https://github.com/biojulia/kmers.jl
Efficient types and methods for K-mer biosequences
https://github.com/biojulia/kmers.jl
Last synced: 8 months ago
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Efficient types and methods for K-mer biosequences
- Host: GitHub
- URL: https://github.com/biojulia/kmers.jl
- Owner: BioJulia
- License: mit
- Created: 2017-04-19T10:28:59.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2025-06-06T16:07:44.000Z (12 months ago)
- Last Synced: 2025-09-21T00:29:30.065Z (9 months ago)
- Language: Julia
- Homepage:
- Size: 1.28 MB
- Stars: 29
- Watchers: 9
- Forks: 7
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# 
Kmers
[](https://github.com/BioJulia/Kmers.jl/releases/latest)
[](https://github.com/BioJulia/Kmers.jl/blob/master/LICENSE)
[](https://biojulia.github.io/Kmers.jl/stable)
## Description
Kmers.jl provide the `Kmer <: BioSequence` type which implement the concept of a
[k-mer](https://en.wikipedia.org/wiki/K-mer), a biological sequence of exactly length `k`.
K-mers are used frequently in bioinformatics because, when k is small and known at
compile time, these sequences can be efficiently represented as integers and stored
directly in CPU registers, allowing for much more efficient computation than arbitrary-length sequences.
In Kmers.jl, the `Kmer` type is parameterized by its length, and its data is stored in an `NTuple`. This makes `Kmers` bitstypes and highly efficient.
Conceptually, one may use the following analogy:
* `BioSequence` is like `AbstractVector`
* `LongSequence` is like `Vector`
* `Kmer` is like [`SVector`](https://github.com/JuliaArrays/StaticArrays.jl) from `StaticArrays`
Kmers.jl is tightly coupled to the
[`BioSequences.jl`](https://github.com/BioJulia/BioSequences.jl) package,
and relies on its internals.
Hence, you should expect strict compat bounds on BioSequences.jl.
## Usage
### ⚠️ WARNING ⚠️
`Kmer`s are parameterized by their length. That means any operation on `Kmer`s that change their length, such as `push`, `pop`, slicing, or masking (logical indexing) will be **type unstable** and hence slow and memory inefficient, unless you write your code in such as way that the compiler can use constant folding.
Further, as `Kmer`s are immutable and their operations are aggressively inlined and unrolled,
they become inefficent as they get longer.
For example, reverse-complementing a 32-mer takes 26 ns, compared to 102 ns for the equivalent `LongSequence`. However, for 512-mers, the `LongSequence` takes 126 ns, and the `Kmer` 16 μs!
Kmers.jl is intended for high-performance computing. If you do not need the extra performance that register-stored sequences provide, you might consider using `LongSequence` from BioSequences.jl instead
## Installation
You can install BioSequences from the julia
REPL. Press `]` to enter pkg mode, and enter the following:
```julia
pkg> add Kmers
```
If you are interested in the cutting edge of development, please check out
the master branch to try new features before release.
## Contributing
We appreciate contributions from users including reporting bugs, fixing
issues, improving performance and adding new features.
Take a look at the [contributing files](https://github.com/BioJulia/Contributing)
detailed contributor and maintainer guidelines, and code of conduct.
## Questions?
If you have a question about contributing or using BioJulia software, come
on over and chat to us on [the Julia Slack workspace](https://julialang.org/slack/), or you can try the
[Bio category of the Julia discourse site](https://discourse.julialang.org/c/domain/bio).