nafcodec 0.3.1
PyO3 bindings and Python interface to nafcodec, an encoder/decoder for Nucleotide Archive Format (NAF) files.
License: MIT
Requires Python: >=3.7
Keywords: nucleotide, archive, biology, bioinformatics

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*Rust coder/decoder for [Nucleotide Archive Format (NAF)](https://github.com/KirillKryukov/naf) files*.

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## 🗺️ Overview

[Nucleotide Archive Format](https://github.com/KirillKryukov/naf) is a file
format proposed in Kryukov *et al.*[\[1\]](#ref1) in 2019 for storing
compressed nucleotide or protein sequences combining 4-bit encoding and
[Zstandard](https://github.com/facebook/zstd) compression. NAF files can
be compressed and decompressed using the
[original C implementation](https://kirill-kryukov.com/study/naf).

This library provides [PyO3](https://pyo3.rs) bindings to the `nafcodec` crate,
a Rust implementation of a NAF decoder using [`nom`](https://crates.io/crates/nom) 
for parsing the binary format, and [`zstd`](https://crates.io/crates/zstd) for 
handling Zstandard decompression. It provides a complete API that allows 
iterating over the contents of a NAF file.

*This is the Python version, there is a [Rust crate](https://crates.io/crates/nafcodec) available as well.*

### 📋 Features

- **streaming decoder**: The decoder is implemented using different readers
  each accessing a region of the compressed file, allowing to stream records
  without having to decode full blocks.
- **file-like decoding**: Allow the decoder to read from a file-like object
  instead of expecting a path.

The following features are planned:

- **optional decoding**: Allow the decoder to skip the decoding of certains
  fields, such as ignoring quality strings when they are not needed.
- **encoder**: Implement an encoder as well, using either in-memory buffers
  or temporary files to grow the archive.

### 🔌 Usage

Use a `nafcodec.Decoder` to iterate over the contents of a Nucleotide Archive 
Format, reading from the given [path-like](https://docs.python.org/3/glossary.html#term-path-like-object)
or [file-like](https://docs.python.org/3/glossary.html#term-file-object) object:

```python
import nafcodec

decoder = nafcodec.Decoder("../data/LuxC.naf")
for record in decoder:
    print(record.id)
```

All fields of the obtained `Record` are optional, and actually depend on the
kind of data that was compressed. 

<!-- ## 🔍 See Also -->

## 💭 Feedback

### ⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the [GitHub issue
tracker](https://github.com/althonos/nafcodec/issues) if you need to report
or ask something. If you are filing in on a bug, please include as much
information as you can about the issue, and try to recreate the same bug
in a simple, easily reproducible situation.

<!-- ### 🏗️ Contributing

Contributions are more than welcome! See [`CONTRIBUTING.md`](https://github.com/althonos/nafcodec/blob/master/CONTRIBUTING.md) for more details. -->


## 📋 Changelog

This project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html)
and provides a [changelog](https://github.com/althonos/nafcodec/blob/master/CHANGELOG.md)
in the [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) format.

## ⚖️ License

This library is provided under the open-source
[MIT license](https://choosealicense.com/licenses/mit/). The
[NAF specification](https://github.com/KirillKryukov/naf/blob/master/NAFv2.pdf)
is in the public domain.

*This project is in no way not affiliated, sponsored, or otherwise endorsed
by the [original NAF authors](https://github.com/KirillKryukov). It was
developed by [Martin Larralde](https://github.com/althonos/) during his PhD
project at the [European Molecular Biology Laboratory](https://www.embl.de/)
in the [Zeller team](https://github.com/zellerlab).*

## 📚 References

- <a id="ref1">\[1\]</a> Kirill Kryukov, Mahoko Takahashi Ueda, So Nakagawa, Tadashi Imanishi. "Nucleotide Archival Format (NAF) enables efficient lossless reference-free compression of DNA sequences". Bioinformatics, Volume 35, Issue 19, October 2019, Pages 3826–3828. [doi:10.1093/bioinformatics/btz144](https://doi.org/10.1093/bioinformatics/btz144)