GenomeKit

run-unit-tests

What is GenomeKit?

GenomeKit is Deep Genomics' Python library for fast and easy access to genomic resources such as sequence, data tracks, and annotations. The goal is to let machine learning researchers build data sets easily, and to be creative about how those data sets are designed.

GenomeKit is also designed to work with genome variants, giving users a powerful way to extract features for different genotypes.

In the future it will include other useful resources like secondary structure, conservation, and more.

Useful Features

  • Fast querying of DNA sequence and genomic tracks.
  • Fast querying and structured access for annotations (GENCODE, RefSeq, ...).
  • Fast querying and structured access for short read alignments (SAM/BAM files).
  • Fast querying and filtering for variants (VCF files).
  • Interval and Variant objects that are convenient, lightweight, and standardized.
  • Scan the genome for motifs.

Resource Available

  • Reference DNA: hg19, hg19.p13.plusMT, hg38, hg38.p12, hg38.p13, hg38.p14, mm10.p6, mm39, rn6, macFas5, susScr11.
  • Annotations: GENCODE, RefSeq, Ensembl, APPRIS, MANE.
  • Tracks: conservation, RNA structure, methylation, nucleosome positions, ...

Installation

Install via conda

The best way to install GenomeKit is via the pre-compiled conda packages available from anaconda.org/conda-forge/genomekit.

You can install GenomeKit with

conda install genomekit

Full Documentation (including developer instructions)

https://deepgenomics.github.io/GenomeKit

Documentation for previous versions is also available, e.g https://deepgenomics.github.io/GenomeKit/v5.0.0

Acknowledgements

We would like to express our gratitude to the following individuals who have contributed to the development of GenomeKit:

Special thanks to the original author, Andrew Delong, for laying the foundation of this project, and to Steve Chan for his continued support and contributions.

We appreciate the efforts and contributions of all past contributors, whose work has been invaluable to the growth and improvement of GenomeKit.

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Credits

Author: Deep Genomics

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License

Apache Software License (Apache License 2.0)

Requires

Python <4, >=3.10

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df

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