][minimap]. It uses a similar
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minimizer-based indexing and seeding algorithm, and improves the original
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minimap with homopolyer-compressed k-mers (see also [SMARTdenovo][smartdenovo]
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and [longISLND][longislnd]), better chaining and the ability to produce CIGAR
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with fast extension alignment (see also [libgaba][gaba] and [ksw2][ksw2]) and
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piece-wise affine gap cost.
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## Installation
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For modern x86-64 CPUs, just type `make` in the source code directory. This
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will compile a binary `minimap2` which you can copy to your desired location.
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If you see compilation errors, try `make sse2only=1` to disable SSE4. Minimap2
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will run a little slower. At present, minimap2 does not work with non-x86 CPUs
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or ancient CPUs that do not support SSE2. SSE2 is critical to the performance
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of minimap2.
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## Algorithm Overview
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In the following, minimap2 command line options have a dash ahead and are
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highlighted in bold.
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1. Read **-I** [=*4G*] reference bases, extract (**-k**,**-w**)-minimizers and
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index them in a hash table.
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2. Read **-K** [=*200M*] query bases. For each query sequence, do step 3
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through 7:
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3. For each (**-k**,**-w**)-minimizer on the query, check against the reference
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index. If a reference minimizer is not among the top **-f** [=*2e-4*] most
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frequent, collect its the occurrences in the reference, which are called
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*seeds*.
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4. Sort seeds by position in the reference. Chain them with dynamic
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programming. Each chain represents a potential mapping. For read
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overlapping, report all chains and then go to step 8. For reference mapping,
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do step 5 through 7:
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5. Let *P* be the set of primary mappings, which is an empty set initially. For
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each chain from the best to the worst according to their chaining scores: if
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on the query, the chain overlaps with a chain in *P* by **--mask-level**
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[=*0.5*] or higher fraction of the shorter chain, mark the chain as
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*secondary* to the chain in *P*; otherwise, add the chain to *P*.
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6. Retain all primary mappings. Also retain up to **-N** [=*5*] top secondary
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mappings if their chaining scores are higher than **-p** [=*0.8*] of their
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corresponding primary mappings.
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7. If alignment is requested, filter out an internal seed if it potentially
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leads to both a long insertion and a long deletion. Extend from the
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left-most seed. Perform global alignments between internal seeds. Split the
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chain if the accumulative score along the global alignment drops by **-z**
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[=*400*], disregarding long gaps. Extend from the right-most seed. Output
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chains and their alignments.
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8. If there are more query sequences in the input, go to step 2 until no more
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queries are left.
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9. If there are more reference sequences, reopen the query file from the start
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and go to step 1; otherwise stop.
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## Limitations
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* At the alignment phase, minimap2 performs global alignments between minimizer
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hits. If the positions of these minimizer hits are incorrect, the final
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alignment may be suboptimal or unnecessarily fragmented. This should happen
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rarely with the latest version.
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* Minimap2 may produce poor alignments that may need post-filtering. We are
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still exploring a reliable and consistent way to report good alignments.
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* Minimap2 does not work well with Illumina short reads as of now.
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* Minimap2 requires SSE2 instructions to compile. It is possible to add
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non-SSE2 support, but it would make minimap2 slower by several times.
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In general, minimap2 is a young project with most code written since June, 2017.
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It may have bugs and room for improvements. Bug reports and suggestions are
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warmly welcomed.
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[paf]: https://github.com/lh3/miniasm/blob/master/PAF.md
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[sam]: https://samtools.github.io/hts-specs/SAMv1.pdf
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[minimap]: https://github.com/lh3/minimap
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[smartdenovo]: https://github.com/ruanjue/smartdenovo
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[longislnd]: https://www.ncbi.nlm.nih.gov/pubmed/27667791
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[gaba]: https://github.com/ocxtal/libgaba
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[ksw2]: https://github.com/lh3/ksw2
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