| Name | genomepy JSON |
| Version |
0.16.1
JSON |
| download |
| home_page | |
| Summary | Genes and genomes at your fingertips |
| upload_time | 2023-06-14 13:37:00 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2016 Simon van Heeringen <simon.vanheeringen@gmail.com> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
ensembl
gencode
ncbi
ucsc
annotation
assembly
gene
genome
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
|
| coveralls test coverage |
No coveralls.
|
# genomepy: genes and genomes at your fingertips
[](http://bioconda.github.io)
[](https://anaconda.org/bioconda/genomepy)
[](https://badge.fury.io/py/genomepy)
[](https://GitHub.com/vanheeringen-lab/genomepy/stargazers/)
[](https://app.travis-ci.com/github/vanheeringen-lab/genomepy/branches)
[](https://codeclimate.com/github/vanheeringen-lab/genomepy/maintainability)
[](https://codeclimate.com/github/vanheeringen-lab/genomepy/test_coverage)
[](https://doi.org/10.1093/bioinformatics/btad119)
[](https://doi.org/10.5281/zenodo.1010458)
<!--- [](https://doi.org/10.48550/arXiv.2209.00842) [](http://joss.theoj.org/papers/df434a15edd00c8c2f4076668575d1cd) -->
genomepy is designed to provide a _simple_ and _straightforward_ way to download and use genomic data.
This includes (1) searching available data,
(2) showing the available metadata,
(3) automatically downloading, preprocessing and matching data and
(4) generating optional aligner indexes.
All with sensible, yet controllable defaults.
Currently, genomepy supports Ensembl, UCSC, NCBI and GENCODE.
[](https://asciinema.org/a/eZttBuf5ly0AnjFVBiEIybbjS)
**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](https://github.com/vanheeringen-lab/genomepy/issues)!
## Table of Contents
1. [Installation](#installation)
2. [Quick usage](#quick-usage)
3. [Command line](#command-line-interface)
1. [Search](#search-genomes--gene-annotations)
2. [Inspect](#inspect-gene-annotations)
3. [Install](#install-a-genome--gene-annotation)
4. [Plugins](#plugins)
4. [Python](#python-api)
5. [Frequently Asked Questions](#frequently-asked-questions)
6. [Getting help](#getting-help)
7. [Contributing](#contributing)
8. [Citation](#citation)
9. [License](#license)
## Installation
genomepy requires Python 3.7+
You can install genomepy via [bioconda](https://bioconda.github.io/), pip or git.
#### Bioconda
```bash
$ conda install -c conda-forge -c bioconda 'genomepy>=0.15'
```
#### Pip
```bash
$ pip install genomepy
```
With the Pip installation, you will have to install additional dependencies, and make them available in your PATH.
To read/write bgzipped genomes you will have to install `pysam`.
If you want to use gene annotation features, you will have to install the following utilities:
* `genePredToBed`
* `genePredToGtf`
* `bedToGenePred`
* `gtfToGenePred`
* `gff3ToGenePred`
You can find the binaries [here](http://hgdownload.cse.ucsc.edu/admin/exe/).
#### Git
```bash
$ git clone https://github.com/vanheeringen-lab/genomepy.git
$ conda env create -n genomepy -f genomepy/environment.yml
$ conda activate genomepy
$ pip install -e genomepy
```
## Quick usage
1. Find your genome: `$ genomepy search zebrafish`
Console output:
```bash
name provider accession tax_id annotation species other_info
GRCz11 Ensembl GCA_000002035.4 7955 ✓ Danio rerio 2017-08-Ensembl/2018-04
^
Use name for genomepy install
```
2. Install your genome (with gene annotation): `$ genomepy install --annotation GRCz11 --provider ensembl `
The default genomes directory: `~/.local/share/genomes/`
## Command line interface
All commands come with a short explanation when appended with `-h`/`--help`.
```bash
$ genomepy --help
Usage: genomepy [OPTIONS] COMMAND [ARGS]...
Options:
--version Show the version and exit.
-h, --help Show this message and exit.
Commands:
annotation show 1st lines of each annotation
clean remove provider data
config manage configuration
genomes list available genomes
install install a genome & run active plugins
plugin manage plugins
providers list available providers
search search for genomes
```
### Search genomes & gene annotations
Let's say we want to download a *Xenopus tropicalis* genome & gene annotation.
First, lets find out what's out there!
You can search by name, taxonomy ID or assembly accession ID.
Additionally, you can limit the search result to one provider with `-p`/`--provider`.
Furthermore, you can get the absolute `--size` of each genome (this option slows down the search).
```bash
$ genomepy search xenopus tro
name provider accession tax_id annotation species other_info
n r e k
Xenopus_tropicalis_v9.1 Ensembl GCA_000004195.3 8364 ✓ Xenopus tropicalis 2019-04-Ensembl/2019-12
xenTro1 UCSC na 8364 ✗ ✗ ✗ ✗ Xenopus tropicalis Oct. 2004 (JGI 3.0/xenTro1)
xenTro2 UCSC na 8364 ✗ ✓ ✓ ✗ Xenopus tropicalis Aug. 2005 (JGI 4.1/xenTro2)
xenTro3 UCSC GCA_000004195.1 8364 ✗ ✓ ✓ ✗ Xenopus tropicalis Nov. 2009 (JGI 4.2/xenTro3)
xenTro7 UCSC GCA_000004195.2 8364 ✓ ✓ ✗ ✗ Xenopus tropicalis Sep. 2012 (JGI 7.0/xenTro7)
xenTro9 UCSC GCA_000004195.3 8364 ✓ ✓ ✓ ✗ Xenopus tropicalis Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9)
Xtropicalis_v7 NCBI GCF_000004195.2 8364 ✓ Xenopus tropicalis DOE Joint Genome Institute
Xenopus_tropicalis_v9.1 NCBI GCF_000004195.3 8364 ✓ Xenopus tropicalis DOE Joint Genome Institute
UCB_Xtro_10.0 NCBI GCF_000004195.4 8364 ✓ Xenopus tropicalis University of California, Berkeley
ASM1336827v1 NCBI GCA_013368275.1 8364 ✗ Xenopus tropicalis Southern University of Science and Technology
^
Use name for genomepy install
```
### Inspect gene annotations
Let's say we want to download the *Xenopus tropicalis* genome & gene annotation from UCSC.
Since we are interested in the gene annotation as well, we should check which gene annotation suits our needs.
As you can see in the search results, UCSC has several gene annotations for us to choose from.
In the search results, `n r e k ` denotes which UCSC annotations are available.
These stand for **n**cbiRefSeq, **r**efGene, **e**nsGene and **k**nownGene, respectively.
We can quickly inspect these with the `genomepy annotation` command:
```bash
$ genomepy annotation xenTro9 -p ucsc
12:04:41 | INFO | UCSC ncbiRefSeq
chr1 genomepy transcript 133270 152620 . - . gene_id "LOC100490505"; transcript_id "XM_012956089.1"; gene_name "LOC100490505";
chr1 genomepy exon 133270 134186 . - . gene_id "LOC100490505"; transcript_id "XM_012956089.1"; exon_number "1"; exon_id "XM_012956089.1.1"; gene_name "LOC100490505";
12:04:45 | INFO | UCSC refGene
chr1 genomepy transcript 193109390 193134311 . + . gene_id "pias2"; transcript_id "NM_001078987"; gene_name "pias2";
chr1 genomepy exon 193109390 193109458 . + . gene_id "pias2"; transcript_id "NM_001078987"; exon_number "1"; exon_id "NM_001078987.1"; gene_name "pias2";
12:04:49 | INFO | UCSC ensGene
chr1 genomepy transcript 133270 152620 . - . gene_id "ENSXETG00000030302.2"; transcript_id "ENSXETT00000061673.2"; gene_name "ENSXETG00000030302.2";
chr1 genomepy exon 133270 134186 . - . gene_id "ENSXETG00000030302.2"; transcript_id "ENSXETT00000061673.2"; exon_number "1"; exon_id "ENSXETT00000061673.2.1"; gene_name "ENSXETG00000030302.2";
```
Here we can see that the `refGene` annotation has actual HGNC gene names, so lets go with this annotation.
This differs between assemblies, so be sure to check!
### Install a genome & gene annotation
Copy the name returned by the search function to install.
```bash
$ genomepy install xenTro9
```
You can choose to download gene annotation files with the `-a`/`--annotation` option.
```bash
$ genomepy install xenTro9 --annotation
```
For UCSC we can also select the annotation type.
See `genomepy install --help` for all provider specific options.
```bash
$ genomepy install xenTro9 --UCSC-annotation refGene
```
Since we did not specify the provider here, genomepy will use the first provider with `xenTro9`.
You can specify a provider by name with `-p`/`--provider`:
```bash
$ genomepy install xenTro9 -p UCSC
Downloading genome from http://hgdownload.soe.ucsc.edu/goldenPath/xenTro9/bigZips/xenTro9.fa.gz...
Genome download successful, starting post processing...
name: xenTro9
local name: xenTro9
fasta: ~/.local/share/genomes/xenTro9/xenTro9.fa
```
Next, the genome is downloaded to the directory specified in the config file (by default `~/.local/share/genomes`).
To choose a different directory, use the `-g`/`--genomes_dir` option:
```bash
$ genomepy install sacCer3 -p UCSC -g /path/to/my/genomes
Downloading genome from http://hgdownload.soe.ucsc.edu/goldenPath/sacCer3/bigZips/chromFa.tar.gz...
Genome download successful, starting post processing...
name: sacCer3
local name: sacCer3
fasta: /path/to/my/genomes/sacCer3/sacCer3.fa
```
#### Regex, masking & compression
You can use a regular expression to filter for matching sequences
(or non-matching sequences by using the `-n`/`--no-match` option).
For instance, the following command downloads hg38 and saves only the major chromosomes:
```bash
$ genomepy install hg38 -p UCSC -r 'chr[0-9XY]+$'
Downloading genome from from http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz...
Genome download successful, starting post processing...
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, genome sequences are soft-masked (ACgtN).
Use `-m hard` for hard masking (ACNNN), or `-m none` for no masking (ACGTN).
```bash
$ genomepy install hg38 --mask hard
```
If you wish to conserve space, you can tell genomepy to compress the downloaded data by passing the `-b`/`--bgzip` option.
See [Configuration](#compression) for details.
```bash
$ genomepy install hg38 --bgzip
```
#### Other providers (any URL/local files)
To use assemblies not on NCBI, UCSC, Ensembl or GENCODE, you can give a URL instead of a name, together with `--provider URL`.
Similarly, if you have a local FASTA file, you can install this using the filepath, together with `--provider Local`:
```bash
$ genomepy install -p url https://research.nhgri.nih.gov/hydra/download/assembly/\Hm105_Dovetail_Assembly_1.0.fa.gz
```
This will install the genome under the filename of the URL/filepath, but can be changed with the `-l`/`--localname` option.
If you add the `--annotation` flag, genomepy will search the (remote) directory for an annotation file as well.
Should this fail, you can also add a URL to the annotation with `--URL-to-annotation` with the `URL` provider,
or a filepath with `--Local-path-to-annotation` with the `Local` provider:
```bash
$ genomepy install -p local /path/to/genome.fa --Local-path-to-annotation /path/to/gene.annotation.gtf
```
#### Reproducibility
All selected options are stored in a `README.txt`.
This includes the original name, download location and other genomepy operations (such as regex filtering and time).
### Plugins
Plugins are optional steps that are executed after installing an assembly with `genomepy install`.
If you already installed an assembly, you can activate a plugin and rerun the install command.
This will not overwrite your local files, unless you use the `--force` option.
Check which plugins are enabled with `genomepy plugin list`.
#### Genome blacklists
For some model organisms, genomepy can download a genome blacklist (generated by the [Kundaje lab](https://www.nature.com/articles/s41598-019-45839-z)).
Blacklists are only available for these model organisms when downloaded from UCSC, and for the human and mouse genomes.
Enable the blacklist plugin to use it:
```bash
$ genomepy plugin enable blacklist
Enabled plugins: blacklist
```
#### Aligner indexes
You can also create aligner indexes for several widely used aligners.
Currently, genomepy supports:
* [Bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)
* [BWA](http://bio-bwa.sourceforge.net/)
* [GMAP](http://research-pub.gene.com/gmap/)
* [HISAT2](https://ccb.jhu.edu/software/hisat2/index.shtml)
* [Minimap2](https://github.com/lh3/minimap2)
* [STAR](https://github.com/alexdobin/STAR)
These programs are not installed by genomepy and need to be installed separately for the indexing to work.
The easiest way to do so is with conda, e.g.: `conda install -c bioconda bwa star`
Splice-aware indexing (required for e.g. RNA-seq) can be performed by STAR and Hisat2.
This will be done automatically if the gene annotation was downloaded as well.
Finally, STAR can further improve mapping to (novel) splice junctions by indexing again (see 2-pass mapping mode in the STAR manual).
The second pass is not supported by genomepy.
You can configure the index creation with `genomepy plugin enable`, e.g.:
```bash
$ genomepy plugin enable bwa star
Enabled plugins: blacklist, bwa, star
```
You can pass the number of threads to use for aligner index creation with `genomepy install --threads` (default is 8).
### Configuration
All defaults can be overwritten on the command line and in Python.
However, you can create & edit the config file to change the default settings ([full description](https://vanheeringen-lab.github.io/genomepy/content/config.html)):
```bash
$ genomepy config generate
Created config file ~/.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:
```yaml
genomes_dir: /data/genomes
```
#### Compression
Genome FASTA files can be stored using bgzip compression.
This means that the FASTA files will take up less space on disk.
Set the following line to your config file:
```yaml
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](https://github.com/mdshw5/pyfaidx)
which comes installed with genomepy.
### List available providers
```bash
$ genomepy providers
GENCODE
Ensembl
UCSC
NCBI
Local
URL
```
### List available genomes
You can constrain the genome list by using the `-p`/`--provider` option to search only a specific provider.
Additionally, you can get the absolute `--size` of each genome (this option slows down the search).
```bash
$ genomepy genomes -p UCSC
name provider accession tax_id annotation species other_info
n r e k
ailMel1 UCSC GCF_000004335.2 9646 ✓ ✗ ✓ ✗ Ailuropoda melanoleuca Dec. 2009 (BGI-Shenzhen 1.0/ailMel1)
allMis1 UCSC GCA_000281125.1 8496 ✗ ✓ ✗ ✗ Alligator mississippiensis Aug. 2012 (allMis0.2/allMis1)
anoCar1 UCSC na 28377 ✗ ✗ ✓ ✗ Anolis carolinensis Feb. 2007 (Broad/anoCar1)
```
### Local cache.
Note that the first time you run `genomepy search` or `list` the command will take a while 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 (so they stay up to date).
This expiration time can be changed in the config file.
You can also delete this directory to clean the cache using `genomepy clean`.
## Python API
Check out our [Python API documentation here](https://vanheeringen-lab.github.io/genomepy/content/api_core.html)
```
>>> import genomepy
>>> for row in genomepy.search("GRCh38"):
... print(row)
...
['GRCh38.p13', 'Ensembl', 'GCA_000001405.28', 9606, True, 'Homo sapiens', '2014-01-Ensembl/2021-03']
['hg38', 'UCSC', 'GCA_000001405.15', 9606, [True, True, False, True], 'Homo sapiens', 'Dec. 2013 (GRCh38/hg38)']
['GRCh38', 'NCBI', 'GCF_000001405.26', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']
['GRCh38.p1', 'NCBI', 'GCF_000001405.27', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']
['GRCh38.p2', 'NCBI', 'GCF_000001405.28', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']
['GRCh38.p3', 'NCBI', 'GCF_000001405.29', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']
>>> genomepy.install_genome("hg38", annotation=True, provider="UCSC", genomes_dir="./data/genomes")
Downloading genome from UCSC. Target URL: http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz...
Genome download successful, starting post processing...
name: hg38
local name: hg38
fasta: ./data/genomes/hg38/hg38.fa
Downloading the ncbiRefSeq annotation from the UCSC MySQL database.
Annotation download successful
>>> a = genomepy.Annotation("hg38", genomes_dir="./data/genomes")
>>> a.named_gtf.head(3)
seqname ... attribute
gene_name ...
DDX11L1 chr1 ... gene_id "DDX11L1"; transcript_id "NR_046018.2"...
DDX11L1 chr1 ... gene_id "DDX11L1"; transcript_id "NR_046018.2"...
DDX11L1 chr1 ... gene_id "DDX11L1"; transcript_id "NR_046018.2"...
>>> start = a.named_gtf.loc["TP63"]["start"].min()
>>> end = a.named_gtf.loc["TP63"]["end"].max()
>>> chrom = a.named_gtf.loc["TP63"]["seqname"][0]
>>> g = genomepy.Genome("hg38", genomes_dir="./data/genomes")
>>> g[chrom][start:end]
>chr3:189596747-189897276
gcaacccgctggggtcaccttccacactgtggaagctttgttcttttgctctttgcagtaaatcttgct...
```
The `genomepy.Genome` class builds on top of the `pyfaidx.Fasta` class,
see the [pyfaidx documentation](https://github.com/mdshw5/pyfaidx) for more details.
The `genomepy.Annotation` class contains pandas Dataframes with GTF and BED files, as well as additional class methods to utilize these.
## Frequently Asked Questions
Genomepy utilizes external databases to obtain your files.
Unfortunately this sometimes causes issues.
Here are some of the more common issues, with solutions.
Let us know if you encounter issues you cannot solve by creating [a new issue](https://github.com/vanheeringen-lab/genomepy/issues).
### Provider is offline
Occasionally one of the providers experience connection issues, which can last anywhere between minutes to hours.
When this happens genomepy will warn that the provider appears offline, or that the URL seems broken.
If the issue does not pass, you can try to reset genomepy.
Simply run `genomepy clean` on the command line, or run `genomepy.clean()` in Python.
### This genome is missing
Genomepy stores provider data on your computer to rerun it faster later.
If a provider was offline during this time, it may miss (parts of) the data.
To re-download the data, remove the local data with `genomepy clean`, then `search` for your genome again.
### This genome is STILL missing/URL is broken
Sadly, not everything (naming, structure, filenames) is always consistent on the provider end.
Contact the provider to get it fixed!
One notable group are Ensembl fungi, which seems to be mostly mislabelled.
In the meantime, you can still use the power of genomepy by manually retrieving the URLs,
and downloading the files with `genomepy install GENOME_URL -p url --url-to-annotation ANNOTATION_URL`.
### The genomepy config was corrupted
You can create a new one with `genomepy config generate` on command line,
or `genomepy.manage_config("generate")` in Python.
### What's genomepy maximum memory usage?
Genomepy does not read a genome fully into memory.
Therefore, installing takes less than 1 GB RAM regardless of the genome's size.
Searching NCBI is the most costly operation, using around 3 GB (the first time).
### Which genome/gene annotation to use
Each provider has its pros and cons:
* Ensembl has excellent gene annotations, but their chromosome names can cause issues with some tools.
* UCSC has an excellent genome browser, but their gene annotations vary in format.
* NCBI allows public submissions, and so has the latest versions, although not always complete or error free.
Use `genomepy search` to see your options, and `genomepy annotation` to check the quality of the gene annotation(s).
## 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](https://github.com/vanheeringen-lab/genomepy/issues).
This is the preferred way of getting support.
Alternatively, you can [mail me](mailto:simon.vanheeringen@gmail.com).
## Contributing
Contributions welcome! Send me a pull request or [get in touch](mailto:simon.vanheeringen@gmail.com).
When contributing a PR, please use the [develop](https://github.com/vanheeringen-lab/genomepy/tree/develop) branch.
### Quick development setup:
1. Fork & download this repo.
2. `cd` into your local repo.
3. `git checkout develop`
4. `conda env create -f environment.yaml`
5. `conda activate genomepy`
6. `pip install -e .`
8. `git checkout -b` your_develop_branch
The command line and python imports will now use the code in your local repo.
To test your changes locally, run the following command: `pytest -vvv`
## Contributors
- Siebren Frölich - [@siebrenf](https://github.com/siebrenf)
- Maarten van der Sande - [@Maarten-vd-Sande](https://github.com/Maarten-vd-Sande)
- Tilman Schäfers [@tilschaef](https://github.com/tilschaef)
- Simon van Heeringen - [@simonvh](https://github.com/simonvh)
- Dohoon Lee - [@dohlee](https://github.com/dohlee)
- Jie Zhu - [@alienzj](https://github.com/alienzj)
## Citation
If you use genomepy in your research, please cite it: [10.21105/joss.00320](http://dx.doi.org/10.21105/joss.00320).
## License
This module is licensed under the terms of the [MIT license](https://opensource.org/licenses/MIT).
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"description": "# genomepy: genes and genomes at your fingertips\n\n[](http://bioconda.github.io)\n[](https://anaconda.org/bioconda/genomepy)\n[](https://badge.fury.io/py/genomepy)\n[](https://GitHub.com/vanheeringen-lab/genomepy/stargazers/)\n\n[](https://app.travis-ci.com/github/vanheeringen-lab/genomepy/branches)\n[](https://codeclimate.com/github/vanheeringen-lab/genomepy/maintainability)\n[](https://codeclimate.com/github/vanheeringen-lab/genomepy/test_coverage)\n\n[](https://doi.org/10.1093/bioinformatics/btad119)\n[](https://doi.org/10.5281/zenodo.1010458)\n<!--- [](https://doi.org/10.48550/arXiv.2209.00842) [](http://joss.theoj.org/papers/df434a15edd00c8c2f4076668575d1cd) -->\n\ngenomepy is designed to provide a _simple_ and _straightforward_ way to download and use genomic data.\nThis includes (1) searching available data, \n(2) showing the available metadata,\n(3) automatically downloading, preprocessing and matching data and\n(4) generating optional aligner indexes.\nAll with sensible, yet controllable defaults.\nCurrently, genomepy supports Ensembl, UCSC, NCBI and GENCODE. \n\n[](https://asciinema.org/a/eZttBuf5ly0AnjFVBiEIybbjS)\n\n**Pssst, hey there!** Is genomepy not doing what you want? Does it fail? Is it clunky?\nIs the documentation unclear? Have any other ideas on how to improve it?\nDon't be shy and [let us know](https://github.com/vanheeringen-lab/genomepy/issues)!\n\n## Table of Contents\n1. [Installation](#installation)\n2. [Quick usage](#quick-usage)\n3. [Command line](#command-line-interface)\n 1. [Search](#search-genomes--gene-annotations)\n 2. [Inspect](#inspect-gene-annotations)\n 3. [Install](#install-a-genome--gene-annotation)\n 4. [Plugins](#plugins)\n4. [Python](#python-api)\n5. [Frequently Asked Questions](#frequently-asked-questions)\n6. [Getting help](#getting-help)\n7. [Contributing](#contributing)\n8. [Citation](#citation)\n9. [License](#license)\n\n## Installation\n\ngenomepy requires Python 3.7+\n\nYou can install genomepy via [bioconda](https://bioconda.github.io/), pip or git.\n\n#### Bioconda\n\n```bash\n$ conda install -c conda-forge -c bioconda 'genomepy>=0.15'\n``` \n\n#### Pip\n\n```bash\n$ pip install genomepy\n```\n\nWith the Pip installation, you will have to install additional dependencies, and make them available in your PATH.\n\nTo read/write bgzipped genomes you will have to install `pysam`.\n\nIf you want to use gene annotation features, you will have to install the following utilities:\n\n* `genePredToBed`\n* `genePredToGtf`\n* `bedToGenePred`\n* `gtfToGenePred`\n* `gff3ToGenePred`\n\nYou can find the binaries [here](http://hgdownload.cse.ucsc.edu/admin/exe/).\n\n#### Git\n\n```bash\n$ git clone https://github.com/vanheeringen-lab/genomepy.git\n$ conda env create -n genomepy -f genomepy/environment.yml\n$ conda activate genomepy\n$ pip install -e genomepy\n```\n\n## Quick usage\n\n1. Find your genome: `$ genomepy search zebrafish`\n\n Console output:\n ```bash\n name provider accession tax_id annotation species other_info\n GRCz11 Ensembl GCA_000002035.4 7955 \u2713 Danio rerio 2017-08-Ensembl/2018-04\n ^\n Use name for genomepy install\n ```\n\n2. Install your genome (with gene annotation): `$ genomepy install --annotation GRCz11 --provider ensembl `\n\nThe default genomes directory: `~/.local/share/genomes/`\n\n## Command line interface\n\nAll commands come with a short explanation when appended with `-h`/`--help`.\n\n```bash\n$ genomepy --help\nUsage: genomepy [OPTIONS] COMMAND [ARGS]...\n\nOptions:\n --version Show the version and exit.\n -h, --help Show this message and exit.\n\nCommands:\n annotation show 1st lines of each annotation\n clean remove provider data\n config manage configuration\n genomes list available genomes\n install install a genome & run active plugins\n plugin manage plugins\n providers list available providers\n search search for genomes\n```\n\n### Search genomes & gene annotations\n\nLet's say we want to download a *Xenopus tropicalis* genome & gene annotation. \nFirst, lets find out what's out there!\n\nYou can search by name, taxonomy ID or assembly accession ID.\nAdditionally, you can limit the search result to one provider with `-p`/`--provider`.\nFurthermore, you can get the absolute `--size` of each genome (this option slows down the search).\n\n```bash\n$ genomepy search xenopus tro\nname provider accession tax_id annotation species other_info\n n r e k\nXenopus_tropicalis_v9.1 Ensembl GCA_000004195.3 8364 \u2713 Xenopus tropicalis 2019-04-Ensembl/2019-12\nxenTro1 UCSC na 8364 \u2717 \u2717 \u2717 \u2717 Xenopus tropicalis Oct. 2004 (JGI 3.0/xenTro1)\nxenTro2 UCSC na 8364 \u2717 \u2713 \u2713 \u2717 Xenopus tropicalis Aug. 2005 (JGI 4.1/xenTro2)\nxenTro3 UCSC GCA_000004195.1 8364 \u2717 \u2713 \u2713 \u2717 Xenopus tropicalis Nov. 2009 (JGI 4.2/xenTro3)\nxenTro7 UCSC GCA_000004195.2 8364 \u2713 \u2713 \u2717 \u2717 Xenopus tropicalis Sep. 2012 (JGI 7.0/xenTro7)\nxenTro9 UCSC GCA_000004195.3 8364 \u2713 \u2713 \u2713 \u2717 Xenopus tropicalis Jul. 2016 (Xenopus_tropicalis_v9.1/xenTro9)\nXtropicalis_v7 NCBI GCF_000004195.2 8364 \u2713 Xenopus tropicalis DOE Joint Genome Institute\nXenopus_tropicalis_v9.1 NCBI GCF_000004195.3 8364 \u2713 Xenopus tropicalis DOE Joint Genome Institute\nUCB_Xtro_10.0 NCBI GCF_000004195.4 8364 \u2713 Xenopus tropicalis University of California, Berkeley\nASM1336827v1 NCBI GCA_013368275.1 8364 \u2717 Xenopus tropicalis Southern University of Science and Technology\n ^\n Use name for genomepy install\n```\n\n### Inspect gene annotations\n\nLet's say we want to download the *Xenopus tropicalis* genome & gene annotation from UCSC.\n\nSince we are interested in the gene annotation as well, we should check which gene annotation suits our needs.\nAs you can see in the search results, UCSC has several gene annotations for us to choose from.\nIn the search results, `n r e k ` denotes which UCSC annotations are available. \nThese stand for **n**cbiRefSeq, **r**efGene, **e**nsGene and **k**nownGene, respectively.\n\nWe can quickly inspect these with the `genomepy annotation` command:\n\n```bash\n$ genomepy annotation xenTro9 -p ucsc\n12:04:41 | INFO | UCSC ncbiRefSeq\nchr1 genomepy transcript 133270 152620 . - . gene_id \"LOC100490505\"; transcript_id \"XM_012956089.1\"; gene_name \"LOC100490505\";\nchr1 genomepy exon 133270 134186 . - . gene_id \"LOC100490505\"; transcript_id \"XM_012956089.1\"; exon_number \"1\"; exon_id \"XM_012956089.1.1\"; gene_name \"LOC100490505\";\n12:04:45 | INFO | UCSC refGene\nchr1 genomepy transcript 193109390 193134311 . + . gene_id \"pias2\"; transcript_id \"NM_001078987\"; gene_name \"pias2\";\nchr1 genomepy exon 193109390 193109458 . + . gene_id \"pias2\"; transcript_id \"NM_001078987\"; exon_number \"1\"; exon_id \"NM_001078987.1\"; gene_name \"pias2\";\n12:04:49 | INFO | UCSC ensGene\nchr1 genomepy transcript 133270 152620 . - . gene_id \"ENSXETG00000030302.2\"; transcript_id \"ENSXETT00000061673.2\"; gene_name \"ENSXETG00000030302.2\";\nchr1 genomepy exon 133270 134186 . - . gene_id \"ENSXETG00000030302.2\"; transcript_id \"ENSXETT00000061673.2\"; exon_number \"1\"; exon_id \"ENSXETT00000061673.2.1\"; gene_name \"ENSXETG00000030302.2\";\n```\n\nHere we can see that the `refGene` annotation has actual HGNC gene names, so lets go with this annotation.\nThis differs between assemblies, so be sure to check!\n\n### Install a genome & gene annotation\n\nCopy the name returned by the search function to install.\n\n```bash\n$ genomepy install xenTro9\n```\n\nYou can choose to download gene annotation files with the `-a`/`--annotation` option.\n\n```bash\n$ genomepy install xenTro9 --annotation\n```\n\nFor UCSC we can also select the annotation type.\nSee `genomepy install --help` for all provider specific options.\n\n```bash\n$ genomepy install xenTro9 --UCSC-annotation refGene\n```\n\nSince we did not specify the provider here, genomepy will use the first provider with `xenTro9`.\nYou can specify a provider by name with `-p`/`--provider`:\n\n```bash\n$ genomepy install xenTro9 -p UCSC\nDownloading genome from http://hgdownload.soe.ucsc.edu/goldenPath/xenTro9/bigZips/xenTro9.fa.gz...\nGenome download successful, starting post processing...\n\nname: xenTro9\nlocal name: xenTro9\nfasta: ~/.local/share/genomes/xenTro9/xenTro9.fa\n```\n\nNext, the genome is downloaded to the directory specified in the config file (by default `~/.local/share/genomes`).\nTo choose a different directory, use the `-g`/`--genomes_dir` option:\n\n```bash\n$ genomepy install sacCer3 -p UCSC -g /path/to/my/genomes\nDownloading genome from http://hgdownload.soe.ucsc.edu/goldenPath/sacCer3/bigZips/chromFa.tar.gz...\nGenome download successful, starting post processing...\n\nname: sacCer3\nlocal name: sacCer3\nfasta: /path/to/my/genomes/sacCer3/sacCer3.fa\n```\n\n#### Regex, masking & compression\n\nYou can use a regular expression to filter for matching sequences\n(or non-matching sequences by using the `-n`/`--no-match` option).\nFor instance, the following command downloads hg38 and saves only the major chromosomes:\n\n```bash\n$ genomepy install hg38 -p UCSC -r 'chr[0-9XY]+$'\nDownloading genome from from http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz...\nGenome download successful, starting post processing...\n\nname: hg38\nlocal name: hg38\nfasta: /data/genomes/hg38/hg38.fa\n\n$ grep \">\" /data/genomes/hg38/hg38.fa\n>chr1\n>chr10\n>chr11\n>chr12\n>chr13\n>chr14\n>chr15\n>chr16\n>chr17\n>chr18\n>chr19\n>chr2\n>chr20\n>chr21\n>chr22\n>chr3\n>chr4\n>chr5\n>chr6\n>chr7\n>chr8\n>chr9\n>chrX\n>chrY\n```\n\nBy default, genome sequences are soft-masked (ACgtN). \nUse `-m hard` for hard masking (ACNNN), or `-m none` for no masking (ACGTN).\n\n```bash\n$ genomepy install hg38 --mask hard\n```\n\nIf you wish to conserve space, you can tell genomepy to compress the downloaded data by passing the `-b`/`--bgzip` option.\nSee [Configuration](#compression) for details.\n\n```bash\n$ genomepy install hg38 --bgzip\n```\n\n#### Other providers (any URL/local files)\n\nTo use assemblies not on NCBI, UCSC, Ensembl or GENCODE, you can give a URL instead of a name, together with `--provider URL`.\nSimilarly, if you have a local FASTA file, you can install this using the filepath, together with `--provider Local`:\n\n```bash\n$ genomepy install -p url https://research.nhgri.nih.gov/hydra/download/assembly/\\Hm105_Dovetail_Assembly_1.0.fa.gz\n```\n\nThis will install the genome under the filename of the URL/filepath, but can be changed with the `-l`/`--localname` option.\n\nIf you add the `--annotation` flag, genomepy will search the (remote) directory for an annotation file as well.\nShould this fail, you can also add a URL to the annotation with `--URL-to-annotation` with the `URL` provider, \nor a filepath with `--Local-path-to-annotation` with the `Local` provider:\n\n```bash\n$ genomepy install -p local /path/to/genome.fa --Local-path-to-annotation /path/to/gene.annotation.gtf\n```\n\n#### Reproducibility\n\nAll selected options are stored in a `README.txt`.\nThis includes the original name, download location and other genomepy operations (such as regex filtering and time).\n\n### Plugins\n\nPlugins are optional steps that are executed after installing an assembly with `genomepy install`.\nIf you already installed an assembly, you can activate a plugin and rerun the install command.\nThis will not overwrite your local files, unless you use the `--force` option.\n\nCheck which plugins are enabled with `genomepy plugin list`.\n\n#### Genome blacklists\n\nFor some model organisms, genomepy can download a genome blacklist (generated by the [Kundaje lab](https://www.nature.com/articles/s41598-019-45839-z)).\nBlacklists are only available for these model organisms when downloaded from UCSC, and for the human and mouse genomes.\n\nEnable the blacklist plugin to use it:\n\n```bash\n$ genomepy plugin enable blacklist\nEnabled plugins: blacklist\n```\n\n#### Aligner indexes\n\nYou can also create aligner indexes for several widely used aligners.\nCurrently, genomepy supports:\n\n* [Bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)\n* [BWA](http://bio-bwa.sourceforge.net/)\n* [GMAP](http://research-pub.gene.com/gmap/)\n* [HISAT2](https://ccb.jhu.edu/software/hisat2/index.shtml)\n* [Minimap2](https://github.com/lh3/minimap2)\n* [STAR](https://github.com/alexdobin/STAR)\n\nThese programs are not installed by genomepy and need to be installed separately for the indexing to work.\nThe easiest way to do so is with conda, e.g.: `conda install -c bioconda bwa star`\n\nSplice-aware indexing (required for e.g. RNA-seq) can be performed by STAR and Hisat2.\nThis will be done automatically if the gene annotation was downloaded as well.\nFinally, STAR can further improve mapping to (novel) splice junctions by indexing again (see 2-pass mapping mode in the STAR manual).\nThe second pass is not supported by genomepy.\n\nYou can configure the index creation with `genomepy plugin enable`, e.g.:\n\n```bash\n$ genomepy plugin enable bwa star\nEnabled plugins: blacklist, bwa, star\n```\n\nYou can pass the number of threads to use for aligner index creation with `genomepy install --threads` (default is 8).\n\n### Configuration\n\nAll defaults can be overwritten on the command line and in Python.\nHowever, you can create & edit the config file to change the default settings ([full description](https://vanheeringen-lab.github.io/genomepy/content/config.html)):\n\n```bash\n$ genomepy config generate\nCreated config file ~/.config/genomepy/genomepy.yaml\n```\n\n#### Genome location\n\nBy default, genomes will be saved in `~/.local/share/genomes`.\n\nTo set the default genome directory, to `/data/genomes` for instance,\nedit `~/.config/genomepy/genomepy.yaml` and change the following line:\n\n```yaml\ngenomes_dir: /data/genomes\n```\n\n#### Compression\n\nGenome FASTA files can be stored using bgzip compression.\nThis means that the FASTA files will take up less space on disk.\nSet the following line to your config file:\n\n```yaml\nbgzip: True\n```\n\nMost tools are able to use bgzip-compressed genome files.\nOne notable exception is `bedtools getfasta`.\nAs an alternative, you can use the `faidx` command-line script from [pyfaidx](https://github.com/mdshw5/pyfaidx)\nwhich comes installed with genomepy.\n\n### List available providers\n\n```bash\n$ genomepy providers\nGENCODE\nEnsembl\nUCSC\nNCBI\nLocal\nURL\n```\n\n### List available genomes\n\nYou can constrain the genome list by using the `-p`/`--provider` option to search only a specific provider.\nAdditionally, you can get the absolute `--size` of each genome (this option slows down the search).\n\n```bash\n$ genomepy genomes -p UCSC\nname provider accession tax_id annotation species other_info\n n r e k\nailMel1 UCSC GCF_000004335.2 9646 \u2713 \u2717 \u2713 \u2717 Ailuropoda melanoleuca Dec. 2009 (BGI-Shenzhen 1.0/ailMel1)\nallMis1 UCSC GCA_000281125.1 8496 \u2717 \u2713 \u2717 \u2717 Alligator mississippiensis Aug. 2012 (allMis0.2/allMis1)\nanoCar1 UCSC na 28377 \u2717 \u2717 \u2713 \u2717 Anolis carolinensis Feb. 2007 (Broad/anoCar1)\n```\n\n### Local cache.\n\nNote that the first time you run `genomepy search` or `list` the command will take a while as the genome lists have to be downloaded.\nThe lists are cached locally, which will save time later.\nThe cached files are stored in `~/.cache/genomepy` and expire after 7 days (so they stay up to date).\nThis expiration time can be changed in the config file.\nYou can also delete this directory to clean the cache using `genomepy clean`.\n\n## Python API\n\nCheck out our [Python API documentation here](https://vanheeringen-lab.github.io/genomepy/content/api_core.html)\n\n```\n>>> import genomepy\n>>> for row in genomepy.search(\"GRCh38\"):\n... print(row)\n... \n['GRCh38.p13', 'Ensembl', 'GCA_000001405.28', 9606, True, 'Homo sapiens', '2014-01-Ensembl/2021-03']\n['hg38', 'UCSC', 'GCA_000001405.15', 9606, [True, True, False, True], 'Homo sapiens', 'Dec. 2013 (GRCh38/hg38)']\n['GRCh38', 'NCBI', 'GCF_000001405.26', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']\n['GRCh38.p1', 'NCBI', 'GCF_000001405.27', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']\n['GRCh38.p2', 'NCBI', 'GCF_000001405.28', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']\n['GRCh38.p3', 'NCBI', 'GCF_000001405.29', 9606, True, 'Homo sapiens', 'Genome Reference Consortium']\n\n>>> genomepy.install_genome(\"hg38\", annotation=True, provider=\"UCSC\", genomes_dir=\"./data/genomes\")\nDownloading genome from UCSC. Target URL: http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz...\nGenome download successful, starting post processing...\nname: hg38\nlocal name: hg38\nfasta: ./data/genomes/hg38/hg38.fa\nDownloading the ncbiRefSeq annotation from the UCSC MySQL database.\nAnnotation download successful\n\n>>> a = genomepy.Annotation(\"hg38\", genomes_dir=\"./data/genomes\")\n>>> a.named_gtf.head(3)\n seqname ... attribute\ngene_name ... \nDDX11L1 chr1 ... gene_id \"DDX11L1\"; transcript_id \"NR_046018.2\"...\nDDX11L1 chr1 ... gene_id \"DDX11L1\"; transcript_id \"NR_046018.2\"...\nDDX11L1 chr1 ... gene_id \"DDX11L1\"; transcript_id \"NR_046018.2\"...\n\n>>> start = a.named_gtf.loc[\"TP63\"][\"start\"].min()\n>>> end = a.named_gtf.loc[\"TP63\"][\"end\"].max()\n>>> chrom = a.named_gtf.loc[\"TP63\"][\"seqname\"][0]\n\n>>> g = genomepy.Genome(\"hg38\", genomes_dir=\"./data/genomes\")\n>>> g[chrom][start:end]\n>chr3:189596747-189897276\ngcaacccgctggggtcaccttccacactgtggaagctttgttcttttgctctttgcagtaaatcttgct...\n\n```\n\nThe `genomepy.Genome` class builds on top of the `pyfaidx.Fasta` class, \nsee the [pyfaidx documentation](https://github.com/mdshw5/pyfaidx) for more details.\nThe `genomepy.Annotation` class contains pandas Dataframes with GTF and BED files, as well as additional class methods to utilize these.\n\n## Frequently Asked Questions\n\nGenomepy utilizes external databases to obtain your files.\nUnfortunately this sometimes causes issues.\nHere are some of the more common issues, with solutions.\n\nLet us know if you encounter issues you cannot solve by creating [a new issue](https://github.com/vanheeringen-lab/genomepy/issues).\n\n### Provider is offline\nOccasionally one of the providers experience connection issues, which can last anywhere between minutes to hours.\nWhen this happens genomepy will warn that the provider appears offline, or that the URL seems broken.\n\nIf the issue does not pass, you can try to reset genomepy.\nSimply run `genomepy clean` on the command line, or run `genomepy.clean()` in Python.\n\n### This genome is missing\nGenomepy stores provider data on your computer to rerun it faster later.\nIf a provider was offline during this time, it may miss (parts of) the data.\n\nTo re-download the data, remove the local data with `genomepy clean`, then `search` for your genome again.\n\n### This genome is STILL missing/URL is broken\nSadly, not everything (naming, structure, filenames) is always consistent on the provider end.\nContact the provider to get it fixed!\nOne notable group are Ensembl fungi, which seems to be mostly mislabelled.\n\nIn the meantime, you can still use the power of genomepy by manually retrieving the URLs,\nand downloading the files with `genomepy install GENOME_URL -p url --url-to-annotation ANNOTATION_URL`.\n\n### The genomepy config was corrupted\nYou can create a new one with `genomepy config generate` on command line,\nor `genomepy.manage_config(\"generate\")` in Python.\n\n### What's genomepy maximum memory usage?\nGenomepy does not read a genome fully into memory. \nTherefore, installing takes less than 1 GB RAM regardless of the genome's size.\nSearching NCBI is the most costly operation, using around 3 GB (the first time).\n\n### Which genome/gene annotation to use\nEach provider has its pros and cons:\n* Ensembl has excellent gene annotations, but their chromosome names can cause issues with some tools.\n* UCSC has an excellent genome browser, but their gene annotations vary in format.\n* NCBI allows public submissions, and so has the latest versions, although not always complete or error free.\n\nUse `genomepy search` to see your options, and `genomepy annotation` to check the quality of the gene annotation(s).\n\n## Getting help\n\nIf you want to report a bug or issue, or have problems with installing or running the software please create [a new issue](https://github.com/vanheeringen-lab/genomepy/issues).\nThis is the preferred way of getting support.\nAlternatively, you can [mail me](mailto:simon.vanheeringen@gmail.com).\n\n## Contributing\n\nContributions welcome! Send me a pull request or [get in touch](mailto:simon.vanheeringen@gmail.com).\n\nWhen contributing a PR, please use the [develop](https://github.com/vanheeringen-lab/genomepy/tree/develop) branch.\n\n### Quick development setup: \n1. Fork & download this repo. \n2. `cd` into your local repo. \n3. `git checkout develop`\n4. `conda env create -f environment.yaml`\n5. `conda activate genomepy`\n6. `pip install -e .`\n8. `git checkout -b` your_develop_branch\n\nThe command line and python imports will now use the code in your local repo. \nTo test your changes locally, run the following command: `pytest -vvv`\n\n## Contributors\n\n- Siebren Fr\u00f6lich - [@siebrenf](https://github.com/siebrenf)\n- Maarten van der Sande - [@Maarten-vd-Sande](https://github.com/Maarten-vd-Sande)\n- Tilman Sch\u00e4fers [@tilschaef](https://github.com/tilschaef)\n- Simon van Heeringen - [@simonvh](https://github.com/simonvh)\n- Dohoon Lee - [@dohlee](https://github.com/dohlee)\n- Jie Zhu - [@alienzj](https://github.com/alienzj)\n\n## Citation\n\nIf you use genomepy in your research, please cite it: [10.21105/joss.00320](http://dx.doi.org/10.21105/joss.00320).\n\n## License\n\nThis module is licensed under the terms of the [MIT license](https://opensource.org/licenses/MIT).\n",
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"license": "MIT License Copyright (c) 2016 Simon van Heeringen <simon.vanheeringen@gmail.com> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
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