Automatic downloading and processing of genomes and metadata in command line and Python
Project description
genomepy
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.
Pssst, hey there! Is genomepy not doing what you want? Does it fail? Is it clunky? Is the documentation unclear? Have any other ideas on how to improve it? Don't be shy and let us know!
Installation
Genomepy works with Python 3.6+. You can install it via bioconda:
$ conda install genomepy
Or via pip:
$ pip install genomepy
To enjoy the full capabilities of genomepy, you will have to install some dependencies. Make sure these dependencies are in your PATH.
To read/write bgzipped genomes you will have to install tabix
.
If you want to use the annotation download feature, you will have to install the following utilities:
genePredToBed
genePredToGtf
bedToGenePred
gtfToGenePred
gff3ToGenePred
You can find the binaries here.
Plugins and indexing
By default genomepy generates a file with chromosome sizes and a BED file with gap locations (Ns in the sequence).
For some genomes genomepy can download blacklist files (generated by the Kundaje lab). This will only work when installing these genomes from UCSC. Enable this plugin to use it.
$ genomepy plugin enable blacklist
You can also create indices for some widely using aligners. Currently, genomepy supports:
Note 1: these programs are not installed by genomepy and need to be installed separately for the indexing to work.
Note 2: the index is based on the genome, annotation (splice sites) is currently not taken into account.
You can configure the index creation using the genomepy plugin
command (see below)
Configuration
To change the default configuration, generate a personal config file:
$ genomepy config generate
Created config file /home/simon/.config/genomepy/genomepy.yaml
Genome location
By default genomes will be saved in ~/.local/share/genomes
.
To set the default genome directory to /data/genomes
for instance,
edit ~/.config/genomepy/genomepy.yaml
and change the following line:
genome_dir: ~/.local/share/genomes/
to:
genome_dir: /data/genomes
The genome directory can also be explicitly specified in both the Python API as well as on the command-line.
Compression
Optionally genome FASTA files can be saved using bgzip compression. This means that the FASTA
files will take up less space on disk. To enable this use the flag --bgzip
on the command line, or add the following line to your config
file:
bgzip: True
Most tools are able to use bgzip-compressed genome files.
One notable exception is bedtools getfasta
. As an alternative, you can use the faidx
command-line
script from pyfaidx which comes installed with genomepy.
Usage
Command line
Usage: genomepy [OPTIONS] COMMAND [ARGS]...
Options:
--version Show the version and exit.
-h, --help Show this message and exit.
Commands:
config manage configuration
genomes list available genomes
install install genome
plugin manage plugins
providers list available providers
search search for genomes
Install a genome.
Find the name of your desired genome:
$ genomepy search xenopus_tropicalis
ensembl Xenopus_tropicalis_v9.1 xenopus_tropicalis
ucsc xenTro9 X. tropicalis Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9) Genome at UCSC
ncbi Xenopus_tropicalis_v9.1 Xenopus tropicalis; DOE Joint Genome Institute
Note that genomes with a space can be searched for either by using "quotation marks"
,
or by replacing the space(s) with and underscore _
.
For example, we can search for Xenopus Tropicalis as "xenopus tropicalis"
,
xenopus_tropicalis
or xenopus
. The search function is case-insensitive.
Lets say we want to download the Xenopus Tropicalis genome from UCSC. Copy the name returned by the search function and it with the provider name to install:
$ genomepy install xenTro9 UCSC
downloading...
done...
name: xenTro9
fasta: /data/genomes/xenTro9/xenTro9.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: /data/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, or -m unmaksed
for no masking.
The chromosome sizes are saved in file called <genome_name>.fa.sizes
.
You can choose to download gene annotation files with the --annotation
option.
These will be saved in BED and GTF format.
$ genomepy install hg38 UCSC --annotation
To facilitate the downloading of genomes not supported by either NCBI, UCSC, or Ensembl, genomes can also be downloaded directly from an url:
$ genomepy install https://research.nhgri.nih.gov/hydra/download/assembly/\Hm105_Dovetail_Assembly_1.0.fa.gz url
This installs the genome under the filename of the link, but can be changed with the --localname
option
Finally, in the spirit of reproducibility all selected options are stored in a README.txt
.
This includes the original name and download location.
Manage plugins.
Use genomepy plugin list
to view the available plugins.
$ genomepy plugin list
plugin enabled
bowtie2
bwa
gmap
hisat2
minimap2
sizes *
star
Enable plugins as follows:
$ genomepy plugin enable bwa hisat2
Enabled plugins: bwa, hisat2, sizes
And disable like this:
$ genomepy plugin disable bwa
Enabled plugins: hisat2, sizes
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.
Note that genomes with a space can be searched for either by using "quotation marks"
,
or by replacing the space(s) with and underscore _
.
Search is case-insensitive.
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
Manage configuration
List the current configuration file that genomepy uses:
$ genomepy config file
/home/simon/.config/genomepy/genomepy.yaml
To show the contents of the config file:
$ genomepy config show
# Directory were downloaded genomes will be stored
genome_dir: ~/.local/share/genomes/
plugin:
- sizes
To generate a personal configuration file (existing file will be overwritten):
$ genomepy config generate
Created config file /home/simon/.config/genomepy/genomepy.yaml
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 Genome object. This has all the
functionality of 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
- Linking genomes to NCBI taxonomy ID
- Optionally: Ensembl bacteria (although there might be better options specifically for bacterial sequences)
Citation
If you use genomepy in your research, please cite it: 10.21105/joss.00320.
Getting help
If you want to report a bug or issue, or have problems with installing or running the software please create a new issue. This is the preferred way of getting support. Alternatively, you can mail me.
Contributing
Contributions welcome! Send me a pull request or get in touch.
When contributing a PR, please use the develop branch.
For style, code will be checked using flake8 and
black. These modules can be
installed via conda, conda install black flake8 flake8-bugbear
or via pip pip install black flake8 flake8-bugbear
.
black --check genomepy/ setup.py tests/
flake8 setup.py genomepy/ tests/
Contributors
- Simon van Heeringen - @simonvh
- Siebren Frölich - @siebrenf
- Maarten van der Sande - @Maarten-vd-Sande
- Dohoon Lee - @dohlee
License
This module is licensed under the terms of the MIT license.
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