genomepy 0.3.1

Genomes in Python

Easily install and use genomes in Python and elsewhere!

The goal is to have a simple and straightforward way to download and use genomic sequences. Currently, genomepy supports UCSC, Ensembl and NCBI.

Installation

Via [bioconda]((https://bioconda.github.io/):

$ conda install genomepy

Or via pip:

$ pip install genomepy

Configuration

By default genomes will be saved in ~/.local/share/genomes. This default can be changed by creating a configuration file called ~/.config/genomepy/genomepy.yaml. For instance, to set the default genome directory to /data/genomes, edit ~/.config/genomepy/genomepy.yaml and add the following line:

genome_dir: /data/genomes

The genome directory can also be explicitly specified in both the Python API as well as on the command-line.

Usage

Command line

$ genomepy

Usage: genomepy [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  genomes    list available genomes
  install    install genome
  providers  list available providers
  search     search for genomes

List available providers

$ genomepy providers
Ensembl
UCSC
NCBI

List available genomes

You can constrain the genome list by using the -p option to search only a specific provider.

$ genomepy genomes -p UCSC
UCSC    hg38    Human Dec. 2013 (GRCh38/hg38) Genome at UCSC
UCSC    hg19    Human Feb. 2009 (GRCh37/hg19) Genome at UCSC
UCSC    hg18    Human Mar. 2006 (NCBI36/hg18) Genome at UCSC
...
UCSC    danRer4 Zebrafish Mar. 2006 (Zv6/danRer4) Genome at UCSC
UCSC    danRer3 Zebrafish May 2005 (Zv5/danRer3) Genome at UCSC

Search for a genome.

$ genomepy search Xenopus
NCBI    Xenopus_tropicalis_v9.1 Xenopus tropicalis; DOE Joint Genome Institute
NCBI    ViralProj30173  Xenopus laevis endogenous retrovirus Xen1;
NCBI    Xenopus_laevis_v2   Xenopus laevis; International Xenopus Sequencing Consortium
NCBI    v4.2    Xenopus tropicalis; DOE Joint Genome Institute
NCBI    Xtropicalis_v7  Xenopus tropicalis; DOE Joint Genome Institute
Ensembl JGI 4.2 Xenopus

Only search a specific provider:

$ genomepy search tropicalis -p UCSC
UCSC    xenTro7 X. tropicalis Sep. 2012 (JGI 7.0/xenTro7) Genome at UCSC
UCSC    xenTro3 X. tropicalis Nov. 2009 (JGI 4.2/xenTro3) Genome at UCSC
UCSC    xenTro2 X. tropicalis Aug. 2005 (JGI 4.1/xenTro2) Genome at UCSC
UCSC    xenTro1 X. tropicalis Oct. 2004 (JGI 3.0/xenTro1) Genome at UCSC

Note that searching doesn’t work flawlessly, so try a few variations if you don’t get any results. Search is case-insensitive.

Install a genome.

The most important command. The most simple form:

$ genomepy  install hg38 UCSC
downloading...
done...
name: hg38
fasta: /data/genomes/hg38/hg38.fa

Here, genomes are downloaded to the directory specified in the config file. To choose a different directory, use the -g option.

$ genomepy install sacCer3 UCSC -g ~/genomes/
downloading from http://hgdownload.soe.ucsc.edu/goldenPath/sacCer3/bigZips/chromFa.tar.gz...
done...
name: sacCer3
local name: sacCer3
fasta: /home/simon/genomes/sacCer3/sacCer3.fa

You can use a regular expression to filter for matching sequences (or non-matching sequences by using the --no-match option). For instance, the following command downloads hg38 and saves only the major chromosomes:

$ genomepy  install hg38 UCSC -r 'chr[0-9XY]+$'
downloading from http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz...
done...
name: hg38
local name: hg38
fasta: /data/genomes/hg38/hg38.fa
$ grep ">" /data/genomes/hg38/hg38.fa
>chr1
>chr10
>chr11
>chr12
>chr13
>chr14
>chr15
>chr16
>chr17
>chr18
>chr19
>chr2
>chr20
>chr21
>chr22
>chr3
>chr4
>chr5
>chr6
>chr7
>chr8
>chr9
>chrX
>chrY

By default, sequences are soft-masked. Use -m hard for hard masking.

The chromosome sizes are saved in file called <genome_name>.fa.sizes.

Finally, in the spirit of reproducibility all selected options are stored in a README.txt. This includes the original name and download location.

Local cache.

Note that the first time you run genomepy search or list the command will take a long time as the genome lists have to be downloaded. The lists are cached locally, which will save time later. The cached files are stored in ~/.cache/genomepy and expire after 7 days. You can also delete this directory to clean the cache.

From Python

>>> import genomepy
>>> for row in genomepy.search("GRCh38"):
...     print("\t".join(row))
...
UCSC    hg38    Human Dec. 2013 (GRCh38/hg38) Genome at UCSC
NCBI    GRCh38.p10  Homo sapiens; Genome Reference Consortium
NCBI    GRCh38  Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p1   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p2   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p3   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p4   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p5   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p6   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p7   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p8   Homo sapiens; Genome Reference Consortium
NCBI    GRCh38.p9   Homo sapiens; Genome Reference Consortium
Ensembl GRCh38.p10  Human
>>> genomepy.install_genome("hg38", "UCSC", genome_dir="/data/genomes")
downloading...
done...
name: hg38
fasta: /data/genomes/hg38/hg38.fa
>>> g = genomepy.genome("hg38", genome_dir="/data/genomes")
>>> g["chr6"][166502000:166503000]
tgtatggtccctagaggggccagagtcacagagatggaaagtggatggcgggtgccgggggctggggagctactgtgcagggggacagagctttagttctgcaagatgaaacagttctggagatggacggtggggatgggggcccagcaatgggaacgtgcttaatgccactgaactgggcacttaaacgtggtgaaaactgtaaaagtcatgtgtatttttctacaattaaaaaaaATCTGCCACAGAGTTAAAAAAATAACCACTATTTTCTGGAAATGGGAAGGAAAAGTTACAGCATGTAATTAAGATGACAATTTATAATGAACAAGGCAAATCTTTTCATCTTTGCCTTTTGGGCATATTCAATCTTTGCCCAGAATTAAGCACCTTTCAAGATTAATTCTCTAATAATTCTAGTTGAACAACACAACCTTTTCCTTCAAGCTTGCAATTAAATAAGGCTATTTTTAGCTGTAAGGATCACGCTGACCTTCAGGAGCAATGAGAACCGGCACTCCCGGCCTGAGTGGATGCACGGGGAGTGTGTCTAACACACAGGCGTCAACAGCCAGGGCCGCACGAGGAGGAGGAGTGGCAACGTCCACACAGACTCACAACACGGCACTCCGACTTGGAGGGTAATTAATACCAGGTTAACTTCTGGGATGACCTTGGCAACGACCCAAGGTGACAGGCCAGGCTCTGCAATCACCTCCCAATTAAGGAGAGGCGAAAGGGGACTCCCAGGGCTCAGAGCACCACGGGGTTCTAGGTCAGACCCACTTTGAAATGGAAATCTGGCCTTGTGCTGCTGCTCTTGTGGGGAGACAGCAGCTGCGGAGGCTGCTCTCTTCATGGGATTACTCTGGATAAAGTCTTTTTTGATTCTACgttgagcatcccttatctgaaatgcctgaaaccggaagtgtttaggatttggggattttgcaatatttacttatatataatgagatatcttggagatgggccacaa

The genomepy.genome() method returns a pyfaidx.Fasta object, see the documentation for more examples on how to use this.

Known issues

There might be issues with specific genome sequences. Sadly, not everything (naming, structure, filenames) is always consistent on the provider end. Let me know if you encounter issues with certain downloads.

Todo

• More tests!

• Automatic indexing (such as bwa)

• Ensembl bacteria

Contributing

Contributions welcome! Send me a pull request or get in touch.

License

This module is licensed under the terms of the MIT license.