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## mm2-fast
### Introduction
mm2-fast is an accelerated implementation of minimap2 on modern CPUs. mm2-fast accelerates all the three major modules of minimap2: (a) seeding, (b) chaining, and (c) pairwise alignment, achieving up to 3.5x speedup over minimap2.
mm2-fast is a drop-in replacement of minimap2, providing the same functionality with the exact same output.
In the current version, all the modules are optimized using **AVX-512** vectorization. Detailed benchmark results are available in our [preprint](https://doi.org/10.1101/2021.07.21.453294).
### System requirement
Operating System: Linux
mm2-fast was tested using g++ (GCC) 9.2.0 and icpc version 19.1.3.304
Architecture: x86\_64 CPUs with [AVX512](https://en.wikipedia.org/wiki/AVX-512)
Memory requirement: ~30GB for human genome
### Installation
Clone the *fast-contrib-v2.22* branch from minimap2 github page. The source code can be compiled by simple using *make* command. It only takes a few seconds.
```
git clone --recursive https://github.com/lh3/minimap2.git -b fast-contrib-v2.22 mm2-fast
cd mm2-fast
make
```
### Usage
The usage of mm2-fast is same as minimap2. Here is an example of mapping ONT reads with test data.
```sh
./minimap2 -ax map-ont test/MT-human.fa test/MT-orang.fa > mm2-fast_output
```
### Accuracy evaluation
As mm2-fast is an accelerated version of minimap2-v2.22, the output of mm2-fast can be verified against minimap2-v2.22. Note that AVX512-based chaining in mm2-fast by default runs with a chaining parameter *max-skip=infinity* for higher chaining precision. Therefore, for correctness verification, minimap2 should run with a larger value of *max-skip* parameter. Follow the below steps to verify the accuracy of mm2-fast.
```sh
git clone https://github.com/lh3/minimap2.git -b v2.22
cd minimap2 && make
./minimap2 -ax map-ont test/MT-human.fa test/MT-orang.fa --max-chain-skip=1000000 > minimap2_output
```
The output generated by minimap2 and mm2-fast should match.
```sh
diff minimap2_output mm2-fast_output > diff_result
```
The file diff\_result should show a clean-diff with the difference of 2 lines, i.e., the lines containing the command-line parameters for minimap2 and mm2-fast.
### Advanced options
The default compilation using make applies two optimizations: AVX512 vectorized chaining and alignment, and learned-indexes based seeding is disabled by default as it requires availability of [Rust](https://en.wikipedia.org/wiki/Rust_(programming_language)). This is because the learned hash-table uses an external training library that runs on Rust. Rust is trivial to install, see https://rustup.rs/ and add its path to .bashrc file. Rust installation only takes a few seconds. Following are the steps to enable learned hash table optimization in mm2-fast:
```sh
# Start by building learned hash table index for optimized seeding module
./build_rmi.sh test/MT-human.fa map-ont ##Takes two arguments: 1. path-to-reference-seq-file 2. preset.
##For human genome, this step should take around 20-30 minutes to finish.
# Next, compile and run the mapping phase
make clean && make lhash=1
./minimap2 -ax map-ont test/MT-human.fa test/MT-orang.fa > mm2-fast-lhash_output
```
To compile mm2-fast with all optimizations turned off and switch back to default minimap2, use the following command during compilation. This could be useful for debugging.
```sh
make clean && make no_opt=1
```
mm2-fast includes preliminary support for AVX2 architecture. Currently, chaining step is not optimized for AVX2 but the seeding and alignment steps are available. To try mm2-fast on AVX2 systems, use the following command to compile.
```sh
make clean && make lhash=1 use_avx2=1
```
### Performance
We have observed up to 1.9x speedup across datasets (please refer to the paper for more details). For example, for the randomly sampled 100K reads from ["HG002\_GM24385\_1\_2\_3\_Guppy\_3.6.0\_prom.fastq.gz"](https://precision.fda.gov/challenges/10/view), minimap2 takes 92 seconds, while mm2-fast takes 54 seconds to map against the human genome on a 28 cores Intel® Xeon® Platinum 8280 CPUs. Our sampled datasets with 100K reads are available [here](https://drive.google.com/drive/folders/1131j7ejHdT7QZnjxLcTLi5qqwYcfFbuv).
### Future Plans
### Citations
["Accelerating long-read analysis on modern CPUs"](https://doi.org/10.1101/2021.07.21.453294); Saurabh Kalikar, Chirag Jain, Vasimuddin Md, Sanchit Misra; BioRxiv 2021
---
The original README content of minimap2 follows.
[![GitHub Downloads](https://img.shields.io/github/downloads/lh3/minimap2/total.svg?style=social&logo=github&label=Download)](https://github.com/lh3/minimap2/releases)
[![BioConda Install](https://img.shields.io/conda/dn/bioconda/minimap2.svg?style=flag&label=BioConda%20install)](https://anaconda.org/bioconda/minimap2)
[![PyPI](https://img.shields.io/pypi/v/mappy.svg?style=flat)](https://pypi.python.org/pypi/mappy)