mappy-rs 0.0.4

A multithreaded python wrapper for rust bindings of minimap2.

Mappy-rs

A multi-threaded minimap2 aligner for python. Built for readfish compatibility.

Heavily leaning on and inspired by Joeseph Guhlin's minimap2-rs repository. They also have a more heavily featured python client, which also provides multithreaded alignment. This client provides a more simple streaming interface for use in pipelines.

pip install mappy-rs

Developers

Start with some Docs on Py03 - https://pyo3.rs/latest/

If you wish to contribute, have a look at CONTRIBUTING.md

In order to build an importable module:

python -m venv .env
source -m .env/bin/activate
pip install ".[tests]"

To run the tests:

# Python
pytest

# Rust
cargo t --no-default-features

Then in your python shell of choice:

import mappy_rs
aligner = mappy_rs.Aligner("resources/test/test.mmi")

The current iteration of mappy-rs serves as a drop in for mappy, implementing all the same methods. However if this is the use case, you may well be better off using mappy, as the extra level of Rust between your python and C++ may well add slightly slower performance.

Multithreading

In order to use multi threading, one must first enable it.

import mappy_rs
aligner = mappy_rs.Aligner("resources/test/test.mmi")
# Use 10 threads
aligner.enable_threading(10)

Enabling threading makes the map_batch method available. This method requires a list or iterable of dictionaries, which can have any number of keys and depth, but must contain the key seq with a string value in the top-level dictionary. Currently, the maximum batch size to be iterated in one call is 20000.

For example:

import mappy_rs
aligner = mappy_rs.Aligner("resources/test/test.mmi")
aligner.enable_threading(10)
seqs = [
    {"seq": "ACGTAGCATCGAGACTACGA", "Other_random_key": "banter"},
    {"seq": "ACGTAGCATCGAGACTACGA", "Other_random_key": "banter"},
]
for (mapping, data) in aligner.map_batch(seqs):
    print(list(mapping))
    print(data)

Benchmarks

A simple benchmark against classic mappy, and mappy_rs with incrementing numbers of threads, run on a 2018 Macbook.

Device

Property	Value
Model Name	MacBook Pro
Model Identifier	MacBookPro15,2
Processor Name	Quad-Core Intel Core i7
Processor Speed	2.7 GHz
Number of Processors	1
Total Number of Cores	4
L2 Cache (per Core)	256 KB
L3 Cache	8 MB
Hyper-Threading Technology	Enabled
Memory	16 GB

Results

Name (time in s)	Min	Max	Mean	StdDev	Median	IQR	Outliers	OPS	Rounds	Iterations
test_benchmark_multi[5]	26.8900 (1.0)	30.0969 (1.0)	28.0622 (1.0)	1.2614 (1.0)	27.9017 (1.0)	1.6081 (1.35)	1;0	0.0356 (1.0)	5	1
test_benchmark_multi[4]	28.5573 (1.06)	43.4543 (1.44)	32.3371 (1.15)	6.2815 (4.98)	29.7480 (1.07)	5.2148 (4.37)	1;1	0.0309 (0.87)	5	1
test_benchmark_multi[3]	31.6497 (1.18)	36.9986 (1.23)	33.5103 (1.19)	2.0542 (1.63)	32.8415 (1.18)	1.9576 (1.64)	1;0	0.0298 (0.84)	5	1
test_benchmark_multi[2]	43.2616 (1.61)	86.3859 (2.87)	53.8572 (1.92)	18.3339 (14.53)	45.9328 (1.65)	14.6382 (12.26)	1;1	0.0186 (0.52)	5	1
test_classic_mappy[mappy_al]	78.5566 (2.92)	82.8876 (2.75)	79.6177 (2.84)	1.8343 (1.45)	78.8350 (2.83)	1.1938 (1.0)	1;1	0.0126 (0.35)	5	1
test_classic_mappy[mappy_al_rs]	83.7239 (3.11)	87.9675 (2.92)	85.4424 (3.04)	1.6806 (1.33)	85.6335 (3.07)	2.3310 (1.95)	2;0	0.0117 (0.33)	5	1
test_benchmark_multi[1]	84.8418 (3.16)	94.0907 (3.13)	86.7404 (3.09)	4.1096 (3.26)	84.8749 (3.04)	2.4310 (2.04)	1;1	0.0115 (0.32)	5	1